From 62c0880cc29516e819aad8d01594d5142f0b43b2 Mon Sep 17 00:00:00 2001 From: Abhishek Raut Date: Sun, 29 Aug 2021 07:03:40 +0530 Subject: [PATCH 1/4] Basic EDA & Analysis Important feature EDA to output and classification with report --- 007/solution/IBM_HR.ipynb | 2929 +++ .../IBM_HR_Employee_Attrition_Profil.html | 17729 ++++++++++++++++ .../catboost_info/catboost_training.json | 804 + .../catboost_info/learn/events.out.tfevents | Bin 0 -> 43870 bytes 007/solution/catboost_info/learn_error.tsv | 801 + 007/solution/catboost_info/time_left.tsv | 801 + 007/solution/employee_attrition.csv | 1471 ++ 007/solution/sweetviz_report.html | 16573 +++++++++++++++ 8 files changed, 41108 insertions(+) create mode 100644 007/solution/IBM_HR.ipynb create mode 100644 007/solution/IBM_HR_Employee_Attrition_Profil.html create mode 100644 007/solution/catboost_info/catboost_training.json create mode 100644 007/solution/catboost_info/learn/events.out.tfevents create mode 100644 007/solution/catboost_info/learn_error.tsv create mode 100644 007/solution/catboost_info/time_left.tsv create mode 100644 007/solution/employee_attrition.csv create mode 100644 007/solution/sweetviz_report.html diff --git a/007/solution/IBM_HR.ipynb b/007/solution/IBM_HR.ipynb new file mode 100644 index 00000000..d77b5f34 --- /dev/null +++ b/007/solution/IBM_HR.ipynb @@ -0,0 +1,2929 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "be9dc5bb", + "metadata": {}, + "source": [ + "# IBM HR Analytics Employee Attrition & Performance\n", + "Predict attrition of your valuable employees\n" + ] + }, + { + "cell_type": "markdown", + "id": "3ab5962a", + "metadata": {}, + "source": [ + "https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "9c6b93b1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import copy\n", + "import time\n", + "import numpy as np\n", + "import pandas as pd\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "from collections import Counter\n", + "\n", + "import pickle\n", + "import sklearn\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,\n", + " GradientBoostingClassifier)\n", + "\n", + "\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from sklearn.svm import SVC\n", + "from sklearn import svm\n", + "from sklearn.model_selection import StratifiedKFold,KFold\n", + "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n", + "from sklearn.datasets import make_moons, make_circles, make_classification\n", + "from sklearn.linear_model import LogisticRegressionCV,LogisticRegression,SGDClassifier,PassiveAggressiveClassifier,Perceptron\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.gaussian_process import GaussianProcessClassifier\n", + "from sklearn.gaussian_process.kernels import RBF\n", + "from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,ExtraTreesClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", + "\n", + "\n", + "from sklearn.metrics import f1_score,accuracy_score\n", + "from sklearn.model_selection import RandomizedSearchCV\n", + "import xgboost\n", + "import catboost as cb\n", + "from catboost import CatBoostClassifier, Pool\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.optim import lr_scheduler\n", + "import numpy as np\n", + "import torchvision\n", + "from torchvision import datasets, models, transforms\n", + "\n", + "\n", + "import seaborn as sns\n", + "sns.set_theme()\n", + "import matplotlib.pyplot as plt\n", + "plt.ion() # interactive mode\n", + "%matplotlib inline\n", + "from IPython.display import Image\n", + "\n", + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import LeakyReLU, PReLU, ELU\n", + "from keras.layers import Dropout\n", + "from matplotlib import pyplot\n", + "from sklearn.metrics import mean_squared_error\n", + "from keras.layers import LSTM, GRU\n", + "from keras.callbacks import EarlyStopping\n", + "from keras.layers import Activation\n", + "\n", + "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "# data.head(2)\n", + "\n", + "import sklearn\n", + "import numpy as np\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "import seaborn as sns; sns.set_theme()\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ea28bfd0", + "metadata": {}, + "outputs": [], + "source": [ + "Train=pd.read_csv(\".\\\\employee_attrition.csv\",na_values=[])\n", + "Train=Train.drop(\"Over18\",axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "4d0e7f33", + "metadata": {}, + "source": [ + "# EDA (Exploratory Data Analysis)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7bd19822", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82686f5e9dbe4a32af50251b25a60b6d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " | | [ 0%] 00:00 ->…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Report sweetviz_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n" + ] + } + ], + "source": [ + "import sweetviz as sv\n", + "\n", + "#EDA using Autoviz\n", + "sweet_report = sv.analyze(pd.read_csv(\"./employee_attrition.csv\"))\n", + "\n", + "#Saving results to HTML file\n", + "sweet_report.show_html('sweetviz_report.html')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "603c2166", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85e9b39633f74187a4e9403976a1e967", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Summarize dataset: 0%| | 0/43 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumber...RelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences11...18008016405
149NoTravel_Frequently279Research & Development81Life Sciences12...4801103310717
237YesTravel_Rarely1373Research & Development22Other14...28007330000
333NoTravel_Frequently1392Research & Development34Life Sciences15...38008338730
427NoTravel_Rarely591Research & Development21Medical17...48016332222
..................................................................
146536NoTravel_Frequently884Research & Development232Medical12061...380117335203
146639NoTravel_Rarely613Research & Development61Medical12062...18019537717
146727NoTravel_Rarely155Research & Development43Life Sciences12064...28016036203
146849NoTravel_Frequently1023Sales23Medical12065...480017329608
146934NoTravel_Rarely628Research & Development83Medical12068...18006344312
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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def label(df):\n", + " df1 = df.select_dtypes(include=['object'])\n", + " for i in df.columns:\n", + " if df[i].dtypes == 'object': \n", + " labelencoder = LabelEncoder()\n", + " df[i] = labelencoder.fit_transform(df[i])\n", + " return df \n", + "\n", + "Train=label(Train)\n", + "Train.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "10888cd9", + "metadata": {}, + "outputs": [], + "source": [ + "def means(df):\n", + " df1=df.columns\n", + " my_imputer = SimpleImputer()\n", + " imputed_X= pd.DataFrame(my_imputer.fit_transform(df))\n", + " df= pd.DataFrame(my_imputer.transform(df))\n", + " df=MinMaxScaler().fit_transform(df)\n", + " df=pd.DataFrame(df)\n", + " df.columns=df1\n", + " return df \n", + " \n", + "# Train=means(Train) \n", + "# Train.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "acec1805", + "metadata": {}, + "outputs": [ + { + "data": { + 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31EmployeeCount0.00
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" + ], + "text/plain": [ + " Features avg\n", + "0 OverTime 273.20\n", + "1 JobLevel 81.99\n", + "2 StockOptionLevel 41.80\n", + "3 TotalWorkingYears 40.53\n", + "4 MonthlyIncome 37.43\n", + "5 MaritalStatus 31.21\n", + "6 YearsWithCurrManager 29.10\n", + "7 Department 29.03\n", + "8 JobRole 26.58\n", + "9 EnvironmentSatisfaction 26.23\n", + "10 YearsAtCompany 24.88\n", + "11 JobInvolvement 24.76\n", + "12 Age 23.92\n", + "13 JobSatisfaction 23.28\n", + "14 WorkLifeBalance 22.71\n", + "15 NumCompaniesWorked 22.43\n", + "16 YearsInCurrentRole 22.26\n", + "17 DistanceFromHome 22.17\n", + "18 BusinessTravel 20.61\n", + "19 YearsSinceLastPromotion 19.75\n", + "20 HourlyRate 19.37\n", + "21 RelationshipSatisfaction 18.69\n", + "22 DailyRate 17.71\n", + "23 EducationField 16.86\n", + "24 PercentSalaryHike 15.72\n", + "25 MonthlyRate 14.53\n", + "26 TrainingTimesLastYear 14.44\n", + "27 Education 14.11\n", + "28 Gender 13.40\n", + "29 PerformanceRating 11.31\n", + "30 StandardHours 0.00\n", + "31 EmployeeCount 0.00" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selector=xgboost.XGBClassifier(n_estimators= 100,verbosity=0, max_depth= 40, learning_rate= 0.01, gamma= 0.07, colsample_bytree= 0.6)\n", + "selector.fit(Train, Y)\n", + "feature_imp = selector.feature_importances_\n", + "\n", + "\n", + "from collections import Counter,defaultdict\n", + "a=[]\n", + "b=[]\n", + "for index, val in enumerate(feature_imp):\n", + " temp=round((val * 1000), 2)\n", + " a.append(Train.columns[index])\n", + " b.append(temp)\n", + "t=pd.DataFrame()\n", + "t[\"Features\"]=a\n", + "t[\"avg\"]=b\n", + "t=t.sort_values(by = 'avg',ascending=False)\n", + "print(\"\\n\",list(t.Features))\n", + "t.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "c60b78ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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OverTimeJobLevelStockOptionLevelTotalWorkingYearsMonthlyIncomeMaritalStatusYearsWithCurrManagerDepartmentJobRoleEnvironmentSatisfaction...DailyRateEducationFieldPercentSalaryHikeMonthlyRateTrainingTimesLastYearEducationGenderPerformanceRatingStandardHoursEmployeeCount
01208599325272...1102111194790203801
102110513017163...279123249073114801
21107209020124...137341523963213801
31108290910164...1392111231593403801
40116346812121...591312166323113801
..................................................................
146502117257113123...884317122903213801
14660319999117104...613315214575113801
14671216614213142...15512051740314801
146802017539018274...1023314132433313801
14690206440412122...628312102283313801
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1470 rows × 32 columns

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" + ], + "text/plain": [ + " OverTime JobLevel StockOptionLevel TotalWorkingYears MonthlyIncome \\\n", + "0 1 2 0 8 5993 \n", + "1 0 2 1 10 5130 \n", + "2 1 1 0 7 2090 \n", + "3 1 1 0 8 2909 \n", + "4 0 1 1 6 3468 \n", + "... ... ... ... ... ... \n", + "1465 0 2 1 17 2571 \n", + "1466 0 3 1 9 9991 \n", + "1467 1 2 1 6 6142 \n", + "1468 0 2 0 17 5390 \n", + "1469 0 2 0 6 4404 \n", + "\n", + " MaritalStatus YearsWithCurrManager Department JobRole \\\n", + "0 2 5 2 7 \n", + "1 1 7 1 6 \n", + "2 2 0 1 2 \n", + "3 1 0 1 6 \n", + "4 1 2 1 2 \n", + "... ... ... ... ... \n", + "1465 1 3 1 2 \n", + "1466 1 7 1 0 \n", + "1467 1 3 1 4 \n", + "1468 1 8 2 7 \n", + "1469 1 2 1 2 \n", + "\n", + " EnvironmentSatisfaction ... DailyRate EducationField \\\n", + "0 2 ... 1102 1 \n", + "1 3 ... 279 1 \n", + "2 4 ... 1373 4 \n", + "3 4 ... 1392 1 \n", + "4 1 ... 591 3 \n", + "... ... ... ... ... \n", + "1465 3 ... 884 3 \n", + "1466 4 ... 613 3 \n", + "1467 2 ... 155 1 \n", + "1468 4 ... 1023 3 \n", + "1469 2 ... 628 3 \n", + "\n", + " PercentSalaryHike MonthlyRate TrainingTimesLastYear Education \\\n", + "0 11 19479 0 2 \n", + "1 23 24907 3 1 \n", + "2 15 2396 3 2 \n", + "3 11 23159 3 4 \n", + "4 12 16632 3 1 \n", + "... ... ... ... ... \n", + "1465 17 12290 3 2 \n", + "1466 15 21457 5 1 \n", + "1467 20 5174 0 3 \n", + "1468 14 13243 3 3 \n", + "1469 12 10228 3 3 \n", + "\n", + " Gender PerformanceRating StandardHours EmployeeCount \n", + "0 0 3 80 1 \n", + "1 1 4 80 1 \n", + "2 1 3 80 1 \n", + "3 0 3 80 1 \n", + "4 1 3 80 1 \n", + "... ... ... ... ... \n", + "1465 1 3 80 1 \n", + "1466 1 3 80 1 \n", + "1467 1 4 80 1 \n", + "1468 1 3 80 1 \n", + "1469 1 3 80 1 \n", + "\n", + "[1470 rows x 32 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train=pd.DataFrame(Train,columns=[i for i in t.Features])\n", + "Train" + ] + }, + { + "cell_type": "markdown", + "id": "93320780", + "metadata": {}, + "source": [ + "## Classification into Groups" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "28483d8c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OverTime 2 0 1 \n", + "JobLevel 5 1 5 \n", + "StockOptionLevel 4 0 3 \n", + "TotalWorkingYears 40 0 40 \n", + "MonthlyIncome 1349 1009 19999 \n", + "MaritalStatus 3 0 2 \n", + "YearsWithCurrManager 18 0 17 \n", + "Department 3 0 2 \n", + "JobRole 9 0 8 \n", + "EnvironmentSatisfaction 4 1 4 \n", + "YearsAtCompany 37 0 40 \n", + "JobInvolvement 4 1 4 \n", + "Age 43 18 60 \n", + "JobSatisfaction 4 1 4 \n", + "WorkLifeBalance 4 1 4 \n", + "NumCompaniesWorked 10 0 9 \n", + "YearsInCurrentRole 19 0 18 \n", + "DistanceFromHome 29 1 29 \n", + "BusinessTravel 3 0 2 \n", + "YearsSinceLastPromotion 16 0 15 \n", + "HourlyRate 71 30 100 \n", + "RelationshipSatisfaction 4 1 4 \n", + "DailyRate 886 102 1499 \n", + "EducationField 6 0 5 \n", + "PercentSalaryHike 15 11 25 \n", + "MonthlyRate 1427 2094 26999 \n", + "TrainingTimesLastYear 7 0 6 \n", + "Education 5 1 5 \n", + "Gender 2 0 1 \n", + "PerformanceRating 2 3 4 \n", + "StandardHours 1 80 80 \n", + "EmployeeCount 1 1 1 \n" + ] + } + ], + "source": [ + "for i in Train.columns:\n", + " print(i,len(Train[i].unique()),\" \" ,min(Train[i]),max(Train[i]) ,\"\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "481fc5f0", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.cut(Train['MonthlyIncome'], 5)\n", + "sorted(df.unique())\n", + "Train.loc[(Train['MonthlyIncome'] > 990) & (Train['MonthlyIncome'] <= 4807), 'MonthlyIncome'] = 0\n", + "Train.loc[(Train['MonthlyIncome'] > 4807) & (Train['MonthlyIncome'] <= 8605), 'MonthlyIncome'] = 1\n", + "Train.loc[(Train['MonthlyIncome'] > 8605) & (Train['MonthlyIncome'] <= 12403), 'MonthlyIncome'] =2\n", + "Train.loc[(Train['MonthlyIncome'] > 12403) & (Train['MonthlyIncome'] <= 16201), 'MonthlyIncome'] =3\n", + "Train.loc[(Train['MonthlyIncome'] > 16201) & (Train['MonthlyIncome'] <= 19999.0), 'MonthlyIncome'] =4" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "64216063", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.cut(Train['TotalWorkingYears'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ (Train['TotalWorkingYears'] <= 8), 'TotalWorkingYears'] = 0\n", + "Train.loc[(Train['TotalWorkingYears'] >8) & (Train['TotalWorkingYears'] <= 16), 'TotalWorkingYears'] = 1\n", + "Train.loc[(Train['TotalWorkingYears'] >16) & (Train['TotalWorkingYears'] <= 24), 'TotalWorkingYears'] = 2\n", + "Train.loc[(Train['TotalWorkingYears'] >24) & (Train['TotalWorkingYears'] <= 32), 'TotalWorkingYears'] = 3\n", + "Train.loc[ Train['TotalWorkingYears'] > 32, 'TotalWorkingYears']=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "84549d94", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.cut(Train['YearsWithCurrManager'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['YearsWithCurrManager'] <= 1.7, 'YearsWithCurrManager'] = 0\n", + "Train.loc[(Train['YearsWithCurrManager'] > 3.4) & (Train['YearsWithCurrManager'] <= 6.8), 'MonthlyIncome'] = 1\n", + "Train.loc[(Train['YearsWithCurrManager'] >6.8) & (Train['YearsWithCurrManager'] <= 10.2), 'MonthlyIncome'] = 2\n", + "Train.loc[(Train['YearsWithCurrManager'] >10.2) & (Train['YearsWithCurrManager'] <= 13.6), 'MonthlyIncome'] = 3\n", + "Train.loc[(Train['YearsWithCurrManager'] >13.6) & (Train['YearsWithCurrManager'] <= 17.0), 'MonthlyIncome'] = 4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "50692bb0", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df=pd.cut(Train['YearsAtCompany'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['YearsAtCompany'] <= 8, 'YearsAtCompany'] = 0\n", + "Train.loc[(Train['YearsAtCompany'] > 8) & (Train['YearsAtCompany'] <= 16), 'YearsAtCompany'] = 1\n", + "Train.loc[(Train['YearsAtCompany'] >16) & (Train['YearsAtCompany'] <= 24), 'YearsAtCompany'] = 2\n", + "Train.loc[(Train['YearsAtCompany'] >24) & (Train['YearsAtCompany'] <= 32), 'YearsAtCompany'] = 3\n", + "Train.loc[(Train['YearsAtCompany'] >32) & (Train['YearsAtCompany'] <= 40), 'YearsAtCompany'] = 4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "ce265bd1", + "metadata": {}, + "outputs": [], + "source": [ + "# DistanceFromHome 5\n", + "df=pd.cut(Train['YearsInCurrentRole'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['YearsInCurrentRole'] <=4 , 'YearsInCurrentRole'] = 0\n", + "Train.loc[(Train['YearsInCurrentRole'] > 4) & (Train['YearsInCurrentRole'] <= 8), 'YearsInCurrentRole'] = 1\n", + "Train.loc[(Train['YearsInCurrentRole'] > 8) & (Train['YearsInCurrentRole'] <= 12), 'YearsInCurrentRole'] = 2\n", + "Train.loc[(Train['YearsInCurrentRole'] >12) & (Train['YearsInCurrentRole'] <= 16), 'YearsInCurrentRole'] = 3\n", + "Train.loc[ Train['YearsInCurrentRole'] > 16, 'YearsInCurrentRole']=4" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "835b397e", + "metadata": {}, + "outputs": [], + "source": [ + "# Age 10\n", + "df=pd.cut(Train['Age'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['Age'] <=27 , 'Age'] = 0\n", + "Train.loc[(Train['Age'] > 27) & (Train['Age'] <= 35), 'Age'] = 1\n", + "Train.loc[(Train['Age'] > 35) & (Train['Age'] <= 44), 'Age'] = 2\n", + "Train.loc[(Train['Age'] >44) & (Train['Age'] <= 52), 'Age'] = 3\n", + "Train.loc[ Train['Age'] > 52, 'Age']=4" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "a885232a", + "metadata": {}, + "outputs": [], + "source": [ + "# Age 10\n", + "df=pd.cut(Train['DistanceFromHome'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['DistanceFromHome'] <=7 , 'DistanceFromHome'] = 0\n", + "Train.loc[(Train['DistanceFromHome'] > 7) & (Train['DistanceFromHome'] <= 13), 'DistanceFromHome'] = 1\n", + "Train.loc[(Train['DistanceFromHome'] > 13) & (Train['DistanceFromHome'] <= 18), 'DistanceFromHome'] = 2\n", + "Train.loc[(Train['DistanceFromHome'] >18) & (Train['DistanceFromHome'] <= 24), 'DistanceFromHome'] = 3\n", + "Train.loc[ Train['DistanceFromHome'] > 24, 'DistanceFromHome']=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "7492ee20", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df=pd.cut(Train['HourlyRate'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['HourlyRate'] <=44 , 'HourlyRate'] = 0\n", + "Train.loc[(Train['HourlyRate'] > 44) & (Train['HourlyRate'] <= 58), 'HourlyRate'] = 1\n", + "Train.loc[(Train['HourlyRate'] > 58) & (Train['HourlyRate'] <= 72), 'HourlyRate'] = 2\n", + "Train.loc[(Train['HourlyRate'] >72) & (Train['HourlyRate'] <= 86), 'HourlyRate'] = 3\n", + "Train.loc[ Train['HourlyRate'] > 86, 'HourlyRate']=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "429c7dcd", + "metadata": {}, + "outputs": [], + "source": [ + "# DailyRate\n", + "df=pd.cut(Train['DailyRate'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['DailyRate'] <=380 , 'DailyRate'] = 0\n", + "Train.loc[(Train['DailyRate'] > 380) & (Train['DailyRate'] <= 660), 'DailyRate'] = 1\n", + "Train.loc[(Train['DailyRate'] > 660) & (Train['DailyRate'] <= 940), 'DailyRate'] = 2\n", + "Train.loc[(Train['DailyRate'] >940) & (Train['DailyRate'] <= 1220), 'DailyRate'] = 3\n", + "Train.loc[ Train['DailyRate'] > 1220, 'DailyRate']=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "765001fc", + "metadata": {}, + "outputs": [], + "source": [ + "# MonthlyRate\n", + "df=pd.cut(Train['MonthlyRate'], 5)\n", + "sorted(df.unique())\n", + "\n", + "Train.loc[ Train['MonthlyRate'] <=7075 , 'MonthlyRate'] = 0\n", + "Train.loc[(Train['MonthlyRate'] > 7075) & (Train['MonthlyRate'] <= 12056), 'MonthlyRate'] = 1\n", + "Train.loc[(Train['MonthlyRate'] > 12056) & (Train['MonthlyRate'] <= 17037), 'MonthlyRate'] = 2\n", + "Train.loc[(Train['MonthlyRate'] > 17037) & (Train['MonthlyRate'] <= 22018), 'MonthlyRate'] = 3\n", + "Train.loc[ Train['MonthlyRate'] > 22018, 'MonthlyRate']=4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "745ee8cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OverTime 2 0 1 \n", + "JobLevel 5 1 5 \n", + "StockOptionLevel 4 0 3 \n", + "TotalWorkingYears 5 0 4 \n", + "MonthlyIncome 5 0 4 \n", + "MaritalStatus 3 0 2 \n", + "YearsWithCurrManager 17 0 17 \n", + "Department 3 0 2 \n", + "JobRole 9 0 8 \n", + "EnvironmentSatisfaction 4 1 4 \n", + "YearsAtCompany 5 0 4 \n", + "JobInvolvement 4 1 4 \n", + "Age 5 0 4 \n", + "JobSatisfaction 4 1 4 \n", + "WorkLifeBalance 4 1 4 \n", + "NumCompaniesWorked 10 0 9 \n", + "YearsInCurrentRole 5 0 4 \n", + "DistanceFromHome 5 0 4 \n", + "BusinessTravel 3 0 2 \n", + "YearsSinceLastPromotion 16 0 15 \n", + "HourlyRate 5 0 4 \n", + "RelationshipSatisfaction 4 1 4 \n", + "DailyRate 5 0 4 \n", + "EducationField 6 0 5 \n", + "PercentSalaryHike 15 11 25 \n", + "MonthlyRate 5 0 4 \n", + "TrainingTimesLastYear 7 0 6 \n", + "Education 5 1 5 \n", + "Gender 2 0 1 \n", + "PerformanceRating 2 3 4 \n", + "StandardHours 1 80 80 \n", + "EmployeeCount 1 1 1 \n" + ] + } + ], + "source": [ + "for i in Train.columns:\n", + " print(i,len(Train[i].unique()),\" \" ,min(Train[i]),max(Train[i]) ,\"\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "17dff408", + "metadata": {}, + "outputs": [], + "source": [ + "Train1=Train\n", + "Train1[\"Output\"]=Y" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "94f9caeb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+viTJ5/PJZrMFXuP3+2Wz2a64vKOOH6+Tz9e+73rwD2NLHs+ZYI8AwEJhYTbjL9eWhKKoqEgej0djxozRqVOnVF9fr6qqKoWHhwde4/F45HA4FBcX1+Jb3jU1NXI4HFaMBQBoB0tCsWbNmsCf8/PztW/fPi1evFgpKSmqrKzU9ddfr8LCQo0bN079+vVTVFSUysrKdNttt6mgoEBJSUlWjAUAaAfLzlH8s6ioKC1btkxz5sxRY2OjkpOTlZaWJknKzc1Vdna26urqFB8fH7iuFAAg+Gz+LnrRpo6eo3h4wRudPNE307oVkzlHAXRxX3eOwvIv3AEAvtkIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDAiFAAAIwIBQDA6F92z2wAnSP62u7qHhUZ7DFCQkNjk86cbgj2GF0eoQC+YbpHRXJP9y+tWzFZZ0QorMahJwCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAkaWhePnll5Wenq709HStWLFCklRaWiqn06mUlBTl5eUFXltRUSGXy6XU1FRlZWXJ6/VaORoAoJUsC0Vpaan27t2rLVu2aOvWrfroo49UWFiozMxMrVy5UkVFRSovL9eePXskSfPnz1dOTo5KSkrk9/u1ceNGq0YDALSBZaGw2+1auHChunXrpsjISA0cOFBHjx7VgAED1L9/f0VERMjpdKq4uFhVVVVqaGhQQkKCJMnlcqm4uNiq0QAAbWDZrVBvvPHGwJ+PHj2qP/zhD3rkkUdkt9sDyx0Oh9xut6qrq1sst9vtcrvdHdp/TEzPDq2PC+z26GCPAFwRP5/Ws/ye2X/729/0xBNPaMGCBQoPD9fRo0cDz/n9ftlsNvl8PtlstkuWd8Tx43Xy+fztWpcfvJY8njPBHgEX4eezJX4+Oy4szGb85drSk9llZWWaOnWq5s2bp7FjxyouLk4ejyfwvMfjkcPhuGR5TU2NHA6HlaMBAFrJslAcO3ZMTz75pHJzc5Weni5JGjp0qI4cOaLKyko1NzersLBQSUlJ6tevn6KiolRWViZJKigoUFJSklWjAQDawLJDT6tXr1ZjY6OWLVsWWDZx4kQtW7ZMc+bMUWNjo5KTk5WWliZJys3NVXZ2turq6hQfH6+MjAyrRgMAtIFlocjOzlZ2dvZln9u2bdslywYPHqzNmzdbNQ4AoJ34ZjYAwIhQAACMCAUAwIhQAACMCAUAwIhQAACMCAUAwIhQAACMCAUAwIhQAACMLL/MOABYxedt4rLrX/Kea1TtqXOWbJtQAPjGCouIVNmKacEeIyTctuA3kqwJBYeeAABGhAIAYMShJxhxDPgCK48BA6GMUMCIY8AXWHkMGAhlHHoCABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABgRCgCAEaEAABiFVCi2b9+u+++/XykpKXrjjTeCPQ4AQFJEsAf4itvtVl5envLz89WtWzdNnDhRd9xxh773ve8FezQAuKqFTChKS0v14x//WNddd50kKTU1VcXFxZo9e3a7thcWZuvQPH16/1uH1u9Kul0bE+wRQkZHf646Cz+fF/DzeUF7fz6/bj2b3+/3t2vLnWzVqlWqr6/X3LlzJUmbNm3SwYMH9Ytf/CLIkwHA1S1kzlH4fD7ZbBeq5vf7WzwGAARHyIQiLi5OHo8n8Njj8cjhcARxIgCAFEKhuPPOO/Xee+/pxIkTOnv2rHbs2KGkpKRgjwUAV72QOZkdGxuruXPnKiMjQ01NTRo/fryGDBkS7LEA4KoXMiezAQChKWQOPQEAQhOhAAAYEQoAgBGhAAAYEQoAgBGhAAAYEQoAgBGhAAAYEQpcETeSQiirq6vT6NGj9fnnnwd7lC6PUOCyvrqR1Lp167R161b9/ve/1yeffBLssQBJ0gcffKBJkybp6NGjwR7lqkAocFkX30jqmmuuCdxICggFGzdu1KJFi7jC9L9IyFwUEKGlurpadrs98NjhcOjgwYNBnAi4YOnSpcEe4arCOwpcFjeSAvAVQoHL4kZSAL5CKHBZ3EgKwFc4R4HL4kZSAL7CjYsAAEYcegIAGBEKAIARoQAAGBEKAIARoQAAGPHxWOAi69ev1/r16+X1emWz2XTzzTdr7ty5+va3v93hbb/66qt66623JEmffvqpevfurejoaEmSy+XSZ599puzs7A7vB+hsfDwW+NLy5ct1+PBhPffcc+rbt698Pp+2bdumX/7yl9q0aZPi4uI6bV9TpkzR5MmTlZaW1mnbBKzCOwpA0j/+8Q9t2LBBu3fvVq9evSRJYWFhevDBB1VeXq7HHntMERER2r59uyTp9OnTGjlypN5++201NDRoyZIlOnbsmJqampSenq4ZM2bo888/1+TJkzVw4EBVVVVp7dq1V7wMSn5+vkpKSrRq1SpNmTJF8fHx+utf/6oTJ07ooYceUk1Njfbt26ezZ8/qV7/6lQYNGqQzZ85o6dKl+vjjj9XU1KTExEQtWLBAERH8b43OxTkKQOfvb3DDDTcEInGxO++8U5GRkfriiy/04YcfSpIKCwuVnJysXr16af78+Ro3bpzy8/O1efNmlZaWqqioSNL5AM2aNUslJSVtulZWVVWVNmzYoBdeeEEvvPCChg0bpvz8fA0fPly/+93vJEnPPfec4uPjlZ+fr61bt6q2tlZr1qzphP8aQEv86gF8yev1Xnb5uXPnZLPZNG7cOG3ZskW33nqr8vPztWDBAtXX12v//v06deqUXnzxRUlSfX29Dh8+rCFDhigiIkIJCQltnuW+++6TJPXv31+SNHz4cEnSd77zHe3bt0+StHv3bn344YfavHmzJKmhoaHN+wFag1AAkhISElRZWSmPx9PiPhyS9P777+sHP/iBxo8fr7Fjx2rChAk6c+aMhg0bprq6Ovn9fm3YsEE9evSQJJ04cUJRUVGqra1Vt27d2nUoqFu3bi0eR0ZGXvIan8+nF198UQMHDpR0/nAYl4KHFTj0BOj8RRCnTJmip59+Wm63O7D8zTff1I4dOzR9+nTFxsZqyJAhysnJ0fjx4yVJPXv2VEJCQuCQz+nTpzVp0iTt3LnT8pnvvvtuvf766/L7/Tp37pxmzpwZOCwFdCZCAXxp3rx5euCBBzRz5kyNHj1aKSkpKi0t1YYNG9SvXz9J0oQJE1RRUaGxY8cG1svNzdUHH3wgp9OpCRMmaPTo0XrggQcsnzcrK0v19fVyOp1yOp266aabNG3aNMv3i6sPH48FABjxjgIAYEQoAABGhAIAYEQoAABGhAIAYEQoAABGhAIAYPS/uIRvd1XIY00AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"OverTime\",hue='Output', data=Train1)\n", + "\n", + "# OverTime\n", + "# 1 NO\n", + "# 2 Yes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "7cdcf1e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"JobLevel\",hue='Output', data=Train1)\n", + "\n", + "# JobInvolvement\n", + "# 1 'Low'\n", + "# 2 'Medium'\n", + "# 3 'High'\n", + "# 4 'Very High'" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "0c12289a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"StockOptionLevel\",hue='Output', data=Train1)\n", + "\n", + "# StockOptionLevel\n", + "# 1 'No'\n", + "# 2 'Low'\n", + "# 3 'Medium'\n", + "# 4 'High'\n", + "\n", + "# Mostly New Freshers (switching jobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "d5e3e14b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"MonthlyIncome\",hue='Output', data=Train1)\n", + "\n", + "# df=pd.cut(df['MonthlyIncome'], 5)\n", + "# sorted(df.unique())\n", + "\n", + "# MonthlyIncome\n", + "# 1 Interval(990.01, 4807.0, closed='right')\n", + "# 2 Interval(4807.0, 8605.0, closed='right')\n", + "# 3 Interval(8605.0, 12403.0, closed='right')\n", + "# 4 Interval(12403.0, 16201.0, closed='right')\n", + "# 5 Interval(16201.0, 19999.0, closed='right')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "38275bff", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"TotalWorkingYears\",hue='Output', data=Train1)\n", + "\n", + "# 0 (990.01, 4807.0]\n", + "# 1 (4807.0, 8605.0]\n", + "# 2 (8605.0, 12403.0]\n", + "# 3 (12403.0, 16201.0]\n", + "# 4 (16201.0, 19999.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "10eab55d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"MaritalStatus\",hue='Output', data=Train1)\n", + "# Mostly Married employees Stays\n", + "# 0 Married \n", + "# 1 Single \n", + "# 2 Other " + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "74280bf0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# YearsWithCurrManager\n", + "sns.countplot(x=\"YearsWithCurrManager\",hue='Output', data=Train1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "3ec57269", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"JobInvolvement\",hue='Output', data=Train1)\n", + "\n", + "# JobInvolvement\n", + "# 1 'Low'\n", + "# 2 'Medium'\n", + "# 3 'High'\n", + "# 4 'Very High'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "a5812c17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"JobSatisfaction\",hue='Output', data=Train1)\n", + "\n", + "# JobSatisfaction\n", + "# 1 'Low'\n", + "# 2 'Medium'\n", + "# 3 'High'\n", + "# 4 'Very High'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "444411e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"Department\",hue='Output', data=Train1)\n", + "\n", + "# 0 Research & Development\n", + "# 1 Sales\n", + "# 2 Other" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "09ae2655", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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DhhATE8PWrVsB8Hg8WCwW3zperxeLxXLR9s5qaGjC4/G2afP3B7fD0ejX/kRELkdQkMXwy3W7guK7D/uysjJfm9EYRWlpKQ6Hg0mTJnHy5EnOnDlDdXU1wcHBvnUcDgd2u52IiAgcDoevvb6+Hrvd3p6yRETED9oVFO+8806Hdvriiy/6Hm/dupW9e/eybNky4uPjqaqq4pprrqGkpIQpU6YwdOhQrFYrFRUVjB49muLiYmJjYzv2KkRExDTtCopzP/jPdf/997e7I6vVSl5eHgsXLsTlchEXF0dCQgIABQUF5Obm0tTURFRUFKmpqe3er4iImKtdQfH3v//d97i5uZmPP/6YmJiYdnWQkpJCSkoKcHasY8eOHeetM3LkSDZv3tyu/YmIiH+1+4K7c9XW1pKTk2NKQSIi0r1c1jTjgwcPprq6uqtrERGRbqjDYxRer5f9+/e3uUpbRER+uDo8RgFnL8DLysoypSAREeleOjRGUV1dTWtrK5GRkaYWJSIi3Ue7gqKqqorf/va31NXV4fF46N+/P2vWrGH48OFm1yciIgHWrsHsxx57jLlz5/Lxxx9TUVHBQw89xLJly8yuTUREuoF2BUVDQwP33HOPb3nKlCkcP37ctKJERKT7aFdQuN1uTpw44Vs+duyYWfWIiEg3064xivvuu49f//rXJCYmYrFYKC0tZfbs2WbXJiIi3UC7giIuLo4XXniBlpYWjhw5Qm1tLb/61a/Mrk1M1LdfOOHWUL/05XS10HjK6Ze+RKTrtSsoFi9ezMyZM0lNTcXlcrFx40ays7N57rnnzK5PTBJuDWVG1nq/9LUhfyaNKChEeqp2jVEcP37cN6Or1Wplzpw5be4hISIiP1ztHsw+9/ak9fX1eL1egy1EROSHol2nnubMmcPkyZO5/fbbsVgslJeXawoPEZErRLuCYurUqdx000189NFHBAcH8+CDD/Kv//qvZtcmIiLdQLuCAs7eXGjkyJFm1iIiIt3QZd2PQkRErhwKChERMWRqUDz11FNMmDCBpKQk382PysvLSU5OJj4+nsLCQt+6Bw8eJCUlhfHjx5OTk0Nra6uZpYmISDuZFhR79+7lo48+YseOHWzZsoV169Zx6NAhsrOzWb16NaWlpezfv5/du3cDkJmZyZIlS9i5cyder5eioiKzShMRkQ4wLSjGjh3Lyy+/TEhICA0NDbjdbk6dOkVkZCTDhg0jJCSE5ORkysrKqK6uxul0Eh0dDUBKSgplZWVmlSYiIh1g6qmn0NBQVq1aRVJSEjExMdTV1WGz2XzP2+12amtrz2u32WxtLvATEZHAaffPYy/XokWLSEtLIz09ncrKSiwWi+85r9eLxWLB4/FcsL0zBg7s06ntu4LN1jfQJXQbei9Eei7TguKrr76iubmZG264gV69ehEfH09ZWRnBwcG+dRwOB3a7nYiIiDZzR9XX12O32zvVf0NDEx5P22lG/P1h5XA0+rW/jtB7ISLfCQqyGH65Nu3U09GjR8nNzaW5uZnm5mbefvttpk2bxuHDh6mqqsLtdlNSUkJsbCxDhw7FarVSUVEBQHFxMbGxsWaVJiIiHWDaEUVcXBz79u1j8uTJBAcHEx8fT1JSEgMGDGDhwoW4XC7i4uJISEgAoKCggNzcXJqamoiKivLNVisiIoFl6hjFwoULWbhwYZu2mJgYduzYcd66I0eOZPPmzWaWIyIil0FXZouIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGDI1KJ555hmSkpJISkoiPz8fgPLycpKTk4mPj6ewsNC37sGDB0lJSWH8+PHk5OTQ2tpqZmkiItJOpgVFeXk5H3zwAdu2bWP79u0cOHCAkpISsrOzWb16NaWlpezfv5/du3cDkJmZyZIlS9i5cyder5eioiKzShMRkQ4wLShsNhuLFy8mLCyM0NBQhg8fTmVlJZGRkQwbNoyQkBCSk5MpKyujuroap9NJdHQ0ACkpKZSVlZlVmoiIdECIWTu+7rrrfI8rKyt58803ue+++7DZbL52u91ObW0tdXV1bdptNhu1tbWd6n/gwD6d2r4r2Gx9A11Ct6H3QqTnMi0ovvOPf/yDefPmkZWVRXBwMJWVlb7nvF4vFosFj8eDxWI5r70zGhqa8Hi8bdr8/WHlcDT6tb+O0HshIt8JCrIYfrk2dTC7oqKCOXPm8PDDD3PPPfcQERGBw+HwPe9wOLDb7ee119fXY7fbzSxNRETaybSgqKmpYf78+RQUFJCUlATAqFGjOHz4MFVVVbjdbkpKSoiNjWXo0KFYrVYqKioAKC4uJjY21qzSRESkA0w79bR27VpcLhd5eXm+tmnTppGXl8fChQtxuVzExcWRkJAAQEFBAbm5uTQ1NREVFUVqaqpZpYmISAeYFhS5ubnk5uZe8LkdO3ac1zZy5Eg2b95sVjl+52lt8ds4QGuzi+Mnm/3Sl4hceUwfzL5SBYWEUpE/1y99jc56HlBQiIg5NIWHiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCHduEiueH37hRNuDfVLX05XC42nnH7pS6SrKCjkihduDWVG1nq/9LUhfyaNKCikZzH11FNTUxMTJ07k6NGjAJSXl5OcnEx8fDyFhYW+9Q4ePEhKSgrjx48nJyeH1tZWM8sSEZEOMC0oPvvsM6ZPn05lZSUATqeT7OxsVq9eTWlpKfv372f37t0AZGZmsmTJEnbu3InX66WoqMisskREpINMC4qioiKWLl2K3W4HYN++fURGRjJs2DBCQkJITk6mrKyM6upqnE4n0dHRAKSkpFBWVmZWWSIi0kGmjVGsWLGizXJdXR02m823bLfbqa2tPa/dZrNRW1vb6f4HDuzT6X30JDZb30CXYKi71+dPei+kp/HbYLbH48FisfiWvV4vFovlou2d1dDQhMfjbdP2Q/4P6nA0dmh9f78XHa3Pn/ReyJUuKMhi+OXab9dRRERE4HA4fMsOhwO73X5ee319ve90lYiIBJ7fgmLUqFEcPnyYqqoq3G43JSUlxMbGMnToUKxWKxUVFQAUFxcTGxvrr7JEROQS/HbqyWq1kpeXx8KFC3G5XMTFxZGQkABAQUEBubm5NDU1ERUVRWpqqr/KEhGRSzA9KN555x3f45iYGHbs2HHeOiNHjmTz5s1mlyIiIpdBcz2JiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIb9NCihXLk9ri9/u+dDa7OL4yWa/9CVypVBQiOmCQkKpyJ/rl75GZz0PKChEupKCQkR8+vYLJ9wa6pe+nK4WGk85/dKXdI6CQkR8wq2hzMha75e+NuTPpBEFRU+gwWwRETGkoBAREUMKChERMaSgEBERQwoKEREx1K1+9fT666/z7LPP0trayuzZs5k5c2agSxLpUrr4UHqibhMUtbW1FBYWsnXrVsLCwpg2bRq33norI0aMCHRpIl1GFx/2HLqm5HvdJijKy8v5+c9/ztVXXw3A+PHjKSsrY8GCBZe1v6AgywXbB/X/0eWW2GFh/Qb6ra+LvV4jei++p/fie/56L/x9dHWysaVD24RbQ1m0crs5Bf0Pqx6ZzOkgl1/6upBL/Z1YvF6v10+1GFqzZg1nzpwhIyMDgE2bNrFv3z7+9Kc/BbgyEZErW7cZzPZ4PFgs36ea1+ttsywiIoHRbYIiIiICh8PhW3Y4HNjt9gBWJCIi0I2C4rbbbmPPnj0cO3aMb7/9ll27dhEbGxvoskRErnjdZjB78ODBZGRkkJqaSktLC1OnTuXmm28OdFkiIle8bjOYLSIi3VO3OfUkIiLdk4JCREQMKShERMSQgkJERAwpKERExJCCQkREDCkoRETEkIJCREQMKSi62Ouvv86ECROIj49n/fr1gS4n4Jqampg4cSJHjx4NdCkB9cwzz5CUlERSUhL5+fmBLiegnnrqKSZMmEBSUhIvvvhioMvpFp544gkWL14c6DIuSkHRhb67+dKGDRvYvn07r732Gl9++WWgywqYzz77jOnTp1NZWRnoUgKqvLycDz74gG3btrF9+3YOHDjAW2+9FeiyAmLv3r189NFH7Nixgy1btrBu3Tq+/vrrQJcVUHv27GHbtm2BLsOQgqILnXvzpd69e/tuvnSlKioqYunSpVf8LMA2m43FixcTFhZGaGgow4cP55///GegywqIsWPH8vLLLxMSEkJDQwNut5vevXsHuqyAOXHiBIWFhaSnpwe6FEPdZlLAH4K6ujpsNptv2W63s2/fvgBWFFgrVqwIdAndwnXXXed7XFlZyZtvvsnGjRsDWFFghYaGsmrVKl544QUSEhIYPHhwoEsKmCVLlpCRkUFNTU2gSzGkI4oupJsviZF//OMfPPDAA2RlZfHjH/840OUE1KJFi9izZw81NTUUFRUFupyA2LRpE0OGDCEmJibQpVySjii6UEREBJ988olvWTdfku9UVFSwaNEisrOzSUpKCnQ5AfPVV1/R3NzMDTfcQK9evYiPj+dvf/tboMsKiNLSUhwOB5MmTeLkyZOcOXOGxx9/nOzs7ECXdh4FRRe67bbbePrppzl27Bi9evVi165duue3UFNTw/z58yksLOwR3x7NdPToUVatWuU79fb2228zZcqUAFcVGOf+4mvr1q3s3bu3W4YEKCi6lG6+JBeydu1aXC4XeXl5vrZp06Yxffr0AFYVGHFxcezbt4/JkycTHBxMfHz8FX2E1VPoxkUiImJIg9kiImJIQSEiIoYUFCIiYkhBISIihhQUIiJiSD+PlW5t2bJlHDhwgI0bNxIcHAyA2+1m5syZ3HrrrWRkZJjS7+LFi7nuuut48MEHL7nutm3bePXVV3E6nbS0tDB69GgyMzPp16+fKbUZaWxsZP78+bz88ssAzJo1i+rqavr27QucnT2gubmZhx56iMmTJxvuqyPvgfyw6YhCurXFixfz7bffsmbNGl/bmjVrCA4OZtGiRQGs7Ky//OUvbNq0iT//+c8UFxdTXFxMSEhIwCZ5O3nyJJ9//nmbtqysLF9tr7/+OgUFBeTm5tLU1BSQGqXn0RGFdGtWq5WCggKmT5/OuHHj8Hq9bNiwgS1btrB161Y2btyIx+Ph6quv5tFHH2X48OEcPnyYxx57jNOnT+NwOBg5ciRPPvkkVquVm266iTvvvJNDhw5RUFDAu+++y1tvvUVoaCj9+/dn5cqV5027MmvWLKKjo/n000+pqakhJiaGP/3pTzidTtasWcO2bdsYNGgQcHbCu6ysLN566y2am5tZs2YNx48fZ8mSJQA8/fTTvuVZs2Zx1VVX8fXXXzN9+nR27drVZnny5MmsWLGCv//977S0tBATE0NWVhYhISH89Kc/5Te/+Q0ffvghdXV1zJ07lxkzZvDII4/gdDqZNGkSW7duveB7euTIEXr37k1YWBgAr732GuvWrSMoKIhBgwbx6KOPcu2117bZ5quvvmLFihWcOHECt9vNrFmzmDp1alf/c0s3paCQbu/6668nIyODnJwcPB4PK1asoKqqiu3bt7N+/Xp69erFBx98wIIFC3jzzTcpKipi8uTJTJo0iZaWFlJSUnjvvfcYP348LS0tjBs3jqeeeoqamhr+8z//kz179hAWFsYLL7zAvn37uOuuu86r4ZtvvmHdunWcOXOGxMRE9u7dS58+fQgPDz9vgr9evXpx9913t+u19evXj9LSUgB27drVZvmRRx4hKiqKvLw83G43ixcv5sUXXyQtLY3m5mb69+/Pq6++yv79+5k+fTpTpkxh5cqVJCcnU1xc7OsjPz+fZ599llOnTuFyufj5z3/OSy+9RFhYGHv27OH555/ntddeY8CAAWzdupX58+fzxhtv+LZvbW1l0aJF5OfnExUVRWNjI7/+9a8ZMWIE0dHRHfzXlJ5IQSE9wqxZs9i5cyfDhw8nLi6O/Px8qqqqmDZtmm+dU6dOceLECTIzM/nwww957rnnqKyspK6ujjNnzvjWGzNmDHB2ypWRI0dyzz33EBsbS2xs7EXnYho3bhxBQUH06dOHyMhITp48Sb9+/fB4PJ16Xd/VcqHl9957j88//5zNmzcD4HQ626x75513AhAVFUVzc3Ob13iurKwsEhISOHbsGGlpaQwePJgbb7wRgPfff58JEyYwYMAAAFJSUlixYkWbOxJWVlbyzTfftJmHyOl08sUXXygorhAKCukxrrnmGv7lX/4FODsoO2nSJDIzM33LdXV1XHXVVWRkZOB2u0lMTOSOO+6gpqaGc2eq+e5GOUFBQbzyyit8/vnn7Nmzh8cff5zbb7+drKys8/oODw/3PbZYLHi9XkaMGEFrayuVlZVtjipcLhcLFixg+fLlvnW/09LS0ma///OmPecuezwennrqKYYPHw6cDcJzp623Wq2+egAuNRvPgAEDePLJJ5k4cSK33HIL8fHxFww6r9dLa2urb9ntdtO3b982Ryn19fW+AXL54dNgtvRIv/zlL3njjTeoq6sDYOPGjcyePRuADz74gPnz5zNhwgTg7C1Z3W73efs4dOgQEydOZPjw4cybN485c+acNxBsJCwsjLS0NHJycqivrwegubmZxx9/nG+//ZbBgwfTv39/Dhw4gNfrpampiXfffbdDr/Gll17C6/X6fqn0yiuvGG4TEhKC2+2+aGgMGzaM9PR0VqxYwZkzZ7j99tspLS3l2LFjAGzZsoWrr76ayMhI3zbXXnst4eHhvqCoqalh4sSJ7N+/v92vRXo2HVFIj/TLX/6StLQ0HnjgASwWC3369OGZZ57BYrGQkZHB/Pnz6d27N3369OHf/u3f+Oabb87bx8iRI0lMTGTKlCn07t2b8PBwcnNzO1RHeno6vXr18v2E1OVyMXbsWFavXg3A3Xffzfvvv098fDyDBw9m7Nixl/zm/52cnBxWrFhBcnIyLS0t3HbbbcydO9dwG5vNxs0330xSUhLr16+/4DoPPvgg27dv59lnn+Xhhx9mzpw5zJ49G4/Hw4ABA1izZg1BQd9/hwwLC2P16tWsWLGC559/ntbWVv7jP/6D0aNHt+t1SM+n2WNFRMSQTj2JiIghBYWIiBhSUIiIiCEFhYiIGFJQiIiIIQWFiIgYUlCIiIih/wfSneE5RjxbtQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"YearsInCurrentRole\",hue='Output', data=Train1)\n", + "\n", + "# 0 0-4 yrs\n", + "# 1 4-8 yrs\n", + "# 2 8-12 yrs\n", + "# 3 12-16 yrs\n", + "# 4 16-20 yrs" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "f3d62b84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=\"Age\",hue='Output', data=Train1)\n", + "\n", + "# 0 18-27\n", + "# 1 27-35\n", + "# 2 35-44\n", + "# 3 44-52\n", + "# 4 52-60" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccb1fb94", + "metadata": {}, + "outputs": [], + "source": [ + "Train2=Train1.iloc[:,:6]\n", + "Train2[\"Output\"]=Y\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccdb3dab", + "metadata": {}, + "outputs": [], + "source": [ + "g = sns.PairGrid(Train2, hue=\"Output\")\n", + "g.map_diag(sns.histplot)\n", + "g.map_offdiag(sns.scatterplot)\n", + "g.add_legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "102deb16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "svm._classes.SVC ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 1.00 0.95 1233\n", + " 1 1.00 0.46 0.63 237\n", + "\n", + " accuracy 0.91 1470\n", + " macro avg 0.95 0.73 0.79 1470\n", + "weighted avg 0.92 0.91 0.90 1470\n", + " \n", + "\n", + "linear_model._stochastic_gradient.SGDClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.91 0.91 1233\n", + " 1 0.53 0.54 0.53 237\n", + "\n", + " accuracy 0.85 1470\n", + " macro avg 0.72 0.72 0.72 1470\n", + "weighted avg 0.85 0.85 0.85 1470\n", + " \n", + "\n", + "linear_model._perceptron.Perceptron ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "naive_bayes.MultinomialNB ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 0.95 0.97 1233\n", + " 1 0.80 1.00 0.89 237\n", + "\n", + " accuracy 0.96 1470\n", + " macro avg 0.90 0.97 0.93 1470\n", + "weighted avg 0.97 0.96 0.96 1470\n", + " \n", + "\n", + "linear_model._passive_aggressive.PassiveAggressiveClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "neighbors._classification.KNeighborsClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.99 0.95 1233\n", + " 1 0.92 0.50 0.65 237\n", + "\n", + " accuracy 0.91 1470\n", + " macro avg 0.92 0.75 0.80 1470\n", + "weighted avg 0.91 0.91 0.90 1470\n", + " \n", + "\n", + "ensemble._forest.RandomForestClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "naive_bayes.GaussianNB ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "gaussian_process._gpc.GaussianProcessClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.96 0.94 1233\n", + " 1 0.73 0.58 0.65 237\n", + "\n", + " accuracy 0.90 1470\n", + " macro avg 0.83 0.77 0.79 1470\n", + "weighted avg 0.89 0.90 0.89 1470\n", + " \n", + "\n", + "ensemble._weight_boosting.AdaBoostClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "ensemble._forest.ExtraTreesClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "ensemble._gb.GradientBoostingClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "neural_network._multilayer_perceptron.MLPClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "discriminant_analysis.QuadraticDiscriminantAnalysis ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + "sklearn.XGBClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n", + ".core.CatBoostClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", + " \n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit\n", + "from catboost import CatBoostClassifier\n", + "from datetime import datetime\n", + "n_split=8\n", + "acc=[]\n", + "\n", + "X=Train\n", + "y=Y\n", + "# Xtrain, Xtest=pd.DataFrame(),pd.DataFrame()\n", + "\n", + "import time\n", + "# cvs=[ KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit]\n", + "cvs =[ShuffleSplit] \n", + "\n", + "def convert_time(sec):\n", + " sec = sec % (24 * 3600)\n", + " hour = sec // 3600\n", + " sec %= 3600\n", + " min = sec // 60\n", + " sec %= 60\n", + " return (\"%02d:%02d:%02d\" % (hour, min, sec) )\n", + "\n", + "def stratified_cv(X, y, clf_class, shuffle=True, **kwargs):\n", + " for cv in cvs:\n", + " stratified_k_fold = cv(n_splits=n_split).split(X,y)\n", + " y_pred = y.copy()\n", + " k=0\n", + " last=0\n", + " start=time.time()\n", + " for ii, jj in (stratified_k_fold): \n", + " curr=time.time()\n", + " k+=1\n", + " Xtrain, Xtest = X.iloc[ii], X.iloc[jj]\n", + " ytrain = y.iloc[ii]\n", + " clf = clf_class(**kwargs)\n", + " clf= clf.fit(Xtrain,ytrain)\n", + "# clf.grid_search(grid,X=Train,y=Y)\n", + " y_pred.iloc[jj] = clf.predict(Xtest)\n", + " last=time.time()\n", + " p=k*100/n_split\n", + " e=convert_time(last-curr)\n", + " u=convert_time(last-start)\n", + " t=u*n_split\n", + "# print(p,\"% percent completed......\",u ,\" \",datetime.now())\n", + "\n", + " print(str(clf_class)[16:-2],str(cv)[39:-2])\n", + " print(classification_report(y,y_pred ),\"\\n\")\n", + "\n", + " # print(classification_report(y, y_pred))\n", + "\n", + " return clf\n", + "\n", + "\n", + "stratified_cv(X, y, svm.SVC)\n", + "stratified_cv(X, y, SGDClassifier,max_iter=10)\n", + "stratified_cv(X, y, Perceptron)\n", + "stratified_cv(X, y, MultinomialNB,alpha=0.01)\n", + "stratified_cv(X, y, PassiveAggressiveClassifier)\n", + "stratified_cv(X, y,GaussianNB)\n", + "stratified_cv(X, y,GaussianProcessClassifier,1.0 * RBF(1.0))\n", + "stratified_cv(X, y,KNeighborsClassifier,n_neighbors=5)\n", + "stratified_cv(X, y,RandomForestClassifier)\n", + "stratified_cv(X, y,AdaBoostClassifier)\n", + "stratified_cv(X, y,ExtraTreesClassifier)\n", + "stratified_cv(X, y,GradientBoostingClassifier)\n", + "stratified_cv(X, y,MLPClassifier,alpha=1, max_iter=1000)\n", + "stratified_cv(X, y,QuadraticDiscriminantAnalysis)\n", + "stratified_cv(X, y,xgboost.XGBClassifier)\n", + "stratified_cv(X, y,cb.CatBoostClassifier,verbose=0,random_seed= 42,\n", + " depth=4, l2_leaf_reg= 4, iterations=800, learning_rate= 0.036)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "f90a39fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1470, 33, 1)\n" + ] + } + ], + "source": [ + "# Train /= np.min(Train) # Normalise data to [0, 1] range\n", + "# Test /= np.max(Test) \n", + "\n", + "Train=np.array(Train)\n", + "Y=np.array(Y)\n", + "\n", + "Train = np.reshape(Train,( Train.shape[0],Train.shape[1], 1 ))\n", + "print(Train.shape)\n", + "\n", + "# define dataloader parameters\n", + "batch_size = 64\n", + "num_workers=0\n", + "train_tensor = torch.utils.data.TensorDataset(torch.Tensor(Train),torch.Tensor(Y)) \n", + "train_loader = torch.utils.data.DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)\n", + "\n", + "train_x, test_x, train_y, test_y = train_test_split(Train, Y,test_size=0.33, random_state=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "36928172", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv1d_10 (Conv1D) (None, 32, 64) 192 \n", + "_________________________________________________________________\n", + "conv1d_11 (Conv1D) (None, 31, 32) 4128 \n", + "_________________________________________________________________\n", + "conv1d_12 (Conv1D) (None, 30, 16) 1040 \n", + "_________________________________________________________________\n", + "conv1d_13 (Conv1D) (None, 29, 8) 264 \n", + "_________________________________________________________________\n", + "conv1d_14 (Conv1D) (None, 28, 4) 68 \n", + "_________________________________________________________________\n", + "flatten_2 (Flatten) (None, 112) 0 \n", + "_________________________________________________________________\n", + "dense_4 (Dense) (None, 2) 226 \n", + "_________________________________________________________________\n", + "dense_5 (Dense) (None, 1) 3 \n", + "=================================================================\n", + "Total params: 5,921\n", + "Trainable params: 5,921\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers import Dense, Conv1D, Flatten\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import mean_squared_error\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# clear_session()\n", + "\n", + "model = Sequential()\n", + "model.add(Conv1D(64, 2, activation=\"relu\", input_shape=(Train.shape[1], 1)))\n", + "# model.add(Flatten())\n", + "model.add(Conv1D(32, 2, activation=\"relu\"))\n", + "model.add(Conv1D(16, 2, activation=\"relu\"))\n", + "model.add(Conv1D(8, 2, activation=\"relu\"))\n", + "model.add(Conv1D(4, 2, activation=\"relu\"))\n", + "model.add(Flatten())\n", + "model.add(Dense(2, activation=\"softmax\"))\n", + "model.add(Dense(1))\n", + "model.compile(loss=\"mse\", optimizer=\"adam\")\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "b407a643", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16/16 [==============================] - 0s 3ms/step - loss: 0.1484\n", + "0.14840067726839717\n", + "MSE: 0.1484\n", + "SCORE= 99.61477191426371 %\n", + "\n" + ] + } + ], + "source": [ + "# from keras.backend import clear_session\n", + "\n", + "pred_y = model.predict(test_x)\n", + "\n", + "print(model.evaluate(test_x,test_y))\n", + " \n", + "print(\"MSE: %.4f\" % mean_squared_error(test_y, pred_y))\n", + "\n", + "score = max(0, 100-np.sqrt(mean_squared_error(test_y,pred_y)))\n", + "print(\"SCORE=\",score,\"%\\n\")\n", + "\n", + "# clear_session()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a64dfc2", + "metadata": {}, + "source": [ + "# Key Features\n", + " ## TOP 14\n", + " 1 'OverTime'\n", + " 2 'JobLevel'\n", + " 3 'StockOptionLevel'\n", + " 4 'MonthlyIncome'\n", + " 5 'TotalWorkingYears'\n", + " 6 'MaritalStatus'\n", + " 7 'YearsWithCurrManager'\n", + " 8 'JobRole'\n", + " 9 'JobInvolvement'\n", + " 10 'EnvironmentSatisfaction'\n", + " 11 'Department'\n", + " 12 'YearsInCurrentRole'\n", + " 13 'NumCompaniesWorked'\n", + " 14 'Age'\n", + " \n", + " ## LESS Priority\n", + " 15 'BusinessTravel'\n", + " 16 'WorkLifeBalance'\n", + " 17 'JobSatisfaction'\n", + " 18 'DistanceFromHome'\n", + " 19 'PerformanceRating'\n", + " 20 'YearsSinceLastPromotion'\n", + " 21 'HourlyRate'\n", + " 22 'DailyRate' \n", + " 23 'RelationshipSatisfaction'\n", + " 24 'PercentSalaryHike'\n", + " 25 'TrainingTimesLastYear'\n", + " 26 'MonthlyRate'\n", + " 27 'Education',\n", + " 26 'EducationField'\n", + " 27 'Gender'\n", + " 28 'EmployeeCount'\n", + " 29 'StandardHours'\n", + " 30 'Over18'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3df1df6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/007/solution/IBM_HR_Employee_Attrition_Profil.html b/007/solution/IBM_HR_Employee_Attrition_Profil.html new file mode 100644 index 00000000..3783750f --- /dev/null +++ b/007/solution/IBM_HR_Employee_Attrition_Profil.html @@ -0,0 +1,17729 @@ +IBM_HR_Employee_Attrition_Report

Overview

Dataset statistics

Number of variables35
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Total size in memory1.1 MiB
Average record size in memory796.8 B

Variable types

Numeric26
Categorical9

Warnings

EmployeeCount has constant value "1" Constant
Over18 has constant value "Y" Constant
StandardHours has constant value "80" Constant
EmployeeNumber has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
StockOptionLevel has 631 (42.9%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2021-08-28 23:32:49.776096
Analysis finished2021-08-28 23:32:50.550593
Duration0.77 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:52.084272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2021-08-29T05:02:52.364340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.1 KiB
No
1233 
Yes
237 

Characters and Unicode

Total characters3177
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No1233
83.9%
Yes237
 
16.1%
ValueCountFrequency (%)
no1233
83.9%
yes237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1707
53.7%
Uppercase Letter1470
46.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1233
72.2%
e237
 
13.9%
s237
 
13.9%
Uppercase Letter
ValueCountFrequency (%)
N1233
83.9%
Y237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3177
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely1043
71.0%
Travel_Frequently277
 
18.8%
Non-Travel150
 
10.2%
ValueCountFrequency (%)
travel_rarely1043
71.0%
travel_frequently277
 
18.8%
non-travel150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15358
77.7%
Uppercase Letter2940
 
14.9%
Connector Punctuation1320
 
6.7%
Dash Punctuation150
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3067
20.0%
r2790
18.2%
l2790
18.2%
a2513
16.4%
v1470
9.6%
y1320
8.6%
n427
 
2.8%
q277
 
1.8%
u277
 
1.8%
t277
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T1470
50.0%
R1043
35.5%
F277
 
9.4%
N150
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_1320
100.0%
Dash Punctuation
ValueCountFrequency (%)
-150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18298
92.6%
Common1470
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3067
16.8%
r2790
15.2%
l2790
15.2%
a2513
13.7%
T1470
8.0%
v1470
8.0%
y1320
7.2%
R1043
 
5.7%
n427
 
2.3%
F277
 
1.5%
Other values (5)1131
 
6.2%
Common
ValueCountFrequency (%)
_1320
89.8%
-150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII19768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:52.869185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5090999
Coefficient of variation (CV)0.5028240288
Kurtosis-1.203822808
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003518568352
Sum1179654
Variance162819.5937
MonotonicityNot monotonic
2021-08-29T05:02:53.125745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916
 
0.4%
4085
 
0.3%
5305
 
0.3%
13295
 
0.3%
10825
 
0.3%
3295
 
0.3%
8294
 
0.3%
14694
 
0.3%
2674
 
0.3%
2174
 
0.3%
Other values (876)1423
96.8%
ValueCountFrequency (%)
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1061
 
0.1%
1071
 
0.1%
1091
 
0.1%
1113
0.2%
1151
 
0.1%
1162
0.1%
ValueCountFrequency (%)
14991
 
0.1%
14981
 
0.1%
14962
0.1%
14953
0.2%
14921
 
0.1%
14904
0.3%
14881
 
0.1%
14853
0.2%
14821
 
0.1%
14802
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size105.7 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18877
77.6%
Uppercase Letter2494
 
10.3%
Space Separator1985
 
8.2%
Other Punctuation961
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5377
28.5%
s1533
 
8.1%
a1470
 
7.8%
l1407
 
7.5%
r1024
 
5.4%
c1024
 
5.4%
o1024
 
5.4%
m1024
 
5.4%
n1024
 
5.4%
h961
 
5.1%
Other values (4)3009
15.9%
Uppercase Letter
ValueCountFrequency (%)
R1024
41.1%
D961
38.5%
S446
17.9%
H63
 
2.5%
Space Separator
ValueCountFrequency (%)
1985
100.0%
Other Punctuation
ValueCountFrequency (%)
&961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21371
87.9%
Common2946
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5377
25.2%
s1533
 
7.2%
a1470
 
6.9%
l1407
 
6.6%
R1024
 
4.8%
r1024
 
4.8%
c1024
 
4.8%
o1024
 
4.8%
m1024
 
4.8%
n1024
 
4.8%
Other values (8)5440
25.5%
Common
ValueCountFrequency (%)
1985
67.4%
&961
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII24317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:53.418707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2021-08-29T05:02:53.604715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Education
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.91292517
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:53.742818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.024164945
Coefficient of variation (CV)0.3515932902
Kurtosis-0.5591149664
Mean2.91292517
Median Absolute Deviation (MAD)1
Skewness-0.289681082
Sum4282
Variance1.048913834
MonotonicityNot monotonic
2021-08-29T05:02:53.867962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%
ValueCountFrequency (%)
1170
 
11.6%
2282
19.2%
3572
38.9%
4398
27.1%
548
 
3.3%
ValueCountFrequency (%)
548
 
3.3%
4398
27.1%
3572
38.9%
2282
19.2%
1170
 
11.6%

EducationField
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.1 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Characters and Unicode

Total characters15484
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences606
41.2%
Medical464
31.6%
Marketing159
 
10.8%
Technical Degree132
 
9.0%
Other82
 
5.6%
Human Resources27
 
1.8%
ValueCountFrequency (%)
life606
27.1%
sciences606
27.1%
medical464
20.8%
marketing159
 
7.1%
technical132
 
5.9%
degree132
 
5.9%
other82
 
3.7%
human27
 
1.2%
resources27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12484
80.6%
Uppercase Letter2235
 
14.4%
Space Separator765
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3105
24.9%
i1967
15.8%
c1967
15.8%
n924
 
7.4%
a782
 
6.3%
s660
 
5.3%
f606
 
4.9%
l596
 
4.8%
d464
 
3.7%
r400
 
3.2%
Other values (7)1013
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M623
27.9%
L606
27.1%
S606
27.1%
T132
 
5.9%
D132
 
5.9%
O82
 
3.7%
H27
 
1.2%
R27
 
1.2%
Space Separator
ValueCountFrequency (%)
765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14719
95.1%
Common765
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3105
21.1%
i1967
13.4%
c1967
13.4%
n924
 
6.3%
a782
 
5.3%
s660
 
4.5%
M623
 
4.2%
L606
 
4.1%
f606
 
4.1%
S606
 
4.1%
Other values (15)2873
19.5%
Common
ValueCountFrequency (%)
765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

EmployeeCount
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:54.208514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1470
Variance0
MonotonicityIncreasing
2021-08-29T05:02:54.308045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%

EmployeeNumber
Real number (ℝ≥0)

UNIQUE

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.865306
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:54.459066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.0243348
Coefficient of variation (CV)0.5874180063
Kurtosis-1.223178906
Mean1024.865306
Median Absolute Deviation (MAD)533.5
Skewness0.01657401958
Sum1506552
Variance362433.2997
MonotonicityStrictly increasing
2021-08-29T05:02:54.685068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
13911
 
0.1%
13891
 
0.1%
13871
 
0.1%
13831
 
0.1%
13821
 
0.1%
13801
 
0.1%
13791
 
0.1%
13771
 
0.1%
13751
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

EnvironmentSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.721768707
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:55.732091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.093082215
Coefficient of variation (CV)0.4016073121
Kurtosis-1.202520522
Mean2.721768707
Median Absolute Deviation (MAD)1
Skewness-0.3216544477
Sum4001
Variance1.194828728
MonotonicityNot monotonic
2021-08-29T05:02:55.861089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
ValueCountFrequency (%)
1284
19.3%
2287
19.5%
3453
30.8%
4446
30.3%
ValueCountFrequency (%)
4446
30.3%
3453
30.8%
2287
19.5%
1284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
Male
882 
Female
588 

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5586
79.2%
Uppercase Letter1470
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2058
36.8%
a1470
26.3%
l1470
26.3%
m588
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.205973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
MonotonicityNot monotonic
2021-08-29T05:02:56.421027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6629
 
2.0%
9828
 
1.9%
4228
 
1.9%
4828
 
1.9%
8428
 
1.9%
5727
 
1.8%
7927
 
1.8%
9627
 
1.8%
5426
 
1.8%
5226
 
1.8%
Other values (61)1196
81.4%
ValueCountFrequency (%)
3019
1.3%
3115
1.0%
3224
1.6%
3319
1.3%
3412
0.8%
3518
1.2%
3618
1.2%
3718
1.2%
3813
0.9%
3917
1.2%
ValueCountFrequency (%)
10019
1.3%
9920
1.4%
9828
1.9%
9721
1.4%
9627
1.8%
9523
1.6%
9422
1.5%
9316
1.1%
9225
1.7%
9118
1.2%

JobInvolvement
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.729931973
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.578028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.711561143
Coefficient of variation (CV)0.2606516023
Kurtosis0.2709987665
Mean2.729931973
Median Absolute Deviation (MAD)0
Skewness-0.498419364
Sum4013
Variance0.5063192602
MonotonicityNot monotonic
2021-08-29T05:02:56.706030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%
ValueCountFrequency (%)
183
 
5.6%
2375
25.5%
3868
59.0%
4144
 
9.8%
ValueCountFrequency (%)
4144
 
9.8%
3868
59.0%
2375
25.5%
183
 
5.6%

JobLevel
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.063945578
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.829028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106939899
Coefficient of variation (CV)0.5363222319
Kurtosis0.3991520554
Mean2.063945578
Median Absolute Deviation (MAD)1
Skewness1.025401283
Sum3034
Variance1.22531594
MonotonicityNot monotonic
2021-08-29T05:02:57.028030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
569
 
4.7%
4106
 
7.2%
3218
14.8%
2534
36.3%
1543
36.9%

JobRole
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size107.9 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22358
84.2%
Uppercase Letter2838
 
10.7%
Space Separator1368
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3905
17.5%
a2580
11.5%
t2098
9.4%
c2061
9.2%
i2012
9.0%
r1984
8.9%
n1468
 
6.6%
s1391
 
6.2%
o795
 
3.6%
h762
 
3.4%
Other values (10)3302
14.8%
Uppercase Letter
ValueCountFrequency (%)
S701
24.7%
R638
22.5%
E326
11.5%
L259
 
9.1%
T259
 
9.1%
M247
 
8.7%
D225
 
7.9%
H183
 
6.4%
Space Separator
ValueCountFrequency (%)
1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25196
94.9%
Common1368
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3905
15.5%
a2580
10.2%
t2098
 
8.3%
c2061
 
8.2%
i2012
 
8.0%
r1984
 
7.9%
n1468
 
5.8%
s1391
 
5.5%
o795
 
3.2%
h762
 
3.0%
Other values (18)6140
24.4%
Common
ValueCountFrequency (%)
1368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

JobSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.728571429
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:57.376150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.102846123
Coefficient of variation (CV)0.404184443
Kurtosis-1.222192568
Mean2.728571429
Median Absolute Deviation (MAD)1
Skewness-0.3296719587
Sum4011
Variance1.216269571
MonotonicityNot monotonic
2021-08-29T05:02:57.519179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
ValueCountFrequency (%)
1289
19.7%
2280
19.0%
3442
30.1%
4459
31.2%
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
2280
19.0%
1289
19.7%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size91.9 KiB
Married
673 
Single
470 
Divorced
327 

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8677
85.5%
Uppercase Letter1470
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1673
19.3%
i1470
16.9%
e1470
16.9%
d1000
11.5%
a673
7.8%
n470
 
5.4%
g470
 
5.4%
l470
 
5.4%
v327
 
3.8%
o327
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M673
45.8%
S470
32.0%
D327
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin10147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

MonthlyIncome
Real number (ℝ≥0)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:57.860181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2021-08-29T05:02:58.055215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:58.361324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
MonotonicityNot monotonic
2021-08-29T05:02:58.586336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42233
 
0.2%
91503
 
0.2%
95582
 
0.1%
128582
 
0.1%
220742
 
0.1%
253262
 
0.1%
90962
 
0.1%
130082
 
0.1%
123552
 
0.1%
77442
 
0.1%
Other values (1417)1448
98.5%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.1%
21371
0.1%
22271
0.1%
22431
0.1%
22531
0.1%
ValueCountFrequency (%)
269991
0.1%
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269331
0.1%
269141
0.1%
268971
0.1%
268941
0.1%
268621
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:58.760371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2021-08-29T05:02:58.870367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

Over18
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size83.4 KiB
Y
1470 

Characters and Unicode

Total characters1470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1470
100.0%
ValueCountFrequency (%)
y1470
100.0%

Most occurring characters

ValueCountFrequency (%)
Y1470
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1470
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y1470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y1470
100.0%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
No
1054 
Yes
416 

Characters and Unicode

Total characters3356
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No1054
71.7%
Yes416
 
28.3%
ValueCountFrequency (%)
no1054
71.7%
yes416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1886
56.2%
Uppercase Letter1470
43.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1054
55.9%
e416
 
22.1%
s416
 
22.1%
Uppercase Letter
ValueCountFrequency (%)
N1054
71.7%
Y416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

PercentSalaryHike
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.468263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
MonotonicityNot monotonic
2021-08-29T05:02:59.615269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Real number (ℝ≥0)

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.153741497
Minimum3
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.746264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile4
Maximum4
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3608235246
Coefficient of variation (CV)0.1144112556
Kurtosis1.69593867
Mean3.153741497
Median Absolute Deviation (MAD)0
Skewness1.921882702
Sum4636
Variance0.1301936159
MonotonicityNot monotonic
2021-08-29T05:02:59.863298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
4226
 
15.4%
31244
84.6%

RelationshipSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.712244898
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.968264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.081208886
Coefficient of variation (CV)0.3986398453
Kurtosis-1.184813982
Mean2.712244898
Median Absolute Deviation (MAD)1
Skewness-0.3028275652
Sum3987
Variance1.169012656
MonotonicityNot monotonic
2021-08-29T05:03:00.103265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
ValueCountFrequency (%)
1276
18.8%
2303
20.6%
3459
31.2%
4432
29.4%
ValueCountFrequency (%)
4432
29.4%
3459
31.2%
2303
20.6%
1276
18.8%

StandardHours
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80
Minimum80
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.231297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile80
Q180
median80
Q380
95-th percentile80
Maximum80
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean80
Median Absolute Deviation (MAD)0
Skewness0
Sum117600
Variance0
MonotonicityIncreasing
2021-08-29T05:03:00.352326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%

StockOptionLevel
Real number (ℝ≥0)

ZEROS

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.793877551
Minimum0
Maximum3
Zeros631
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.481292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8520766679
Coefficient of variation (CV)1.073309942
Kurtosis0.3646343338
Mean0.793877551
Median Absolute Deviation (MAD)1
Skewness0.9689803168
Sum1167
Variance0.726034648
MonotonicityNot monotonic
2021-08-29T05:03:00.600291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
385
 
5.8%
2158
 
10.7%
1596
40.5%
0631
42.9%

TotalWorkingYears
Real number (ℝ≥0)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.789573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2021-08-29T05:03:00.976547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.123560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
MonotonicityNot monotonic
2021-08-29T05:03:01.222559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

WorkLifeBalance
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.76122449
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.340562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7064758297
Coefficient of variation (CV)0.2558559915
Kurtosis0.4194604953
Mean2.76122449
Median Absolute Deviation (MAD)0
Skewness-0.5524802991
Sum4059
Variance0.499108098
MonotonicityNot monotonic
2021-08-29T05:03:01.459699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%
ValueCountFrequency (%)
180
 
5.4%
2344
 
23.4%
3893
60.7%
4153
 
10.4%
ValueCountFrequency (%)
4153
 
10.4%
3893
60.7%
2344
 
23.4%
180
 
5.4%

YearsAtCompany
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.597768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityNot monotonic
2021-08-29T05:03:01.773307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.917293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
MonotonicityNot monotonic
2021-08-29T05:03:02.054959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:02.184954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2021-08-29T05:03:02.377955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:02.513957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2021-08-29T05:03:02.721961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%
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00000000..7838fe03 --- /dev/null +++ b/007/solution/employee_attrition.csv @@ -0,0 +1,1471 @@ +Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager +41,Yes,Travel_Rarely,1102,Sales,1,2,Life Sciences,1,1,2,Female,94,3,2,Sales Executive,4,Single,5993,19479,8,Y,Yes,11,3,1,80,0,8,0,1,6,4,0,5 +49,No,Travel_Frequently,279,Research & Development,8,1,Life Sciences,1,2,3,Male,61,2,2,Research Scientist,2,Married,5130,24907,1,Y,No,23,4,4,80,1,10,3,3,10,7,1,7 +37,Yes,Travel_Rarely,1373,Research & Development,2,2,Other,1,4,4,Male,92,2,1,Laboratory Technician,3,Single,2090,2396,6,Y,Yes,15,3,2,80,0,7,3,3,0,0,0,0 +33,No,Travel_Frequently,1392,Research & Development,3,4,Life Sciences,1,5,4,Female,56,3,1,Research Scientist,3,Married,2909,23159,1,Y,Yes,11,3,3,80,0,8,3,3,8,7,3,0 +27,No,Travel_Rarely,591,Research & Development,2,1,Medical,1,7,1,Male,40,3,1,Laboratory Technician,2,Married,3468,16632,9,Y,No,12,3,4,80,1,6,3,3,2,2,2,2 +32,No,Travel_Frequently,1005,Research & Development,2,2,Life Sciences,1,8,4,Male,79,3,1,Laboratory Technician,4,Single,3068,11864,0,Y,No,13,3,3,80,0,8,2,2,7,7,3,6 +59,No,Travel_Rarely,1324,Research & Development,3,3,Medical,1,10,3,Female,81,4,1,Laboratory Technician,1,Married,2670,9964,4,Y,Yes,20,4,1,80,3,12,3,2,1,0,0,0 +30,No,Travel_Rarely,1358,Research & Development,24,1,Life Sciences,1,11,4,Male,67,3,1,Laboratory Technician,3,Divorced,2693,13335,1,Y,No,22,4,2,80,1,1,2,3,1,0,0,0 +38,No,Travel_Frequently,216,Research & Development,23,3,Life Sciences,1,12,4,Male,44,2,3,Manufacturing Director,3,Single,9526,8787,0,Y,No,21,4,2,80,0,10,2,3,9,7,1,8 +36,No,Travel_Rarely,1299,Research & Development,27,3,Medical,1,13,3,Male,94,3,2,Healthcare Representative,3,Married,5237,16577,6,Y,No,13,3,2,80,2,17,3,2,7,7,7,7 +35,No,Travel_Rarely,809,Research & Development,16,3,Medical,1,14,1,Male,84,4,1,Laboratory Technician,2,Married,2426,16479,0,Y,No,13,3,3,80,1,6,5,3,5,4,0,3 +29,No,Travel_Rarely,153,Research & Development,15,2,Life Sciences,1,15,4,Female,49,2,2,Laboratory Technician,3,Single,4193,12682,0,Y,Yes,12,3,4,80,0,10,3,3,9,5,0,8 +31,No,Travel_Rarely,670,Research & Development,26,1,Life Sciences,1,16,1,Male,31,3,1,Research Scientist,3,Divorced,2911,15170,1,Y,No,17,3,4,80,1,5,1,2,5,2,4,3 +34,No,Travel_Rarely,1346,Research & Development,19,2,Medical,1,18,2,Male,93,3,1,Laboratory Technician,4,Divorced,2661,8758,0,Y,No,11,3,3,80,1,3,2,3,2,2,1,2 +28,Yes,Travel_Rarely,103,Research & Development,24,3,Life Sciences,1,19,3,Male,50,2,1,Laboratory Technician,3,Single,2028,12947,5,Y,Yes,14,3,2,80,0,6,4,3,4,2,0,3 +29,No,Travel_Rarely,1389,Research & Development,21,4,Life Sciences,1,20,2,Female,51,4,3,Manufacturing Director,1,Divorced,9980,10195,1,Y,No,11,3,3,80,1,10,1,3,10,9,8,8 +32,No,Travel_Rarely,334,Research & Development,5,2,Life Sciences,1,21,1,Male,80,4,1,Research Scientist,2,Divorced,3298,15053,0,Y,Yes,12,3,4,80,2,7,5,2,6,2,0,5 +22,No,Non-Travel,1123,Research & Development,16,2,Medical,1,22,4,Male,96,4,1,Laboratory Technician,4,Divorced,2935,7324,1,Y,Yes,13,3,2,80,2,1,2,2,1,0,0,0 +53,No,Travel_Rarely,1219,Sales,2,4,Life Sciences,1,23,1,Female,78,2,4,Manager,4,Married,15427,22021,2,Y,No,16,3,3,80,0,31,3,3,25,8,3,7 +38,No,Travel_Rarely,371,Research & Development,2,3,Life Sciences,1,24,4,Male,45,3,1,Research Scientist,4,Single,3944,4306,5,Y,Yes,11,3,3,80,0,6,3,3,3,2,1,2 +24,No,Non-Travel,673,Research & Development,11,2,Other,1,26,1,Female,96,4,2,Manufacturing Director,3,Divorced,4011,8232,0,Y,No,18,3,4,80,1,5,5,2,4,2,1,3 +36,Yes,Travel_Rarely,1218,Sales,9,4,Life Sciences,1,27,3,Male,82,2,1,Sales Representative,1,Single,3407,6986,7,Y,No,23,4,2,80,0,10,4,3,5,3,0,3 +34,No,Travel_Rarely,419,Research & Development,7,4,Life Sciences,1,28,1,Female,53,3,3,Research Director,2,Single,11994,21293,0,Y,No,11,3,3,80,0,13,4,3,12,6,2,11 +21,No,Travel_Rarely,391,Research & Development,15,2,Life Sciences,1,30,3,Male,96,3,1,Research Scientist,4,Single,1232,19281,1,Y,No,14,3,4,80,0,0,6,3,0,0,0,0 +34,Yes,Travel_Rarely,699,Research & Development,6,1,Medical,1,31,2,Male,83,3,1,Research Scientist,1,Single,2960,17102,2,Y,No,11,3,3,80,0,8,2,3,4,2,1,3 +53,No,Travel_Rarely,1282,Research & Development,5,3,Other,1,32,3,Female,58,3,5,Manager,3,Divorced,19094,10735,4,Y,No,11,3,4,80,1,26,3,2,14,13,4,8 +32,Yes,Travel_Frequently,1125,Research & Development,16,1,Life Sciences,1,33,2,Female,72,1,1,Research Scientist,1,Single,3919,4681,1,Y,Yes,22,4,2,80,0,10,5,3,10,2,6,7 +42,No,Travel_Rarely,691,Sales,8,4,Marketing,1,35,3,Male,48,3,2,Sales Executive,2,Married,6825,21173,0,Y,No,11,3,4,80,1,10,2,3,9,7,4,2 +44,No,Travel_Rarely,477,Research & Development,7,4,Medical,1,36,1,Female,42,2,3,Healthcare Representative,4,Married,10248,2094,3,Y,No,14,3,4,80,1,24,4,3,22,6,5,17 +46,No,Travel_Rarely,705,Sales,2,4,Marketing,1,38,2,Female,83,3,5,Manager,1,Single,18947,22822,3,Y,No,12,3,4,80,0,22,2,2,2,2,2,1 +33,No,Travel_Rarely,924,Research & Development,2,3,Medical,1,39,3,Male,78,3,1,Laboratory Technician,4,Single,2496,6670,4,Y,No,11,3,4,80,0,7,3,3,1,1,0,0 +44,No,Travel_Rarely,1459,Research & Development,10,4,Other,1,40,4,Male,41,3,2,Healthcare Representative,4,Married,6465,19121,2,Y,Yes,13,3,4,80,0,9,5,4,4,2,1,3 +30,No,Travel_Rarely,125,Research & Development,9,2,Medical,1,41,4,Male,83,2,1,Laboratory Technician,3,Single,2206,16117,1,Y,No,13,3,1,80,0,10,5,3,10,0,1,8 +39,Yes,Travel_Rarely,895,Sales,5,3,Technical Degree,1,42,4,Male,56,3,2,Sales Representative,4,Married,2086,3335,3,Y,No,14,3,3,80,1,19,6,4,1,0,0,0 +24,Yes,Travel_Rarely,813,Research & Development,1,3,Medical,1,45,2,Male,61,3,1,Research Scientist,4,Married,2293,3020,2,Y,Yes,16,3,1,80,1,6,2,2,2,0,2,0 +43,No,Travel_Rarely,1273,Research & Development,2,2,Medical,1,46,4,Female,72,4,1,Research Scientist,3,Divorced,2645,21923,1,Y,No,12,3,4,80,2,6,3,2,5,3,1,4 +50,Yes,Travel_Rarely,869,Sales,3,2,Marketing,1,47,1,Male,86,2,1,Sales Representative,3,Married,2683,3810,1,Y,Yes,14,3,3,80,0,3,2,3,3,2,0,2 +35,No,Travel_Rarely,890,Sales,2,3,Marketing,1,49,4,Female,97,3,1,Sales Representative,4,Married,2014,9687,1,Y,No,13,3,1,80,0,2,3,3,2,2,2,2 +36,No,Travel_Rarely,852,Research & Development,5,4,Life Sciences,1,51,2,Female,82,2,1,Research Scientist,1,Married,3419,13072,9,Y,Yes,14,3,4,80,1,6,3,4,1,1,0,0 +33,No,Travel_Frequently,1141,Sales,1,3,Life Sciences,1,52,3,Female,42,4,2,Sales Executive,1,Married,5376,3193,2,Y,No,19,3,1,80,2,10,3,3,5,3,1,3 +35,No,Travel_Rarely,464,Research & Development,4,2,Other,1,53,3,Male,75,3,1,Laboratory Technician,4,Divorced,1951,10910,1,Y,No,12,3,3,80,1,1,3,3,1,0,0,0 +27,No,Travel_Rarely,1240,Research & Development,2,4,Life Sciences,1,54,4,Female,33,3,1,Laboratory Technician,1,Divorced,2341,19715,1,Y,No,13,3,4,80,1,1,6,3,1,0,0,0 +26,Yes,Travel_Rarely,1357,Research & Development,25,3,Life Sciences,1,55,1,Male,48,1,1,Laboratory Technician,3,Single,2293,10558,1,Y,No,12,3,3,80,0,1,2,2,1,0,0,1 +27,No,Travel_Frequently,994,Sales,8,3,Life Sciences,1,56,4,Male,37,3,3,Sales Executive,3,Single,8726,2975,1,Y,No,15,3,4,80,0,9,0,3,9,8,1,7 +30,No,Travel_Frequently,721,Research & Development,1,2,Medical,1,57,3,Female,58,3,2,Laboratory Technician,4,Single,4011,10781,1,Y,No,23,4,4,80,0,12,2,3,12,8,3,7 +41,Yes,Travel_Rarely,1360,Research & Development,12,3,Technical Degree,1,58,2,Female,49,3,5,Research Director,3,Married,19545,16280,1,Y,No,12,3,4,80,0,23,0,3,22,15,15,8 +34,No,Non-Travel,1065,Sales,23,4,Marketing,1,60,2,Male,72,3,2,Sales Executive,3,Single,4568,10034,0,Y,No,20,4,3,80,0,10,2,3,9,5,8,7 +37,No,Travel_Rarely,408,Research & Development,19,2,Life Sciences,1,61,2,Male,73,3,1,Research Scientist,2,Married,3022,10227,4,Y,No,21,4,1,80,0,8,1,3,1,0,0,0 +46,No,Travel_Frequently,1211,Sales,5,4,Marketing,1,62,1,Male,98,3,2,Sales Executive,4,Single,5772,20445,4,Y,Yes,21,4,3,80,0,14,4,3,9,6,0,8 +35,No,Travel_Rarely,1229,Research & Development,8,1,Life Sciences,1,63,4,Male,36,4,1,Laboratory Technician,4,Married,2269,4892,1,Y,No,19,3,4,80,0,1,2,3,1,0,0,1 +48,Yes,Travel_Rarely,626,Research & Development,1,2,Life Sciences,1,64,1,Male,98,2,3,Laboratory Technician,3,Single,5381,19294,9,Y,Yes,13,3,4,80,0,23,2,3,1,0,0,0 +28,Yes,Travel_Rarely,1434,Research & Development,5,4,Technical Degree,1,65,3,Male,50,3,1,Laboratory Technician,3,Single,3441,11179,1,Y,Yes,13,3,3,80,0,2,3,2,2,2,2,2 +44,No,Travel_Rarely,1488,Sales,1,5,Marketing,1,68,2,Female,75,3,2,Sales Executive,1,Divorced,5454,4009,5,Y,Yes,21,4,3,80,1,9,2,2,4,3,1,3 +35,No,Non-Travel,1097,Research & Development,11,2,Medical,1,70,3,Male,79,2,3,Healthcare Representative,1,Married,9884,8302,2,Y,Yes,13,3,3,80,1,10,3,3,4,0,2,3 +26,No,Travel_Rarely,1443,Sales,23,3,Marketing,1,72,3,Female,47,2,2,Sales Executive,4,Married,4157,21436,7,Y,Yes,19,3,3,80,1,5,2,2,2,2,0,0 +33,No,Travel_Frequently,515,Research & Development,1,2,Life Sciences,1,73,1,Female,98,3,3,Research Director,4,Single,13458,15146,1,Y,Yes,12,3,3,80,0,15,1,3,15,14,8,12 +35,No,Travel_Frequently,853,Sales,18,5,Life Sciences,1,74,2,Male,71,3,3,Sales Executive,1,Married,9069,11031,1,Y,No,22,4,4,80,1,9,3,2,9,8,1,8 +35,No,Travel_Rarely,1142,Research & Development,23,4,Medical,1,75,3,Female,30,3,1,Laboratory Technician,1,Married,4014,16002,3,Y,Yes,15,3,3,80,1,4,3,3,2,2,2,2 +31,No,Travel_Rarely,655,Research & Development,7,4,Life Sciences,1,76,4,Male,48,3,2,Laboratory Technician,4,Divorced,5915,9528,3,Y,No,22,4,4,80,1,10,3,2,7,7,1,7 +37,No,Travel_Rarely,1115,Research & Development,1,4,Life Sciences,1,77,1,Male,51,2,2,Manufacturing Director,3,Divorced,5993,2689,1,Y,No,18,3,3,80,1,7,2,4,7,5,0,7 +32,No,Travel_Rarely,427,Research & Development,1,3,Medical,1,78,1,Male,33,3,2,Manufacturing Director,4,Married,6162,10877,1,Y,Yes,22,4,2,80,1,9,3,3,9,8,7,8 +38,No,Travel_Frequently,653,Research & Development,29,5,Life Sciences,1,79,4,Female,50,3,2,Laboratory Technician,4,Single,2406,5456,1,Y,No,11,3,4,80,0,10,2,3,10,3,9,9 +50,No,Travel_Rarely,989,Research & Development,7,2,Medical,1,80,2,Female,43,2,5,Research Director,3,Divorced,18740,16701,5,Y,Yes,12,3,4,80,1,29,2,2,27,3,13,8 +59,No,Travel_Rarely,1435,Sales,25,3,Life Sciences,1,81,1,Female,99,3,3,Sales Executive,1,Single,7637,2354,7,Y,No,11,3,4,80,0,28,3,2,21,16,7,9 +36,No,Travel_Rarely,1223,Research & Development,8,3,Technical Degree,1,83,3,Female,59,3,3,Healthcare Representative,3,Divorced,10096,8202,1,Y,No,13,3,2,80,3,17,2,3,17,14,12,8 +55,No,Travel_Rarely,836,Research & Development,8,3,Medical,1,84,4,Female,33,3,4,Manager,3,Divorced,14756,19730,2,Y,Yes,14,3,3,80,3,21,2,3,5,0,0,2 +36,No,Travel_Frequently,1195,Research & Development,11,3,Life Sciences,1,85,2,Male,95,2,2,Manufacturing Director,2,Single,6499,22656,1,Y,No,13,3,3,80,0,6,3,3,6,5,0,3 +45,No,Travel_Rarely,1339,Research & Development,7,3,Life Sciences,1,86,2,Male,59,3,3,Research Scientist,1,Divorced,9724,18787,2,Y,No,17,3,3,80,1,25,2,3,1,0,0,0 +35,No,Travel_Frequently,664,Research & Development,1,3,Medical,1,88,2,Male,79,3,1,Research Scientist,1,Married,2194,5868,4,Y,No,13,3,4,80,1,5,2,2,3,2,1,2 +36,Yes,Travel_Rarely,318,Research & Development,9,3,Medical,1,90,4,Male,79,2,1,Research Scientist,3,Married,3388,21777,0,Y,Yes,17,3,1,80,1,2,0,2,1,0,0,0 +59,No,Travel_Frequently,1225,Sales,1,1,Life Sciences,1,91,1,Female,57,2,2,Sales Executive,3,Single,5473,24668,7,Y,No,11,3,4,80,0,20,2,2,4,3,1,3 +29,No,Travel_Rarely,1328,Research & Development,2,3,Life Sciences,1,94,3,Male,76,3,1,Research Scientist,2,Married,2703,4956,0,Y,No,23,4,4,80,1,6,3,3,5,4,0,4 +31,No,Travel_Rarely,1082,Research & Development,1,4,Medical,1,95,3,Male,87,3,1,Research Scientist,2,Single,2501,18775,1,Y,No,17,3,2,80,0,1,4,3,1,1,1,0 +32,No,Travel_Rarely,548,Research & Development,1,3,Life Sciences,1,96,2,Male,66,3,2,Research Scientist,2,Married,6220,7346,1,Y,No,17,3,2,80,2,10,3,3,10,4,0,9 +36,No,Travel_Rarely,132,Research & Development,6,3,Life Sciences,1,97,2,Female,55,4,1,Laboratory Technician,4,Married,3038,22002,3,Y,No,12,3,2,80,0,5,3,3,1,0,0,0 +31,No,Travel_Rarely,746,Research & Development,8,4,Life Sciences,1,98,3,Female,61,3,2,Manufacturing Director,4,Single,4424,20682,1,Y,No,23,4,4,80,0,11,2,3,11,7,1,8 +35,No,Travel_Rarely,776,Sales,1,4,Marketing,1,100,3,Male,32,2,2,Sales Executive,1,Single,4312,23016,0,Y,No,14,3,2,80,0,16,2,3,15,13,2,8 +45,No,Travel_Rarely,193,Research & Development,6,4,Other,1,101,4,Male,52,3,3,Research Director,1,Married,13245,15067,4,Y,Yes,14,3,2,80,0,17,3,4,0,0,0,0 +37,No,Travel_Rarely,397,Research & Development,7,4,Medical,1,102,1,Male,30,3,3,Research Director,3,Single,13664,25258,4,Y,No,13,3,1,80,0,16,3,4,5,2,0,2 +46,No,Travel_Rarely,945,Human Resources,5,2,Medical,1,103,2,Male,80,3,2,Human Resources,2,Divorced,5021,10425,8,Y,Yes,22,4,4,80,1,16,2,3,4,2,0,2 +30,No,Travel_Rarely,852,Research & Development,1,1,Life Sciences,1,104,4,Male,55,2,2,Laboratory Technician,4,Married,5126,15998,1,Y,Yes,12,3,3,80,2,10,1,2,10,8,3,0 +35,No,Travel_Rarely,1214,Research & Development,1,3,Medical,1,105,2,Male,30,2,1,Research Scientist,3,Single,2859,26278,1,Y,No,18,3,1,80,0,6,3,3,6,4,0,4 +55,No,Travel_Rarely,111,Sales,1,2,Life Sciences,1,106,1,Male,70,3,3,Sales Executive,4,Married,10239,18092,3,Y,No,14,3,4,80,1,24,4,3,1,0,1,0 +38,No,Non-Travel,573,Research & Development,6,3,Medical,1,107,2,Female,79,1,2,Research Scientist,4,Divorced,5329,15717,7,Y,Yes,12,3,4,80,3,17,3,3,13,11,1,9 +34,No,Travel_Rarely,1153,Research & Development,1,2,Medical,1,110,1,Male,94,3,2,Manufacturing Director,2,Married,4325,17736,1,Y,No,15,3,3,80,0,5,2,3,5,2,1,3 +56,No,Travel_Rarely,1400,Research & Development,7,3,Life Sciences,1,112,4,Male,49,1,3,Manufacturing Director,4,Single,7260,21698,4,Y,No,11,3,1,80,0,37,3,2,6,4,0,2 +23,No,Travel_Rarely,541,Sales,2,1,Technical Degree,1,113,3,Male,62,3,1,Sales Representative,1,Divorced,2322,9518,3,Y,No,13,3,3,80,1,3,3,3,0,0,0,0 +51,No,Travel_Rarely,432,Research & Development,9,4,Life Sciences,1,116,4,Male,96,3,1,Laboratory Technician,4,Married,2075,18725,3,Y,No,23,4,2,80,2,10,4,3,4,2,0,3 +30,No,Travel_Rarely,288,Research & Development,2,3,Life Sciences,1,117,3,Male,99,2,2,Healthcare Representative,4,Married,4152,15830,1,Y,No,19,3,1,80,3,11,3,3,11,10,10,8 +46,Yes,Travel_Rarely,669,Sales,9,2,Medical,1,118,3,Male,64,2,3,Sales Executive,4,Single,9619,13596,1,Y,No,16,3,4,80,0,9,3,3,9,8,4,7 +40,No,Travel_Frequently,530,Research & Development,1,4,Life Sciences,1,119,3,Male,78,2,4,Healthcare Representative,2,Married,13503,14115,1,Y,No,22,4,4,80,1,22,3,2,22,3,11,11 +51,No,Travel_Rarely,632,Sales,21,4,Marketing,1,120,3,Male,71,3,2,Sales Executive,4,Single,5441,8423,0,Y,Yes,22,4,4,80,0,11,2,1,10,7,1,0 +30,No,Travel_Rarely,1334,Sales,4,2,Medical,1,121,3,Female,63,2,2,Sales Executive,2,Divorced,5209,19760,1,Y,Yes,12,3,2,80,3,11,4,2,11,8,2,7 +46,No,Travel_Frequently,638,Research & Development,1,3,Medical,1,124,3,Male,40,2,3,Healthcare Representative,1,Married,10673,3142,2,Y,Yes,13,3,3,80,1,21,5,2,10,9,9,5 +32,No,Travel_Rarely,1093,Sales,6,4,Medical,1,125,2,Male,87,3,2,Sales Executive,3,Single,5010,24301,1,Y,No,16,3,1,80,0,12,0,3,11,8,5,7 +54,No,Travel_Rarely,1217,Research & Development,2,4,Technical Degree,1,126,1,Female,60,3,3,Research Director,3,Married,13549,24001,9,Y,No,12,3,1,80,1,16,5,1,4,3,0,3 +24,No,Travel_Rarely,1353,Sales,3,2,Other,1,128,1,Female,33,3,2,Sales Executive,3,Married,4999,17519,0,Y,No,21,4,1,80,1,4,2,2,3,2,0,2 +28,No,Non-Travel,120,Sales,4,3,Medical,1,129,2,Male,43,3,2,Sales Executive,3,Married,4221,8863,1,Y,No,15,3,2,80,0,5,3,4,5,4,0,4 +58,No,Travel_Rarely,682,Sales,10,4,Medical,1,131,4,Male,37,3,4,Sales Executive,3,Single,13872,24409,0,Y,No,13,3,3,80,0,38,1,2,37,10,1,8 +44,No,Non-Travel,489,Research & Development,23,3,Medical,1,132,2,Male,67,3,2,Laboratory Technician,2,Married,2042,25043,4,Y,No,12,3,3,80,1,17,3,4,3,2,1,2 +37,Yes,Travel_Rarely,807,Human Resources,6,4,Human Resources,1,133,3,Male,63,3,1,Human Resources,1,Divorced,2073,23648,4,Y,Yes,22,4,4,80,0,7,3,3,3,2,0,2 +32,No,Travel_Rarely,827,Research & Development,1,1,Life Sciences,1,134,4,Male,71,3,1,Research Scientist,1,Single,2956,15178,1,Y,No,13,3,4,80,0,1,2,3,1,0,0,0 +20,Yes,Travel_Frequently,871,Research & Development,6,3,Life Sciences,1,137,4,Female,66,2,1,Laboratory Technician,4,Single,2926,19783,1,Y,Yes,18,3,2,80,0,1,5,3,1,0,1,0 +34,No,Travel_Rarely,665,Research & Development,6,4,Other,1,138,1,Female,41,3,2,Research Scientist,3,Single,4809,12482,1,Y,No,14,3,3,80,0,16,3,3,16,13,2,10 +37,No,Non-Travel,1040,Research & Development,2,2,Life Sciences,1,139,3,Male,100,2,2,Healthcare Representative,4,Divorced,5163,15850,5,Y,No,14,3,4,80,1,17,2,4,1,0,0,0 +59,No,Non-Travel,1420,Human Resources,2,4,Human Resources,1,140,3,Female,32,2,5,Manager,4,Married,18844,21922,9,Y,No,21,4,4,80,1,30,3,3,3,2,2,2 +50,No,Travel_Frequently,1115,Research & Development,1,3,Life Sciences,1,141,1,Female,73,3,5,Research Director,2,Married,18172,9755,3,Y,Yes,19,3,1,80,0,28,1,2,8,3,0,7 +25,Yes,Travel_Rarely,240,Sales,5,3,Marketing,1,142,3,Male,46,2,2,Sales Executive,3,Single,5744,26959,1,Y,Yes,11,3,4,80,0,6,1,3,6,4,0,3 +25,No,Travel_Rarely,1280,Research & Development,7,1,Medical,1,143,4,Male,64,2,1,Research Scientist,4,Married,2889,26897,1,Y,No,11,3,3,80,2,2,2,3,2,2,2,1 +22,No,Travel_Rarely,534,Research & Development,15,3,Medical,1,144,2,Female,59,3,1,Laboratory Technician,4,Single,2871,23785,1,Y,No,15,3,3,80,0,1,5,3,0,0,0,0 +51,No,Travel_Frequently,1456,Research & Development,1,4,Medical,1,145,1,Female,30,2,3,Healthcare Representative,1,Single,7484,25796,3,Y,No,20,4,3,80,0,23,1,2,13,12,12,8 +34,Yes,Travel_Frequently,658,Research & Development,7,3,Life Sciences,1,147,1,Male,66,1,2,Laboratory Technician,3,Single,6074,22887,1,Y,Yes,24,4,4,80,0,9,3,3,9,7,0,6 +54,No,Non-Travel,142,Human Resources,26,3,Human Resources,1,148,4,Female,30,4,4,Manager,4,Single,17328,13871,2,Y,Yes,12,3,3,80,0,23,3,3,5,3,4,4 +24,No,Travel_Rarely,1127,Research & Development,18,1,Life Sciences,1,150,2,Male,52,3,1,Laboratory Technician,3,Married,2774,13257,0,Y,No,12,3,3,80,1,6,2,3,5,3,1,2 +34,No,Travel_Rarely,1031,Research & Development,6,4,Life Sciences,1,151,3,Female,45,2,2,Research Scientist,2,Divorced,4505,15000,6,Y,No,15,3,3,80,1,12,3,3,1,0,0,0 +37,No,Travel_Rarely,1189,Sales,3,3,Life Sciences,1,152,3,Male,87,3,3,Sales Executive,4,Single,7428,14506,2,Y,No,12,3,1,80,0,12,3,3,5,3,1,3 +34,No,Travel_Rarely,1354,Research & Development,5,3,Medical,1,153,3,Female,45,2,3,Manager,1,Single,11631,5615,2,Y,No,12,3,4,80,0,14,6,3,11,10,5,8 +36,No,Travel_Frequently,1467,Sales,11,2,Technical Degree,1,154,2,Female,92,3,3,Sales Executive,4,Married,9738,22952,0,Y,No,14,3,3,80,1,10,6,3,9,7,2,8 +36,No,Travel_Rarely,922,Research & Development,3,2,Life Sciences,1,155,1,Female,39,3,1,Laboratory Technician,4,Divorced,2835,2561,5,Y,No,22,4,1,80,1,7,2,3,1,0,0,0 +43,No,Travel_Frequently,394,Sales,26,2,Life Sciences,1,158,3,Male,92,3,4,Manager,4,Married,16959,19494,1,Y,Yes,12,3,4,80,2,25,3,4,25,12,4,12 +30,No,Travel_Frequently,1312,Research & Development,23,3,Life Sciences,1,159,1,Male,96,1,1,Research Scientist,3,Divorced,2613,22310,1,Y,No,25,4,3,80,3,10,2,2,10,7,0,9 +33,No,Non-Travel,750,Sales,22,2,Marketing,1,160,3,Male,95,3,2,Sales Executive,2,Married,6146,15480,0,Y,No,13,3,1,80,1,8,2,4,7,7,0,7 +56,Yes,Travel_Rarely,441,Research & Development,14,4,Life Sciences,1,161,2,Female,72,3,1,Research Scientist,2,Married,4963,4510,9,Y,Yes,18,3,1,80,3,7,2,3,5,4,4,3 +51,No,Travel_Rarely,684,Research & Development,6,3,Life Sciences,1,162,1,Male,51,3,5,Research Director,3,Single,19537,6462,7,Y,No,13,3,3,80,0,23,5,3,20,18,15,15 +31,Yes,Travel_Rarely,249,Sales,6,4,Life Sciences,1,163,2,Male,76,1,2,Sales Executive,3,Married,6172,20739,4,Y,Yes,18,3,2,80,0,12,3,2,7,7,7,7 +26,No,Travel_Rarely,841,Research & Development,6,3,Other,1,164,3,Female,46,2,1,Research Scientist,2,Married,2368,23300,1,Y,No,19,3,3,80,0,5,3,2,5,4,4,3 +58,Yes,Travel_Rarely,147,Research & Development,23,4,Medical,1,165,4,Female,94,3,3,Healthcare Representative,4,Married,10312,3465,1,Y,No,12,3,4,80,1,40,3,2,40,10,15,6 +19,Yes,Travel_Rarely,528,Sales,22,1,Marketing,1,167,4,Male,50,3,1,Sales Representative,3,Single,1675,26820,1,Y,Yes,19,3,4,80,0,0,2,2,0,0,0,0 +22,No,Travel_Rarely,594,Research & Development,2,1,Technical Degree,1,169,3,Male,100,3,1,Laboratory Technician,4,Married,2523,19299,0,Y,No,14,3,3,80,1,3,2,3,2,1,2,1 +49,No,Travel_Rarely,470,Research & Development,20,4,Medical,1,170,3,Female,96,3,2,Manufacturing Director,1,Married,6567,5549,1,Y,No,14,3,3,80,0,16,2,2,15,11,5,11 +43,No,Travel_Frequently,957,Research & Development,28,3,Medical,1,171,2,Female,72,4,1,Research Scientist,3,Single,4739,16090,4,Y,No,12,3,4,80,0,18,2,3,3,2,1,2 +50,No,Travel_Frequently,809,Sales,12,3,Marketing,1,174,3,Female,77,3,3,Sales Executive,4,Single,9208,6645,4,Y,No,11,3,4,80,0,16,3,3,2,2,2,1 +31,Yes,Travel_Rarely,542,Sales,20,3,Life Sciences,1,175,2,Female,71,1,2,Sales Executive,3,Married,4559,24788,3,Y,Yes,11,3,3,80,1,4,2,3,2,2,2,2 +41,No,Travel_Rarely,802,Sales,9,1,Life Sciences,1,176,3,Male,96,3,3,Sales Executive,3,Divorced,8189,21196,3,Y,Yes,13,3,3,80,1,12,2,3,9,7,0,7 +26,No,Travel_Rarely,1355,Human Resources,25,1,Life Sciences,1,177,3,Female,61,3,1,Human Resources,3,Married,2942,8916,1,Y,No,23,4,4,80,1,8,3,3,8,7,5,7 +36,No,Travel_Rarely,216,Research & Development,6,2,Medical,1,178,2,Male,84,3,2,Manufacturing Director,2,Divorced,4941,2819,6,Y,No,20,4,4,80,2,7,0,3,3,2,0,1 +51,Yes,Travel_Frequently,1150,Research & Development,8,4,Life Sciences,1,179,1,Male,53,1,3,Manufacturing Director,4,Single,10650,25150,2,Y,No,15,3,4,80,0,18,2,3,4,2,0,3 +39,No,Travel_Rarely,1329,Sales,4,4,Life Sciences,1,182,4,Female,47,2,2,Sales Executive,3,Married,5902,14590,4,Y,No,14,3,3,80,1,17,1,4,15,11,5,9 +25,No,Travel_Rarely,959,Sales,28,3,Life Sciences,1,183,1,Male,41,2,2,Sales Executive,3,Married,8639,24835,2,Y,No,18,3,4,80,0,6,3,3,2,2,2,2 +30,No,Travel_Rarely,1240,Human Resources,9,3,Human Resources,1,184,3,Male,48,3,2,Human Resources,4,Married,6347,13982,0,Y,Yes,19,3,4,80,0,12,2,1,11,9,4,7 +32,Yes,Travel_Rarely,1033,Research & Development,9,3,Medical,1,190,1,Female,41,3,1,Laboratory Technician,1,Single,4200,10224,7,Y,No,22,4,1,80,0,10,2,4,5,4,0,4 +45,No,Travel_Rarely,1316,Research & Development,29,3,Medical,1,192,3,Male,83,3,1,Research Scientist,4,Single,3452,9752,5,Y,No,13,3,2,80,0,9,2,2,6,5,0,3 +38,No,Travel_Rarely,364,Research & Development,3,5,Technical Degree,1,193,4,Female,32,3,2,Research Scientist,3,Single,4317,2302,3,Y,Yes,20,4,2,80,0,19,2,3,3,2,2,2 +30,No,Travel_Rarely,438,Research & Development,18,3,Life Sciences,1,194,1,Female,75,3,1,Research Scientist,3,Single,2632,23910,1,Y,No,14,3,3,80,0,5,4,2,5,4,0,4 +32,No,Travel_Frequently,689,Sales,9,2,Medical,1,195,4,Male,35,1,2,Sales Executive,4,Divorced,4668,22812,0,Y,No,17,3,4,80,3,9,2,4,8,7,0,7 +30,No,Travel_Rarely,201,Research & Development,5,3,Technical Degree,1,197,4,Female,84,3,1,Research Scientist,1,Divorced,3204,10415,5,Y,No,14,3,4,80,1,8,3,3,3,2,2,2 +30,No,Travel_Rarely,1427,Research & Development,2,1,Medical,1,198,2,Male,35,2,1,Laboratory Technician,4,Single,2720,11162,0,Y,No,13,3,4,80,0,6,3,3,5,3,1,2 +41,No,Travel_Frequently,857,Research & Development,10,3,Life Sciences,1,199,4,Male,91,2,4,Manager,1,Divorced,17181,12888,4,Y,No,13,3,2,80,1,21,2,2,7,6,7,7 +41,No,Travel_Rarely,933,Research & Development,9,4,Life Sciences,1,200,3,Male,94,3,1,Laboratory Technician,1,Married,2238,6961,2,Y,No,21,4,4,80,1,7,2,3,5,0,1,4 +19,No,Travel_Rarely,1181,Research & Development,3,1,Medical,1,201,2,Female,79,3,1,Laboratory Technician,2,Single,1483,16102,1,Y,No,14,3,4,80,0,1,3,3,1,0,0,0 +40,No,Travel_Frequently,1395,Research & Development,26,3,Medical,1,202,2,Female,54,3,2,Research Scientist,2,Divorced,5605,8504,1,Y,No,11,3,1,80,1,20,2,3,20,7,2,13 +35,No,Travel_Rarely,662,Sales,1,5,Marketing,1,204,3,Male,94,3,3,Sales Executive,2,Married,7295,11439,1,Y,No,13,3,1,80,2,10,3,3,10,8,0,6 +53,No,Travel_Rarely,1436,Sales,6,2,Marketing,1,205,2,Male,34,3,2,Sales Representative,3,Married,2306,16047,2,Y,Yes,20,4,4,80,1,13,3,1,7,7,4,5 +45,No,Travel_Rarely,194,Research & Development,9,3,Life Sciences,1,206,2,Male,60,3,2,Laboratory Technician,2,Divorced,2348,10901,8,Y,No,18,3,3,80,1,20,2,1,17,9,0,15 +32,No,Travel_Frequently,967,Sales,8,3,Marketing,1,207,2,Female,43,3,3,Sales Executive,4,Single,8998,15589,1,Y,No,14,3,4,80,0,9,2,3,9,8,3,7 +29,No,Non-Travel,1496,Research & Development,1,1,Technical Degree,1,208,4,Male,41,3,2,Manufacturing Director,3,Married,4319,26283,1,Y,No,13,3,1,80,1,10,1,3,10,7,0,9 +51,No,Travel_Rarely,1169,Research & Development,7,4,Medical,1,211,2,Male,34,2,2,Manufacturing Director,3,Married,6132,13983,2,Y,No,17,3,3,80,0,10,2,3,1,0,0,0 +58,No,Travel_Rarely,1145,Research & Development,9,3,Medical,1,214,2,Female,75,2,1,Research Scientist,2,Married,3346,11873,4,Y,Yes,20,4,2,80,1,9,3,2,1,0,0,0 +40,No,Travel_Rarely,630,Sales,4,4,Marketing,1,215,3,Male,67,2,3,Sales Executive,4,Married,10855,8552,7,Y,No,11,3,1,80,1,15,2,2,12,11,2,11 +34,No,Travel_Frequently,303,Sales,2,4,Marketing,1,216,3,Female,75,3,1,Sales Representative,3,Married,2231,11314,6,Y,No,18,3,4,80,1,6,3,3,4,3,1,2 +22,No,Travel_Rarely,1256,Research & Development,19,1,Medical,1,217,3,Male,80,3,1,Research Scientist,4,Married,2323,11992,1,Y,No,24,4,1,80,2,2,6,3,2,2,2,2 +27,No,Non-Travel,691,Research & Development,9,3,Medical,1,218,4,Male,57,3,1,Research Scientist,2,Divorced,2024,5970,6,Y,No,18,3,4,80,1,6,1,1,2,2,2,2 +28,No,Travel_Rarely,440,Research & Development,21,3,Medical,1,221,3,Male,42,3,1,Research Scientist,4,Married,2713,6672,1,Y,No,11,3,3,80,1,5,2,1,5,2,0,2 +57,No,Travel_Rarely,334,Research & Development,24,2,Life Sciences,1,223,3,Male,83,4,3,Healthcare Representative,4,Divorced,9439,23402,3,Y,Yes,16,3,2,80,1,12,2,1,5,3,1,4 +27,No,Non-Travel,1450,Research & Development,3,3,Medical,1,224,3,Male,79,2,1,Research Scientist,3,Divorced,2566,25326,1,Y,Yes,15,3,4,80,1,1,2,2,1,1,0,1 +50,No,Travel_Rarely,1452,Research & Development,11,3,Life Sciences,1,226,3,Female,53,3,5,Manager,2,Single,19926,17053,3,Y,No,15,3,2,80,0,21,5,3,5,4,4,4 +41,No,Travel_Rarely,465,Research & Development,14,3,Life Sciences,1,227,1,Male,56,3,1,Research Scientist,3,Divorced,2451,4609,4,Y,No,12,3,1,80,1,13,2,3,9,8,1,8 +30,No,Travel_Rarely,1339,Sales,5,3,Life Sciences,1,228,2,Female,41,3,3,Sales Executive,4,Married,9419,8053,2,Y,No,12,3,3,80,1,12,2,3,10,9,7,4 +38,No,Travel_Rarely,702,Sales,1,4,Life Sciences,1,230,1,Female,59,2,2,Sales Executive,4,Single,8686,12930,4,Y,No,22,4,3,80,0,12,2,4,8,3,0,7 +32,No,Travel_Rarely,120,Research & Development,6,5,Life Sciences,1,231,3,Male,43,3,1,Research Scientist,3,Single,3038,12430,3,Y,No,20,4,1,80,0,8,2,3,5,4,1,4 +27,No,Travel_Rarely,1157,Research & Development,17,3,Technical Degree,1,233,3,Male,51,3,1,Research Scientist,2,Married,3058,13364,0,Y,Yes,16,3,4,80,1,6,3,2,5,2,1,1 +19,Yes,Travel_Frequently,602,Sales,1,1,Technical Degree,1,235,3,Female,100,1,1,Sales Representative,1,Single,2325,20989,0,Y,No,21,4,1,80,0,1,5,4,0,0,0,0 +36,No,Travel_Frequently,1480,Research & Development,3,2,Medical,1,238,4,Male,30,3,1,Laboratory Technician,2,Single,2088,15062,4,Y,No,12,3,3,80,0,13,3,2,8,7,7,2 +30,No,Non-Travel,111,Research & Development,9,3,Medical,1,239,3,Male,66,3,2,Laboratory Technician,1,Divorced,3072,11012,1,Y,No,11,3,3,80,2,12,4,3,12,9,6,10 +45,No,Travel_Rarely,1268,Sales,4,2,Life Sciences,1,240,3,Female,30,3,2,Sales Executive,1,Divorced,5006,6319,4,Y,Yes,11,3,1,80,1,9,3,4,5,4,0,3 +56,No,Travel_Rarely,713,Research & Development,8,3,Life Sciences,1,241,3,Female,67,3,1,Research Scientist,1,Divorced,4257,13939,4,Y,Yes,18,3,3,80,1,19,3,3,2,2,2,2 +33,No,Travel_Rarely,134,Research & Development,2,3,Life Sciences,1,242,3,Male,90,3,1,Research Scientist,4,Single,2500,10515,0,Y,No,14,3,1,80,0,4,2,4,3,1,0,2 +19,Yes,Travel_Rarely,303,Research & Development,2,3,Life Sciences,1,243,2,Male,47,2,1,Laboratory Technician,4,Single,1102,9241,1,Y,No,22,4,3,80,0,1,3,2,1,0,1,0 +46,No,Travel_Rarely,526,Sales,1,2,Marketing,1,244,2,Female,92,3,3,Sales Executive,1,Divorced,10453,2137,1,Y,No,25,4,3,80,3,24,2,3,24,13,15,7 +38,No,Travel_Rarely,1380,Research & Development,9,2,Life Sciences,1,245,3,Female,75,3,1,Laboratory Technician,4,Single,2288,6319,1,Y,No,12,3,3,80,0,2,3,3,2,2,2,1 +31,No,Travel_Rarely,140,Research & Development,12,1,Medical,1,246,3,Female,95,3,1,Research Scientist,4,Married,3929,6984,8,Y,Yes,23,4,3,80,1,7,0,3,4,2,0,2 +34,No,Travel_Rarely,629,Research & Development,27,2,Medical,1,247,4,Female,95,3,1,Research Scientist,2,Single,2311,5711,2,Y,No,15,3,4,80,0,9,3,3,3,2,1,2 +41,Yes,Travel_Rarely,1356,Sales,20,2,Marketing,1,248,2,Female,70,3,1,Sales Representative,2,Single,3140,21728,1,Y,Yes,22,4,4,80,0,4,5,2,4,3,0,2 +50,No,Travel_Rarely,328,Research & Development,1,3,Medical,1,249,3,Male,86,2,1,Laboratory Technician,3,Married,3690,3425,2,Y,No,15,3,4,80,1,5,2,2,3,2,0,2 +53,No,Travel_Rarely,1084,Research & Development,13,2,Medical,1,250,4,Female,57,4,2,Manufacturing Director,1,Divorced,4450,26250,1,Y,No,11,3,3,80,2,5,3,3,4,2,1,3 +33,No,Travel_Rarely,931,Research & Development,14,3,Medical,1,252,4,Female,72,3,1,Research Scientist,2,Married,2756,4673,1,Y,No,13,3,4,80,1,8,5,3,8,7,1,6 +40,No,Travel_Rarely,989,Research & Development,4,1,Medical,1,253,4,Female,46,3,5,Manager,3,Married,19033,6499,1,Y,No,14,3,2,80,1,21,2,3,20,8,9,9 +55,No,Travel_Rarely,692,Research & Development,14,4,Medical,1,254,3,Male,61,4,5,Research Director,2,Single,18722,13339,8,Y,No,11,3,4,80,0,36,3,3,24,15,2,15 +34,No,Travel_Frequently,1069,Research & Development,2,1,Life Sciences,1,256,4,Male,45,2,2,Manufacturing Director,3,Married,9547,14074,1,Y,No,17,3,3,80,0,10,2,2,10,9,1,9 +51,No,Travel_Rarely,313,Research & Development,3,3,Medical,1,258,4,Female,98,3,4,Healthcare Representative,2,Single,13734,7192,3,Y,No,18,3,3,80,0,21,6,3,7,7,1,0 +52,No,Travel_Rarely,699,Research & Development,1,4,Life Sciences,1,259,3,Male,65,2,5,Manager,3,Married,19999,5678,0,Y,No,14,3,1,80,1,34,5,3,33,18,11,9 +27,No,Travel_Rarely,894,Research & Development,9,3,Medical,1,260,4,Female,99,3,1,Research Scientist,2,Single,2279,11781,1,Y,No,16,3,4,80,0,7,2,2,7,7,0,3 +35,Yes,Travel_Rarely,556,Research & Development,23,2,Life Sciences,1,261,2,Male,50,2,2,Manufacturing Director,3,Married,5916,15497,3,Y,Yes,13,3,1,80,0,8,1,3,1,0,0,1 +43,No,Non-Travel,1344,Research & Development,7,3,Medical,1,262,4,Male,37,4,1,Research Scientist,4,Divorced,2089,5228,4,Y,No,14,3,4,80,3,7,3,4,5,4,2,2 +45,No,Non-Travel,1195,Research & Development,2,2,Medical,1,264,1,Male,65,2,4,Manager,4,Married,16792,20462,9,Y,No,23,4,4,80,1,22,1,3,20,8,11,8 +37,No,Travel_Rarely,290,Research & Development,21,3,Life Sciences,1,267,2,Male,65,4,1,Research Scientist,1,Married,3564,22977,1,Y,Yes,12,3,1,80,1,8,3,2,8,7,1,7 +35,No,Travel_Frequently,138,Research & Development,2,3,Medical,1,269,2,Female,37,3,2,Laboratory Technician,2,Single,4425,15986,5,Y,No,11,3,4,80,0,10,5,3,6,2,1,2 +42,No,Non-Travel,926,Research & Development,21,2,Medical,1,270,3,Female,36,3,2,Manufacturing Director,3,Divorced,5265,16439,2,Y,No,16,3,2,80,1,11,5,3,5,3,0,2 +38,No,Travel_Rarely,1261,Research & Development,2,4,Life Sciences,1,271,4,Male,88,3,2,Manufacturing Director,3,Married,6553,7259,9,Y,No,14,3,2,80,0,14,3,3,1,0,0,0 +38,No,Travel_Rarely,1084,Research & Development,29,3,Technical Degree,1,273,4,Male,54,3,2,Manufacturing Director,4,Married,6261,4185,3,Y,No,18,3,1,80,1,9,3,1,7,7,1,7 +27,No,Travel_Frequently,472,Research & Development,1,1,Technical Degree,1,274,3,Male,60,2,2,Manufacturing Director,1,Married,4298,9679,5,Y,No,19,3,3,80,1,6,1,3,2,2,2,0 +49,No,Non-Travel,1002,Research & Development,18,4,Life Sciences,1,275,4,Male,92,3,2,Manufacturing Director,4,Divorced,6804,23793,1,Y,Yes,15,3,1,80,2,7,0,3,7,7,1,7 +34,No,Travel_Frequently,878,Research & Development,10,4,Medical,1,277,4,Male,43,3,1,Research Scientist,3,Divorced,3815,5972,1,Y,Yes,17,3,4,80,1,5,4,4,5,3,2,0 +40,No,Travel_Rarely,905,Research & Development,19,2,Medical,1,281,3,Male,99,3,2,Laboratory Technician,4,Married,2741,16523,8,Y,Yes,15,3,3,80,1,15,2,4,7,2,3,7 +38,Yes,Travel_Rarely,1180,Research & Development,29,1,Medical,1,282,2,Male,70,3,2,Healthcare Representative,1,Married,6673,11354,7,Y,Yes,19,3,2,80,0,17,2,3,1,0,0,0 +29,Yes,Travel_Rarely,121,Sales,27,3,Marketing,1,283,2,Female,35,3,3,Sales Executive,4,Married,7639,24525,1,Y,No,22,4,4,80,3,10,3,2,10,4,1,9 +22,No,Travel_Rarely,1136,Research & Development,5,3,Life Sciences,1,284,4,Male,60,4,1,Research Scientist,2,Divorced,2328,12392,1,Y,Yes,16,3,1,80,1,4,2,2,4,2,2,2 +36,No,Travel_Frequently,635,Research & Development,18,1,Medical,1,286,2,Female,73,3,1,Laboratory Technician,4,Single,2153,7703,1,Y,No,13,3,1,80,0,8,2,3,8,1,1,7 +40,No,Non-Travel,1151,Research & Development,9,5,Life Sciences,1,287,4,Male,63,2,2,Healthcare Representative,4,Married,4876,14242,9,Y,No,14,3,4,80,1,5,5,1,3,2,0,2 +46,No,Travel_Rarely,644,Research & Development,1,4,Medical,1,288,4,Male,97,3,3,Healthcare Representative,1,Divorced,9396,12368,7,Y,No,16,3,3,80,1,17,3,3,4,2,0,3 +32,Yes,Travel_Rarely,1045,Sales,4,4,Medical,1,291,4,Male,32,1,3,Sales Executive,4,Married,10400,25812,1,Y,No,11,3,3,80,0,14,2,2,14,8,9,8 +30,No,Non-Travel,829,Research & Development,1,1,Life Sciences,1,292,3,Male,88,2,3,Manufacturing Director,3,Single,8474,20925,1,Y,No,22,4,3,80,0,12,2,3,11,8,5,8 +27,No,Travel_Frequently,1242,Sales,20,3,Life Sciences,1,293,4,Female,90,3,2,Sales Executive,3,Single,9981,12916,1,Y,No,14,3,4,80,0,7,2,3,7,7,0,7 +51,No,Travel_Rarely,1469,Research & Development,8,4,Life Sciences,1,296,2,Male,81,2,3,Research Director,2,Married,12490,15736,5,Y,No,16,3,4,80,2,16,5,1,10,9,4,7 +30,Yes,Travel_Rarely,1005,Research & Development,3,3,Technical Degree,1,297,4,Female,88,3,1,Research Scientist,1,Single,2657,8556,5,Y,Yes,11,3,3,80,0,8,5,3,5,2,0,4 +41,No,Travel_Rarely,896,Sales,6,3,Life Sciences,1,298,4,Female,75,3,3,Manager,4,Single,13591,14674,3,Y,Yes,18,3,3,80,0,16,3,3,1,0,0,0 +30,Yes,Travel_Frequently,334,Sales,26,4,Marketing,1,299,3,Female,52,2,2,Sales Executive,1,Single,6696,22967,5,Y,No,15,3,3,80,0,9,5,2,6,3,0,1 +29,Yes,Travel_Rarely,992,Research & Development,1,3,Technical Degree,1,300,3,Male,85,3,1,Research Scientist,3,Single,2058,19757,0,Y,No,14,3,4,80,0,7,1,2,6,2,1,5 +45,No,Non-Travel,1052,Sales,6,3,Medical,1,302,4,Female,57,2,3,Sales Executive,4,Single,8865,16840,6,Y,No,12,3,4,80,0,23,2,3,19,7,12,8 +54,No,Travel_Rarely,1147,Sales,3,3,Marketing,1,303,4,Female,52,3,2,Sales Executive,1,Married,5940,17011,2,Y,No,14,3,4,80,1,16,4,3,6,2,0,5 +36,No,Travel_Rarely,1396,Research & Development,5,2,Life Sciences,1,304,4,Male,62,3,2,Laboratory Technician,2,Single,5914,9945,8,Y,No,16,3,4,80,0,16,3,4,13,11,3,7 +33,No,Travel_Rarely,147,Research & Development,4,4,Medical,1,305,3,Female,47,2,1,Research Scientist,2,Married,2622,13248,6,Y,No,21,4,4,80,0,7,3,3,3,2,1,1 +37,No,Travel_Frequently,663,Research & Development,11,3,Other,1,306,2,Male,47,3,3,Research Director,4,Divorced,12185,10056,1,Y,Yes,14,3,3,80,3,10,1,3,10,8,0,7 +38,No,Travel_Rarely,119,Sales,3,3,Life Sciences,1,307,1,Male,76,3,3,Sales Executive,3,Divorced,10609,9647,0,Y,No,12,3,3,80,2,17,6,2,16,10,5,13 +31,No,Non-Travel,979,Research & Development,1,4,Medical,1,308,3,Male,90,1,2,Manufacturing Director,3,Married,4345,4381,0,Y,No,12,3,4,80,1,6,2,3,5,4,1,4 +59,No,Travel_Rarely,142,Research & Development,3,3,Life Sciences,1,309,3,Male,70,2,1,Research Scientist,4,Married,2177,8456,3,Y,No,17,3,1,80,1,7,6,3,1,0,0,0 +37,No,Travel_Frequently,319,Sales,4,4,Marketing,1,311,1,Male,41,3,1,Sales Representative,4,Divorced,2793,2539,4,Y,No,17,3,3,80,1,13,2,3,9,8,5,8 +29,No,Travel_Frequently,1413,Sales,1,1,Medical,1,312,2,Female,42,3,3,Sales Executive,4,Married,7918,6599,1,Y,No,14,3,4,80,1,11,5,3,11,10,4,1 +35,No,Travel_Frequently,944,Sales,1,3,Marketing,1,314,3,Female,92,3,3,Sales Executive,3,Single,8789,9096,1,Y,No,14,3,1,80,0,10,3,4,10,7,0,8 +29,Yes,Travel_Rarely,896,Research & Development,18,1,Medical,1,315,3,Male,86,2,1,Research Scientist,4,Single,2389,14961,1,Y,Yes,13,3,3,80,0,4,3,2,4,3,0,1 +52,No,Travel_Rarely,1323,Research & Development,2,3,Life Sciences,1,316,3,Female,89,2,1,Laboratory Technician,4,Single,3212,3300,7,Y,No,15,3,2,80,0,6,3,2,2,2,2,2 +42,No,Travel_Rarely,532,Research & Development,4,2,Technical Degree,1,319,3,Male,58,3,5,Manager,4,Married,19232,4933,1,Y,No,11,3,4,80,0,22,3,3,22,17,11,15 +59,No,Travel_Rarely,818,Human Resources,6,2,Medical,1,321,2,Male,52,3,1,Human Resources,3,Married,2267,25657,8,Y,No,17,3,4,80,0,7,2,2,2,2,2,2 +50,No,Travel_Rarely,854,Sales,1,4,Medical,1,323,4,Female,68,3,5,Manager,4,Divorced,19517,24118,3,Y,No,11,3,3,80,1,32,3,2,7,0,0,6 +33,Yes,Travel_Rarely,813,Research & Development,14,3,Medical,1,325,3,Male,58,3,1,Laboratory Technician,4,Married,2436,22149,5,Y,Yes,13,3,3,80,1,8,2,1,5,4,0,4 +43,No,Travel_Rarely,1034,Sales,16,3,Marketing,1,327,4,Female,80,3,4,Manager,4,Married,16064,7744,5,Y,Yes,22,4,3,80,1,22,3,3,17,13,1,9 +33,Yes,Travel_Rarely,465,Research & Development,2,2,Life Sciences,1,328,1,Female,39,3,1,Laboratory Technician,1,Married,2707,21509,7,Y,No,20,4,1,80,0,13,3,4,9,7,1,7 +52,No,Non-Travel,771,Sales,2,4,Life Sciences,1,329,1,Male,79,2,5,Manager,3,Single,19068,21030,1,Y,Yes,18,3,4,80,0,33,2,4,33,7,15,12 +32,No,Travel_Rarely,1401,Sales,4,2,Life Sciences,1,330,3,Female,56,3,1,Sales Representative,2,Married,3931,20990,2,Y,No,11,3,1,80,1,6,5,3,4,3,1,2 +32,Yes,Travel_Rarely,515,Research & Development,1,3,Life Sciences,1,331,4,Male,62,2,1,Laboratory Technician,3,Single,3730,9571,0,Y,Yes,14,3,4,80,0,4,2,1,3,2,1,2 +39,No,Travel_Rarely,1431,Research & Development,1,4,Medical,1,332,3,Female,96,3,1,Laboratory Technician,3,Divorced,2232,15417,7,Y,No,14,3,3,80,3,7,1,3,3,2,1,2 +32,No,Non-Travel,976,Sales,26,4,Marketing,1,333,3,Male,100,3,2,Sales Executive,4,Married,4465,12069,0,Y,No,18,3,1,80,0,4,2,3,3,2,2,2 +41,No,Travel_Rarely,1411,Research & Development,19,2,Life Sciences,1,334,3,Male,36,3,2,Research Scientist,1,Divorced,3072,19877,2,Y,No,16,3,1,80,2,17,2,2,1,0,0,0 +40,No,Travel_Rarely,1300,Research & Development,24,2,Technical Degree,1,335,1,Male,62,3,2,Research Scientist,4,Divorced,3319,24447,1,Y,No,17,3,1,80,2,9,3,3,9,8,4,7 +45,No,Travel_Rarely,252,Research & Development,1,3,Other,1,336,3,Male,70,4,5,Manager,4,Married,19202,15970,0,Y,No,11,3,3,80,1,25,2,3,24,0,1,7 +31,No,Travel_Frequently,1327,Research & Development,3,4,Medical,1,337,2,Male,73,3,3,Research Director,3,Divorced,13675,13523,9,Y,No,12,3,1,80,1,9,3,3,2,2,2,2 +33,No,Travel_Rarely,832,Research & Development,5,4,Life Sciences,1,338,3,Female,63,2,1,Research Scientist,4,Married,2911,14776,1,Y,No,13,3,3,80,1,2,2,2,2,2,0,2 +34,No,Travel_Rarely,470,Research & Development,2,4,Life Sciences,1,339,4,Male,84,2,2,Manufacturing Director,1,Married,5957,23687,6,Y,No,13,3,2,80,1,13,3,3,11,9,5,9 +37,No,Travel_Rarely,1017,Research & Development,1,2,Medical,1,340,3,Female,83,2,1,Research Scientist,1,Married,3920,18697,2,Y,No,14,3,1,80,1,17,2,2,3,1,0,2 +45,No,Travel_Frequently,1199,Research & Development,7,4,Life Sciences,1,341,1,Male,77,4,2,Manufacturing Director,3,Married,6434,5118,4,Y,No,17,3,4,80,1,9,1,3,3,2,0,2 +37,Yes,Travel_Frequently,504,Research & Development,10,3,Medical,1,342,1,Male,61,3,3,Manufacturing Director,3,Divorced,10048,22573,6,Y,No,11,3,2,80,2,17,5,3,1,0,0,0 +39,No,Travel_Frequently,505,Research & Development,2,4,Technical Degree,1,343,3,Female,64,3,3,Healthcare Representative,3,Single,10938,6420,0,Y,No,25,4,4,80,0,20,1,3,19,6,11,8 +29,No,Travel_Rarely,665,Research & Development,15,3,Life Sciences,1,346,3,Male,60,3,1,Research Scientist,4,Single,2340,22673,1,Y,No,19,3,1,80,0,6,1,3,6,5,1,5 +42,No,Travel_Rarely,916,Research & Development,17,2,Life Sciences,1,347,4,Female,82,4,2,Research Scientist,1,Single,6545,23016,3,Y,Yes,13,3,3,80,0,10,1,3,3,2,0,2 +29,No,Travel_Rarely,1247,Sales,20,2,Marketing,1,349,4,Male,45,3,2,Sales Executive,4,Divorced,6931,10732,2,Y,No,14,3,4,80,1,10,2,3,3,2,0,2 +25,No,Travel_Rarely,685,Research & Development,1,3,Life Sciences,1,350,1,Female,62,3,2,Manufacturing Director,3,Married,4898,7505,0,Y,No,12,3,4,80,2,5,3,3,4,2,1,2 +42,No,Travel_Rarely,269,Research & Development,2,3,Medical,1,351,4,Female,56,2,1,Laboratory Technician,1,Divorced,2593,8007,0,Y,Yes,11,3,3,80,1,10,4,3,9,6,7,8 +40,No,Travel_Rarely,1416,Research & Development,2,2,Medical,1,352,1,Male,49,3,5,Research Director,3,Divorced,19436,5949,0,Y,No,19,3,4,80,1,22,5,3,21,7,3,9 +51,No,Travel_Rarely,833,Research & Development,1,3,Life Sciences,1,353,3,Male,96,3,1,Research Scientist,4,Married,2723,23231,1,Y,No,11,3,2,80,0,1,0,2,1,0,0,0 +31,Yes,Travel_Frequently,307,Research & Development,29,2,Medical,1,355,3,Male,71,2,1,Laboratory Technician,2,Single,3479,11652,0,Y,No,11,3,2,80,0,6,2,4,5,4,1,4 +32,No,Travel_Frequently,1311,Research & Development,7,3,Life Sciences,1,359,2,Male,100,4,1,Laboratory Technician,2,Married,2794,26062,1,Y,No,20,4,3,80,0,5,3,1,5,1,0,3 +38,No,Non-Travel,1327,Sales,2,2,Life Sciences,1,361,4,Male,39,2,2,Sales Executive,4,Married,5249,19682,3,Y,No,18,3,4,80,1,13,0,3,8,7,7,5 +32,No,Travel_Rarely,128,Research & Development,2,1,Technical Degree,1,362,4,Male,84,2,2,Laboratory Technician,1,Single,2176,19737,4,Y,No,13,3,4,80,0,9,5,3,6,2,0,4 +46,No,Travel_Rarely,488,Sales,2,3,Technical Degree,1,363,3,Female,75,1,4,Manager,2,Married,16872,14977,3,Y,Yes,12,3,2,80,1,28,2,2,7,7,7,7 +28,Yes,Travel_Rarely,529,Research & Development,2,4,Life Sciences,1,364,1,Male,79,3,1,Laboratory Technician,3,Single,3485,14935,2,Y,No,11,3,3,80,0,5,5,1,0,0,0,0 +29,No,Travel_Rarely,1210,Sales,2,3,Medical,1,366,1,Male,78,2,2,Sales Executive,2,Married,6644,3687,2,Y,No,19,3,2,80,2,10,2,3,0,0,0,0 +31,No,Travel_Rarely,1463,Research & Development,23,3,Medical,1,367,2,Male,64,2,2,Healthcare Representative,4,Married,5582,14408,0,Y,No,21,4,2,80,1,10,2,3,9,0,7,8 +25,No,Non-Travel,675,Research & Development,5,2,Life Sciences,1,369,2,Male,85,4,2,Healthcare Representative,1,Divorced,4000,18384,1,Y,No,12,3,4,80,2,6,2,3,6,3,1,5 +45,No,Travel_Rarely,1385,Research & Development,20,2,Medical,1,372,3,Male,79,3,4,Healthcare Representative,4,Married,13496,7501,0,Y,Yes,14,3,2,80,0,21,2,3,20,7,4,10 +36,No,Travel_Rarely,1403,Research & Development,6,3,Life Sciences,1,373,4,Male,47,3,1,Laboratory Technician,4,Married,3210,20251,0,Y,No,11,3,3,80,1,16,4,3,15,13,10,11 +55,No,Travel_Rarely,452,Research & Development,1,3,Medical,1,374,4,Male,81,3,5,Manager,1,Single,19045,18938,0,Y,Yes,14,3,3,80,0,37,2,3,36,10,4,13 +47,Yes,Non-Travel,666,Research & Development,29,4,Life Sciences,1,376,1,Male,88,3,3,Manager,2,Married,11849,10268,1,Y,Yes,12,3,4,80,1,10,2,2,10,7,9,9 +28,No,Travel_Rarely,1158,Research & Development,9,3,Medical,1,377,4,Male,94,3,1,Research Scientist,4,Married,2070,2613,1,Y,No,23,4,4,80,1,5,3,2,5,2,0,4 +37,No,Travel_Rarely,228,Sales,6,4,Medical,1,378,3,Male,98,3,2,Sales Executive,4,Married,6502,22825,4,Y,No,14,3,2,80,1,7,5,4,5,4,0,1 +21,No,Travel_Rarely,996,Research & Development,3,2,Medical,1,379,4,Male,100,2,1,Research Scientist,3,Single,3230,10531,1,Y,No,17,3,1,80,0,3,4,4,3,2,1,0 +37,No,Non-Travel,728,Research & Development,1,4,Medical,1,380,1,Female,80,3,3,Research Director,4,Divorced,13603,11677,2,Y,Yes,18,3,1,80,2,15,2,3,5,2,0,2 +35,No,Travel_Rarely,1315,Research & Development,22,3,Life Sciences,1,381,2,Female,71,4,3,Manager,2,Divorced,11996,19100,7,Y,No,18,3,2,80,1,10,6,2,7,7,6,2 +38,No,Travel_Rarely,322,Sales,7,2,Medical,1,382,1,Female,44,4,2,Sales Executive,1,Divorced,5605,19191,1,Y,Yes,24,4,3,80,1,8,3,3,8,0,7,7 +26,No,Travel_Frequently,1479,Research & Development,1,3,Life Sciences,1,384,3,Female,84,3,2,Manufacturing Director,2,Divorced,6397,26767,1,Y,No,20,4,1,80,1,6,6,1,6,5,1,4 +50,No,Travel_Rarely,797,Research & Development,4,1,Life Sciences,1,385,1,Male,96,3,5,Research Director,2,Divorced,19144,15815,3,Y,No,14,3,1,80,2,28,4,2,10,4,1,6 +53,No,Travel_Rarely,1070,Research & Development,3,4,Medical,1,386,3,Male,45,3,4,Research Director,3,Married,17584,21016,3,Y,Yes,16,3,4,80,3,21,5,2,5,3,1,3 +42,No,Travel_Rarely,635,Sales,1,1,Life Sciences,1,387,2,Male,99,3,2,Sales Executive,3,Married,4907,24532,1,Y,No,25,4,3,80,0,20,3,3,20,16,11,6 +29,No,Travel_Frequently,442,Sales,2,2,Life Sciences,1,388,2,Male,44,3,2,Sales Executive,4,Single,4554,20260,1,Y,No,18,3,1,80,0,10,3,2,10,7,0,9 +55,No,Travel_Rarely,147,Research & Development,20,2,Technical Degree,1,389,2,Male,37,3,2,Laboratory Technician,4,Married,5415,15972,3,Y,Yes,19,3,4,80,1,12,4,3,10,7,0,8 +26,No,Travel_Frequently,496,Research & Development,11,2,Medical,1,390,1,Male,60,3,2,Healthcare Representative,1,Married,4741,22722,1,Y,Yes,13,3,3,80,1,5,3,3,5,3,3,3 +37,No,Travel_Rarely,1372,Research & Development,1,3,Life Sciences,1,391,4,Female,42,3,1,Research Scientist,4,Single,2115,15881,1,Y,No,12,3,2,80,0,17,3,3,17,12,5,7 +44,Yes,Travel_Frequently,920,Research & Development,24,3,Life Sciences,1,392,4,Male,43,3,1,Laboratory Technician,3,Divorced,3161,19920,3,Y,Yes,22,4,4,80,1,19,0,1,1,0,0,0 +38,No,Travel_Rarely,688,Research & Development,23,4,Life Sciences,1,393,4,Male,82,3,2,Healthcare Representative,4,Divorced,5745,18899,9,Y,No,14,3,2,80,1,10,2,3,2,2,1,2 +26,Yes,Travel_Rarely,1449,Research & Development,16,4,Medical,1,394,1,Male,45,3,1,Laboratory Technician,2,Divorced,2373,14180,2,Y,Yes,13,3,4,80,1,5,2,3,3,2,0,2 +28,No,Travel_Rarely,1117,Research & Development,8,2,Life Sciences,1,395,4,Female,66,3,1,Research Scientist,4,Single,3310,4488,1,Y,No,21,4,4,80,0,5,3,3,5,3,0,2 +49,No,Travel_Frequently,636,Research & Development,10,4,Life Sciences,1,396,3,Female,35,3,5,Research Director,1,Single,18665,25594,9,Y,Yes,11,3,4,80,0,22,4,3,3,2,1,2 +36,No,Travel_Rarely,506,Research & Development,3,3,Technical Degree,1,397,3,Male,30,3,2,Research Scientist,2,Single,4485,26285,4,Y,No,12,3,4,80,0,10,2,3,8,0,7,7 +31,No,Travel_Frequently,444,Sales,5,3,Marketing,1,399,4,Female,84,3,1,Sales Representative,2,Divorced,2789,3909,1,Y,No,11,3,3,80,1,2,5,2,2,2,2,2 +26,Yes,Travel_Rarely,950,Sales,4,4,Marketing,1,401,4,Male,48,2,2,Sales Executive,4,Single,5828,8450,1,Y,Yes,12,3,2,80,0,8,0,3,8,7,7,4 +37,No,Travel_Frequently,889,Research & Development,9,3,Medical,1,403,2,Male,53,3,1,Research Scientist,4,Married,2326,11411,1,Y,Yes,12,3,3,80,3,4,3,2,4,2,1,2 +42,No,Travel_Frequently,555,Sales,26,3,Marketing,1,404,3,Female,77,3,4,Sales Executive,2,Married,13525,14864,5,Y,No,14,3,4,80,1,23,2,4,20,4,4,8 +18,Yes,Travel_Rarely,230,Research & Development,3,3,Life Sciences,1,405,3,Male,54,3,1,Laboratory Technician,3,Single,1420,25233,1,Y,No,13,3,3,80,0,0,2,3,0,0,0,0 +35,No,Travel_Rarely,1232,Sales,16,3,Marketing,1,406,3,Male,96,3,3,Sales Executive,2,Married,8020,5100,0,Y,No,15,3,3,80,2,12,3,2,11,9,6,9 +36,No,Travel_Frequently,566,Research & Development,18,4,Life Sciences,1,407,3,Male,81,4,1,Laboratory Technician,4,Married,3688,7122,4,Y,No,18,3,4,80,2,4,2,3,1,0,0,0 +51,No,Travel_Rarely,1302,Research & Development,2,3,Medical,1,408,4,Male,84,1,2,Manufacturing Director,2,Divorced,5482,16321,5,Y,No,18,3,4,80,1,13,3,3,4,1,1,2 +41,No,Travel_Rarely,334,Sales,2,4,Life Sciences,1,410,4,Male,88,3,4,Manager,2,Single,16015,15896,1,Y,No,19,3,2,80,0,22,2,3,22,10,0,4 +18,No,Travel_Rarely,812,Sales,10,3,Medical,1,411,4,Female,69,2,1,Sales Representative,3,Single,1200,9724,1,Y,No,12,3,1,80,0,0,2,3,0,0,0,0 +28,No,Travel_Rarely,1476,Research & Development,16,2,Medical,1,412,2,Male,68,4,2,Healthcare Representative,1,Single,5661,4824,0,Y,No,19,3,3,80,0,9,2,3,8,3,0,7 +31,No,Travel_Rarely,218,Sales,7,3,Technical Degree,1,416,2,Male,100,4,2,Sales Executive,4,Married,6929,12241,4,Y,No,11,3,2,80,1,10,3,2,8,7,7,7 +39,No,Travel_Rarely,1132,Research & Development,1,3,Medical,1,417,3,Male,48,4,3,Healthcare Representative,4,Divorced,9613,10942,0,Y,No,17,3,1,80,3,19,5,2,18,10,3,7 +36,No,Non-Travel,1105,Research & Development,24,4,Life Sciences,1,419,2,Female,47,3,2,Laboratory Technician,2,Married,5674,6927,7,Y,No,15,3,3,80,1,11,3,3,9,8,0,8 +32,No,Travel_Rarely,906,Sales,7,3,Life Sciences,1,420,4,Male,91,2,2,Sales Executive,3,Married,5484,16985,1,Y,No,14,3,3,80,1,13,3,2,13,8,4,8 +38,No,Travel_Rarely,849,Research & Development,25,2,Life Sciences,1,421,1,Female,81,2,3,Research Director,2,Married,12061,26707,3,Y,No,17,3,3,80,1,19,2,3,10,8,0,1 +58,No,Non-Travel,390,Research & Development,1,4,Life Sciences,1,422,4,Male,32,1,2,Healthcare Representative,3,Divorced,5660,17056,2,Y,Yes,13,3,4,80,1,12,2,3,5,3,1,2 +31,No,Travel_Rarely,691,Research & Development,5,4,Technical Degree,1,423,3,Male,86,3,1,Research Scientist,4,Married,4821,10077,0,Y,Yes,12,3,3,80,1,6,4,3,5,2,0,3 +31,No,Travel_Rarely,106,Human Resources,2,3,Human Resources,1,424,1,Male,62,2,2,Human Resources,1,Married,6410,17822,3,Y,No,12,3,4,80,0,9,1,3,2,2,1,0 +45,No,Travel_Frequently,1249,Research & Development,7,3,Life Sciences,1,425,1,Male,97,3,3,Laboratory Technician,1,Divorced,5210,20308,1,Y,No,18,3,1,80,1,24,2,3,24,9,9,11 +31,No,Travel_Rarely,192,Research & Development,2,4,Life Sciences,1,426,3,Male,32,3,1,Research Scientist,4,Divorced,2695,7747,0,Y,Yes,18,3,2,80,1,3,2,1,2,2,2,2 +33,No,Travel_Frequently,553,Research & Development,5,4,Life Sciences,1,428,4,Female,74,3,3,Manager,2,Married,11878,23364,6,Y,No,11,3,2,80,2,12,2,3,10,6,8,8 +39,No,Travel_Rarely,117,Research & Development,10,1,Medical,1,429,3,Male,99,3,4,Manager,1,Married,17068,5355,1,Y,Yes,14,3,4,80,0,21,3,3,21,9,11,10 +43,No,Travel_Frequently,185,Research & Development,10,4,Life Sciences,1,430,3,Female,33,3,1,Laboratory Technician,4,Single,2455,10675,0,Y,No,19,3,1,80,0,9,5,3,8,7,1,7 +49,No,Travel_Rarely,1091,Research & Development,1,2,Technical Degree,1,431,3,Female,90,2,4,Healthcare Representative,3,Single,13964,17810,7,Y,Yes,12,3,4,80,0,25,2,3,7,1,0,7 +52,Yes,Travel_Rarely,723,Research & Development,8,4,Medical,1,433,3,Male,85,2,2,Research Scientist,2,Married,4941,17747,2,Y,No,15,3,1,80,0,11,3,2,8,2,7,7 +27,No,Travel_Rarely,1220,Research & Development,5,3,Life Sciences,1,434,3,Female,85,3,1,Research Scientist,2,Single,2478,20938,1,Y,Yes,12,3,2,80,0,4,2,2,4,3,1,2 +32,No,Travel_Rarely,588,Sales,8,2,Technical Degree,1,436,3,Female,65,2,2,Sales Executive,2,Married,5228,24624,1,Y,Yes,11,3,4,80,0,13,2,3,13,12,11,9 +27,No,Travel_Rarely,1377,Sales,2,3,Life Sciences,1,437,4,Male,74,3,2,Sales Executive,3,Single,4478,5242,1,Y,Yes,11,3,1,80,0,5,3,3,5,4,0,4 +31,No,Travel_Rarely,691,Sales,7,3,Marketing,1,438,4,Male,73,3,2,Sales Executive,4,Divorced,7547,7143,4,Y,No,12,3,4,80,3,13,3,3,7,7,1,7 +32,No,Travel_Rarely,1018,Research & Development,2,4,Medical,1,439,1,Female,74,4,2,Research Scientist,4,Single,5055,10557,7,Y,No,16,3,3,80,0,10,0,2,7,7,0,7 +28,Yes,Travel_Rarely,1157,Research & Development,2,4,Medical,1,440,1,Male,84,1,1,Research Scientist,4,Married,3464,24737,5,Y,Yes,13,3,4,80,0,5,4,2,3,2,2,2 +30,No,Travel_Rarely,1275,Research & Development,28,2,Medical,1,441,4,Female,64,3,2,Research Scientist,4,Married,5775,11934,1,Y,No,13,3,4,80,2,11,2,3,10,8,1,9 +31,No,Travel_Frequently,798,Research & Development,7,2,Life Sciences,1,442,3,Female,48,2,3,Manufacturing Director,3,Married,8943,14034,1,Y,No,24,4,1,80,1,10,2,3,10,9,8,9 +39,No,Travel_Frequently,672,Research & Development,7,2,Medical,1,444,3,Male,54,2,5,Manager,4,Married,19272,21141,1,Y,No,15,3,1,80,1,21,2,3,21,9,13,3 +39,Yes,Travel_Rarely,1162,Sales,3,2,Medical,1,445,4,Female,41,3,2,Sales Executive,3,Married,5238,17778,4,Y,Yes,18,3,1,80,0,12,3,2,1,0,0,0 +33,No,Travel_Frequently,508,Sales,10,3,Marketing,1,446,2,Male,46,2,2,Sales Executive,4,Single,4682,4317,3,Y,No,14,3,3,80,0,9,6,2,7,7,0,1 +47,No,Travel_Rarely,1482,Research & Development,5,5,Life Sciences,1,447,4,Male,42,3,5,Research Director,3,Married,18300,16375,4,Y,No,11,3,2,80,1,21,2,3,3,2,1,1 +43,No,Travel_Frequently,559,Research & Development,10,4,Life Sciences,1,448,3,Female,82,2,2,Laboratory Technician,3,Divorced,5257,6227,1,Y,No,11,3,2,80,1,9,3,4,9,7,0,0 +27,No,Non-Travel,210,Sales,1,1,Marketing,1,449,3,Male,73,3,2,Sales Executive,2,Married,6349,22107,0,Y,Yes,13,3,4,80,1,6,0,3,5,4,1,4 +54,No,Travel_Frequently,928,Research & Development,20,4,Life Sciences,1,450,4,Female,31,3,2,Research Scientist,3,Single,4869,16885,3,Y,No,12,3,4,80,0,20,4,2,4,3,0,3 +43,No,Travel_Rarely,1001,Research & Development,7,3,Life Sciences,1,451,3,Female,43,3,3,Healthcare Representative,1,Married,9985,9262,8,Y,No,16,3,1,80,1,10,1,2,1,0,0,0 +45,No,Travel_Rarely,549,Research & Development,8,4,Other,1,452,4,Male,75,3,2,Research Scientist,4,Married,3697,9278,9,Y,No,14,3,1,80,2,12,3,3,10,9,9,8 +40,No,Travel_Rarely,1124,Sales,1,2,Medical,1,453,2,Male,57,1,2,Sales Executive,4,Married,7457,13273,2,Y,Yes,22,4,3,80,3,6,2,2,4,3,0,2 +29,Yes,Travel_Rarely,318,Research & Development,8,4,Other,1,454,2,Male,77,1,1,Laboratory Technician,1,Married,2119,4759,1,Y,Yes,11,3,4,80,0,7,4,2,7,7,0,7 +29,No,Travel_Rarely,738,Research & Development,9,5,Other,1,455,2,Male,30,2,1,Laboratory Technician,4,Single,3983,7621,0,Y,No,17,3,3,80,0,4,2,3,3,2,2,2 +30,No,Travel_Rarely,570,Sales,5,3,Marketing,1,456,4,Female,30,2,2,Sales Executive,3,Divorced,6118,5431,1,Y,No,13,3,3,80,3,10,2,3,10,9,1,2 +27,No,Travel_Rarely,1130,Sales,8,4,Marketing,1,458,2,Female,56,3,2,Sales Executive,2,Married,6214,3415,1,Y,No,18,3,1,80,1,8,3,3,8,7,0,7 +37,No,Travel_Rarely,1192,Research & Development,5,2,Medical,1,460,4,Male,61,3,2,Manufacturing Director,4,Divorced,6347,23177,7,Y,No,16,3,3,80,2,8,2,2,6,2,0,4 +38,No,Travel_Rarely,343,Research & Development,15,2,Life Sciences,1,461,3,Male,92,2,3,Research Director,4,Divorced,11510,15682,0,Y,Yes,14,3,2,80,1,12,3,3,11,10,2,9 +31,No,Travel_Rarely,1232,Research & Development,7,4,Medical,1,462,3,Female,39,3,3,Manufacturing Director,4,Single,7143,25713,1,Y,Yes,14,3,3,80,0,11,2,2,11,9,4,10 +29,No,Travel_Rarely,144,Sales,10,1,Marketing,1,463,4,Female,39,2,2,Sales Executive,2,Divorced,8268,11866,1,Y,Yes,14,3,1,80,2,7,2,3,7,7,1,7 +35,No,Travel_Rarely,1296,Research & Development,5,4,Technical Degree,1,464,3,Male,62,3,3,Manufacturing Director,2,Single,8095,18264,0,Y,No,13,3,4,80,0,17,5,3,16,6,0,13 +23,No,Travel_Rarely,1309,Research & Development,26,1,Life Sciences,1,465,3,Male,83,3,1,Research Scientist,4,Divorced,2904,16092,1,Y,No,12,3,3,80,2,4,2,2,4,2,0,2 +41,No,Travel_Rarely,483,Research & Development,6,3,Medical,1,466,4,Male,95,2,2,Manufacturing Director,2,Single,6032,10110,6,Y,Yes,15,3,4,80,0,8,3,3,5,4,1,2 +47,No,Travel_Frequently,1309,Sales,4,1,Medical,1,467,2,Male,99,3,2,Sales Representative,3,Single,2976,25751,3,Y,No,19,3,1,80,0,5,3,3,0,0,0,0 +42,No,Travel_Rarely,810,Research & Development,23,5,Life Sciences,1,468,1,Female,44,3,4,Research Director,4,Single,15992,15901,2,Y,No,14,3,2,80,0,16,2,3,1,0,0,0 +29,No,Non-Travel,746,Sales,2,3,Life Sciences,1,469,4,Male,61,3,2,Sales Executive,3,Married,4649,16928,1,Y,No,14,3,1,80,1,4,3,2,4,3,0,2 +42,No,Travel_Rarely,544,Human Resources,2,1,Technical Degree,1,470,3,Male,52,3,1,Human Resources,3,Divorced,2696,24017,0,Y,Yes,11,3,3,80,1,4,5,3,3,2,1,0 +32,No,Travel_Rarely,1062,Research & Development,2,3,Medical,1,471,3,Female,75,3,1,Laboratory Technician,2,Married,2370,3956,1,Y,No,13,3,3,80,1,8,4,3,8,0,0,7 +48,No,Travel_Rarely,530,Sales,29,1,Medical,1,473,1,Female,91,3,3,Manager,3,Married,12504,23978,3,Y,No,21,4,2,80,1,15,3,1,0,0,0,0 +37,No,Travel_Rarely,1319,Research & Development,6,3,Medical,1,474,3,Male,51,4,2,Research Scientist,1,Divorced,5974,17001,4,Y,Yes,13,3,1,80,2,13,2,3,7,7,6,7 +30,No,Non-Travel,641,Sales,25,2,Technical Degree,1,475,4,Female,85,3,2,Sales Executive,3,Married,4736,6069,7,Y,Yes,12,3,2,80,1,4,2,4,2,2,2,2 +26,No,Travel_Rarely,933,Sales,1,3,Life Sciences,1,476,3,Male,57,3,2,Sales Executive,3,Married,5296,20156,1,Y,No,17,3,2,80,1,8,3,3,8,7,7,7 +42,No,Travel_Rarely,1332,Research & Development,2,4,Other,1,477,1,Male,98,2,2,Healthcare Representative,4,Single,6781,17078,3,Y,No,23,4,2,80,0,14,6,3,1,0,0,0 +21,Yes,Travel_Frequently,756,Sales,1,1,Technical Degree,1,478,1,Female,99,2,1,Sales Representative,2,Single,2174,9150,1,Y,Yes,11,3,3,80,0,3,3,3,3,2,1,2 +36,No,Non-Travel,845,Sales,1,5,Medical,1,479,4,Female,45,3,2,Sales Executive,4,Single,6653,15276,4,Y,No,15,3,2,80,0,7,6,3,1,0,0,0 +36,No,Travel_Frequently,541,Sales,3,4,Medical,1,481,1,Male,48,2,3,Sales Executive,4,Married,9699,7246,4,Y,No,11,3,1,80,1,16,2,3,13,9,1,12 +57,No,Travel_Rarely,593,Research & Development,1,4,Medical,1,482,4,Male,88,3,2,Healthcare Representative,3,Married,6755,2967,2,Y,No,11,3,3,80,0,15,2,3,3,2,1,2 +40,No,Travel_Rarely,1171,Research & Development,10,4,Life Sciences,1,483,4,Female,46,4,1,Laboratory Technician,3,Married,2213,22495,3,Y,Yes,13,3,3,80,1,10,3,3,7,7,1,7 +21,No,Non-Travel,895,Sales,9,2,Medical,1,484,1,Male,39,3,1,Sales Representative,4,Single,2610,2851,1,Y,No,24,4,3,80,0,3,3,2,3,2,2,2 +33,Yes,Travel_Rarely,350,Sales,5,3,Marketing,1,485,4,Female,34,3,1,Sales Representative,3,Single,2851,9150,1,Y,Yes,13,3,2,80,0,1,2,3,1,0,0,0 +37,No,Travel_Rarely,921,Research & Development,10,3,Medical,1,486,3,Female,98,3,1,Laboratory Technician,1,Married,3452,17663,6,Y,No,20,4,2,80,1,17,3,3,5,4,0,3 +46,No,Non-Travel,1144,Research & Development,7,4,Medical,1,487,3,Female,30,3,2,Manufacturing Director,3,Married,5258,16044,2,Y,No,14,3,3,80,0,7,2,4,1,0,0,0 +41,Yes,Travel_Frequently,143,Sales,4,3,Marketing,1,488,1,Male,56,3,2,Sales Executive,2,Single,9355,9558,1,Y,No,18,3,3,80,0,8,5,3,8,7,7,7 +50,No,Travel_Rarely,1046,Research & Development,10,3,Technical Degree,1,491,4,Male,100,2,3,Healthcare Representative,4,Single,10496,2755,6,Y,No,15,3,4,80,0,20,2,3,4,3,1,3 +40,Yes,Travel_Rarely,575,Sales,22,2,Marketing,1,492,3,Male,68,2,2,Sales Executive,3,Married,6380,6110,2,Y,Yes,12,3,1,80,2,8,6,3,6,4,1,0 +31,No,Travel_Rarely,408,Research & Development,9,4,Life Sciences,1,493,3,Male,42,2,1,Research Scientist,2,Single,2657,7551,0,Y,Yes,16,3,4,80,0,3,5,3,2,2,2,2 +21,Yes,Travel_Rarely,156,Sales,12,3,Life Sciences,1,494,3,Female,90,4,1,Sales Representative,2,Single,2716,25422,1,Y,No,15,3,4,80,0,1,0,3,1,0,0,0 +29,No,Travel_Rarely,1283,Research & Development,23,3,Life Sciences,1,495,4,Male,54,3,1,Research Scientist,4,Single,2201,18168,9,Y,No,16,3,4,80,0,6,4,3,3,2,1,2 +35,No,Travel_Rarely,755,Research & Development,9,4,Life Sciences,1,496,3,Male,97,2,2,Healthcare Representative,2,Single,6540,19394,9,Y,No,19,3,3,80,0,10,5,3,1,1,0,0 +27,No,Travel_Rarely,1469,Research & Development,1,2,Medical,1,497,4,Male,82,3,1,Laboratory Technician,2,Divorced,3816,17881,1,Y,No,11,3,2,80,1,5,2,3,5,2,0,4 +28,No,Travel_Rarely,304,Sales,9,4,Life Sciences,1,498,2,Male,92,3,2,Sales Executive,4,Single,5253,20750,1,Y,No,16,3,4,80,0,7,1,3,7,5,0,7 +49,No,Travel_Rarely,1261,Research & Development,7,3,Other,1,499,2,Male,31,2,3,Healthcare Representative,3,Single,10965,12066,8,Y,No,24,4,3,80,0,26,2,3,5,2,0,0 +51,No,Travel_Rarely,1178,Sales,14,2,Life Sciences,1,500,3,Female,87,3,2,Sales Executive,4,Married,4936,14862,4,Y,No,11,3,3,80,1,18,2,2,7,7,0,7 +36,No,Travel_Rarely,329,Research & Development,2,3,Life Sciences,1,501,4,Female,96,3,1,Research Scientist,3,Married,2543,11868,4,Y,No,13,3,2,80,1,6,3,3,2,2,2,2 +34,Yes,Non-Travel,1362,Sales,19,3,Marketing,1,502,1,Male,67,4,2,Sales Executive,4,Single,5304,4652,8,Y,Yes,13,3,2,80,0,9,3,2,5,2,0,4 +55,No,Travel_Rarely,1311,Research & Development,2,3,Life Sciences,1,505,3,Female,97,3,4,Manager,4,Single,16659,23258,2,Y,Yes,13,3,3,80,0,30,2,3,5,4,1,2 +24,No,Travel_Rarely,1371,Sales,10,4,Marketing,1,507,4,Female,77,3,2,Sales Executive,3,Divorced,4260,5915,1,Y,Yes,12,3,4,80,1,5,2,4,5,2,0,3 +30,No,Travel_Rarely,202,Sales,2,1,Technical Degree,1,508,3,Male,72,3,1,Sales Representative,2,Married,2476,17434,1,Y,No,18,3,1,80,1,1,3,3,1,0,0,0 +26,Yes,Travel_Frequently,575,Research & Development,3,1,Technical Degree,1,510,3,Male,73,3,1,Research Scientist,1,Single,3102,6582,0,Y,No,22,4,3,80,0,7,2,3,6,4,0,4 +22,No,Travel_Rarely,253,Research & Development,11,3,Medical,1,511,1,Female,43,3,1,Research Scientist,2,Married,2244,24440,1,Y,No,13,3,4,80,1,2,1,3,2,1,1,2 +36,No,Travel_Rarely,164,Sales,2,2,Medical,1,513,2,Male,61,2,3,Sales Executive,3,Married,7596,3809,1,Y,No,13,3,2,80,2,10,2,3,10,9,9,0 +30,Yes,Travel_Frequently,464,Research & Development,4,3,Technical Degree,1,514,3,Male,40,3,1,Research Scientist,4,Single,2285,3427,9,Y,Yes,23,4,3,80,0,3,4,3,1,0,0,0 +37,No,Travel_Rarely,1107,Research & Development,14,3,Life Sciences,1,515,4,Female,95,3,1,Laboratory Technician,1,Divorced,3034,26914,1,Y,No,12,3,3,80,1,18,2,2,18,7,12,17 +40,No,Travel_Rarely,759,Sales,2,2,Marketing,1,516,4,Female,46,3,2,Sales Executive,2,Divorced,5715,22553,7,Y,No,12,3,3,80,2,8,5,3,5,4,1,3 +42,No,Travel_Rarely,201,Research & Development,1,4,Life Sciences,1,517,2,Female,95,3,1,Laboratory Technician,1,Divorced,2576,20490,3,Y,No,16,3,2,80,1,8,5,3,5,2,1,2 +37,No,Travel_Rarely,1305,Research & Development,10,4,Life Sciences,1,518,3,Male,49,3,2,Manufacturing Director,2,Single,4197,21123,2,Y,Yes,12,3,4,80,0,18,2,2,1,0,0,1 +43,No,Travel_Rarely,982,Research & Development,12,3,Life Sciences,1,520,1,Male,59,2,4,Research Director,2,Divorced,14336,4345,1,Y,No,11,3,3,80,1,25,3,3,25,10,3,9 +40,No,Travel_Rarely,555,Research & Development,2,3,Medical,1,521,2,Female,78,2,2,Laboratory Technician,3,Married,3448,13436,6,Y,No,22,4,2,80,1,20,3,3,1,0,0,0 +54,No,Travel_Rarely,821,Research & Development,5,2,Medical,1,522,1,Male,86,3,5,Research Director,1,Married,19406,8509,4,Y,No,11,3,3,80,1,24,4,2,4,2,1,2 +34,No,Non-Travel,1381,Sales,4,4,Marketing,1,523,3,Female,72,3,2,Sales Executive,3,Married,6538,12740,9,Y,No,15,3,1,80,1,6,3,3,3,2,1,2 +31,No,Travel_Rarely,480,Research & Development,7,2,Medical,1,524,2,Female,31,3,2,Manufacturing Director,1,Married,4306,4156,1,Y,No,12,3,2,80,1,13,5,1,13,10,3,12 +43,No,Travel_Frequently,313,Research & Development,21,3,Medical,1,525,4,Male,61,3,1,Laboratory Technician,4,Married,2258,15238,7,Y,No,20,4,1,80,1,8,1,3,3,2,1,2 +43,No,Travel_Rarely,1473,Research & Development,8,4,Other,1,526,3,Female,74,3,2,Healthcare Representative,3,Divorced,4522,2227,4,Y,Yes,14,3,4,80,0,8,3,3,5,2,0,2 +25,No,Travel_Rarely,891,Sales,4,2,Life Sciences,1,527,2,Female,99,2,2,Sales Executive,4,Single,4487,12090,1,Y,Yes,11,3,2,80,0,5,3,3,5,4,1,3 +37,No,Non-Travel,1063,Research & Development,25,5,Medical,1,529,2,Female,72,3,2,Research Scientist,3,Married,4449,23866,3,Y,Yes,15,3,1,80,2,15,2,3,13,11,10,7 +31,No,Travel_Rarely,329,Research & Development,1,2,Life Sciences,1,530,4,Male,98,2,1,Laboratory Technician,1,Married,2218,16193,1,Y,No,12,3,3,80,1,4,3,3,4,2,3,2 +39,No,Travel_Frequently,1218,Research & Development,1,1,Life Sciences,1,531,2,Male,52,3,5,Manager,3,Divorced,19197,8213,1,Y,Yes,14,3,3,80,1,21,3,3,21,8,1,6 +56,No,Travel_Frequently,906,Sales,6,3,Life Sciences,1,532,3,Female,86,4,4,Sales Executive,1,Married,13212,18256,9,Y,No,11,3,4,80,3,36,0,2,7,7,7,7 +30,No,Travel_Rarely,1082,Sales,12,3,Technical Degree,1,533,2,Female,83,3,2,Sales Executive,3,Single,6577,19558,0,Y,No,11,3,2,80,0,6,6,3,5,4,4,4 +41,No,Travel_Rarely,645,Sales,1,3,Marketing,1,534,2,Male,49,4,3,Sales Executive,1,Married,8392,19566,1,Y,No,16,3,3,80,1,10,2,3,10,7,0,7 +28,No,Travel_Rarely,1300,Research & Development,17,2,Medical,1,536,3,Male,79,3,2,Laboratory Technician,1,Divorced,4558,13535,1,Y,No,12,3,4,80,1,10,2,3,10,0,1,8 +25,Yes,Travel_Rarely,688,Research & Development,3,3,Medical,1,538,1,Male,91,3,1,Laboratory Technician,1,Married,4031,9396,5,Y,No,13,3,3,80,1,6,5,3,2,2,0,2 +52,No,Travel_Rarely,319,Research & Development,3,3,Medical,1,543,4,Male,39,2,3,Manufacturing Director,3,Married,7969,19609,2,Y,Yes,14,3,3,80,0,28,4,3,5,4,0,4 +45,No,Travel_Rarely,192,Research & Development,10,2,Life Sciences,1,544,1,Male,69,3,1,Research Scientist,4,Married,2654,9655,3,Y,No,21,4,4,80,2,8,3,2,2,2,0,2 +52,No,Travel_Rarely,1490,Research & Development,4,2,Life Sciences,1,546,4,Female,30,3,4,Manager,4,Married,16555,10310,2,Y,No,13,3,4,80,0,31,2,1,5,2,1,4 +42,No,Travel_Frequently,532,Research & Development,29,2,Life Sciences,1,547,1,Female,92,3,2,Research Scientist,3,Divorced,4556,12932,2,Y,No,11,3,2,80,1,19,3,3,5,4,0,2 +30,No,Travel_Rarely,317,Research & Development,2,3,Life Sciences,1,548,3,Female,43,1,2,Manufacturing Director,4,Single,6091,24793,2,Y,No,20,4,3,80,0,11,2,3,5,4,0,2 +60,No,Travel_Rarely,422,Research & Development,7,3,Life Sciences,1,549,1,Female,41,3,5,Manager,1,Married,19566,3854,5,Y,No,11,3,4,80,0,33,5,1,29,8,11,10 +46,No,Travel_Rarely,1485,Research & Development,18,3,Medical,1,550,3,Female,87,3,2,Manufacturing Director,3,Divorced,4810,26314,2,Y,No,14,3,3,80,1,19,5,2,10,7,0,8 +42,No,Travel_Frequently,1368,Research & Development,28,4,Technical Degree,1,551,4,Female,88,2,2,Healthcare Representative,4,Married,4523,4386,0,Y,No,11,3,4,80,3,7,4,4,6,5,0,4 +24,Yes,Travel_Rarely,1448,Sales,1,1,Technical Degree,1,554,1,Female,62,3,1,Sales Representative,2,Single,3202,21972,1,Y,Yes,16,3,2,80,0,6,4,3,5,3,1,4 +34,Yes,Travel_Frequently,296,Sales,6,2,Marketing,1,555,4,Female,33,1,1,Sales Representative,3,Divorced,2351,12253,0,Y,No,16,3,4,80,1,3,3,2,2,2,1,0 +38,No,Travel_Frequently,1490,Research & Development,2,2,Life Sciences,1,556,4,Male,42,3,1,Laboratory Technician,4,Married,1702,12106,1,Y,Yes,23,4,3,80,1,1,3,3,1,0,0,0 +40,No,Travel_Rarely,1398,Sales,2,4,Life Sciences,1,558,3,Female,79,3,5,Manager,3,Married,18041,13022,0,Y,No,14,3,4,80,0,21,2,3,20,15,1,12 +26,No,Travel_Rarely,1349,Research & Development,23,3,Life Sciences,1,560,1,Female,90,3,1,Research Scientist,4,Divorced,2886,3032,1,Y,No,22,4,2,80,2,3,3,1,3,2,0,2 +30,No,Non-Travel,1400,Research & Development,3,3,Life Sciences,1,562,3,Male,53,3,1,Laboratory Technician,4,Married,2097,16734,4,Y,No,15,3,3,80,1,9,3,1,5,3,1,4 +29,No,Travel_Rarely,986,Research & Development,3,4,Medical,1,564,2,Male,93,2,3,Research Director,3,Married,11935,21526,1,Y,No,18,3,3,80,0,10,2,3,10,2,0,7 +29,Yes,Travel_Rarely,408,Research & Development,25,5,Technical Degree,1,565,3,Female,71,2,1,Research Scientist,2,Married,2546,18300,5,Y,No,16,3,2,80,0,6,2,4,2,2,1,1 +19,Yes,Travel_Rarely,489,Human Resources,2,2,Technical Degree,1,566,1,Male,52,2,1,Human Resources,4,Single,2564,18437,1,Y,No,12,3,3,80,0,1,3,4,1,0,0,0 +30,No,Non-Travel,1398,Sales,22,4,Other,1,567,3,Female,69,3,3,Sales Executive,1,Married,8412,2890,0,Y,No,11,3,3,80,0,10,3,3,9,8,7,8 +57,No,Travel_Rarely,210,Sales,29,3,Marketing,1,568,1,Male,56,2,4,Manager,4,Divorced,14118,22102,3,Y,No,12,3,3,80,1,32,3,2,1,0,0,0 +50,No,Travel_Rarely,1099,Research & Development,29,4,Life Sciences,1,569,2,Male,88,2,4,Manager,3,Married,17046,9314,0,Y,No,15,3,2,80,1,28,2,3,27,10,15,7 +30,No,Non-Travel,1116,Research & Development,2,3,Medical,1,571,3,Female,49,3,1,Laboratory Technician,4,Single,2564,7181,0,Y,No,14,3,3,80,0,12,2,2,11,7,6,7 +60,No,Travel_Frequently,1499,Sales,28,3,Marketing,1,573,3,Female,80,2,3,Sales Executive,1,Married,10266,2845,4,Y,No,19,3,4,80,0,22,5,4,18,13,13,11 +47,No,Travel_Rarely,983,Research & Development,2,2,Medical,1,574,1,Female,65,3,2,Manufacturing Director,4,Divorced,5070,7389,5,Y,No,13,3,3,80,3,20,2,3,5,0,0,4 +46,No,Travel_Rarely,1009,Research & Development,2,3,Life Sciences,1,575,1,Male,51,3,4,Research Director,3,Married,17861,2288,6,Y,No,13,3,3,80,0,26,2,1,3,2,0,1 +35,No,Travel_Rarely,144,Research & Development,22,3,Life Sciences,1,577,4,Male,46,1,1,Laboratory Technician,3,Single,4230,19225,0,Y,No,15,3,3,80,0,6,2,3,5,4,4,3 +54,No,Travel_Rarely,548,Research & Development,8,4,Life Sciences,1,578,3,Female,42,3,2,Laboratory Technician,3,Single,3780,23428,7,Y,No,11,3,3,80,0,19,3,3,1,0,0,0 +34,No,Travel_Rarely,1303,Research & Development,2,4,Life Sciences,1,579,4,Male,62,2,1,Research Scientist,3,Divorced,2768,8416,3,Y,No,12,3,3,80,1,14,3,3,7,3,5,7 +46,No,Travel_Rarely,1125,Sales,10,3,Marketing,1,580,3,Female,94,2,3,Sales Executive,4,Married,9071,11563,2,Y,Yes,19,3,3,80,1,15,3,3,3,2,1,2 +31,No,Travel_Rarely,1274,Research & Development,9,1,Life Sciences,1,581,3,Male,33,3,3,Manufacturing Director,2,Divorced,10648,14394,1,Y,No,25,4,4,80,1,13,6,4,13,8,0,8 +33,Yes,Travel_Rarely,1277,Research & Development,15,1,Medical,1,582,2,Male,56,3,3,Manager,3,Married,13610,24619,7,Y,Yes,12,3,4,80,0,15,2,4,7,6,7,7 +33,Yes,Travel_Rarely,587,Research & Development,10,1,Medical,1,584,1,Male,38,1,1,Laboratory Technician,4,Divorced,3408,6705,7,Y,No,13,3,1,80,3,8,2,3,4,3,1,3 +30,No,Travel_Rarely,413,Sales,7,1,Marketing,1,585,4,Male,57,3,1,Sales Representative,2,Single,2983,18398,0,Y,No,14,3,1,80,0,4,3,3,3,2,1,2 +35,No,Travel_Rarely,1276,Research & Development,16,3,Life Sciences,1,586,4,Male,72,3,3,Healthcare Representative,3,Married,7632,14295,4,Y,Yes,12,3,3,80,0,10,2,3,8,7,0,0 +31,Yes,Travel_Frequently,534,Research & Development,20,3,Life Sciences,1,587,1,Male,66,3,3,Healthcare Representative,3,Married,9824,22908,3,Y,No,12,3,1,80,0,12,2,3,1,0,0,0 +34,Yes,Travel_Frequently,988,Human Resources,23,3,Human Resources,1,590,2,Female,43,3,3,Human Resources,1,Divorced,9950,11533,9,Y,Yes,15,3,3,80,3,11,2,3,3,2,0,2 +42,No,Travel_Frequently,1474,Research & Development,5,2,Other,1,591,2,Male,97,3,1,Laboratory Technician,3,Married,2093,9260,4,Y,No,17,3,4,80,1,8,4,3,2,2,2,0 +36,No,Non-Travel,635,Sales,10,4,Medical,1,592,2,Male,32,3,3,Sales Executive,4,Single,9980,15318,1,Y,No,14,3,4,80,0,10,3,2,10,3,9,7 +22,Yes,Travel_Frequently,1368,Research & Development,4,1,Technical Degree,1,593,3,Male,99,2,1,Laboratory Technician,3,Single,3894,9129,5,Y,No,16,3,3,80,0,4,3,3,2,2,1,2 +48,No,Travel_Rarely,163,Sales,2,5,Marketing,1,595,2,Female,37,3,2,Sales Executive,4,Married,4051,19658,2,Y,No,14,3,1,80,1,14,2,3,9,7,6,7 +55,No,Travel_Rarely,1117,Sales,18,5,Life Sciences,1,597,1,Female,83,3,4,Manager,2,Single,16835,9873,3,Y,No,23,4,4,80,0,37,2,3,10,9,7,7 +41,No,Non-Travel,267,Sales,10,2,Life Sciences,1,599,4,Male,56,3,2,Sales Executive,4,Single,6230,13430,7,Y,No,14,3,4,80,0,16,3,3,14,3,1,10 +35,No,Travel_Rarely,619,Sales,1,3,Marketing,1,600,2,Male,85,3,2,Sales Executive,3,Married,4717,18659,9,Y,No,11,3,3,80,0,15,2,3,11,9,6,9 +40,No,Travel_Rarely,302,Research & Development,6,3,Life Sciences,1,601,2,Female,75,3,4,Manufacturing Director,3,Single,13237,20364,7,Y,No,15,3,3,80,0,22,3,3,20,6,5,13 +39,No,Travel_Frequently,443,Research & Development,8,1,Life Sciences,1,602,3,Female,48,3,1,Laboratory Technician,3,Married,3755,17872,1,Y,No,11,3,1,80,1,8,3,3,8,3,0,7 +31,No,Travel_Rarely,828,Sales,2,1,Life Sciences,1,604,2,Male,77,3,2,Sales Executive,4,Single,6582,8346,4,Y,Yes,13,3,3,80,0,10,2,4,6,5,0,5 +42,No,Travel_Rarely,319,Research & Development,24,3,Medical,1,605,4,Male,56,3,3,Manufacturing Director,1,Married,7406,6950,1,Y,Yes,21,4,4,80,1,10,5,2,10,9,5,8 +45,No,Travel_Rarely,561,Sales,2,3,Other,1,606,4,Male,61,3,2,Sales Executive,2,Married,4805,16177,0,Y,No,19,3,2,80,1,9,3,4,8,7,3,7 +26,Yes,Travel_Frequently,426,Human Resources,17,4,Life Sciences,1,608,2,Female,58,3,1,Human Resources,3,Divorced,2741,22808,0,Y,Yes,11,3,2,80,1,8,2,2,7,7,1,0 +29,No,Travel_Rarely,232,Research & Development,19,3,Technical Degree,1,611,4,Male,34,3,2,Manufacturing Director,4,Divorced,4262,22645,4,Y,No,12,3,2,80,2,8,2,4,3,2,1,2 +33,No,Travel_Rarely,922,Research & Development,1,5,Medical,1,612,1,Female,95,4,4,Research Director,3,Divorced,16184,22578,4,Y,No,19,3,3,80,1,10,2,3,6,1,0,5 +31,No,Travel_Rarely,688,Sales,7,3,Life Sciences,1,613,3,Male,44,2,3,Manager,4,Divorced,11557,25291,9,Y,No,21,4,3,80,1,10,3,2,5,4,0,1 +18,Yes,Travel_Frequently,1306,Sales,5,3,Marketing,1,614,2,Male,69,3,1,Sales Representative,2,Single,1878,8059,1,Y,Yes,14,3,4,80,0,0,3,3,0,0,0,0 +40,No,Non-Travel,1094,Sales,28,3,Other,1,615,3,Male,58,1,3,Sales Executive,1,Divorced,10932,11373,3,Y,No,15,3,3,80,1,20,2,3,1,0,0,1 +41,No,Non-Travel,509,Research & Development,2,4,Other,1,616,1,Female,62,2,2,Healthcare Representative,3,Single,6811,2112,2,Y,Yes,17,3,1,80,0,10,3,3,8,7,0,7 +26,No,Travel_Rarely,775,Sales,29,2,Medical,1,618,1,Male,45,3,2,Sales Executive,3,Divorced,4306,4267,5,Y,No,12,3,1,80,2,8,5,3,0,0,0,0 +35,No,Travel_Rarely,195,Sales,1,3,Medical,1,620,1,Female,80,3,2,Sales Executive,3,Single,4859,6698,1,Y,No,16,3,4,80,0,5,3,3,5,4,0,3 +34,No,Travel_Rarely,258,Sales,21,4,Life Sciences,1,621,4,Male,74,4,2,Sales Executive,4,Single,5337,19921,1,Y,No,12,3,4,80,0,10,3,3,10,7,5,7 +26,Yes,Travel_Rarely,471,Research & Development,24,3,Technical Degree,1,622,3,Male,66,1,1,Laboratory Technician,4,Single,2340,23213,1,Y,Yes,18,3,2,80,0,1,3,1,1,0,0,0 +37,No,Travel_Rarely,799,Research & Development,1,3,Technical Degree,1,623,2,Female,59,3,3,Manufacturing Director,4,Single,7491,23848,4,Y,No,17,3,4,80,0,12,3,4,6,5,1,2 +46,No,Travel_Frequently,1034,Research & Development,18,1,Medical,1,624,1,Female,86,3,3,Healthcare Representative,3,Married,10527,8984,5,Y,No,11,3,4,80,0,28,3,2,2,2,1,2 +41,No,Travel_Rarely,1276,Sales,2,5,Life Sciences,1,625,2,Female,91,3,4,Manager,1,Married,16595,5626,7,Y,No,16,3,2,80,1,22,2,3,18,16,11,8 +37,No,Non-Travel,142,Sales,9,4,Medical,1,626,1,Male,69,3,3,Sales Executive,2,Divorced,8834,24666,1,Y,No,13,3,4,80,1,9,6,3,9,5,7,7 +52,No,Travel_Rarely,956,Research & Development,6,2,Technical Degree,1,630,4,Male,78,3,2,Research Scientist,1,Divorced,5577,22087,3,Y,Yes,12,3,2,80,2,18,3,3,10,9,6,9 +32,Yes,Non-Travel,1474,Sales,11,4,Other,1,631,4,Male,60,4,2,Sales Executive,3,Married,4707,23914,8,Y,No,12,3,4,80,0,6,2,3,4,2,1,2 +24,No,Travel_Frequently,535,Sales,24,3,Medical,1,632,4,Male,38,3,1,Sales Representative,4,Married,2400,5530,0,Y,No,13,3,3,80,2,3,3,3,2,2,2,1 +38,No,Travel_Rarely,1495,Research & Development,10,3,Medical,1,634,3,Female,76,3,2,Healthcare Representative,3,Married,9824,22174,3,Y,No,19,3,3,80,1,18,4,3,1,0,0,0 +37,No,Travel_Rarely,446,Research & Development,1,4,Life Sciences,1,635,2,Female,65,3,2,Manufacturing Director,2,Married,6447,15701,6,Y,No,12,3,2,80,1,8,2,2,6,5,4,3 +49,No,Travel_Rarely,1245,Research & Development,18,4,Life Sciences,1,638,4,Male,58,2,5,Research Director,3,Divorced,19502,2125,1,Y,Yes,17,3,3,80,1,31,5,3,31,9,0,9 +24,No,Travel_Rarely,691,Research & Development,23,3,Medical,1,639,2,Male,89,4,1,Research Scientist,4,Married,2725,21630,1,Y,Yes,11,3,2,80,2,6,3,3,6,5,1,4 +26,No,Travel_Rarely,703,Sales,28,2,Marketing,1,641,1,Male,66,3,2,Sales Executive,2,Married,6272,7428,1,Y,No,20,4,4,80,2,6,5,4,5,3,1,4 +24,No,Travel_Rarely,823,Research & Development,17,2,Other,1,643,4,Male,94,2,1,Laboratory Technician,2,Married,2127,9100,1,Y,No,21,4,4,80,1,1,2,3,1,0,0,0 +50,No,Travel_Frequently,1246,Human Resources,3,3,Medical,1,644,1,Male,99,3,5,Manager,2,Married,18200,7999,1,Y,No,11,3,3,80,1,32,2,3,32,5,10,7 +25,No,Travel_Rarely,622,Sales,13,1,Medical,1,645,2,Male,40,3,1,Sales Representative,3,Married,2096,26376,1,Y,No,11,3,3,80,0,7,1,3,7,4,0,6 +24,Yes,Travel_Frequently,1287,Research & Development,7,3,Life Sciences,1,647,1,Female,55,3,1,Laboratory Technician,3,Married,2886,14168,1,Y,Yes,16,3,4,80,1,6,4,3,6,3,1,2 +30,Yes,Travel_Frequently,448,Sales,12,4,Life Sciences,1,648,2,Male,74,2,1,Sales Representative,1,Married,2033,14470,1,Y,No,18,3,3,80,1,1,2,4,1,0,0,0 +34,No,Travel_Rarely,254,Research & Development,1,2,Life Sciences,1,649,2,Male,83,2,1,Research Scientist,4,Married,3622,22794,1,Y,Yes,13,3,4,80,1,6,3,3,6,5,1,3 +31,Yes,Travel_Rarely,1365,Sales,13,4,Medical,1,650,2,Male,46,3,2,Sales Executive,1,Divorced,4233,11512,2,Y,No,17,3,3,80,0,9,2,1,3,1,1,2 +35,No,Travel_Rarely,538,Research & Development,25,2,Other,1,652,1,Male,54,2,2,Laboratory Technician,4,Single,3681,14004,4,Y,No,14,3,4,80,0,9,3,3,3,2,0,2 +31,No,Travel_Rarely,525,Sales,6,4,Medical,1,653,1,Male,66,4,2,Sales Executive,4,Divorced,5460,6219,4,Y,No,22,4,4,80,2,13,4,4,7,7,5,7 +27,No,Travel_Rarely,798,Research & Development,6,4,Medical,1,655,1,Female,66,2,1,Research Scientist,3,Divorced,2187,5013,0,Y,No,12,3,3,80,2,6,5,2,5,3,0,3 +37,No,Travel_Rarely,558,Sales,2,3,Marketing,1,656,4,Male,75,3,2,Sales Executive,3,Married,9602,3010,4,Y,Yes,11,3,3,80,1,17,3,2,3,0,1,0 +20,No,Travel_Rarely,959,Research & Development,1,3,Life Sciences,1,657,4,Female,83,2,1,Research Scientist,2,Single,2836,11757,1,Y,No,13,3,4,80,0,1,0,4,1,0,0,0 +42,No,Travel_Rarely,622,Research & Development,2,4,Life Sciences,1,659,3,Female,81,3,2,Healthcare Representative,4,Married,4089,5718,1,Y,No,13,3,2,80,2,10,4,3,10,2,2,2 +43,No,Travel_Rarely,782,Research & Development,6,4,Other,1,661,2,Male,50,2,4,Research Director,4,Divorced,16627,2671,4,Y,Yes,14,3,3,80,1,21,3,2,1,0,0,0 +38,No,Travel_Rarely,362,Research & Development,1,1,Life Sciences,1,662,3,Female,43,3,1,Research Scientist,1,Single,2619,14561,3,Y,No,17,3,4,80,0,8,3,2,0,0,0,0 +43,No,Travel_Frequently,1001,Research & Development,9,5,Medical,1,663,4,Male,72,3,2,Laboratory Technician,3,Divorced,5679,19627,3,Y,Yes,13,3,2,80,1,10,3,3,8,7,4,7 +48,No,Travel_Rarely,1236,Research & Development,1,4,Life Sciences,1,664,4,Female,40,2,4,Manager,1,Married,15402,17997,7,Y,No,11,3,1,80,1,21,3,1,3,2,0,2 +44,No,Travel_Rarely,1112,Human Resources,1,4,Life Sciences,1,665,1,Female,50,2,2,Human Resources,3,Single,5985,26894,4,Y,No,11,3,2,80,0,10,1,4,2,2,0,2 +34,No,Travel_Rarely,204,Sales,14,3,Technical Degree,1,666,3,Female,31,3,1,Sales Representative,3,Divorced,2579,2912,1,Y,Yes,18,3,4,80,2,8,3,3,8,2,0,6 +27,Yes,Travel_Rarely,1420,Sales,2,1,Marketing,1,667,3,Male,85,3,1,Sales Representative,1,Divorced,3041,16346,0,Y,No,11,3,2,80,1,5,3,3,4,3,0,2 +21,No,Travel_Rarely,1343,Sales,22,1,Technical Degree,1,669,3,Male,49,3,1,Sales Representative,3,Single,3447,24444,1,Y,No,11,3,3,80,0,3,2,3,3,2,1,2 +44,No,Travel_Rarely,1315,Research & Development,3,4,Other,1,671,4,Male,35,3,5,Manager,4,Married,19513,9358,4,Y,Yes,12,3,1,80,1,26,2,4,2,2,0,1 +22,No,Travel_Rarely,604,Research & Development,6,1,Medical,1,675,1,Male,69,3,1,Research Scientist,3,Married,2773,12145,0,Y,No,20,4,4,80,0,3,3,3,2,2,2,2 +33,No,Travel_Rarely,1216,Sales,8,4,Marketing,1,677,3,Male,39,3,2,Sales Executive,3,Divorced,7104,20431,0,Y,No,12,3,4,80,0,6,3,3,5,0,1,2 +32,No,Travel_Rarely,646,Research & Development,9,4,Life Sciences,1,679,1,Female,92,3,2,Research Scientist,4,Married,6322,18089,1,Y,Yes,12,3,4,80,1,6,2,2,6,4,0,5 +30,No,Travel_Frequently,160,Research & Development,3,3,Medical,1,680,3,Female,71,3,1,Research Scientist,3,Divorced,2083,22653,1,Y,No,20,4,3,80,1,1,2,3,1,0,0,0 +53,No,Travel_Rarely,238,Sales,1,1,Medical,1,682,4,Female,34,3,2,Sales Executive,1,Single,8381,7507,7,Y,No,20,4,4,80,0,18,2,4,14,7,8,10 +34,No,Travel_Rarely,1397,Research & Development,1,5,Life Sciences,1,683,2,Male,42,3,1,Research Scientist,4,Married,2691,7660,1,Y,No,12,3,4,80,1,10,4,2,10,9,8,8 +45,Yes,Travel_Frequently,306,Sales,26,4,Life Sciences,1,684,1,Female,100,3,2,Sales Executive,1,Married,4286,5630,2,Y,No,14,3,4,80,2,5,4,3,1,1,0,0 +26,No,Travel_Rarely,991,Research & Development,6,3,Life Sciences,1,686,3,Female,71,3,1,Laboratory Technician,4,Married,2659,17759,1,Y,Yes,13,3,3,80,1,3,2,3,3,2,0,2 +37,No,Travel_Rarely,482,Research & Development,3,3,Other,1,689,3,Male,36,3,3,Manufacturing Director,3,Married,9434,9606,1,Y,No,15,3,3,80,1,10,2,3,10,7,7,8 +29,No,Travel_Rarely,1176,Sales,3,2,Medical,1,690,2,Female,62,3,2,Sales Executive,3,Married,5561,3487,1,Y,No,14,3,1,80,1,6,5,2,6,0,1,2 +35,No,Travel_Rarely,1017,Research & Development,6,4,Life Sciences,1,691,2,Male,82,1,2,Research Scientist,4,Single,6646,19368,1,Y,No,13,3,2,80,0,17,3,3,17,11,11,8 +33,No,Travel_Frequently,1296,Research & Development,6,3,Life Sciences,1,692,3,Male,30,3,2,Healthcare Representative,4,Divorced,7725,5335,3,Y,No,23,4,3,80,1,15,2,1,13,11,4,7 +54,No,Travel_Rarely,397,Human Resources,19,4,Medical,1,698,3,Male,88,3,3,Human Resources,2,Married,10725,6729,2,Y,No,15,3,3,80,1,16,1,4,9,7,7,1 +36,No,Travel_Rarely,913,Research & Development,9,2,Medical,1,699,2,Male,48,2,2,Manufacturing Director,2,Divorced,8847,13934,2,Y,Yes,11,3,3,80,1,13,2,3,3,2,0,2 +27,No,Travel_Rarely,1115,Research & Development,3,4,Medical,1,700,1,Male,54,2,1,Research Scientist,4,Single,2045,15174,0,Y,No,13,3,4,80,0,5,0,3,4,2,1,1 +20,Yes,Travel_Rarely,1362,Research & Development,10,1,Medical,1,701,4,Male,32,3,1,Research Scientist,3,Single,1009,26999,1,Y,Yes,11,3,4,80,0,1,5,3,1,0,1,1 +33,Yes,Travel_Frequently,1076,Research & Development,3,3,Life Sciences,1,702,1,Male,70,3,1,Research Scientist,1,Single,3348,3164,1,Y,Yes,11,3,1,80,0,10,3,3,10,8,9,7 +35,No,Non-Travel,727,Research & Development,3,3,Life Sciences,1,704,3,Male,41,2,1,Laboratory Technician,3,Married,1281,16900,1,Y,No,18,3,3,80,2,1,3,3,1,0,0,0 +23,No,Travel_Rarely,885,Research & Development,4,3,Medical,1,705,1,Male,58,4,1,Research Scientist,1,Married,2819,8544,2,Y,No,16,3,1,80,1,5,3,4,3,2,0,2 +25,No,Travel_Rarely,810,Sales,8,3,Life Sciences,1,707,4,Male,57,4,2,Sales Executive,2,Married,4851,15678,0,Y,No,22,4,3,80,1,4,4,3,3,2,1,2 +38,No,Travel_Rarely,243,Sales,7,4,Marketing,1,709,4,Female,46,2,2,Sales Executive,4,Single,4028,7791,0,Y,No,20,4,1,80,0,8,2,3,7,7,0,5 +29,No,Travel_Frequently,806,Research & Development,1,4,Life Sciences,1,710,2,Male,76,1,1,Research Scientist,4,Divorced,2720,18959,1,Y,No,18,3,4,80,1,10,5,3,10,7,2,8 +48,No,Travel_Rarely,817,Sales,2,1,Marketing,1,712,2,Male,56,4,2,Sales Executive,2,Married,8120,18597,3,Y,No,12,3,4,80,0,12,3,3,2,2,2,2 +27,No,Travel_Frequently,1410,Sales,3,1,Medical,1,714,4,Female,71,4,2,Sales Executive,4,Divorced,4647,16673,1,Y,Yes,20,4,2,80,2,6,3,3,6,5,0,4 +37,No,Travel_Rarely,1225,Research & Development,10,2,Life Sciences,1,715,4,Male,80,4,1,Research Scientist,4,Single,4680,15232,3,Y,No,17,3,1,80,0,4,2,3,1,0,0,0 +50,No,Travel_Rarely,1207,Research & Development,28,1,Medical,1,716,4,Male,74,4,1,Laboratory Technician,3,Married,3221,3297,1,Y,Yes,11,3,3,80,3,20,3,3,20,8,3,8 +34,No,Travel_Rarely,1442,Research & Development,9,3,Medical,1,717,4,Female,46,2,3,Healthcare Representative,2,Single,8621,17654,1,Y,No,14,3,2,80,0,9,3,4,8,7,7,7 +24,Yes,Travel_Rarely,693,Sales,3,2,Life Sciences,1,720,1,Female,65,3,2,Sales Executive,3,Single,4577,24785,9,Y,No,14,3,1,80,0,4,3,3,2,2,2,0 +39,No,Travel_Rarely,408,Research & Development,2,4,Technical Degree,1,721,4,Female,80,2,2,Healthcare Representative,3,Single,4553,20978,1,Y,No,11,3,1,80,0,20,4,3,20,7,11,10 +32,No,Travel_Rarely,929,Sales,10,3,Marketing,1,722,4,Male,55,3,2,Sales Executive,4,Single,5396,21703,1,Y,No,12,3,4,80,0,10,2,2,10,7,0,8 +50,Yes,Travel_Frequently,562,Sales,8,2,Technical Degree,1,723,2,Male,50,3,2,Sales Executive,3,Married,6796,23452,3,Y,Yes,14,3,1,80,1,18,4,3,4,3,1,3 +38,No,Travel_Rarely,827,Research & Development,1,4,Life Sciences,1,724,2,Female,33,4,2,Healthcare Representative,4,Single,7625,19383,0,Y,No,13,3,3,80,0,10,4,2,9,7,1,8 +27,No,Travel_Rarely,608,Research & Development,1,2,Life Sciences,1,725,3,Female,68,3,3,Manufacturing Director,1,Married,7412,6009,1,Y,No,11,3,4,80,0,9,3,3,9,7,0,7 +32,No,Travel_Rarely,1018,Research & Development,3,2,Life Sciences,1,727,3,Female,39,3,3,Research Director,4,Single,11159,19373,3,Y,No,15,3,4,80,0,10,6,3,7,7,7,7 +47,No,Travel_Rarely,703,Sales,14,4,Marketing,1,728,4,Male,42,3,2,Sales Executive,1,Single,4960,11825,2,Y,No,12,3,4,80,0,20,2,3,7,7,1,7 +40,No,Travel_Frequently,580,Sales,5,4,Life Sciences,1,729,4,Male,48,2,3,Sales Executive,1,Married,10475,23772,5,Y,Yes,21,4,3,80,1,20,2,3,18,13,1,12 +53,No,Travel_Rarely,970,Research & Development,7,3,Life Sciences,1,730,3,Male,59,4,4,Research Director,3,Married,14814,13514,3,Y,No,19,3,3,80,0,32,3,3,5,1,1,3 +41,No,Travel_Rarely,427,Human Resources,10,4,Human Resources,1,731,2,Male,73,2,5,Manager,4,Divorced,19141,8861,3,Y,No,15,3,2,80,3,23,2,2,21,6,12,6 +60,No,Travel_Rarely,1179,Sales,16,4,Marketing,1,732,1,Male,84,3,2,Sales Executive,1,Single,5405,11924,8,Y,No,14,3,4,80,0,10,1,3,2,2,2,2 +27,No,Travel_Frequently,294,Research & Development,10,2,Life Sciences,1,733,4,Male,32,3,3,Manufacturing Director,1,Divorced,8793,4809,1,Y,No,21,4,3,80,2,9,4,2,9,7,1,7 +41,No,Travel_Rarely,314,Human Resources,1,3,Human Resources,1,734,4,Male,59,2,5,Manager,3,Married,19189,19562,1,Y,No,12,3,2,80,1,22,3,3,22,7,2,10 +50,No,Travel_Rarely,316,Sales,8,4,Marketing,1,738,4,Male,54,3,1,Sales Representative,2,Married,3875,9983,7,Y,No,15,3,4,80,1,4,2,3,2,2,2,2 +28,Yes,Travel_Rarely,654,Research & Development,1,2,Life Sciences,1,741,1,Female,67,1,1,Research Scientist,2,Single,2216,3872,7,Y,Yes,13,3,4,80,0,10,4,3,7,7,3,7 +36,No,Non-Travel,427,Research & Development,8,3,Life Sciences,1,742,1,Female,63,4,3,Research Director,1,Married,11713,20335,9,Y,No,14,3,1,80,1,10,2,3,8,7,0,5 +38,No,Travel_Rarely,168,Research & Development,1,3,Life Sciences,1,743,3,Female,81,3,3,Manufacturing Director,3,Single,7861,15397,4,Y,Yes,14,3,4,80,0,10,4,4,1,0,0,0 +44,No,Non-Travel,381,Research & Development,24,3,Medical,1,744,1,Male,49,1,1,Laboratory Technician,3,Single,3708,2104,2,Y,No,14,3,3,80,0,9,5,3,5,2,1,4 +47,No,Travel_Frequently,217,Sales,3,3,Medical,1,746,4,Female,49,3,4,Sales Executive,3,Divorced,13770,10225,9,Y,Yes,12,3,4,80,2,28,2,2,22,2,11,13 +30,No,Travel_Rarely,501,Sales,27,5,Marketing,1,747,3,Male,99,3,2,Sales Executive,4,Divorced,5304,25275,7,Y,No,23,4,4,80,1,10,2,2,8,7,7,7 +29,No,Travel_Rarely,1396,Sales,10,3,Life Sciences,1,749,3,Male,99,3,1,Sales Representative,3,Single,2642,2755,1,Y,No,11,3,3,80,0,1,6,3,1,0,0,0 +42,Yes,Travel_Frequently,933,Research & Development,19,3,Medical,1,752,3,Male,57,4,1,Research Scientist,3,Divorced,2759,20366,6,Y,Yes,12,3,4,80,0,7,2,3,2,2,2,2 +43,No,Travel_Frequently,775,Sales,15,3,Life Sciences,1,754,4,Male,47,2,2,Sales Executive,4,Married,6804,23683,3,Y,No,18,3,3,80,1,7,5,3,2,2,2,2 +34,No,Travel_Rarely,970,Research & Development,8,2,Medical,1,757,2,Female,96,3,2,Healthcare Representative,3,Single,6142,7360,3,Y,No,11,3,4,80,0,10,2,3,5,1,4,3 +23,No,Travel_Rarely,650,Research & Development,9,1,Medical,1,758,2,Male,37,3,1,Laboratory Technician,1,Married,2500,4344,1,Y,No,14,3,4,80,1,5,2,4,4,3,0,2 +39,No,Travel_Rarely,141,Human Resources,3,3,Human Resources,1,760,3,Female,44,4,2,Human Resources,2,Married,6389,18767,9,Y,No,15,3,3,80,1,12,3,1,8,3,3,6 +56,No,Travel_Rarely,832,Research & Development,9,3,Medical,1,762,3,Male,81,3,4,Healthcare Representative,4,Married,11103,20420,7,Y,No,11,3,3,80,0,30,1,2,10,7,1,1 +40,No,Travel_Rarely,804,Research & Development,2,1,Medical,1,763,4,Female,86,2,1,Research Scientist,4,Single,2342,22929,0,Y,Yes,20,4,4,80,0,5,2,2,4,2,2,3 +27,No,Travel_Rarely,975,Research & Development,7,3,Medical,1,764,4,Female,55,2,2,Healthcare Representative,1,Single,6811,23398,8,Y,No,19,3,1,80,0,9,2,1,7,6,0,7 +29,No,Travel_Rarely,1090,Sales,10,3,Marketing,1,766,4,Male,83,3,1,Sales Representative,2,Divorced,2297,17967,1,Y,No,14,3,4,80,2,2,2,3,2,2,2,2 +53,No,Travel_Rarely,346,Research & Development,6,3,Life Sciences,1,769,4,Male,86,3,2,Laboratory Technician,4,Single,2450,10919,2,Y,No,17,3,4,80,0,19,4,3,2,2,2,2 +35,No,Non-Travel,1225,Research & Development,2,4,Life Sciences,1,771,4,Female,61,3,2,Healthcare Representative,1,Divorced,5093,4761,2,Y,No,11,3,1,80,1,16,2,4,1,0,0,0 +32,No,Travel_Frequently,430,Research & Development,24,4,Life Sciences,1,772,1,Male,80,3,2,Laboratory Technician,4,Married,5309,21146,1,Y,No,15,3,4,80,2,10,2,3,10,8,4,7 +38,No,Travel_Rarely,268,Research & Development,2,5,Medical,1,773,4,Male,92,3,1,Research Scientist,3,Married,3057,20471,6,Y,Yes,13,3,2,80,1,6,0,1,1,0,0,1 +34,No,Travel_Rarely,167,Research & Development,8,5,Life Sciences,1,775,2,Female,32,3,2,Manufacturing Director,1,Divorced,5121,4187,3,Y,No,14,3,3,80,1,7,3,3,0,0,0,0 +52,No,Travel_Rarely,621,Sales,3,4,Marketing,1,776,3,Male,31,2,4,Manager,1,Married,16856,10084,1,Y,No,11,3,1,80,0,34,3,4,34,6,1,16 +33,Yes,Travel_Rarely,527,Research & Development,1,4,Other,1,780,4,Male,63,3,1,Research Scientist,4,Single,2686,5207,1,Y,Yes,13,3,3,80,0,10,2,2,10,9,7,8 +25,No,Travel_Rarely,883,Sales,26,1,Medical,1,781,3,Female,32,3,2,Sales Executive,4,Single,6180,22807,1,Y,No,23,4,2,80,0,6,5,2,6,5,1,4 +45,No,Travel_Rarely,954,Sales,2,2,Technical Degree,1,783,2,Male,46,1,2,Sales Representative,3,Single,6632,12388,0,Y,No,13,3,1,80,0,9,3,3,8,7,3,1 +23,No,Travel_Rarely,310,Research & Development,10,1,Medical,1,784,1,Male,79,4,1,Research Scientist,3,Single,3505,19630,1,Y,No,18,3,4,80,0,2,3,3,2,2,0,2 +47,Yes,Travel_Frequently,719,Sales,27,2,Life Sciences,1,785,2,Female,77,4,2,Sales Executive,3,Single,6397,10339,4,Y,Yes,12,3,4,80,0,8,2,3,5,4,1,3 +34,No,Travel_Rarely,304,Sales,2,3,Other,1,786,4,Male,60,3,2,Sales Executive,4,Single,6274,18686,1,Y,No,22,4,3,80,0,6,5,3,6,5,1,4 +55,Yes,Travel_Rarely,725,Research & Development,2,3,Medical,1,787,4,Male,78,3,5,Manager,1,Married,19859,21199,5,Y,Yes,13,3,4,80,1,24,2,3,5,2,1,4 +36,No,Non-Travel,1434,Sales,8,4,Life Sciences,1,789,1,Male,76,2,3,Sales Executive,1,Single,7587,14229,1,Y,No,15,3,2,80,0,10,1,3,10,7,0,9 +52,No,Non-Travel,715,Research & Development,19,4,Medical,1,791,4,Male,41,3,1,Research Scientist,4,Married,4258,26589,0,Y,No,18,3,1,80,1,5,3,3,4,3,1,2 +26,No,Travel_Frequently,575,Research & Development,1,2,Life Sciences,1,792,1,Female,71,1,1,Laboratory Technician,4,Divorced,4364,5288,3,Y,No,14,3,1,80,1,5,2,3,2,2,2,0 +29,No,Travel_Rarely,657,Research & Development,27,3,Medical,1,793,2,Female,66,3,2,Healthcare Representative,3,Married,4335,25549,4,Y,No,12,3,1,80,1,11,3,2,8,7,1,1 +26,Yes,Travel_Rarely,1146,Sales,8,3,Technical Degree,1,796,4,Male,38,2,2,Sales Executive,1,Single,5326,3064,6,Y,No,17,3,3,80,0,6,2,2,4,3,1,2 +34,No,Travel_Rarely,182,Research & Development,1,4,Life Sciences,1,797,2,Female,72,4,1,Research Scientist,4,Single,3280,13551,2,Y,No,16,3,3,80,0,10,2,3,4,2,1,3 +54,No,Travel_Rarely,376,Research & Development,19,4,Medical,1,799,4,Female,95,3,2,Manufacturing Director,1,Divorced,5485,22670,9,Y,Yes,11,3,2,80,2,9,4,3,5,3,1,4 +27,No,Travel_Frequently,829,Sales,8,1,Marketing,1,800,3,Male,84,3,2,Sales Executive,4,Married,4342,24008,0,Y,No,19,3,2,80,1,5,3,3,4,2,1,1 +37,No,Travel_Rarely,571,Research & Development,10,1,Life Sciences,1,802,4,Female,82,3,1,Research Scientist,1,Divorced,2782,19905,0,Y,Yes,13,3,2,80,2,6,3,2,5,3,4,3 +38,No,Travel_Frequently,240,Research & Development,2,4,Life Sciences,1,803,1,Female,75,4,2,Manufacturing Director,1,Single,5980,26085,6,Y,Yes,12,3,4,80,0,17,2,3,15,7,4,12 +34,No,Travel_Rarely,121,Research & Development,2,4,Medical,1,804,3,Female,86,2,1,Research Scientist,1,Single,4381,7530,1,Y,No,11,3,3,80,0,6,3,3,6,5,1,3 +35,No,Travel_Rarely,384,Sales,8,4,Life Sciences,1,805,1,Female,72,3,1,Sales Representative,4,Married,2572,20317,1,Y,No,16,3,2,80,1,3,1,2,3,2,0,2 +30,No,Travel_Rarely,921,Research & Development,1,3,Life Sciences,1,806,4,Male,38,1,1,Laboratory Technician,3,Married,3833,24375,3,Y,No,21,4,3,80,2,7,2,3,2,2,0,2 +40,No,Travel_Frequently,791,Research & Development,2,2,Medical,1,807,3,Female,38,4,2,Healthcare Representative,2,Married,4244,9931,1,Y,No,24,4,4,80,1,8,2,3,8,7,3,7 +34,No,Travel_Rarely,1111,Sales,8,2,Life Sciences,1,808,3,Female,93,3,2,Sales Executive,1,Married,6500,13305,5,Y,No,17,3,2,80,1,6,1,3,3,2,1,2 +42,No,Travel_Frequently,570,Research & Development,8,3,Life Sciences,1,809,2,Male,66,3,5,Manager,4,Divorced,18430,16225,1,Y,No,13,3,2,80,1,24,4,2,24,7,14,9 +23,Yes,Travel_Rarely,1243,Research & Development,6,3,Life Sciences,1,811,3,Male,63,4,1,Laboratory Technician,1,Married,1601,3445,1,Y,Yes,21,4,3,80,2,1,2,3,0,0,0,0 +24,No,Non-Travel,1092,Research & Development,9,3,Life Sciences,1,812,3,Male,60,2,1,Laboratory Technician,2,Divorced,2694,26551,1,Y,No,11,3,3,80,3,1,4,3,1,0,0,0 +52,No,Travel_Rarely,1325,Research & Development,11,4,Life Sciences,1,813,4,Female,82,3,2,Laboratory Technician,3,Married,3149,21821,8,Y,No,20,4,2,80,1,9,3,3,5,2,1,4 +50,No,Travel_Rarely,691,Research & Development,2,3,Medical,1,815,3,Male,64,3,4,Research Director,3,Married,17639,6881,5,Y,No,16,3,4,80,0,30,3,3,4,3,0,3 +29,Yes,Travel_Rarely,805,Research & Development,1,2,Life Sciences,1,816,2,Female,36,2,1,Laboratory Technician,1,Married,2319,6689,1,Y,Yes,11,3,4,80,1,1,1,3,1,0,0,0 +33,No,Travel_Rarely,213,Research & Development,7,3,Medical,1,817,3,Male,49,3,3,Research Director,3,Married,11691,25995,0,Y,No,11,3,4,80,0,14,3,4,13,9,3,7 +33,Yes,Travel_Rarely,118,Sales,16,3,Marketing,1,819,1,Female,69,3,2,Sales Executive,1,Single,5324,26507,5,Y,No,15,3,3,80,0,6,3,3,3,2,0,2 +47,No,Travel_Rarely,202,Research & Development,2,2,Other,1,820,3,Female,33,3,4,Manager,4,Married,16752,12982,1,Y,Yes,11,3,3,80,1,26,3,2,26,14,3,0 +36,No,Travel_Rarely,676,Research & Development,1,3,Other,1,823,3,Female,35,3,2,Manufacturing Director,2,Married,5228,23361,0,Y,No,15,3,1,80,1,10,2,3,9,7,0,5 +29,No,Travel_Rarely,1252,Research & Development,23,2,Life Sciences,1,824,3,Male,81,4,1,Research Scientist,3,Married,2700,23779,1,Y,No,24,4,3,80,1,10,3,3,10,7,0,7 +58,Yes,Travel_Rarely,286,Research & Development,2,4,Life Sciences,1,825,4,Male,31,3,5,Research Director,2,Single,19246,25761,7,Y,Yes,12,3,4,80,0,40,2,3,31,15,13,8 +35,No,Travel_Rarely,1258,Research & Development,1,4,Life Sciences,1,826,4,Female,40,4,1,Research Scientist,3,Single,2506,13301,3,Y,No,13,3,3,80,0,7,0,3,2,2,2,2 +42,No,Travel_Rarely,932,Research & Development,1,2,Life Sciences,1,827,4,Female,43,2,2,Manufacturing Director,4,Married,6062,4051,9,Y,Yes,13,3,4,80,1,8,4,3,4,3,0,2 +28,Yes,Travel_Rarely,890,Research & Development,2,4,Medical,1,828,3,Male,46,3,1,Research Scientist,3,Single,4382,16374,6,Y,No,17,3,4,80,0,5,3,2,2,2,2,1 +36,No,Travel_Rarely,1041,Human Resources,13,3,Human Resources,1,829,3,Male,36,3,1,Human Resources,2,Married,2143,25527,4,Y,No,13,3,2,80,1,8,2,3,5,2,0,4 +32,No,Travel_Rarely,859,Research & Development,4,3,Life Sciences,1,830,3,Female,98,2,2,Manufacturing Director,3,Married,6162,19124,1,Y,No,12,3,3,80,1,14,3,3,14,13,6,8 +40,No,Travel_Frequently,720,Research & Development,16,4,Medical,1,832,1,Male,51,2,2,Laboratory Technician,3,Single,5094,11983,6,Y,No,14,3,4,80,0,10,6,3,1,0,0,0 +30,No,Travel_Rarely,946,Research & Development,2,3,Medical,1,833,3,Female,52,2,2,Manufacturing Director,4,Single,6877,20234,5,Y,Yes,24,4,2,80,0,12,4,2,0,0,0,0 +45,No,Travel_Rarely,252,Research & Development,2,3,Life Sciences,1,834,2,Female,95,2,1,Research Scientist,3,Single,2274,6153,1,Y,No,14,3,4,80,0,1,3,3,1,0,0,0 +42,No,Travel_Rarely,933,Research & Development,29,3,Life Sciences,1,836,2,Male,98,3,2,Manufacturing Director,2,Married,4434,11806,1,Y,No,13,3,4,80,1,10,3,2,9,8,7,8 +38,No,Travel_Frequently,471,Research & Development,12,3,Life Sciences,1,837,1,Male,45,2,2,Healthcare Representative,1,Divorced,6288,4284,2,Y,No,15,3,3,80,1,13,3,2,4,3,1,2 +34,No,Travel_Frequently,702,Research & Development,16,4,Life Sciences,1,838,3,Female,100,2,1,Research Scientist,4,Single,2553,8306,1,Y,No,16,3,3,80,0,6,3,3,5,2,1,3 +49,Yes,Travel_Rarely,1184,Sales,11,3,Marketing,1,840,3,Female,43,3,3,Sales Executive,4,Married,7654,5860,1,Y,No,18,3,1,80,2,9,3,4,9,8,7,7 +55,Yes,Travel_Rarely,436,Sales,2,1,Medical,1,842,3,Male,37,3,2,Sales Executive,4,Single,5160,21519,4,Y,No,16,3,3,80,0,12,3,2,9,7,7,3 +43,No,Travel_Rarely,589,Research & Development,14,2,Life Sciences,1,843,2,Male,94,3,4,Research Director,1,Married,17159,5200,6,Y,No,24,4,3,80,1,22,3,3,4,1,1,0 +27,No,Travel_Rarely,269,Research & Development,5,1,Technical Degree,1,844,3,Male,42,2,3,Research Director,4,Divorced,12808,8842,1,Y,Yes,16,3,2,80,1,9,3,3,9,8,0,8 +35,No,Travel_Rarely,950,Research & Development,7,3,Other,1,845,3,Male,59,3,3,Manufacturing Director,3,Single,10221,18869,3,Y,No,21,4,2,80,0,17,3,4,8,5,1,6 +28,No,Travel_Rarely,760,Sales,2,4,Marketing,1,846,2,Female,81,3,2,Sales Executive,2,Married,4779,3698,1,Y,Yes,20,4,1,80,0,8,2,3,8,7,7,5 +34,No,Travel_Rarely,829,Human Resources,3,2,Human Resources,1,847,3,Male,88,3,1,Human Resources,4,Married,3737,2243,0,Y,No,19,3,3,80,1,4,1,1,3,2,0,2 +26,Yes,Travel_Frequently,887,Research & Development,5,2,Medical,1,848,3,Female,88,2,1,Research Scientist,3,Married,2366,20898,1,Y,Yes,14,3,1,80,1,8,2,3,8,7,1,7 +27,No,Non-Travel,443,Research & Development,3,3,Medical,1,850,4,Male,50,3,1,Research Scientist,4,Married,1706,16571,1,Y,No,11,3,3,80,3,0,6,2,0,0,0,0 +51,No,Travel_Rarely,1318,Sales,26,4,Marketing,1,851,1,Female,66,3,4,Manager,3,Married,16307,5594,2,Y,No,14,3,3,80,1,29,2,2,20,6,4,17 +44,No,Travel_Rarely,625,Research & Development,4,3,Medical,1,852,4,Male,50,3,2,Healthcare Representative,2,Single,5933,5197,9,Y,No,12,3,4,80,0,10,2,2,5,2,2,3 +25,No,Travel_Rarely,180,Research & Development,2,1,Medical,1,854,1,Male,65,4,1,Research Scientist,1,Single,3424,21632,7,Y,No,13,3,3,80,0,6,3,2,4,3,0,1 +33,No,Travel_Rarely,586,Sales,1,3,Medical,1,855,1,Male,48,4,2,Sales Executive,1,Divorced,4037,21816,1,Y,No,22,4,1,80,1,9,5,3,9,8,0,8 +35,No,Travel_Rarely,1343,Research & Development,27,1,Medical,1,856,3,Female,53,2,1,Research Scientist,1,Single,2559,17852,1,Y,No,11,3,4,80,0,6,3,2,6,5,1,1 +36,No,Travel_Rarely,928,Sales,1,2,Life Sciences,1,857,2,Male,56,3,2,Sales Executive,4,Married,6201,2823,1,Y,Yes,14,3,4,80,1,18,1,2,18,14,4,11 +32,No,Travel_Rarely,117,Sales,13,4,Life Sciences,1,859,2,Male,73,3,2,Sales Executive,4,Divorced,4403,9250,2,Y,No,11,3,3,80,1,8,3,2,5,2,0,3 +30,No,Travel_Frequently,1012,Research & Development,5,4,Life Sciences,1,861,2,Male,75,2,1,Research Scientist,4,Divorced,3761,2373,9,Y,No,12,3,2,80,1,10,3,2,5,4,0,3 +53,No,Travel_Rarely,661,Sales,7,2,Marketing,1,862,1,Female,78,2,3,Sales Executive,4,Married,10934,20715,7,Y,Yes,18,3,4,80,1,35,3,3,5,2,0,4 +45,No,Travel_Rarely,930,Sales,9,3,Marketing,1,864,4,Male,74,3,3,Sales Executive,1,Divorced,10761,19239,4,Y,Yes,12,3,3,80,1,18,2,3,5,4,0,2 +32,No,Travel_Rarely,638,Research & Development,8,2,Medical,1,865,3,Female,91,4,2,Research Scientist,3,Married,5175,22162,5,Y,No,12,3,3,80,1,9,3,2,5,3,1,3 +52,No,Travel_Frequently,890,Research & Development,25,4,Medical,1,867,3,Female,81,2,4,Manufacturing Director,4,Married,13826,19028,3,Y,No,22,4,3,80,0,31,3,3,9,8,0,0 +37,No,Travel_Rarely,342,Sales,16,4,Marketing,1,868,4,Male,66,2,2,Sales Executive,3,Divorced,6334,24558,4,Y,No,19,3,4,80,2,9,2,3,1,0,0,0 +28,No,Travel_Rarely,1169,Human Resources,8,2,Medical,1,869,2,Male,63,2,1,Human Resources,4,Divorced,4936,23965,1,Y,No,13,3,4,80,1,6,6,3,5,1,0,4 +22,No,Travel_Rarely,1230,Research & Development,1,2,Life Sciences,1,872,4,Male,33,2,2,Manufacturing Director,4,Married,4775,19146,6,Y,No,22,4,1,80,2,4,2,1,2,2,2,2 +44,No,Travel_Rarely,986,Research & Development,8,4,Life Sciences,1,874,1,Male,62,4,1,Laboratory Technician,4,Married,2818,5044,2,Y,Yes,24,4,3,80,1,10,2,2,3,2,0,2 +42,No,Travel_Frequently,1271,Research & Development,2,1,Medical,1,875,2,Male,35,3,1,Research Scientist,4,Single,2515,9068,5,Y,Yes,14,3,4,80,0,8,2,3,2,1,2,2 +36,No,Travel_Rarely,1278,Human Resources,8,3,Life Sciences,1,878,1,Male,77,2,1,Human Resources,1,Married,2342,8635,0,Y,No,21,4,3,80,0,6,3,3,5,4,0,3 +25,No,Travel_Rarely,141,Sales,3,1,Other,1,879,3,Male,98,3,2,Sales Executive,1,Married,4194,14363,1,Y,Yes,18,3,4,80,0,5,3,3,5,3,0,3 +35,No,Travel_Rarely,607,Research & Development,9,3,Life Sciences,1,880,4,Female,66,2,3,Manufacturing Director,3,Married,10685,23457,1,Y,Yes,20,4,2,80,1,17,2,3,17,14,5,15 +35,Yes,Travel_Frequently,130,Research & Development,25,4,Life Sciences,1,881,4,Female,96,3,1,Research Scientist,2,Divorced,2022,16612,1,Y,Yes,19,3,1,80,1,10,3,2,10,2,7,8 +32,No,Non-Travel,300,Research & Development,1,3,Life Sciences,1,882,4,Male,61,3,1,Laboratory Technician,4,Divorced,2314,9148,0,Y,No,12,3,2,80,1,4,2,3,3,0,0,2 +25,No,Travel_Rarely,583,Sales,4,1,Marketing,1,885,3,Male,87,2,2,Sales Executive,1,Married,4256,18154,1,Y,No,12,3,1,80,0,5,1,4,5,2,0,3 +49,No,Travel_Rarely,1418,Research & Development,1,3,Technical Degree,1,887,3,Female,36,3,1,Research Scientist,1,Married,3580,10554,2,Y,No,16,3,2,80,1,7,2,3,4,2,0,2 +24,No,Non-Travel,1269,Research & Development,4,1,Life Sciences,1,888,1,Male,46,2,1,Laboratory Technician,4,Married,3162,10778,0,Y,No,17,3,4,80,0,6,2,2,5,2,3,4 +32,No,Travel_Frequently,379,Sales,5,2,Life Sciences,1,889,2,Male,48,3,2,Sales Executive,2,Married,6524,8891,1,Y,No,14,3,4,80,1,10,3,3,10,8,5,3 +38,No,Travel_Rarely,395,Sales,9,3,Marketing,1,893,2,Male,98,2,1,Sales Representative,2,Married,2899,12102,0,Y,No,19,3,4,80,1,3,3,3,2,2,1,2 +42,No,Travel_Rarely,1265,Research & Development,3,3,Life Sciences,1,894,3,Female,95,4,2,Laboratory Technician,4,Married,5231,23726,2,Y,Yes,13,3,2,80,1,17,1,2,5,3,1,3 +31,No,Travel_Rarely,1222,Research & Development,11,4,Life Sciences,1,895,4,Male,48,3,1,Research Scientist,4,Married,2356,14871,3,Y,Yes,19,3,2,80,1,8,2,3,6,4,0,2 +29,Yes,Travel_Rarely,341,Sales,1,3,Medical,1,896,2,Female,48,2,1,Sales Representative,3,Divorced,2800,23522,6,Y,Yes,19,3,3,80,3,5,3,3,3,2,0,2 +53,No,Travel_Rarely,868,Sales,8,3,Marketing,1,897,1,Male,73,3,4,Sales Executive,4,Married,11836,22789,5,Y,No,14,3,3,80,1,28,3,3,2,0,2,2 +35,No,Travel_Rarely,672,Research & Development,25,3,Technical Degree,1,899,4,Male,78,2,3,Manufacturing Director,2,Married,10903,9129,3,Y,No,16,3,1,80,0,16,2,3,13,10,4,8 +37,No,Travel_Frequently,1231,Sales,21,2,Medical,1,900,3,Female,54,3,1,Sales Representative,4,Married,2973,21222,5,Y,No,15,3,2,80,1,10,3,3,5,4,0,0 +53,No,Travel_Rarely,102,Research & Development,23,4,Life Sciences,1,901,4,Female,72,3,4,Research Director,4,Single,14275,20206,6,Y,No,18,3,3,80,0,33,0,3,12,9,3,8 +43,No,Travel_Frequently,422,Research & Development,1,3,Life Sciences,1,902,4,Female,33,3,2,Healthcare Representative,4,Married,5562,21782,4,Y,No,13,3,2,80,1,12,2,2,5,2,2,2 +47,No,Travel_Rarely,249,Sales,2,2,Marketing,1,903,3,Female,35,3,2,Sales Executive,4,Married,4537,17783,0,Y,Yes,22,4,1,80,1,8,2,3,7,6,7,7 +37,No,Non-Travel,1252,Sales,19,2,Medical,1,904,1,Male,32,3,3,Sales Executive,2,Single,7642,4814,1,Y,Yes,13,3,4,80,0,10,2,3,10,0,0,9 +50,No,Non-Travel,881,Research & Development,2,4,Life Sciences,1,905,1,Male,98,3,4,Manager,1,Divorced,17924,4544,1,Y,No,11,3,4,80,1,31,3,3,31,6,14,7 +39,No,Travel_Rarely,1383,Human Resources,2,3,Life Sciences,1,909,4,Female,42,2,2,Human Resources,4,Married,5204,7790,8,Y,No,11,3,3,80,2,13,2,3,5,4,0,4 +33,No,Travel_Rarely,1075,Human Resources,3,2,Human Resources,1,910,4,Male,57,3,1,Human Resources,2,Divorced,2277,22650,3,Y,Yes,11,3,3,80,1,7,4,4,4,3,0,3 +32,Yes,Travel_Rarely,374,Research & Development,25,4,Life Sciences,1,911,1,Male,87,3,1,Laboratory Technician,4,Single,2795,18016,1,Y,Yes,24,4,3,80,0,1,2,1,1,0,0,1 +29,No,Travel_Rarely,1086,Research & Development,7,1,Medical,1,912,1,Female,62,2,1,Laboratory Technician,4,Divorced,2532,6054,6,Y,No,14,3,3,80,3,8,5,3,4,3,0,3 +44,No,Travel_Rarely,661,Research & Development,9,2,Life Sciences,1,913,2,Male,61,3,1,Research Scientist,1,Married,2559,7508,1,Y,Yes,13,3,4,80,0,8,0,3,8,7,7,1 +28,No,Travel_Rarely,821,Sales,5,4,Medical,1,916,1,Male,98,3,2,Sales Executive,4,Single,4908,24252,1,Y,No,14,3,2,80,0,4,3,3,4,2,0,2 +58,Yes,Travel_Frequently,781,Research & Development,2,1,Life Sciences,1,918,4,Male,57,2,1,Laboratory Technician,4,Divorced,2380,13384,9,Y,Yes,14,3,4,80,1,3,3,2,1,0,0,0 +43,No,Travel_Rarely,177,Research & Development,8,3,Life Sciences,1,920,1,Female,55,3,2,Manufacturing Director,2,Divorced,4765,23814,4,Y,No,21,4,3,80,1,4,2,4,1,0,0,0 +20,Yes,Travel_Rarely,500,Sales,2,3,Medical,1,922,3,Female,49,2,1,Sales Representative,3,Single,2044,22052,1,Y,No,13,3,4,80,0,2,3,2,2,2,0,2 +21,Yes,Travel_Rarely,1427,Research & Development,18,1,Other,1,923,4,Female,65,3,1,Research Scientist,4,Single,2693,8870,1,Y,No,19,3,1,80,0,1,3,2,1,0,0,0 +36,No,Travel_Rarely,1425,Research & Development,14,1,Life Sciences,1,924,3,Male,68,3,2,Healthcare Representative,4,Married,6586,4821,0,Y,Yes,17,3,1,80,1,17,2,2,16,8,4,11 +47,No,Travel_Rarely,1454,Sales,2,4,Life Sciences,1,925,4,Female,65,2,1,Sales Representative,4,Single,3294,13137,1,Y,Yes,18,3,1,80,0,3,3,2,3,2,1,2 +22,Yes,Travel_Rarely,617,Research & Development,3,1,Life Sciences,1,926,2,Female,34,3,2,Manufacturing Director,3,Married,4171,10022,0,Y,Yes,19,3,1,80,1,4,3,4,3,2,0,2 +41,Yes,Travel_Rarely,1085,Research & Development,2,4,Life Sciences,1,927,2,Female,57,1,1,Laboratory Technician,4,Divorced,2778,17725,4,Y,Yes,13,3,3,80,1,10,1,2,7,7,1,0 +28,No,Travel_Rarely,995,Research & Development,9,3,Medical,1,930,3,Female,77,3,1,Research Scientist,3,Divorced,2377,9834,5,Y,No,18,3,2,80,1,6,2,3,2,2,2,2 +39,Yes,Travel_Rarely,1122,Research & Development,6,3,Medical,1,932,4,Male,70,3,1,Laboratory Technician,1,Married,2404,4303,7,Y,Yes,21,4,4,80,0,8,2,1,2,2,2,2 +27,No,Travel_Rarely,618,Research & Development,4,3,Life Sciences,1,933,2,Female,76,3,1,Research Scientist,3,Single,2318,17808,1,Y,No,19,3,3,80,0,1,2,3,1,1,0,0 +34,No,Travel_Rarely,546,Research & Development,10,3,Life Sciences,1,934,2,Male,83,3,1,Laboratory Technician,2,Divorced,2008,6896,1,Y,No,14,3,2,80,2,1,3,3,1,0,1,0 +42,No,Travel_Rarely,462,Sales,14,2,Medical,1,936,3,Female,68,2,2,Sales Executive,3,Single,6244,7824,7,Y,No,17,3,1,80,0,10,6,3,5,4,0,3 +33,No,Travel_Rarely,1198,Research & Development,1,4,Other,1,939,3,Male,100,2,1,Research Scientist,1,Single,2799,3339,3,Y,Yes,11,3,2,80,0,6,1,3,3,2,0,2 +58,No,Travel_Rarely,1272,Research & Development,5,3,Technical Degree,1,940,3,Female,37,2,3,Healthcare Representative,2,Divorced,10552,9255,2,Y,Yes,13,3,4,80,1,24,3,3,6,0,0,4 +31,No,Travel_Rarely,154,Sales,7,4,Life Sciences,1,941,2,Male,41,2,1,Sales Representative,3,Married,2329,11737,3,Y,No,15,3,2,80,0,13,2,4,7,7,5,2 +35,No,Travel_Rarely,1137,Research & Development,21,1,Life Sciences,1,942,4,Female,51,3,2,Healthcare Representative,4,Married,4014,19170,1,Y,Yes,25,4,4,80,1,10,2,1,10,6,0,7 +49,No,Travel_Rarely,527,Research & Development,8,2,Other,1,944,1,Female,51,3,3,Laboratory Technician,2,Married,7403,22477,4,Y,No,11,3,3,80,1,29,3,2,26,9,1,7 +48,No,Travel_Rarely,1469,Research & Development,20,4,Medical,1,945,4,Male,51,3,1,Research Scientist,3,Married,2259,5543,4,Y,No,17,3,1,80,2,13,2,2,0,0,0,0 +31,No,Non-Travel,1188,Sales,20,2,Marketing,1,947,4,Female,45,3,2,Sales Executive,3,Married,6932,24406,1,Y,No,13,3,4,80,1,9,2,2,9,8,0,0 +36,No,Travel_Rarely,188,Research & Development,7,4,Other,1,949,2,Male,65,3,1,Research Scientist,4,Single,4678,23293,2,Y,No,18,3,3,80,0,8,6,3,6,2,0,1 +38,No,Travel_Rarely,1333,Research & Development,1,3,Technical Degree,1,950,4,Female,80,3,3,Research Director,1,Married,13582,16292,1,Y,No,13,3,2,80,1,15,3,3,15,12,5,11 +32,No,Non-Travel,1184,Research & Development,1,3,Life Sciences,1,951,3,Female,70,2,1,Laboratory Technician,2,Married,2332,3974,6,Y,No,20,4,3,80,0,5,3,3,3,0,0,2 +25,Yes,Travel_Rarely,867,Sales,19,2,Marketing,1,952,3,Male,36,2,1,Sales Representative,2,Married,2413,18798,1,Y,Yes,18,3,3,80,3,1,2,3,1,0,0,0 +40,No,Travel_Rarely,658,Sales,10,4,Marketing,1,954,1,Male,67,2,3,Sales Executive,2,Divorced,9705,20652,2,Y,No,12,3,2,80,1,11,2,2,1,0,0,0 +26,No,Travel_Frequently,1283,Sales,1,3,Medical,1,956,3,Male,52,2,2,Sales Executive,1,Single,4294,11148,1,Y,No,12,3,2,80,0,7,2,3,7,7,0,7 +41,No,Travel_Rarely,263,Research & Development,6,3,Medical,1,957,4,Male,59,3,1,Laboratory Technician,1,Single,4721,3119,2,Y,Yes,13,3,3,80,0,20,3,3,18,13,2,17 +36,No,Travel_Rarely,938,Research & Development,2,4,Medical,1,958,3,Male,79,3,1,Laboratory Technician,3,Single,2519,12287,4,Y,No,21,4,3,80,0,16,6,3,11,8,3,9 +19,Yes,Travel_Rarely,419,Sales,21,3,Other,1,959,4,Male,37,2,1,Sales Representative,2,Single,2121,9947,1,Y,Yes,13,3,2,80,0,1,3,4,1,0,0,0 +20,Yes,Travel_Rarely,129,Research & Development,4,3,Technical Degree,1,960,1,Male,84,3,1,Laboratory Technician,1,Single,2973,13008,1,Y,No,19,3,2,80,0,1,2,3,1,0,0,0 +31,No,Travel_Rarely,616,Research & Development,12,3,Medical,1,961,4,Female,41,3,2,Healthcare Representative,4,Married,5855,17369,0,Y,Yes,11,3,3,80,2,10,2,1,9,7,8,5 +40,No,Travel_Frequently,1469,Research & Development,9,4,Medical,1,964,4,Male,35,3,1,Research Scientist,2,Divorced,3617,25063,8,Y,Yes,14,3,4,80,1,3,2,3,1,1,0,0 +32,No,Travel_Rarely,498,Research & Development,3,4,Medical,1,966,3,Female,93,3,2,Manufacturing Director,1,Married,6725,13554,1,Y,No,12,3,3,80,1,8,2,4,8,7,6,3 +36,Yes,Travel_Rarely,530,Sales,3,1,Life Sciences,1,967,3,Male,51,2,3,Sales Executive,4,Married,10325,5518,1,Y,Yes,11,3,1,80,1,16,6,3,16,7,3,7 +33,No,Travel_Rarely,1069,Research & Development,1,3,Life Sciences,1,969,2,Female,42,2,2,Healthcare Representative,4,Single,6949,12291,0,Y,No,14,3,1,80,0,6,3,3,5,0,1,4 +37,Yes,Travel_Rarely,625,Sales,1,4,Life Sciences,1,970,1,Male,46,2,3,Sales Executive,3,Married,10609,14922,5,Y,No,11,3,3,80,0,17,2,1,14,1,11,7 +45,No,Non-Travel,805,Research & Development,4,2,Life Sciences,1,972,3,Male,57,3,2,Laboratory Technician,2,Married,4447,23163,1,Y,No,12,3,2,80,0,9,5,2,9,7,0,8 +29,No,Travel_Frequently,1404,Sales,20,3,Technical Degree,1,974,3,Female,84,3,1,Sales Representative,4,Married,2157,18203,1,Y,No,15,3,2,80,1,3,5,3,3,1,0,2 +35,No,Travel_Rarely,1219,Sales,18,3,Medical,1,975,3,Female,86,3,2,Sales Executive,3,Married,4601,6179,1,Y,No,16,3,2,80,0,5,3,3,5,2,1,0 +52,No,Travel_Rarely,1053,Research & Development,1,2,Life Sciences,1,976,4,Male,70,3,4,Manager,4,Married,17099,13829,2,Y,No,15,3,2,80,1,26,2,2,9,8,7,8 +58,Yes,Travel_Rarely,289,Research & Development,2,3,Technical Degree,1,977,4,Male,51,3,1,Research Scientist,3,Single,2479,26227,4,Y,No,24,4,1,80,0,7,4,3,1,0,0,0 +53,No,Travel_Rarely,1376,Sales,2,2,Medical,1,981,3,Male,45,3,4,Manager,3,Divorced,14852,13938,6,Y,No,13,3,3,80,1,22,3,4,17,13,15,2 +30,No,Travel_Rarely,231,Sales,8,2,Other,1,982,3,Male,62,3,3,Sales Executive,3,Divorced,7264,9977,5,Y,No,11,3,1,80,1,10,2,4,8,4,7,7 +38,No,Non-Travel,152,Sales,10,3,Technical Degree,1,983,3,Female,85,3,2,Sales Executive,4,Single,5666,19899,1,Y,Yes,13,3,2,80,0,6,1,3,5,3,1,3 +35,No,Travel_Rarely,882,Sales,3,4,Life Sciences,1,984,4,Male,92,3,3,Sales Executive,4,Divorced,7823,6812,6,Y,No,13,3,2,80,1,12,2,3,10,9,0,8 +39,No,Travel_Rarely,903,Sales,2,5,Life Sciences,1,985,1,Male,41,4,3,Sales Executive,3,Single,7880,2560,0,Y,No,18,3,4,80,0,9,3,3,8,7,0,7 +40,Yes,Non-Travel,1479,Sales,24,3,Life Sciences,1,986,2,Female,100,4,4,Sales Executive,2,Single,13194,17071,4,Y,Yes,16,3,4,80,0,22,2,2,1,0,0,0 +47,No,Travel_Frequently,1379,Research & Development,16,4,Medical,1,987,3,Male,64,4,2,Manufacturing Director,3,Divorced,5067,6759,1,Y,Yes,19,3,3,80,0,20,3,4,19,10,2,7 +36,No,Non-Travel,1229,Sales,8,4,Technical Degree,1,990,1,Male,84,3,2,Sales Executive,4,Divorced,5079,25952,4,Y,No,13,3,4,80,2,12,3,3,7,7,0,7 +31,Yes,Non-Travel,335,Research & Development,9,2,Medical,1,991,3,Male,46,2,1,Research Scientist,1,Single,2321,10322,0,Y,Yes,22,4,1,80,0,4,0,3,3,2,1,2 +33,No,Non-Travel,722,Sales,17,3,Life Sciences,1,992,4,Male,38,3,4,Manager,3,Single,17444,20489,1,Y,No,11,3,4,80,0,10,2,3,10,8,6,0 +29,Yes,Travel_Rarely,906,Research & Development,10,3,Life Sciences,1,994,4,Female,92,2,1,Research Scientist,1,Single,2404,11479,6,Y,Yes,20,4,3,80,0,3,5,3,0,0,0,0 +33,No,Travel_Rarely,461,Research & Development,13,1,Life Sciences,1,995,2,Female,53,3,1,Research Scientist,4,Single,3452,17241,3,Y,No,18,3,1,80,0,5,4,3,3,2,0,2 +45,No,Travel_Rarely,974,Research & Development,1,4,Medical,1,996,4,Female,91,3,1,Laboratory Technician,4,Divorced,2270,11005,3,Y,No,14,3,4,80,2,8,2,3,5,3,0,2 +50,No,Travel_Rarely,1126,Research & Development,1,2,Medical,1,997,4,Male,66,3,4,Research Director,4,Divorced,17399,6615,9,Y,No,22,4,3,80,1,32,1,2,5,4,1,3 +33,No,Travel_Frequently,827,Research & Development,1,4,Other,1,998,3,Female,84,4,2,Healthcare Representative,2,Married,5488,20161,1,Y,Yes,13,3,1,80,1,6,2,3,6,5,1,2 +41,No,Travel_Frequently,840,Research & Development,9,3,Medical,1,999,1,Male,64,3,5,Research Director,3,Divorced,19419,3735,2,Y,No,17,3,2,80,1,21,2,4,18,16,0,11 +27,No,Travel_Rarely,1134,Research & Development,16,4,Technical Degree,1,1001,3,Female,37,3,1,Laboratory Technician,2,Married,2811,12086,9,Y,No,14,3,2,80,1,4,2,3,2,2,2,2 +45,No,Non-Travel,248,Research & Development,23,2,Life Sciences,1,1002,4,Male,42,3,2,Laboratory Technician,1,Married,3633,14039,1,Y,Yes,15,3,3,80,1,9,2,3,9,8,0,8 +47,No,Travel_Rarely,955,Sales,4,2,Life Sciences,1,1003,4,Female,83,3,2,Sales Executive,4,Single,4163,8571,1,Y,Yes,17,3,3,80,0,9,0,3,9,0,0,7 +30,Yes,Travel_Rarely,138,Research & Development,22,3,Life Sciences,1,1004,1,Female,48,3,1,Research Scientist,3,Married,2132,11539,4,Y,Yes,11,3,2,80,0,7,2,3,5,2,0,1 +50,No,Travel_Rarely,939,Research & Development,24,3,Life Sciences,1,1005,4,Male,95,3,4,Manufacturing Director,3,Married,13973,4161,3,Y,Yes,18,3,4,80,1,22,2,3,12,11,1,5 +38,No,Travel_Frequently,1391,Research & Development,10,1,Medical,1,1006,3,Male,66,3,1,Research Scientist,3,Married,2684,12127,0,Y,No,17,3,2,80,1,3,0,2,2,1,0,2 +46,No,Travel_Rarely,566,Research & Development,7,2,Medical,1,1007,4,Male,75,3,3,Manufacturing Director,3,Divorced,10845,24208,6,Y,No,13,3,2,80,1,13,3,3,8,7,0,7 +24,No,Travel_Rarely,1206,Research & Development,17,1,Medical,1,1009,4,Female,41,2,2,Manufacturing Director,3,Divorced,4377,24117,1,Y,No,15,3,2,80,2,5,6,3,4,2,3,2 +35,Yes,Travel_Rarely,622,Research & Development,14,4,Other,1,1010,3,Male,39,2,1,Laboratory Technician,2,Divorced,3743,10074,1,Y,Yes,24,4,4,80,1,5,2,1,4,2,0,2 +31,No,Travel_Frequently,853,Research & Development,1,1,Life Sciences,1,1011,3,Female,96,3,2,Manufacturing Director,1,Married,4148,11275,1,Y,No,12,3,3,80,1,4,1,3,4,3,0,3 +18,No,Non-Travel,287,Research & Development,5,2,Life Sciences,1,1012,2,Male,73,3,1,Research Scientist,4,Single,1051,13493,1,Y,No,15,3,4,80,0,0,2,3,0,0,0,0 +54,No,Travel_Rarely,1441,Research & Development,17,3,Technical Degree,1,1013,3,Female,56,3,3,Manufacturing Director,3,Married,10739,13943,8,Y,No,11,3,3,80,1,22,2,3,10,7,0,8 +35,No,Travel_Rarely,583,Research & Development,25,4,Medical,1,1014,3,Female,57,3,3,Healthcare Representative,3,Divorced,10388,6975,1,Y,Yes,11,3,3,80,1,16,3,2,16,10,10,1 +30,No,Travel_Rarely,153,Research & Development,8,2,Life Sciences,1,1015,2,Female,73,4,3,Research Director,1,Married,11416,17802,0,Y,Yes,12,3,3,80,3,9,4,2,8,7,1,7 +20,Yes,Travel_Rarely,1097,Research & Development,11,3,Medical,1,1016,4,Female,98,2,1,Research Scientist,1,Single,2600,18275,1,Y,Yes,15,3,1,80,0,1,2,3,1,0,0,0 +30,Yes,Travel_Frequently,109,Research & Development,5,3,Medical,1,1017,2,Female,60,3,1,Laboratory Technician,2,Single,2422,25725,0,Y,No,17,3,1,80,0,4,3,3,3,2,1,2 +26,No,Travel_Rarely,1066,Research & Development,2,2,Medical,1,1018,4,Male,32,4,2,Manufacturing Director,4,Married,5472,3334,1,Y,No,12,3,2,80,0,8,2,3,8,7,1,3 +22,No,Travel_Rarely,217,Research & Development,8,1,Life Sciences,1,1019,2,Male,94,1,1,Laboratory Technician,1,Married,2451,6881,1,Y,No,15,3,1,80,1,4,3,2,4,3,1,1 +48,No,Travel_Rarely,277,Research & Development,6,3,Life Sciences,1,1022,1,Male,97,2,2,Healthcare Representative,3,Single,4240,13119,2,Y,No,13,3,4,80,0,19,0,3,2,2,2,2 +48,No,Travel_Rarely,1355,Research & Development,4,4,Life Sciences,1,1024,3,Male,78,2,3,Healthcare Representative,3,Single,10999,22245,7,Y,No,14,3,2,80,0,27,3,3,15,11,4,8 +41,No,Travel_Rarely,549,Research & Development,7,2,Medical,1,1025,4,Female,42,3,2,Manufacturing Director,3,Single,5003,23371,6,Y,No,14,3,2,80,0,8,6,3,2,2,2,1 +39,No,Travel_Rarely,466,Research & Development,1,1,Life Sciences,1,1026,4,Female,65,2,4,Manufacturing Director,4,Married,12742,7060,1,Y,No,16,3,3,80,1,21,3,3,21,6,11,8 +27,No,Travel_Rarely,1055,Research & Development,2,4,Life Sciences,1,1027,1,Female,47,3,2,Manufacturing Director,4,Married,4227,4658,0,Y,No,18,3,2,80,1,4,2,3,3,2,2,2 +35,No,Travel_Rarely,802,Research & Development,10,3,Other,1,1028,2,Male,45,3,1,Laboratory Technician,4,Divorced,3917,9541,1,Y,No,20,4,1,80,1,3,4,2,3,2,1,2 +42,No,Travel_Rarely,265,Sales,5,2,Marketing,1,1029,4,Male,90,3,5,Manager,3,Married,18303,7770,6,Y,No,13,3,2,80,0,21,3,4,1,0,0,0 +50,No,Travel_Rarely,804,Research & Development,9,3,Life Sciences,1,1030,1,Male,64,3,1,Laboratory Technician,4,Married,2380,20165,4,Y,No,18,3,2,80,0,8,5,3,1,0,0,0 +59,No,Travel_Rarely,715,Research & Development,2,3,Life Sciences,1,1032,3,Female,69,2,4,Manufacturing Director,4,Single,13726,21829,3,Y,Yes,13,3,1,80,0,30,4,3,5,3,4,3 +37,Yes,Travel_Rarely,1141,Research & Development,11,2,Medical,1,1033,1,Female,61,1,2,Healthcare Representative,2,Married,4777,14382,5,Y,No,15,3,1,80,0,15,2,1,1,0,0,0 +55,No,Travel_Frequently,135,Research & Development,18,4,Medical,1,1034,3,Male,62,3,2,Healthcare Representative,2,Married,6385,12992,3,Y,Yes,14,3,4,80,2,17,3,3,8,7,6,7 +41,No,Non-Travel,247,Research & Development,7,1,Life Sciences,1,1035,2,Female,55,1,5,Research Director,3,Divorced,19973,20284,1,Y,No,22,4,2,80,2,21,3,3,21,16,5,10 +38,No,Travel_Rarely,1035,Sales,3,4,Life Sciences,1,1036,2,Male,42,3,2,Sales Executive,4,Single,6861,4981,8,Y,Yes,12,3,3,80,0,19,1,3,1,0,0,0 +26,Yes,Non-Travel,265,Sales,29,2,Medical,1,1037,2,Male,79,1,2,Sales Executive,1,Single,4969,21813,8,Y,No,18,3,4,80,0,7,6,3,2,2,2,2 +52,Yes,Travel_Rarely,266,Sales,2,1,Marketing,1,1038,1,Female,57,1,5,Manager,4,Married,19845,25846,1,Y,No,15,3,4,80,1,33,3,3,32,14,6,9 +44,No,Travel_Rarely,1448,Sales,28,3,Medical,1,1039,4,Female,53,4,4,Sales Executive,4,Married,13320,11737,3,Y,Yes,18,3,3,80,1,23,2,3,12,11,11,11 +50,No,Non-Travel,145,Sales,1,3,Life Sciences,1,1040,4,Female,95,3,2,Sales Executive,3,Married,6347,24920,0,Y,No,12,3,1,80,1,19,3,3,18,7,0,13 +36,Yes,Travel_Rarely,885,Research & Development,16,4,Life Sciences,1,1042,3,Female,43,4,1,Laboratory Technician,1,Single,2743,8269,1,Y,No,16,3,3,80,0,18,1,3,17,13,15,14 +39,No,Travel_Frequently,945,Research & Development,22,3,Medical,1,1043,4,Female,82,3,3,Manufacturing Director,1,Single,10880,5083,1,Y,Yes,13,3,3,80,0,21,2,3,21,6,2,8 +33,No,Non-Travel,1038,Sales,8,1,Life Sciences,1,1044,2,Female,88,2,1,Sales Representative,4,Single,2342,21437,0,Y,No,19,3,4,80,0,3,2,2,2,2,2,2 +45,No,Travel_Rarely,1234,Sales,11,2,Life Sciences,1,1045,4,Female,90,3,4,Manager,4,Married,17650,5404,3,Y,No,13,3,2,80,1,26,4,4,9,3,1,1 +32,No,Non-Travel,1109,Research & Development,29,4,Medical,1,1046,4,Female,69,3,1,Laboratory Technician,3,Single,4025,11135,9,Y,No,12,3,2,80,0,10,2,3,8,7,7,7 +34,No,Travel_Rarely,216,Sales,1,4,Marketing,1,1047,2,Male,75,4,2,Sales Executive,4,Divorced,9725,12278,0,Y,No,11,3,4,80,1,16,2,2,15,1,0,9 +59,No,Travel_Rarely,1089,Sales,1,2,Technical Degree,1,1048,2,Male,66,3,3,Manager,4,Married,11904,11038,3,Y,Yes,14,3,3,80,1,14,1,1,6,4,0,4 +45,No,Travel_Rarely,788,Human Resources,24,4,Medical,1,1049,2,Male,36,3,1,Human Resources,2,Single,2177,8318,1,Y,No,16,3,1,80,0,6,3,3,6,3,0,4 +53,No,Travel_Frequently,124,Sales,2,3,Marketing,1,1050,3,Female,38,2,3,Sales Executive,2,Married,7525,23537,2,Y,No,12,3,1,80,1,30,2,3,15,7,6,12 +36,Yes,Travel_Rarely,660,Research & Development,15,3,Other,1,1052,1,Male,81,3,2,Laboratory Technician,3,Divorced,4834,7858,7,Y,No,14,3,2,80,1,9,3,2,1,0,0,0 +26,Yes,Travel_Frequently,342,Research & Development,2,3,Life Sciences,1,1053,1,Male,57,3,1,Research Scientist,1,Married,2042,15346,6,Y,Yes,14,3,2,80,1,6,2,3,3,2,1,2 +34,No,Travel_Rarely,1333,Sales,10,4,Life Sciences,1,1055,3,Female,87,3,1,Sales Representative,3,Married,2220,18410,1,Y,Yes,19,3,4,80,1,1,2,3,1,1,0,0 +28,No,Travel_Rarely,1144,Sales,10,1,Medical,1,1056,4,Male,74,3,1,Sales Representative,2,Married,1052,23384,1,Y,No,22,4,2,80,0,1,5,3,1,0,0,0 +38,No,Travel_Frequently,1186,Research & Development,3,4,Other,1,1060,3,Male,44,3,1,Research Scientist,3,Married,2821,2997,3,Y,No,16,3,1,80,1,8,2,3,2,2,2,2 +50,No,Travel_Rarely,1464,Research & Development,2,4,Medical,1,1061,2,Male,62,3,5,Research Director,3,Married,19237,12853,2,Y,Yes,11,3,4,80,1,29,2,2,8,1,7,7 +37,No,Travel_Rarely,124,Research & Development,3,3,Other,1,1062,4,Female,35,3,2,Healthcare Representative,2,Single,4107,13848,3,Y,No,15,3,1,80,0,8,3,2,4,3,0,1 +40,No,Travel_Rarely,300,Sales,26,3,Marketing,1,1066,3,Male,74,3,2,Sales Executive,1,Married,8396,22217,1,Y,No,14,3,2,80,1,8,3,2,7,7,7,5 +26,No,Travel_Frequently,921,Research & Development,1,1,Medical,1,1068,1,Female,66,2,1,Research Scientist,3,Divorced,2007,25265,1,Y,No,13,3,3,80,2,5,5,3,5,3,1,3 +46,No,Travel_Rarely,430,Research & Development,1,4,Medical,1,1069,4,Male,40,3,5,Research Director,4,Divorced,19627,21445,9,Y,No,17,3,4,80,2,23,0,3,2,2,2,2 +54,No,Travel_Rarely,1082,Sales,2,4,Life Sciences,1,1070,3,Female,41,2,3,Sales Executive,3,Married,10686,8392,6,Y,No,11,3,2,80,1,13,4,3,9,4,7,0 +56,No,Travel_Frequently,1240,Research & Development,9,3,Medical,1,1071,1,Female,63,3,1,Research Scientist,3,Married,2942,12154,2,Y,No,19,3,2,80,1,18,4,3,5,4,0,3 +36,No,Travel_Rarely,796,Research & Development,12,5,Medical,1,1073,4,Female,51,2,3,Manufacturing Director,4,Single,8858,15669,0,Y,No,11,3,2,80,0,15,2,2,14,8,7,8 +55,No,Non-Travel,444,Research & Development,2,1,Medical,1,1074,3,Male,40,2,4,Manager,1,Single,16756,17323,7,Y,No,15,3,2,80,0,31,3,4,9,7,6,2 +43,No,Travel_Rarely,415,Sales,25,3,Medical,1,1076,3,Male,79,2,3,Sales Executive,4,Divorced,10798,5268,5,Y,No,13,3,3,80,1,18,5,3,1,0,0,0 +20,Yes,Travel_Frequently,769,Sales,9,3,Marketing,1,1077,4,Female,54,3,1,Sales Representative,4,Single,2323,17205,1,Y,Yes,14,3,2,80,0,2,3,3,2,2,0,2 +21,Yes,Travel_Rarely,1334,Research & Development,10,3,Life Sciences,1,1079,3,Female,36,2,1,Laboratory Technician,1,Single,1416,17258,1,Y,No,13,3,1,80,0,1,6,2,1,0,1,0 +46,No,Travel_Rarely,1003,Research & Development,8,4,Life Sciences,1,1080,4,Female,74,2,2,Research Scientist,1,Divorced,4615,21029,8,Y,Yes,23,4,1,80,3,19,2,3,16,13,1,7 +51,Yes,Travel_Rarely,1323,Research & Development,4,4,Life Sciences,1,1081,1,Male,34,3,1,Research Scientist,3,Married,2461,10332,9,Y,Yes,12,3,3,80,3,18,2,4,10,0,2,7 +28,Yes,Non-Travel,1366,Research & Development,24,2,Technical Degree,1,1082,2,Male,72,2,3,Healthcare Representative,1,Single,8722,12355,1,Y,No,12,3,1,80,0,10,2,2,10,7,1,9 +26,No,Travel_Rarely,192,Research & Development,1,2,Medical,1,1083,1,Male,59,2,1,Laboratory Technician,1,Married,3955,11141,1,Y,No,16,3,1,80,2,6,2,3,5,3,1,3 +30,No,Travel_Rarely,1176,Research & Development,20,3,Other,1,1084,3,Male,85,3,2,Manufacturing Director,1,Married,9957,9096,0,Y,No,15,3,3,80,1,7,1,2,6,2,0,2 +41,No,Travel_Rarely,509,Research & Development,7,2,Technical Degree,1,1085,2,Female,43,4,1,Research Scientist,3,Married,3376,18863,1,Y,No,13,3,3,80,0,10,3,3,10,6,0,8 +38,No,Travel_Rarely,330,Research & Development,17,1,Life Sciences,1,1088,3,Female,65,2,3,Healthcare Representative,3,Married,8823,24608,0,Y,No,18,3,1,80,1,20,4,2,19,9,1,9 +40,No,Travel_Rarely,1492,Research & Development,20,4,Technical Degree,1,1092,1,Male,61,3,3,Healthcare Representative,4,Married,10322,26542,4,Y,No,20,4,4,80,1,14,6,3,11,10,11,1 +27,No,Non-Travel,1277,Research & Development,8,5,Life Sciences,1,1094,1,Male,87,1,1,Laboratory Technician,3,Married,4621,5869,1,Y,No,19,3,4,80,3,3,4,3,3,2,1,2 +55,No,Travel_Frequently,1091,Research & Development,2,1,Life Sciences,1,1096,4,Male,65,3,3,Manufacturing Director,2,Married,10976,15813,3,Y,No,18,3,2,80,1,23,4,3,3,2,1,2 +28,No,Travel_Rarely,857,Research & Development,10,3,Other,1,1097,3,Female,59,3,2,Research Scientist,3,Single,3660,7909,3,Y,No,13,3,4,80,0,10,4,4,8,7,1,7 +44,Yes,Travel_Rarely,1376,Human Resources,1,2,Medical,1,1098,2,Male,91,2,3,Human Resources,1,Married,10482,2326,9,Y,No,14,3,4,80,1,24,1,3,20,6,3,6 +33,No,Travel_Rarely,654,Research & Development,5,3,Life Sciences,1,1099,4,Male,34,2,3,Healthcare Representative,4,Divorced,7119,21214,4,Y,No,15,3,3,80,1,9,2,3,3,2,1,2 +35,Yes,Travel_Rarely,1204,Sales,4,3,Technical Degree,1,1100,4,Male,86,3,3,Sales Executive,1,Single,9582,10333,0,Y,Yes,22,4,1,80,0,9,2,3,8,7,4,7 +33,Yes,Travel_Frequently,827,Research & Development,29,4,Medical,1,1101,1,Female,54,2,2,Research Scientist,3,Single,4508,3129,1,Y,No,22,4,2,80,0,14,4,3,13,7,3,8 +28,No,Travel_Rarely,895,Research & Development,15,2,Life Sciences,1,1102,1,Male,50,3,1,Laboratory Technician,3,Divorced,2207,22482,1,Y,No,16,3,4,80,1,4,5,2,4,2,2,2 +34,No,Travel_Frequently,618,Research & Development,3,1,Life Sciences,1,1103,1,Male,45,3,2,Healthcare Representative,4,Single,7756,22266,0,Y,No,17,3,3,80,0,7,1,2,6,2,0,4 +37,No,Travel_Rarely,309,Sales,10,4,Life Sciences,1,1105,4,Female,88,2,2,Sales Executive,4,Divorced,6694,24223,2,Y,Yes,14,3,3,80,3,8,5,3,1,0,0,0 +25,Yes,Travel_Rarely,1219,Research & Development,4,1,Technical Degree,1,1106,4,Male,32,3,1,Laboratory Technician,4,Married,3691,4605,1,Y,Yes,15,3,2,80,1,7,3,4,7,7,5,6 +26,Yes,Travel_Rarely,1330,Research & Development,21,3,Medical,1,1107,1,Male,37,3,1,Laboratory Technician,3,Divorced,2377,19373,1,Y,No,20,4,3,80,1,1,0,2,1,1,0,0 +33,Yes,Travel_Rarely,1017,Research & Development,25,3,Medical,1,1108,1,Male,55,2,1,Research Scientist,2,Single,2313,2993,4,Y,Yes,20,4,2,80,0,5,0,3,2,2,2,2 +42,No,Travel_Rarely,469,Research & Development,2,2,Medical,1,1109,4,Male,35,3,4,Manager,1,Married,17665,14399,0,Y,No,17,3,4,80,1,23,3,3,22,6,13,7 +28,Yes,Travel_Frequently,1009,Research & Development,1,3,Medical,1,1111,1,Male,45,2,1,Laboratory Technician,2,Divorced,2596,7160,1,Y,No,15,3,1,80,2,1,2,3,1,0,0,0 +50,Yes,Travel_Frequently,959,Sales,1,4,Other,1,1113,4,Male,81,3,2,Sales Executive,3,Single,4728,17251,3,Y,Yes,14,3,4,80,0,5,4,3,0,0,0,0 +33,No,Travel_Frequently,970,Sales,7,3,Life Sciences,1,1114,4,Female,30,3,2,Sales Executive,2,Married,4302,13401,0,Y,No,17,3,3,80,1,4,3,3,3,2,0,2 +34,No,Non-Travel,697,Research & Development,3,4,Life Sciences,1,1115,3,Male,40,2,1,Research Scientist,4,Married,2979,22478,3,Y,No,17,3,4,80,3,6,2,3,0,0,0,0 +48,No,Non-Travel,1262,Research & Development,1,4,Medical,1,1116,1,Male,35,4,4,Manager,4,Single,16885,16154,2,Y,No,22,4,3,80,0,27,3,2,5,4,2,1 +45,No,Non-Travel,1050,Sales,9,4,Life Sciences,1,1117,2,Female,65,2,2,Sales Executive,3,Married,5593,17970,1,Y,No,13,3,4,80,1,15,2,3,15,10,4,12 +52,No,Travel_Rarely,994,Research & Development,7,4,Life Sciences,1,1118,2,Male,87,3,3,Healthcare Representative,2,Single,10445,15322,7,Y,No,19,3,4,80,0,18,4,3,8,6,4,0 +38,No,Travel_Rarely,770,Sales,10,4,Marketing,1,1119,3,Male,73,2,3,Sales Executive,3,Divorced,8740,5569,0,Y,Yes,14,3,2,80,2,9,2,3,8,7,2,7 +29,No,Travel_Rarely,1107,Research & Development,28,4,Life Sciences,1,1120,3,Female,93,3,1,Research Scientist,4,Divorced,2514,26968,4,Y,No,22,4,1,80,1,11,1,3,7,5,1,7 +28,No,Travel_Rarely,950,Research & Development,3,3,Medical,1,1121,4,Female,93,3,3,Manufacturing Director,2,Divorced,7655,8039,0,Y,No,17,3,2,80,3,10,3,2,9,7,1,7 +46,No,Travel_Rarely,406,Sales,3,1,Marketing,1,1124,1,Male,52,3,4,Manager,3,Married,17465,15596,3,Y,No,12,3,4,80,1,23,3,3,12,9,4,9 +38,No,Travel_Rarely,130,Sales,2,2,Marketing,1,1125,4,Male,32,3,3,Sales Executive,2,Single,7351,20619,7,Y,No,16,3,3,80,0,10,2,3,1,0,0,0 +43,No,Travel_Frequently,1082,Research & Development,27,3,Life Sciences,1,1126,3,Female,83,3,3,Manufacturing Director,1,Married,10820,11535,8,Y,No,11,3,3,80,1,18,1,3,8,7,0,1 +39,Yes,Travel_Frequently,203,Research & Development,2,3,Life Sciences,1,1127,1,Male,84,3,4,Healthcare Representative,4,Divorced,12169,13547,7,Y,No,11,3,4,80,3,21,4,3,18,7,11,5 +40,No,Travel_Rarely,1308,Research & Development,14,3,Medical,1,1128,3,Male,44,2,5,Research Director,3,Single,19626,17544,1,Y,No,14,3,1,80,0,21,2,4,20,7,4,9 +21,No,Travel_Rarely,984,Research & Development,1,1,Technical Degree,1,1131,4,Female,70,2,1,Research Scientist,2,Single,2070,25326,1,Y,Yes,11,3,3,80,0,2,6,4,2,2,2,2 +39,No,Non-Travel,439,Research & Development,9,3,Life Sciences,1,1132,3,Male,70,3,2,Laboratory Technician,2,Single,6782,8770,9,Y,No,15,3,3,80,0,9,2,2,5,4,0,3 +36,No,Non-Travel,217,Research & Development,18,4,Life Sciences,1,1133,1,Male,78,3,2,Manufacturing Director,4,Single,7779,23238,2,Y,No,20,4,1,80,0,18,0,3,11,9,0,9 +31,No,Travel_Frequently,793,Sales,20,3,Life Sciences,1,1135,3,Male,67,4,1,Sales Representative,4,Married,2791,21981,0,Y,No,12,3,1,80,1,3,4,3,2,2,2,2 +28,No,Travel_Rarely,1451,Research & Development,2,1,Life Sciences,1,1136,1,Male,67,2,1,Research Scientist,2,Married,3201,19911,0,Y,No,17,3,1,80,0,6,2,1,5,3,0,4 +35,No,Travel_Frequently,1182,Sales,11,2,Marketing,1,1137,4,Male,54,3,2,Sales Executive,4,Divorced,4968,18500,1,Y,No,11,3,4,80,1,5,3,3,5,2,0,2 +49,No,Travel_Rarely,174,Sales,8,4,Technical Degree,1,1138,4,Male,56,2,4,Sales Executive,2,Married,13120,11879,6,Y,No,17,3,2,80,1,22,3,3,9,8,2,3 +34,No,Travel_Frequently,1003,Research & Development,2,2,Life Sciences,1,1140,4,Male,95,3,2,Manufacturing Director,3,Single,4033,15834,2,Y,No,11,3,4,80,0,5,3,2,3,2,0,2 +29,No,Travel_Frequently,490,Research & Development,10,3,Life Sciences,1,1143,4,Female,61,3,1,Research Scientist,2,Divorced,3291,17940,0,Y,No,14,3,4,80,2,8,2,2,7,5,1,1 +42,No,Travel_Rarely,188,Research & Development,29,3,Medical,1,1148,2,Male,56,1,2,Laboratory Technician,4,Single,4272,9558,4,Y,No,19,3,1,80,0,16,3,3,1,0,0,0 +29,No,Travel_Rarely,718,Research & Development,8,1,Medical,1,1150,2,Male,79,2,2,Manufacturing Director,4,Married,5056,17689,1,Y,Yes,15,3,3,80,1,10,2,2,10,7,1,2 +38,No,Travel_Rarely,433,Human Resources,1,3,Human Resources,1,1152,3,Male,37,4,1,Human Resources,3,Married,2844,6004,1,Y,No,13,3,4,80,1,7,2,4,7,6,5,0 +28,No,Travel_Frequently,773,Research & Development,6,3,Life Sciences,1,1154,3,Male,39,2,1,Research Scientist,3,Divorced,2703,22088,1,Y,Yes,14,3,4,80,1,3,2,3,3,1,0,2 +18,Yes,Non-Travel,247,Research & Development,8,1,Medical,1,1156,3,Male,80,3,1,Laboratory Technician,3,Single,1904,13556,1,Y,No,12,3,4,80,0,0,0,3,0,0,0,0 +33,Yes,Travel_Rarely,603,Sales,9,4,Marketing,1,1157,1,Female,77,3,2,Sales Executive,1,Single,8224,18385,0,Y,Yes,17,3,1,80,0,6,3,3,5,2,0,3 +41,No,Travel_Rarely,167,Research & Development,12,4,Life Sciences,1,1158,2,Male,46,3,1,Laboratory Technician,4,Married,4766,9051,3,Y,Yes,11,3,1,80,1,6,4,3,1,0,0,0 +31,Yes,Travel_Frequently,874,Research & Development,15,3,Medical,1,1160,3,Male,72,3,1,Laboratory Technician,3,Married,2610,6233,1,Y,No,12,3,3,80,1,2,5,2,2,2,2,2 +37,No,Travel_Rarely,367,Research & Development,25,2,Medical,1,1161,3,Female,52,2,2,Healthcare Representative,4,Divorced,5731,17171,7,Y,No,13,3,3,80,2,9,2,3,6,2,1,3 +27,No,Travel_Rarely,199,Research & Development,6,3,Life Sciences,1,1162,4,Male,55,2,1,Research Scientist,3,Married,2539,7950,1,Y,No,13,3,3,80,1,4,0,3,4,2,2,2 +34,No,Travel_Rarely,1400,Sales,9,1,Life Sciences,1,1163,2,Female,70,3,2,Sales Executive,3,Married,5714,5829,1,Y,No,20,4,1,80,0,6,3,2,6,5,1,3 +35,No,Travel_Rarely,528,Human Resources,8,4,Technical Degree,1,1164,3,Male,100,3,1,Human Resources,3,Single,4323,7108,1,Y,No,17,3,2,80,0,6,2,1,5,4,1,4 +29,Yes,Travel_Rarely,408,Sales,23,1,Life Sciences,1,1165,4,Female,45,2,3,Sales Executive,1,Married,7336,11162,1,Y,No,13,3,1,80,1,11,3,1,11,8,3,10 +40,No,Travel_Frequently,593,Research & Development,9,4,Medical,1,1166,2,Female,88,3,3,Research Director,3,Single,13499,13782,9,Y,No,17,3,3,80,0,20,3,2,18,7,2,13 +42,Yes,Travel_Frequently,481,Sales,12,3,Life Sciences,1,1167,3,Male,44,3,4,Sales Executive,1,Single,13758,2447,0,Y,Yes,12,3,2,80,0,22,2,2,21,9,13,14 +42,No,Travel_Rarely,647,Sales,4,4,Marketing,1,1171,2,Male,45,3,2,Sales Executive,1,Single,5155,2253,7,Y,No,13,3,4,80,0,9,3,4,6,4,1,5 +35,No,Travel_Rarely,982,Research & Development,1,4,Medical,1,1172,4,Male,58,2,1,Laboratory Technician,3,Married,2258,16340,6,Y,No,12,3,2,80,1,10,2,3,8,0,1,7 +24,No,Travel_Rarely,477,Research & Development,24,3,Medical,1,1173,4,Male,49,3,1,Laboratory Technician,2,Single,3597,6409,8,Y,No,22,4,4,80,0,6,2,3,4,3,1,2 +28,Yes,Travel_Rarely,1485,Research & Development,12,1,Life Sciences,1,1175,3,Female,79,3,1,Laboratory Technician,4,Married,2515,22955,1,Y,Yes,11,3,4,80,0,1,4,2,1,1,0,0 +26,No,Travel_Rarely,1384,Research & Development,3,4,Medical,1,1177,1,Male,82,4,1,Laboratory Technician,4,Married,4420,13421,1,Y,No,22,4,2,80,1,8,2,3,8,7,0,7 +30,No,Travel_Rarely,852,Sales,10,3,Marketing,1,1179,3,Male,72,2,2,Sales Executive,3,Married,6578,2706,1,Y,No,18,3,1,80,1,10,3,3,10,3,1,4 +40,No,Travel_Frequently,902,Research & Development,26,2,Medical,1,1180,3,Female,92,2,2,Research Scientist,4,Married,4422,21203,3,Y,Yes,13,3,4,80,1,16,3,1,1,1,0,0 +35,No,Travel_Rarely,819,Research & Development,2,3,Life Sciences,1,1182,3,Male,44,2,3,Manufacturing Director,2,Divorced,10274,19588,2,Y,No,18,3,2,80,1,15,2,4,7,7,6,4 +34,No,Travel_Frequently,669,Research & Development,1,3,Medical,1,1184,4,Male,97,2,2,Healthcare Representative,1,Single,5343,25755,0,Y,No,20,4,3,80,0,14,3,3,13,9,4,9 +35,No,Travel_Frequently,636,Research & Development,4,4,Other,1,1185,4,Male,47,2,1,Laboratory Technician,4,Married,2376,26537,1,Y,No,13,3,2,80,1,2,2,4,2,2,2,2 +43,Yes,Travel_Rarely,1372,Sales,9,3,Marketing,1,1188,1,Female,85,1,2,Sales Executive,3,Single,5346,9489,8,Y,No,13,3,2,80,0,7,2,2,4,3,1,3 +32,No,Non-Travel,862,Sales,2,1,Life Sciences,1,1190,3,Female,76,3,1,Sales Representative,1,Divorced,2827,14947,1,Y,No,12,3,3,80,3,1,3,3,1,0,0,0 +56,No,Travel_Rarely,718,Research & Development,4,4,Technical Degree,1,1191,4,Female,92,3,5,Manager,1,Divorced,19943,18575,4,Y,No,13,3,4,80,1,28,2,3,5,2,4,2 +29,No,Travel_Rarely,1401,Research & Development,6,1,Medical,1,1192,2,Female,54,3,1,Laboratory Technician,4,Married,3131,26342,1,Y,No,13,3,1,80,1,10,5,3,10,8,0,8 +19,No,Travel_Rarely,645,Research & Development,9,2,Life Sciences,1,1193,3,Male,54,3,1,Research Scientist,1,Single,2552,7172,1,Y,No,25,4,3,80,0,1,4,3,1,1,0,0 +45,No,Travel_Rarely,1457,Research & Development,7,3,Medical,1,1195,1,Female,83,3,1,Research Scientist,3,Married,4477,20100,4,Y,Yes,19,3,3,80,1,7,2,2,3,2,0,2 +37,No,Travel_Rarely,977,Research & Development,1,3,Life Sciences,1,1196,4,Female,56,2,2,Manufacturing Director,4,Married,6474,9961,1,Y,No,13,3,2,80,1,14,2,2,14,8,3,11 +20,No,Travel_Rarely,805,Research & Development,3,3,Life Sciences,1,1198,1,Male,87,2,1,Laboratory Technician,3,Single,3033,12828,1,Y,No,12,3,1,80,0,2,2,2,2,2,1,2 +44,Yes,Travel_Rarely,1097,Research & Development,10,4,Life Sciences,1,1200,3,Male,96,3,1,Research Scientist,3,Single,2936,10826,1,Y,Yes,11,3,3,80,0,6,4,3,6,4,0,2 +53,No,Travel_Rarely,1223,Research & Development,7,2,Medical,1,1201,4,Female,50,3,5,Manager,3,Divorced,18606,18640,3,Y,No,18,3,2,80,1,26,6,3,7,7,4,7 +29,No,Travel_Rarely,942,Research & Development,15,1,Life Sciences,1,1202,2,Female,69,1,1,Research Scientist,4,Married,2168,26933,0,Y,Yes,18,3,1,80,1,6,2,2,5,4,1,3 +22,Yes,Travel_Frequently,1256,Research & Development,3,4,Life Sciences,1,1203,3,Male,48,2,1,Research Scientist,4,Married,2853,4223,0,Y,Yes,11,3,2,80,1,1,5,3,0,0,0,0 +46,No,Travel_Rarely,1402,Sales,2,3,Marketing,1,1204,3,Female,69,3,4,Manager,1,Married,17048,24097,8,Y,No,23,4,1,80,0,28,2,3,26,15,15,9 +44,No,Non-Travel,111,Research & Development,17,3,Life Sciences,1,1206,4,Male,74,1,1,Research Scientist,3,Single,2290,4279,2,Y,No,13,3,4,80,0,6,3,3,0,0,0,0 +33,No,Travel_Rarely,147,Human Resources,2,3,Human Resources,1,1207,2,Male,99,3,1,Human Resources,3,Married,3600,8429,1,Y,No,13,3,4,80,1,5,2,3,5,4,1,4 +41,Yes,Non-Travel,906,Research & Development,5,2,Life Sciences,1,1210,1,Male,95,2,1,Research Scientist,1,Divorced,2107,20293,6,Y,No,17,3,1,80,1,5,2,1,1,0,0,0 +30,No,Travel_Rarely,1329,Sales,29,4,Life Sciences,1,1211,3,Male,61,3,2,Sales Executive,1,Divorced,4115,13192,8,Y,No,19,3,3,80,3,8,3,3,4,3,0,3 +40,No,Travel_Frequently,1184,Sales,2,4,Medical,1,1212,2,Male,62,3,2,Sales Executive,2,Married,4327,25440,5,Y,No,12,3,4,80,3,5,2,3,0,0,0,0 +50,No,Travel_Frequently,1421,Research & Development,2,3,Medical,1,1215,4,Female,30,3,4,Manager,1,Married,17856,9490,2,Y,No,22,4,3,80,1,32,3,3,2,2,2,2 +28,No,Travel_Rarely,1179,Research & Development,19,4,Medical,1,1216,4,Male,78,2,1,Laboratory Technician,1,Married,3196,12449,1,Y,No,12,3,3,80,3,6,2,3,6,5,3,3 +46,No,Travel_Rarely,1450,Research & Development,15,2,Life Sciences,1,1217,4,Male,52,3,5,Research Director,2,Married,19081,10849,5,Y,No,11,3,1,80,1,25,2,3,4,2,0,3 +35,No,Travel_Rarely,1361,Sales,17,4,Life Sciences,1,1218,3,Male,94,3,2,Sales Executive,1,Married,8966,21026,3,Y,Yes,15,3,4,80,3,15,2,3,7,7,1,7 +24,Yes,Travel_Rarely,984,Research & Development,17,2,Life Sciences,1,1219,4,Female,97,3,1,Laboratory Technician,2,Married,2210,3372,1,Y,No,13,3,1,80,1,1,3,1,1,0,0,0 +33,No,Travel_Frequently,1146,Sales,25,3,Medical,1,1220,2,Female,82,3,2,Sales Executive,3,Married,4539,4905,1,Y,No,12,3,1,80,1,10,3,2,10,7,0,1 +36,No,Travel_Rarely,917,Research & Development,6,4,Life Sciences,1,1221,3,Male,60,1,1,Laboratory Technician,3,Divorced,2741,6865,1,Y,No,14,3,3,80,1,7,4,3,7,7,1,7 +30,No,Travel_Rarely,853,Research & Development,7,4,Life Sciences,1,1224,3,Male,49,3,2,Laboratory Technician,3,Divorced,3491,11309,1,Y,No,13,3,1,80,3,10,4,2,10,7,8,9 +44,No,Travel_Rarely,200,Research & Development,29,4,Other,1,1225,4,Male,32,3,2,Research Scientist,4,Single,4541,7744,1,Y,No,25,4,2,80,0,20,3,3,20,11,13,17 +20,No,Travel_Rarely,654,Sales,21,3,Marketing,1,1226,3,Male,43,4,1,Sales Representative,4,Single,2678,5050,1,Y,No,17,3,4,80,0,2,2,3,2,1,2,2 +46,No,Travel_Rarely,150,Research & Development,2,4,Technical Degree,1,1228,4,Male,60,3,2,Manufacturing Director,4,Divorced,7379,17433,2,Y,No,11,3,3,80,1,12,3,2,6,3,1,4 +42,No,Non-Travel,179,Human Resources,2,5,Medical,1,1231,4,Male,79,4,2,Human Resources,1,Married,6272,12858,7,Y,No,16,3,1,80,1,10,3,4,4,3,0,3 +60,No,Travel_Rarely,696,Sales,7,4,Marketing,1,1233,2,Male,52,4,2,Sales Executive,4,Divorced,5220,10893,0,Y,Yes,18,3,2,80,1,12,3,3,11,7,1,9 +32,No,Travel_Frequently,116,Research & Development,13,3,Other,1,1234,3,Female,77,2,1,Laboratory Technician,2,Married,2743,7331,1,Y,No,20,4,3,80,1,2,2,3,2,2,2,2 +32,No,Travel_Frequently,1316,Research & Development,2,2,Life Sciences,1,1235,4,Female,38,3,2,Research Scientist,3,Single,4998,2338,4,Y,Yes,14,3,4,80,0,10,2,3,8,7,0,7 +36,No,Travel_Rarely,363,Research & Development,1,3,Technical Degree,1,1237,3,Female,77,1,3,Manufacturing Director,1,Divorced,10252,4235,2,Y,Yes,21,4,3,80,1,17,2,3,7,7,7,7 +33,No,Travel_Rarely,117,Research & Development,9,3,Medical,1,1238,1,Male,60,3,1,Research Scientist,4,Married,2781,6311,0,Y,No,13,3,2,80,1,15,5,3,14,10,4,10 +40,No,Travel_Rarely,107,Sales,10,3,Technical Degree,1,1239,2,Female,84,2,2,Sales Executive,2,Divorced,6852,11591,7,Y,No,12,3,2,80,1,7,2,4,5,1,1,3 +25,No,Travel_Rarely,1356,Sales,10,4,Life Sciences,1,1240,3,Male,57,3,2,Sales Executive,4,Single,4950,20623,0,Y,No,14,3,2,80,0,5,4,3,4,3,1,1 +30,No,Travel_Rarely,1465,Research & Development,1,3,Medical,1,1241,4,Male,63,3,1,Research Scientist,2,Married,3579,9369,0,Y,Yes,21,4,1,80,1,12,2,3,11,9,5,7 +42,No,Travel_Frequently,458,Research & Development,26,5,Medical,1,1242,1,Female,60,3,3,Research Director,1,Married,13191,23281,3,Y,Yes,17,3,3,80,0,20,6,3,1,0,0,0 +35,No,Non-Travel,1212,Sales,8,2,Marketing,1,1243,3,Female,78,2,3,Sales Executive,4,Married,10377,13755,4,Y,Yes,11,3,2,80,1,16,6,2,13,2,4,12 +27,No,Travel_Rarely,1103,Research & Development,14,3,Life Sciences,1,1244,1,Male,42,3,1,Research Scientist,1,Married,2235,14377,1,Y,Yes,14,3,4,80,2,9,3,2,9,7,6,8 +54,No,Travel_Frequently,966,Research & Development,1,4,Life Sciences,1,1245,4,Female,53,3,3,Manufacturing Director,3,Divorced,10502,9659,7,Y,No,17,3,1,80,1,33,2,1,5,4,1,4 +44,No,Travel_Rarely,1117,Research & Development,2,1,Life Sciences,1,1246,1,Female,72,4,1,Research Scientist,4,Married,2011,19982,1,Y,No,13,3,4,80,1,10,5,3,10,5,7,7 +19,Yes,Non-Travel,504,Research & Development,10,3,Medical,1,1248,1,Female,96,2,1,Research Scientist,2,Single,1859,6148,1,Y,Yes,25,4,2,80,0,1,2,4,1,1,0,0 +29,No,Travel_Rarely,1010,Research & Development,1,3,Life Sciences,1,1249,1,Female,97,3,1,Research Scientist,4,Divorced,3760,5598,1,Y,No,15,3,1,80,3,3,5,3,3,2,1,2 +54,No,Travel_Rarely,685,Research & Development,3,3,Life Sciences,1,1250,4,Male,85,3,4,Research Director,4,Married,17779,23474,3,Y,No,14,3,1,80,0,36,2,3,10,9,0,9 +31,No,Travel_Rarely,1332,Research & Development,11,2,Medical,1,1251,3,Male,80,3,2,Healthcare Representative,1,Married,6833,17089,1,Y,Yes,12,3,4,80,0,6,2,2,6,5,0,1 +31,No,Travel_Rarely,1062,Research & Development,24,3,Medical,1,1252,3,Female,96,2,2,Healthcare Representative,1,Single,6812,17198,1,Y,No,19,3,2,80,0,10,2,3,10,9,1,8 +59,No,Travel_Rarely,326,Sales,3,3,Life Sciences,1,1254,3,Female,48,2,2,Sales Executive,4,Single,5171,16490,5,Y,No,17,3,4,80,0,13,2,3,6,1,0,5 +43,No,Travel_Rarely,920,Research & Development,3,3,Life Sciences,1,1255,3,Male,96,1,5,Research Director,4,Married,19740,18625,3,Y,No,14,3,2,80,1,25,2,3,8,7,0,7 +49,No,Travel_Rarely,1098,Research & Development,4,2,Medical,1,1256,1,Male,85,2,5,Manager,3,Married,18711,12124,2,Y,No,13,3,3,80,1,23,2,4,1,0,0,0 +36,No,Travel_Frequently,469,Research & Development,3,3,Technical Degree,1,1257,3,Male,46,3,1,Research Scientist,2,Married,3692,9256,1,Y,No,12,3,3,80,0,12,2,2,11,10,0,7 +48,No,Travel_Rarely,969,Research & Development,2,2,Technical Degree,1,1258,4,Male,76,4,1,Laboratory Technician,2,Single,2559,16620,5,Y,No,11,3,3,80,0,7,4,2,1,0,0,0 +27,No,Travel_Rarely,1167,Research & Development,4,2,Life Sciences,1,1259,1,Male,76,3,1,Research Scientist,3,Divorced,2517,3208,1,Y,No,11,3,2,80,3,5,2,3,5,3,0,3 +29,No,Travel_Rarely,1329,Research & Development,7,3,Life Sciences,1,1260,3,Male,82,3,2,Healthcare Representative,4,Divorced,6623,4204,1,Y,Yes,11,3,2,80,2,6,2,3,6,0,1,0 +48,No,Travel_Rarely,715,Research & Development,1,3,Life Sciences,1,1263,4,Male,76,2,5,Research Director,4,Single,18265,8733,6,Y,No,12,3,3,80,0,25,3,4,1,0,0,0 +29,No,Travel_Rarely,694,Research & Development,1,3,Life Sciences,1,1264,4,Female,87,2,4,Research Director,4,Divorced,16124,3423,3,Y,No,14,3,2,80,2,9,2,2,7,7,1,7 +34,No,Travel_Rarely,1320,Research & Development,20,3,Technical Degree,1,1265,3,Female,89,4,1,Research Scientist,3,Married,2585,21643,0,Y,No,17,3,4,80,0,2,5,2,1,0,0,0 +44,No,Travel_Rarely,1099,Sales,5,3,Marketing,1,1267,2,Male,88,3,5,Manager,2,Married,18213,8751,7,Y,No,11,3,3,80,1,26,5,3,22,9,3,10 +33,No,Travel_Rarely,536,Sales,10,5,Marketing,1,1268,4,Male,82,4,3,Sales Executive,3,Divorced,8380,21708,0,Y,Yes,14,3,4,80,2,10,3,3,9,8,0,8 +19,No,Travel_Rarely,265,Research & Development,25,3,Life Sciences,1,1269,2,Female,57,4,1,Research Scientist,4,Single,2994,21221,1,Y,Yes,12,3,4,80,0,1,2,3,1,0,0,1 +23,No,Travel_Rarely,373,Research & Development,1,2,Life Sciences,1,1270,4,Male,47,3,1,Research Scientist,3,Married,1223,16901,1,Y,No,22,4,4,80,1,1,2,3,1,0,0,1 +25,Yes,Travel_Frequently,599,Sales,24,1,Life Sciences,1,1273,3,Male,73,1,1,Sales Representative,4,Single,1118,8040,1,Y,Yes,14,3,4,80,0,1,4,3,1,0,1,0 +26,No,Travel_Rarely,583,Research & Development,4,2,Life Sciences,1,1275,3,Male,53,3,1,Research Scientist,4,Single,2875,9973,1,Y,Yes,20,4,2,80,0,8,2,2,8,5,2,2 +45,Yes,Travel_Rarely,1449,Sales,2,3,Marketing,1,1277,1,Female,94,1,5,Manager,2,Single,18824,2493,2,Y,Yes,16,3,1,80,0,26,2,3,24,10,1,11 +55,No,Non-Travel,177,Research & Development,8,1,Medical,1,1278,4,Male,37,2,4,Healthcare Representative,2,Divorced,13577,25592,1,Y,Yes,15,3,4,80,1,34,3,3,33,9,15,0 +21,Yes,Travel_Frequently,251,Research & Development,10,2,Life Sciences,1,1279,1,Female,45,2,1,Laboratory Technician,3,Single,2625,25308,1,Y,No,20,4,3,80,0,2,2,1,2,2,2,2 +46,No,Travel_Rarely,168,Sales,4,2,Marketing,1,1280,4,Female,33,2,5,Manager,2,Married,18789,9946,2,Y,No,14,3,3,80,1,26,2,3,11,4,0,8 +34,No,Travel_Rarely,131,Sales,2,3,Marketing,1,1281,3,Female,86,3,2,Sales Executive,1,Single,4538,6039,0,Y,Yes,12,3,4,80,0,4,3,3,3,2,0,2 +51,No,Travel_Frequently,237,Sales,9,3,Life Sciences,1,1282,4,Male,83,3,5,Manager,2,Divorced,19847,19196,4,Y,Yes,24,4,1,80,1,31,5,2,29,10,11,10 +59,No,Travel_Rarely,1429,Research & Development,18,4,Medical,1,1283,4,Male,67,3,3,Manufacturing Director,4,Single,10512,20002,6,Y,No,12,3,4,80,0,25,6,2,9,7,5,4 +34,No,Travel_Frequently,135,Research & Development,19,3,Medical,1,1285,3,Female,46,3,2,Laboratory Technician,2,Divorced,4444,22534,4,Y,No,13,3,3,80,2,15,2,4,11,8,5,10 +28,No,Travel_Frequently,791,Research & Development,1,4,Medical,1,1286,4,Male,44,3,1,Laboratory Technician,3,Single,2154,6842,0,Y,Yes,11,3,3,80,0,5,2,2,4,2,0,2 +44,No,Travel_Rarely,1199,Research & Development,4,2,Life Sciences,1,1288,3,Male,92,4,5,Manager,1,Divorced,19190,17477,1,Y,No,14,3,4,80,2,26,4,2,25,9,14,13 +34,No,Travel_Frequently,648,Human Resources,11,3,Life Sciences,1,1289,3,Male,56,2,2,Human Resources,2,Married,4490,21833,4,Y,No,11,3,4,80,2,14,5,4,10,9,1,8 +35,No,Travel_Rarely,735,Research & Development,6,1,Life Sciences,1,1291,3,Male,66,3,1,Research Scientist,3,Married,3506,6020,0,Y,Yes,14,3,4,80,0,4,3,3,3,2,2,2 +42,No,Travel_Rarely,603,Research & Development,7,4,Medical,1,1292,2,Female,78,4,2,Research Scientist,2,Married,2372,5628,6,Y,Yes,16,3,4,80,0,18,2,3,1,0,0,0 +43,No,Travel_Rarely,531,Sales,4,4,Marketing,1,1293,4,Female,56,2,3,Sales Executive,4,Single,10231,20364,3,Y,No,14,3,4,80,0,23,3,4,21,7,15,17 +36,No,Travel_Rarely,429,Research & Development,2,4,Life Sciences,1,1294,3,Female,53,3,2,Manufacturing Director,2,Single,5410,2323,9,Y,Yes,11,3,4,80,0,18,2,3,16,14,5,12 +44,Yes,Travel_Rarely,621,Research & Development,15,3,Medical,1,1295,1,Female,73,3,3,Healthcare Representative,4,Married,7978,14075,1,Y,No,11,3,4,80,1,10,2,3,10,7,0,5 +28,No,Travel_Frequently,193,Research & Development,2,3,Life Sciences,1,1296,4,Male,52,2,1,Laboratory Technician,4,Married,3867,14222,1,Y,Yes,12,3,2,80,1,2,2,3,2,2,2,2 +51,No,Travel_Frequently,968,Research & Development,6,2,Medical,1,1297,2,Female,40,2,1,Laboratory Technician,3,Single,2838,4257,0,Y,No,14,3,2,80,0,8,6,2,7,0,7,7 +30,No,Non-Travel,879,Research & Development,9,2,Medical,1,1298,3,Female,72,3,2,Manufacturing Director,3,Single,4695,12858,7,Y,Yes,18,3,3,80,0,10,3,3,8,4,1,7 +29,Yes,Travel_Rarely,806,Research & Development,7,3,Technical Degree,1,1299,2,Female,39,3,1,Laboratory Technician,3,Divorced,3339,17285,3,Y,Yes,13,3,1,80,2,10,2,3,7,7,7,7 +28,No,Travel_Rarely,640,Research & Development,1,3,Technical Degree,1,1301,4,Male,84,3,1,Research Scientist,1,Single,2080,4732,2,Y,No,11,3,2,80,0,5,2,2,3,2,1,2 +25,No,Travel_Rarely,266,Research & Development,1,3,Medical,1,1303,4,Female,40,3,1,Research Scientist,2,Single,2096,18830,1,Y,No,18,3,4,80,0,2,3,2,2,2,2,1 +32,No,Travel_Rarely,604,Sales,8,3,Medical,1,1304,3,Male,56,4,2,Sales Executive,4,Married,6209,11693,1,Y,No,15,3,3,80,2,10,4,4,10,7,0,8 +45,No,Travel_Frequently,364,Research & Development,25,3,Medical,1,1306,2,Female,83,3,5,Manager,2,Single,18061,13035,3,Y,No,22,4,3,80,0,22,4,3,0,0,0,0 +39,No,Travel_Rarely,412,Research & Development,13,4,Medical,1,1307,3,Female,94,2,4,Manager,2,Divorced,17123,17334,6,Y,Yes,13,3,4,80,2,21,4,3,19,9,15,2 +58,No,Travel_Rarely,848,Research & Development,23,4,Life Sciences,1,1308,1,Male,88,3,1,Research Scientist,3,Divorced,2372,26076,1,Y,No,12,3,4,80,2,2,3,3,2,2,2,2 +32,Yes,Travel_Rarely,1089,Research & Development,7,2,Life Sciences,1,1309,4,Male,79,3,2,Laboratory Technician,3,Married,4883,22845,1,Y,No,18,3,1,80,1,10,3,3,10,4,1,1 +39,Yes,Travel_Rarely,360,Research & Development,23,3,Medical,1,1310,3,Male,93,3,1,Research Scientist,1,Single,3904,22154,0,Y,No,13,3,1,80,0,6,2,3,5,2,0,3 +30,No,Travel_Rarely,1138,Research & Development,6,3,Technical Degree,1,1311,1,Female,48,2,2,Laboratory Technician,4,Married,4627,23631,0,Y,No,12,3,1,80,1,10,6,3,9,2,6,7 +36,No,Travel_Rarely,325,Research & Development,10,4,Technical Degree,1,1312,4,Female,63,3,3,Healthcare Representative,3,Married,7094,5747,3,Y,No,12,3,1,80,0,10,0,3,7,7,1,7 +46,No,Travel_Rarely,991,Human Resources,1,2,Life Sciences,1,1314,4,Female,44,3,1,Human Resources,1,Single,3423,22957,6,Y,No,12,3,3,80,0,10,3,4,7,6,5,7 +28,No,Non-Travel,1476,Research & Development,1,3,Life Sciences,1,1315,3,Female,55,1,2,Laboratory Technician,4,Married,6674,16392,0,Y,No,11,3,1,80,3,10,6,3,9,8,7,5 +50,No,Travel_Rarely,1322,Research & Development,28,3,Life Sciences,1,1317,4,Female,43,3,4,Research Director,1,Married,16880,22422,4,Y,Yes,11,3,2,80,0,25,2,3,3,2,1,2 +40,Yes,Travel_Rarely,299,Sales,25,4,Marketing,1,1318,4,Male,57,2,3,Sales Executive,2,Single,9094,17235,2,Y,Yes,12,3,3,80,0,9,2,3,5,4,1,0 +52,Yes,Travel_Rarely,1030,Sales,5,3,Life Sciences,1,1319,2,Male,64,3,3,Sales Executive,2,Single,8446,21534,9,Y,Yes,19,3,3,80,0,10,2,2,8,7,7,7 +30,No,Travel_Rarely,634,Research & Development,17,4,Medical,1,1321,2,Female,95,3,3,Manager,1,Married,11916,25927,1,Y,Yes,23,4,4,80,2,9,2,3,9,1,0,8 +39,No,Travel_Rarely,524,Research & Development,18,2,Life Sciences,1,1322,1,Male,32,3,2,Manufacturing Director,3,Single,4534,13352,0,Y,No,11,3,1,80,0,9,6,3,8,7,1,7 +31,No,Non-Travel,587,Sales,2,4,Life Sciences,1,1324,4,Female,57,3,3,Sales Executive,3,Divorced,9852,8935,1,Y,Yes,19,3,1,80,1,10,5,2,10,8,9,6 +41,No,Non-Travel,256,Sales,10,2,Medical,1,1329,3,Male,40,1,2,Sales Executive,2,Single,6151,22074,1,Y,No,13,3,1,80,0,19,4,3,19,2,11,9 +31,Yes,Travel_Frequently,1060,Sales,1,3,Life Sciences,1,1331,4,Female,54,3,1,Sales Representative,2,Single,2302,8319,1,Y,Yes,11,3,1,80,0,3,2,4,3,2,2,2 +44,Yes,Travel_Rarely,935,Research & Development,3,3,Life Sciences,1,1333,1,Male,89,3,1,Laboratory Technician,1,Married,2362,14669,4,Y,No,12,3,3,80,0,10,4,4,3,2,1,2 +42,No,Non-Travel,495,Research & Development,2,1,Life Sciences,1,1334,3,Male,37,3,4,Manager,3,Married,17861,26582,0,Y,Yes,13,3,4,80,0,21,3,2,20,8,2,10 +55,No,Travel_Rarely,282,Research & Development,2,2,Medical,1,1336,4,Female,58,1,5,Manager,3,Married,19187,6992,4,Y,No,14,3,4,80,1,23,5,3,19,9,9,11 +56,No,Travel_Rarely,206,Human Resources,8,4,Life Sciences,1,1338,4,Male,99,3,5,Manager,2,Single,19717,4022,6,Y,No,14,3,1,80,0,36,4,3,7,3,7,7 +40,No,Non-Travel,458,Research & Development,16,2,Life Sciences,1,1340,3,Male,74,3,1,Research Scientist,3,Divorced,3544,8532,9,Y,No,16,3,2,80,1,6,0,3,4,2,0,0 +34,No,Travel_Rarely,943,Research & Development,9,3,Life Sciences,1,1344,4,Male,86,3,3,Healthcare Representative,4,Divorced,8500,5494,0,Y,No,11,3,4,80,1,10,0,2,9,7,1,6 +40,No,Travel_Rarely,523,Research & Development,2,3,Life Sciences,1,1346,3,Male,98,3,2,Research Scientist,4,Single,4661,22455,1,Y,No,13,3,3,80,0,9,4,3,9,8,8,8 +41,No,Travel_Frequently,1018,Sales,1,3,Marketing,1,1349,3,Female,66,3,2,Sales Executive,1,Divorced,4103,4297,0,Y,No,17,3,4,80,1,10,2,3,9,3,1,7 +35,No,Travel_Frequently,482,Research & Development,4,4,Life Sciences,1,1350,3,Male,87,3,2,Research Scientist,3,Single,4249,2690,1,Y,Yes,11,3,2,80,0,9,3,3,9,6,1,1 +51,No,Travel_Rarely,770,Human Resources,5,3,Life Sciences,1,1352,3,Male,84,3,4,Manager,2,Divorced,14026,17588,1,Y,Yes,11,3,2,80,1,33,2,3,33,9,0,10 +38,No,Travel_Rarely,1009,Sales,2,2,Life Sciences,1,1355,2,Female,31,3,2,Sales Executive,1,Divorced,6893,19461,3,Y,No,15,3,4,80,1,11,3,3,7,7,1,7 +34,No,Travel_Rarely,507,Sales,15,2,Medical,1,1356,3,Female,66,3,2,Sales Executive,1,Single,6125,23553,1,Y,No,12,3,4,80,0,10,6,4,10,8,9,6 +25,No,Travel_Rarely,882,Research & Development,19,1,Medical,1,1358,4,Male,67,3,1,Laboratory Technician,4,Married,3669,9075,3,Y,No,11,3,3,80,3,7,6,2,3,2,1,2 +58,Yes,Travel_Rarely,601,Research & Development,7,4,Medical,1,1360,3,Female,53,2,3,Manufacturing Director,1,Married,10008,12023,7,Y,Yes,14,3,4,80,0,31,0,2,10,9,5,9 +40,No,Travel_Rarely,329,Research & Development,1,4,Life Sciences,1,1361,2,Male,88,3,1,Laboratory Technician,2,Married,2387,6762,3,Y,No,22,4,3,80,1,7,3,3,4,2,0,3 +36,No,Travel_Frequently,607,Sales,7,3,Marketing,1,1362,1,Female,83,4,2,Sales Executive,1,Married,4639,2261,2,Y,No,16,3,4,80,1,17,2,2,15,7,6,13 +48,No,Travel_Rarely,855,Research & Development,4,3,Life Sciences,1,1363,4,Male,54,3,3,Manufacturing Director,4,Single,7898,18706,1,Y,No,11,3,3,80,0,11,2,3,10,9,0,8 +27,No,Travel_Rarely,1291,Sales,11,3,Medical,1,1364,3,Female,98,4,1,Sales Representative,4,Married,2534,6527,8,Y,No,14,3,2,80,1,5,4,3,1,0,0,0 +51,No,Travel_Rarely,1405,Research & Development,11,2,Technical Degree,1,1367,4,Female,82,2,4,Manufacturing Director,2,Single,13142,24439,3,Y,No,16,3,2,80,0,29,1,2,5,2,0,3 +18,No,Non-Travel,1124,Research & Development,1,3,Life Sciences,1,1368,4,Female,97,3,1,Laboratory Technician,4,Single,1611,19305,1,Y,No,15,3,3,80,0,0,5,4,0,0,0,0 +35,No,Travel_Rarely,817,Research & Development,1,3,Medical,1,1369,4,Female,60,2,2,Laboratory Technician,4,Married,5363,10846,0,Y,No,12,3,2,80,1,10,0,3,9,7,0,0 +27,No,Travel_Frequently,793,Sales,2,1,Life Sciences,1,1371,4,Male,43,1,2,Sales Executive,4,Single,5071,20392,3,Y,No,20,4,2,80,0,8,3,3,6,2,0,0 +55,Yes,Travel_Rarely,267,Sales,13,4,Marketing,1,1372,1,Male,85,4,4,Sales Executive,3,Single,13695,9277,6,Y,Yes,17,3,3,80,0,24,2,2,19,7,3,8 +56,No,Travel_Rarely,1369,Research & Development,23,3,Life Sciences,1,1373,4,Male,68,3,4,Manufacturing Director,2,Married,13402,18235,4,Y,Yes,12,3,1,80,1,33,0,3,19,16,15,9 +34,No,Non-Travel,999,Research & Development,26,1,Technical Degree,1,1374,1,Female,92,2,1,Research Scientist,3,Divorced,2029,15891,1,Y,No,20,4,3,80,3,5,2,3,5,4,0,0 +40,No,Travel_Rarely,1202,Research & Development,2,1,Medical,1,1375,2,Female,89,4,2,Healthcare Representative,3,Divorced,6377,13888,5,Y,No,20,4,2,80,3,15,0,3,12,11,11,8 +34,No,Travel_Rarely,285,Research & Development,29,3,Medical,1,1377,2,Male,86,3,2,Laboratory Technician,3,Married,5429,17491,4,Y,No,13,3,1,80,2,10,1,3,8,7,7,7 +31,Yes,Travel_Frequently,703,Sales,2,3,Life Sciences,1,1379,3,Female,90,2,1,Sales Representative,4,Single,2785,11882,7,Y,No,14,3,3,80,0,3,3,4,1,0,0,0 +35,Yes,Travel_Frequently,662,Sales,18,4,Marketing,1,1380,4,Female,67,3,2,Sales Executive,3,Married,4614,23288,0,Y,Yes,18,3,3,80,1,5,0,2,4,2,3,2 +38,No,Travel_Frequently,693,Research & Development,7,3,Life Sciences,1,1382,4,Male,57,4,1,Research Scientist,3,Divorced,2610,15748,1,Y,No,11,3,4,80,3,4,2,3,4,2,0,3 +34,No,Travel_Rarely,404,Research & Development,2,4,Technical Degree,1,1383,3,Female,98,3,2,Healthcare Representative,4,Single,6687,6163,1,Y,No,11,3,4,80,0,14,2,4,14,11,4,11 +28,No,Travel_Rarely,736,Sales,26,3,Life Sciences,1,1387,3,Male,48,2,2,Sales Executive,1,Married,4724,24232,1,Y,No,11,3,3,80,1,5,0,3,5,3,0,4 +31,Yes,Travel_Rarely,330,Research & Development,22,4,Medical,1,1389,4,Male,98,3,2,Manufacturing Director,3,Married,6179,21057,1,Y,Yes,15,3,4,80,2,10,3,2,10,2,6,7 +39,No,Travel_Rarely,1498,Sales,21,4,Life Sciences,1,1390,1,Male,44,2,2,Sales Executive,4,Married,6120,3567,3,Y,Yes,12,3,4,80,2,8,2,4,5,4,1,4 +51,No,Travel_Frequently,541,Sales,2,3,Marketing,1,1391,2,Male,52,3,3,Sales Executive,2,Married,10596,15395,2,Y,No,11,3,2,80,0,14,5,3,4,2,3,2 +41,No,Travel_Frequently,1200,Research & Development,22,3,Life Sciences,1,1392,4,Female,75,3,2,Research Scientist,4,Divorced,5467,13953,3,Y,Yes,14,3,1,80,2,12,4,2,6,2,3,3 +37,No,Travel_Rarely,1439,Research & Development,4,1,Life Sciences,1,1394,3,Male,54,3,1,Research Scientist,3,Married,2996,5182,7,Y,Yes,15,3,4,80,0,8,2,3,6,4,1,3 +33,No,Travel_Frequently,1111,Sales,5,1,Life Sciences,1,1395,2,Male,61,3,2,Sales Executive,4,Married,9998,19293,6,Y,No,13,3,1,80,0,8,2,4,5,4,1,2 +32,No,Travel_Rarely,499,Sales,2,1,Marketing,1,1396,3,Male,36,3,2,Sales Executive,2,Married,4078,20497,0,Y,Yes,13,3,1,80,3,4,3,2,3,2,1,2 +39,No,Non-Travel,1485,Research & Development,25,2,Life Sciences,1,1397,3,Male,71,3,3,Healthcare Representative,3,Married,10920,3449,3,Y,No,21,4,2,80,1,13,2,3,6,4,0,5 +25,No,Travel_Rarely,1372,Sales,18,1,Life Sciences,1,1399,1,Male,93,4,2,Sales Executive,3,Married,6232,12477,2,Y,No,11,3,2,80,0,6,3,2,3,2,1,2 +52,No,Travel_Frequently,322,Research & Development,28,2,Medical,1,1401,4,Female,59,4,4,Manufacturing Director,3,Married,13247,9731,2,Y,Yes,11,3,2,80,1,24,3,2,5,3,0,2 +43,No,Travel_Rarely,930,Research & Development,6,3,Medical,1,1402,1,Female,73,2,2,Research Scientist,3,Single,4081,20003,1,Y,Yes,14,3,1,80,0,20,3,1,20,7,1,8 +27,No,Travel_Rarely,205,Sales,10,3,Marketing,1,1403,4,Female,98,2,2,Sales Executive,4,Married,5769,7100,1,Y,Yes,11,3,4,80,0,6,3,3,6,2,4,4 +27,Yes,Travel_Rarely,135,Research & Development,17,4,Life Sciences,1,1405,4,Female,51,3,1,Research Scientist,3,Single,2394,25681,1,Y,Yes,13,3,4,80,0,8,2,3,8,2,7,7 +26,No,Travel_Rarely,683,Research & Development,2,1,Medical,1,1407,1,Male,36,2,1,Research Scientist,4,Single,3904,4050,0,Y,No,12,3,4,80,0,5,2,3,4,3,1,1 +42,No,Travel_Rarely,1147,Human Resources,10,3,Human Resources,1,1408,3,Female,31,3,4,Manager,1,Married,16799,16616,0,Y,No,14,3,3,80,1,21,5,3,20,7,0,9 +52,No,Travel_Rarely,258,Research & Development,8,4,Other,1,1409,3,Female,54,3,1,Laboratory Technician,1,Married,2950,17363,9,Y,No,13,3,3,80,0,12,2,1,5,4,0,4 +37,No,Travel_Rarely,1462,Research & Development,11,3,Medical,1,1411,1,Female,94,3,1,Laboratory Technician,3,Single,3629,19106,4,Y,No,18,3,1,80,0,8,6,3,3,2,0,2 +35,No,Travel_Frequently,200,Research & Development,18,2,Life Sciences,1,1412,3,Male,60,3,3,Manufacturing Director,4,Single,9362,19944,2,Y,No,11,3,3,80,0,10,2,3,2,2,2,2 +25,No,Travel_Rarely,949,Research & Development,1,3,Technical Degree,1,1415,1,Male,81,3,1,Laboratory Technician,4,Married,3229,4910,4,Y,No,11,3,2,80,1,7,2,2,3,2,0,2 +26,No,Travel_Rarely,652,Research & Development,7,3,Other,1,1417,3,Male,100,4,1,Laboratory Technician,1,Single,3578,23577,0,Y,No,12,3,4,80,0,8,2,3,7,7,0,7 +29,No,Travel_Rarely,332,Human Resources,17,3,Other,1,1419,2,Male,51,2,3,Human Resources,1,Single,7988,9769,1,Y,No,13,3,1,80,0,10,3,2,10,9,0,9 +49,Yes,Travel_Frequently,1475,Research & Development,28,2,Life Sciences,1,1420,1,Male,97,2,2,Laboratory Technician,1,Single,4284,22710,3,Y,No,20,4,1,80,0,20,2,3,4,3,1,3 +29,Yes,Travel_Frequently,337,Research & Development,14,1,Other,1,1421,3,Female,84,3,3,Healthcare Representative,4,Single,7553,22930,0,Y,Yes,12,3,1,80,0,9,1,3,8,7,7,7 +54,No,Travel_Rarely,971,Research & Development,1,3,Medical,1,1422,4,Female,54,3,4,Research Director,4,Single,17328,5652,6,Y,No,19,3,4,80,0,29,3,2,20,7,12,7 +58,No,Travel_Rarely,1055,Research & Development,1,3,Medical,1,1423,4,Female,76,3,5,Research Director,1,Married,19701,22456,3,Y,Yes,21,4,3,80,1,32,3,3,9,8,1,5 +55,No,Travel_Rarely,1136,Research & Development,1,4,Medical,1,1424,2,Male,81,4,4,Research Director,4,Divorced,14732,12414,2,Y,No,13,3,4,80,2,31,4,4,7,7,0,0 +36,No,Travel_Rarely,1174,Sales,3,4,Marketing,1,1425,1,Female,99,3,2,Sales Executive,2,Single,9278,20763,3,Y,Yes,16,3,4,80,0,15,3,3,5,4,0,1 +31,Yes,Travel_Frequently,667,Sales,1,4,Life Sciences,1,1427,2,Female,50,1,1,Sales Representative,3,Single,1359,16154,1,Y,No,12,3,2,80,0,1,3,3,1,0,0,0 +30,No,Travel_Rarely,855,Sales,7,4,Marketing,1,1428,4,Female,73,3,2,Sales Executive,1,Divorced,4779,12761,7,Y,No,14,3,2,80,2,8,3,3,3,2,0,2 +31,No,Travel_Rarely,182,Research & Development,8,5,Life Sciences,1,1430,1,Female,93,3,4,Research Director,2,Single,16422,8847,3,Y,No,11,3,3,80,0,9,3,4,3,2,1,0 +34,No,Travel_Frequently,560,Research & Development,1,4,Other,1,1431,4,Male,91,3,1,Research Scientist,1,Divorced,2996,20284,5,Y,No,14,3,3,80,2,10,2,3,4,3,1,3 +31,Yes,Travel_Rarely,202,Research & Development,8,3,Life Sciences,1,1433,1,Female,34,2,1,Research Scientist,2,Single,1261,22262,1,Y,No,12,3,3,80,0,1,3,4,1,0,0,0 +27,No,Travel_Rarely,1377,Research & Development,11,1,Life Sciences,1,1434,2,Male,91,3,1,Laboratory Technician,1,Married,2099,7679,0,Y,No,14,3,2,80,0,6,3,4,5,0,1,4 +36,No,Travel_Rarely,172,Research & Development,4,4,Life Sciences,1,1435,1,Male,37,2,2,Laboratory Technician,4,Single,5810,22604,1,Y,No,16,3,3,80,0,10,2,2,10,4,1,8 +36,No,Travel_Rarely,329,Sales,16,4,Marketing,1,1436,3,Female,98,2,2,Sales Executive,1,Married,5647,13494,4,Y,No,13,3,1,80,2,11,3,2,3,2,0,2 +47,No,Travel_Rarely,465,Research & Development,1,3,Technical Degree,1,1438,1,Male,74,3,1,Research Scientist,4,Married,3420,10205,7,Y,No,12,3,3,80,1,17,2,2,6,5,1,2 +25,Yes,Travel_Rarely,383,Sales,9,2,Life Sciences,1,1439,1,Male,68,2,1,Sales Representative,1,Married,4400,15182,3,Y,No,12,3,1,80,0,6,2,3,3,2,2,2 +37,No,Non-Travel,1413,Research & Development,5,2,Technical Degree,1,1440,3,Male,84,4,1,Laboratory Technician,3,Single,3500,25470,0,Y,No,14,3,1,80,0,7,2,1,6,5,1,3 +56,No,Travel_Rarely,1255,Research & Development,1,2,Life Sciences,1,1441,1,Female,90,3,1,Research Scientist,1,Married,2066,10494,2,Y,No,22,4,4,80,1,5,3,4,3,2,1,0 +47,No,Travel_Rarely,359,Research & Development,2,4,Medical,1,1443,1,Female,82,3,4,Research Director,3,Married,17169,26703,3,Y,No,19,3,2,80,2,26,2,4,20,17,5,6 +24,No,Travel_Rarely,1476,Sales,4,1,Medical,1,1445,4,Female,42,3,2,Sales Executive,3,Married,4162,15211,1,Y,Yes,12,3,3,80,2,5,3,3,5,4,0,3 +32,No,Travel_Rarely,601,Sales,7,5,Marketing,1,1446,4,Male,97,3,2,Sales Executive,4,Married,9204,23343,4,Y,No,12,3,3,80,1,7,3,2,4,3,0,3 +34,No,Travel_Rarely,401,Research & Development,1,3,Life Sciences,1,1447,4,Female,86,2,1,Laboratory Technician,2,Married,3294,3708,5,Y,No,17,3,1,80,1,7,2,2,5,4,0,2 +41,No,Travel_Rarely,1283,Research & Development,5,5,Medical,1,1448,2,Male,90,4,1,Research Scientist,3,Married,2127,5561,2,Y,Yes,12,3,1,80,0,7,5,2,4,2,0,3 +40,No,Non-Travel,663,Research & Development,9,4,Other,1,1449,3,Male,81,3,2,Laboratory Technician,3,Divorced,3975,23099,3,Y,No,11,3,3,80,2,11,2,4,8,7,0,7 +31,No,Travel_Rarely,326,Sales,8,2,Life Sciences,1,1453,1,Male,31,3,3,Sales Executive,4,Divorced,10793,8386,1,Y,No,18,3,1,80,1,13,5,3,13,7,9,9 +46,Yes,Travel_Rarely,377,Sales,9,3,Marketing,1,1457,1,Male,52,3,3,Sales Executive,4,Divorced,10096,15986,4,Y,No,11,3,1,80,1,28,1,4,7,7,4,3 +39,Yes,Non-Travel,592,Research & Development,2,3,Life Sciences,1,1458,1,Female,54,2,1,Laboratory Technician,1,Single,3646,17181,2,Y,Yes,23,4,2,80,0,11,2,4,1,0,0,0 +31,Yes,Travel_Frequently,1445,Research & Development,1,5,Life Sciences,1,1459,3,Female,100,4,3,Manufacturing Director,2,Single,7446,8931,1,Y,No,11,3,1,80,0,10,2,3,10,8,4,7 +45,No,Travel_Rarely,1038,Research & Development,20,3,Medical,1,1460,2,Male,95,1,3,Healthcare Representative,1,Divorced,10851,19863,2,Y,Yes,18,3,2,80,1,24,2,3,7,7,0,7 +31,No,Travel_Rarely,1398,Human Resources,8,2,Medical,1,1461,4,Female,96,4,1,Human Resources,2,Single,2109,24609,9,Y,No,18,3,4,80,0,8,3,3,3,2,0,2 +31,Yes,Travel_Frequently,523,Research & Development,2,3,Life Sciences,1,1464,2,Male,94,3,1,Laboratory Technician,4,Married,3722,21081,6,Y,Yes,13,3,3,80,1,7,2,1,2,2,2,2 +45,No,Travel_Rarely,1448,Research & Development,29,3,Technical Degree,1,1465,2,Male,55,3,3,Manufacturing Director,4,Married,9380,14720,4,Y,Yes,18,3,4,80,2,10,4,4,3,1,1,2 +48,No,Travel_Rarely,1221,Sales,7,3,Marketing,1,1466,3,Male,96,3,2,Sales Executive,1,Divorced,5486,24795,4,Y,No,11,3,1,80,3,15,3,3,2,2,2,2 +34,Yes,Travel_Rarely,1107,Human Resources,9,4,Technical Degree,1,1467,1,Female,52,3,1,Human Resources,3,Married,2742,3072,1,Y,No,15,3,4,80,0,2,0,3,2,2,2,2 +40,No,Non-Travel,218,Research & Development,8,1,Medical,1,1468,4,Male,55,2,3,Research Director,2,Divorced,13757,25178,2,Y,No,11,3,3,80,1,16,5,3,9,8,4,8 +28,No,Travel_Rarely,866,Sales,5,3,Medical,1,1469,4,Male,84,3,2,Sales Executive,1,Single,8463,23490,0,Y,No,18,3,4,80,0,6,4,3,5,4,1,3 +44,No,Non-Travel,981,Research & Development,5,3,Life Sciences,1,1471,3,Male,90,2,1,Laboratory Technician,3,Single,3162,7973,3,Y,No,14,3,4,80,0,7,5,3,5,2,0,3 +53,No,Travel_Rarely,447,Research & Development,2,3,Medical,1,1472,4,Male,39,4,4,Research Director,2,Single,16598,19764,4,Y,No,12,3,2,80,0,35,2,2,9,8,8,8 +49,No,Travel_Rarely,1495,Research & Development,5,4,Technical Degree,1,1473,1,Male,96,3,2,Healthcare Representative,3,Married,6651,21534,2,Y,No,14,3,2,80,1,20,0,2,3,2,1,2 +40,No,Travel_Rarely,896,Research & Development,2,3,Medical,1,1474,3,Male,68,3,1,Research Scientist,3,Divorced,2345,8045,2,Y,No,14,3,3,80,1,8,3,4,3,1,1,2 +44,No,Travel_Rarely,1467,Research & Development,20,3,Life Sciences,1,1475,4,Male,49,3,1,Research Scientist,2,Single,3420,21158,1,Y,No,13,3,3,80,0,6,3,2,5,2,1,3 +33,No,Travel_Frequently,430,Sales,7,3,Medical,1,1477,4,Male,54,3,2,Sales Executive,1,Married,4373,17456,0,Y,No,14,3,1,80,2,5,2,3,4,3,0,3 +34,No,Travel_Rarely,1326,Sales,3,3,Other,1,1478,4,Male,81,1,2,Sales Executive,1,Single,4759,15891,3,Y,No,18,3,4,80,0,15,2,3,13,9,3,12 +30,No,Travel_Rarely,1358,Sales,16,1,Life Sciences,1,1479,4,Male,96,3,2,Sales Executive,3,Married,5301,2939,8,Y,No,15,3,3,80,2,4,2,2,2,1,2,2 +42,No,Travel_Frequently,748,Research & Development,9,2,Medical,1,1480,1,Female,74,3,1,Laboratory Technician,4,Single,3673,16458,1,Y,No,13,3,3,80,0,12,3,3,12,9,5,8 +44,No,Travel_Frequently,383,Sales,1,5,Marketing,1,1481,1,Female,79,3,2,Sales Executive,3,Married,4768,9282,7,Y,No,12,3,3,80,1,11,4,2,1,0,0,0 +30,No,Non-Travel,990,Research & Development,7,3,Technical Degree,1,1482,3,Male,64,3,1,Research Scientist,3,Divorced,1274,7152,1,Y,No,13,3,2,80,2,1,2,2,1,0,0,0 +57,No,Travel_Rarely,405,Research & Development,1,2,Life Sciences,1,1483,2,Male,93,4,2,Research Scientist,3,Married,4900,2721,0,Y,No,24,4,1,80,1,13,2,2,12,9,2,8 +49,No,Travel_Rarely,1490,Research & Development,7,4,Life Sciences,1,1484,3,Male,35,3,3,Healthcare Representative,2,Divorced,10466,20948,3,Y,No,14,3,2,80,2,29,3,3,8,7,0,7 +34,No,Travel_Frequently,829,Research & Development,15,3,Medical,1,1485,2,Male,71,3,4,Research Director,1,Divorced,17007,11929,7,Y,No,14,3,4,80,2,16,3,2,14,8,6,9 +28,Yes,Travel_Frequently,1496,Sales,1,3,Technical Degree,1,1486,1,Male,92,3,1,Sales Representative,3,Married,2909,15747,3,Y,No,15,3,4,80,1,5,3,4,3,2,1,2 +29,Yes,Travel_Frequently,115,Sales,13,3,Technical Degree,1,1487,1,Female,51,3,2,Sales Executive,2,Single,5765,17485,5,Y,No,11,3,1,80,0,7,4,1,5,3,0,0 +34,Yes,Travel_Rarely,790,Sales,24,4,Medical,1,1489,1,Female,40,2,2,Sales Executive,2,Single,4599,7815,0,Y,Yes,23,4,3,80,0,16,2,4,15,9,10,10 +35,No,Travel_Rarely,660,Sales,7,1,Life Sciences,1,1492,4,Male,76,3,1,Sales Representative,3,Married,2404,16192,1,Y,No,13,3,1,80,1,1,3,3,1,0,0,0 +24,Yes,Travel_Frequently,381,Research & Development,9,3,Medical,1,1494,2,Male,89,3,1,Laboratory Technician,1,Single,3172,16998,2,Y,Yes,11,3,3,80,0,4,2,2,0,0,0,0 +24,No,Non-Travel,830,Sales,13,2,Life Sciences,1,1495,4,Female,78,3,1,Sales Representative,2,Married,2033,7103,1,Y,No,13,3,3,80,1,1,2,3,1,0,0,0 +44,No,Travel_Frequently,1193,Research & Development,2,1,Medical,1,1496,2,Male,86,3,3,Manufacturing Director,3,Single,10209,19719,5,Y,Yes,18,3,2,80,0,16,2,2,2,2,2,2 +29,No,Travel_Rarely,1246,Sales,19,3,Life Sciences,1,1497,3,Male,77,2,2,Sales Executive,3,Divorced,8620,23757,1,Y,No,14,3,3,80,2,10,3,3,10,7,0,4 +30,No,Travel_Rarely,330,Human Resources,1,3,Life Sciences,1,1499,3,Male,46,3,1,Human Resources,3,Divorced,2064,15428,0,Y,No,21,4,1,80,1,6,3,4,5,3,1,3 +55,No,Travel_Rarely,1229,Research & Development,4,4,Life Sciences,1,1501,4,Male,30,3,2,Healthcare Representative,3,Married,4035,16143,0,Y,Yes,16,3,2,80,0,4,2,3,3,2,1,2 +33,No,Travel_Rarely,1099,Research & Development,4,4,Medical,1,1502,1,Female,82,2,1,Laboratory Technician,2,Married,3838,8192,8,Y,No,11,3,4,80,0,8,5,3,5,4,0,2 +47,No,Travel_Rarely,571,Sales,14,3,Medical,1,1503,3,Female,78,3,2,Sales Executive,3,Married,4591,24200,3,Y,Yes,17,3,3,80,1,11,4,2,5,4,1,2 +28,Yes,Travel_Frequently,289,Research & Development,2,2,Medical,1,1504,3,Male,38,2,1,Laboratory Technician,1,Single,2561,5355,7,Y,No,11,3,3,80,0,8,2,2,0,0,0,0 +28,No,Travel_Rarely,1423,Research & Development,1,3,Life Sciences,1,1506,1,Male,72,2,1,Research Scientist,3,Divorced,1563,12530,1,Y,No,14,3,4,80,1,1,2,1,1,0,0,0 +28,No,Travel_Frequently,467,Sales,7,3,Life Sciences,1,1507,3,Male,55,3,2,Sales Executive,1,Single,4898,11827,0,Y,No,14,3,4,80,0,5,5,3,4,2,1,3 +49,No,Travel_Rarely,271,Research & Development,3,2,Medical,1,1509,3,Female,43,2,2,Laboratory Technician,1,Married,4789,23070,4,Y,No,25,4,1,80,1,10,3,3,3,2,1,2 +29,No,Travel_Frequently,410,Research & Development,2,1,Life Sciences,1,1513,4,Female,97,3,1,Laboratory Technician,2,Married,3180,4668,0,Y,No,13,3,3,80,3,4,3,3,3,2,0,2 +28,No,Travel_Rarely,1083,Research & Development,29,1,Life Sciences,1,1514,3,Male,96,1,2,Manufacturing Director,2,Married,6549,3173,1,Y,No,14,3,2,80,2,8,2,2,8,6,1,7 +33,No,Travel_Rarely,516,Research & Development,8,5,Life Sciences,1,1515,4,Male,69,3,2,Healthcare Representative,3,Single,6388,22049,2,Y,Yes,17,3,1,80,0,14,6,3,0,0,0,0 +32,No,Travel_Rarely,495,Research & Development,10,3,Medical,1,1516,3,Male,64,3,3,Manager,4,Single,11244,21072,2,Y,No,25,4,2,80,0,10,5,4,5,2,0,0 +54,No,Travel_Frequently,1050,Research & Development,11,4,Medical,1,1520,2,Female,87,3,4,Manager,4,Divorced,16032,24456,3,Y,No,20,4,1,80,1,26,2,3,14,9,1,12 +29,Yes,Travel_Rarely,224,Research & Development,1,4,Technical Degree,1,1522,1,Male,100,2,1,Research Scientist,1,Single,2362,7568,6,Y,No,13,3,3,80,0,11,2,1,9,7,0,7 +44,No,Travel_Rarely,136,Research & Development,28,3,Life Sciences,1,1523,4,Male,32,3,4,Research Director,1,Married,16328,22074,3,Y,No,13,3,3,80,1,24,1,4,20,6,14,17 +39,No,Travel_Rarely,1089,Research & Development,6,3,Life Sciences,1,1525,2,Female,32,3,3,Manufacturing Director,2,Single,8376,9150,4,Y,No,18,3,4,80,0,9,3,3,2,0,2,2 +46,No,Travel_Rarely,228,Sales,3,3,Life Sciences,1,1527,3,Female,51,3,4,Manager,2,Married,16606,11380,8,Y,No,12,3,4,80,1,23,2,4,13,12,5,1 +35,No,Travel_Rarely,1029,Research & Development,16,3,Life Sciences,1,1529,4,Female,91,2,3,Healthcare Representative,2,Single,8606,21195,1,Y,No,19,3,4,80,0,11,3,1,11,8,3,3 +23,No,Travel_Rarely,507,Research & Development,20,1,Life Sciences,1,1533,1,Male,97,3,2,Laboratory Technician,3,Single,2272,24812,0,Y,No,14,3,2,80,0,5,2,3,4,3,1,2 +40,Yes,Travel_Rarely,676,Research & Development,9,4,Life Sciences,1,1534,4,Male,86,3,1,Laboratory Technician,1,Single,2018,21831,3,Y,No,14,3,2,80,0,15,3,1,5,4,1,0 +34,No,Travel_Rarely,971,Sales,1,3,Technical Degree,1,1535,4,Male,64,2,3,Sales Executive,3,Married,7083,12288,1,Y,Yes,14,3,4,80,0,10,3,3,10,9,8,6 +31,Yes,Travel_Frequently,561,Research & Development,3,3,Life Sciences,1,1537,4,Female,33,3,1,Research Scientist,3,Single,4084,4156,1,Y,No,12,3,1,80,0,7,2,1,7,2,7,7 +50,No,Travel_Frequently,333,Research & Development,22,5,Medical,1,1539,3,Male,88,1,4,Research Director,4,Single,14411,24450,1,Y,Yes,13,3,4,80,0,32,2,3,32,6,13,9 +34,No,Travel_Rarely,1440,Sales,7,2,Technical Degree,1,1541,2,Male,55,3,1,Sales Representative,3,Married,2308,4944,0,Y,Yes,25,4,2,80,1,12,4,3,11,10,5,7 +42,No,Travel_Rarely,1210,Research & Development,2,3,Medical,1,1542,3,Male,68,2,1,Laboratory Technician,2,Married,4841,24052,4,Y,No,14,3,2,80,1,4,3,3,1,0,0,0 +37,No,Travel_Rarely,674,Research & Development,13,3,Medical,1,1543,1,Male,47,3,2,Research Scientist,4,Married,4285,3031,1,Y,No,17,3,1,80,0,10,2,3,10,8,3,7 +29,No,Travel_Rarely,441,Research & Development,8,1,Other,1,1544,3,Female,39,1,2,Healthcare Representative,1,Married,9715,7288,3,Y,No,13,3,3,80,1,9,3,3,7,7,0,7 +33,No,Travel_Rarely,575,Research & Development,25,3,Life Sciences,1,1545,4,Male,44,2,2,Manufacturing Director,2,Single,4320,24152,1,Y,No,13,3,4,80,0,5,2,3,5,3,0,2 +45,No,Travel_Rarely,950,Research & Development,28,3,Technical Degree,1,1546,4,Male,97,3,1,Research Scientist,4,Married,2132,4585,4,Y,No,20,4,4,80,1,8,3,3,5,4,0,3 +42,No,Travel_Frequently,288,Research & Development,2,3,Life Sciences,1,1547,4,Male,40,3,3,Healthcare Representative,4,Married,10124,18611,2,Y,Yes,14,3,3,80,1,24,3,1,20,8,13,9 +40,No,Travel_Rarely,1342,Sales,9,2,Medical,1,1548,1,Male,47,3,2,Sales Executive,1,Married,5473,19345,0,Y,No,12,3,4,80,0,9,5,4,8,4,7,1 +33,No,Travel_Rarely,589,Research & Development,28,4,Life Sciences,1,1549,2,Male,79,3,2,Laboratory Technician,3,Married,5207,22949,1,Y,Yes,12,3,2,80,1,15,3,3,15,14,5,7 +40,No,Travel_Rarely,898,Human Resources,6,2,Medical,1,1550,3,Male,38,3,4,Manager,4,Single,16437,17381,1,Y,Yes,21,4,4,80,0,21,2,3,21,7,7,7 +24,No,Travel_Rarely,350,Research & Development,21,2,Technical Degree,1,1551,3,Male,57,2,1,Laboratory Technician,1,Divorced,2296,10036,0,Y,No,14,3,2,80,3,2,3,3,1,1,0,0 +40,No,Non-Travel,1142,Research & Development,8,2,Life Sciences,1,1552,4,Male,72,3,2,Healthcare Representative,4,Divorced,4069,8841,3,Y,Yes,18,3,3,80,0,8,2,3,2,2,2,2 +45,No,Travel_Rarely,538,Research & Development,1,4,Technical Degree,1,1553,1,Male,66,3,3,Healthcare Representative,2,Divorced,7441,20933,1,Y,No,12,3,1,80,3,10,4,3,10,8,7,7 +35,No,Travel_Rarely,1402,Sales,28,4,Life Sciences,1,1554,2,Female,98,2,1,Sales Representative,3,Married,2430,26204,0,Y,No,23,4,1,80,2,6,5,3,5,3,4,2 +32,No,Travel_Rarely,824,Research & Development,5,2,Life Sciences,1,1555,4,Female,67,2,2,Research Scientist,2,Married,5878,15624,3,Y,No,12,3,1,80,1,12,2,3,7,1,2,5 +36,No,Travel_Rarely,1157,Sales,2,4,Life Sciences,1,1556,3,Male,70,3,1,Sales Representative,4,Single,2644,17001,3,Y,Yes,21,4,4,80,0,7,3,2,3,2,1,2 +48,No,Travel_Rarely,492,Sales,16,4,Life Sciences,1,1557,3,Female,96,3,2,Sales Executive,3,Divorced,6439,13693,8,Y,No,14,3,3,80,1,18,2,3,8,7,7,7 +29,No,Travel_Rarely,598,Research & Development,9,3,Life Sciences,1,1558,3,Male,91,4,1,Research Scientist,3,Married,2451,22376,6,Y,No,18,3,1,80,2,5,2,2,1,0,0,0 +33,No,Travel_Rarely,1242,Sales,8,4,Life Sciences,1,1560,1,Male,46,3,2,Sales Executive,1,Married,6392,10589,2,Y,No,13,3,4,80,1,8,6,1,2,2,2,2 +30,Yes,Travel_Rarely,740,Sales,1,3,Life Sciences,1,1562,2,Male,64,2,2,Sales Executive,1,Married,9714,5323,1,Y,No,11,3,4,80,1,10,4,3,10,8,6,7 +38,No,Travel_Frequently,888,Human Resources,10,4,Human Resources,1,1563,3,Male,71,3,2,Human Resources,3,Married,6077,14814,3,Y,No,11,3,3,80,0,10,2,3,6,3,1,2 +35,No,Travel_Rarely,992,Research & Development,1,3,Medical,1,1564,4,Male,68,2,1,Laboratory Technician,1,Single,2450,21731,1,Y,No,19,3,2,80,0,3,3,3,3,0,1,2 +30,No,Travel_Rarely,1288,Sales,29,4,Technical Degree,1,1568,3,Male,33,3,3,Sales Executive,2,Married,9250,17799,3,Y,No,12,3,2,80,1,9,3,3,4,2,1,3 +35,Yes,Travel_Rarely,104,Research & Development,2,3,Life Sciences,1,1569,1,Female,69,3,1,Laboratory Technician,1,Divorced,2074,26619,1,Y,Yes,12,3,4,80,1,1,2,3,1,0,0,0 +53,Yes,Travel_Rarely,607,Research & Development,2,5,Technical Degree,1,1572,3,Female,78,2,3,Manufacturing Director,4,Married,10169,14618,0,Y,No,16,3,2,80,1,34,4,3,33,7,1,9 +38,Yes,Travel_Rarely,903,Research & Development,2,3,Medical,1,1573,3,Male,81,3,2,Manufacturing Director,2,Married,4855,7653,4,Y,No,11,3,1,80,2,7,2,3,5,2,1,4 +32,No,Non-Travel,1200,Research & Development,1,4,Technical Degree,1,1574,4,Male,62,3,2,Research Scientist,1,Married,4087,25174,4,Y,No,14,3,2,80,1,9,3,2,6,5,1,2 +48,No,Travel_Rarely,1108,Research & Development,15,4,Other,1,1576,3,Female,65,3,1,Research Scientist,1,Married,2367,16530,8,Y,No,12,3,4,80,1,10,3,2,8,2,7,6 +34,No,Travel_Rarely,479,Research & Development,7,4,Medical,1,1577,1,Male,35,3,1,Research Scientist,4,Single,2972,22061,1,Y,No,13,3,3,80,0,1,4,1,1,0,0,0 +55,No,Travel_Rarely,685,Sales,26,5,Marketing,1,1578,3,Male,60,2,5,Manager,4,Married,19586,23037,1,Y,No,21,4,3,80,1,36,3,3,36,6,2,13 +34,No,Travel_Rarely,1351,Research & Development,1,4,Life Sciences,1,1580,2,Male,45,3,2,Research Scientist,4,Married,5484,13008,9,Y,No,17,3,2,80,1,9,3,2,2,2,2,1 +26,No,Travel_Rarely,474,Research & Development,3,3,Life Sciences,1,1581,1,Female,89,3,1,Research Scientist,4,Married,2061,11133,1,Y,No,21,4,1,80,0,1,5,3,1,0,0,0 +38,No,Travel_Rarely,1245,Sales,14,3,Life Sciences,1,1582,3,Male,80,3,2,Sales Executive,2,Married,9924,12355,0,Y,No,11,3,4,80,1,10,3,3,9,8,7,7 +38,No,Travel_Rarely,437,Sales,16,3,Life Sciences,1,1583,2,Female,90,3,2,Sales Executive,2,Single,4198,16379,2,Y,No,12,3,2,80,0,8,5,4,3,2,1,2 +36,No,Travel_Rarely,884,Sales,1,4,Life Sciences,1,1585,2,Female,73,3,2,Sales Executive,3,Single,6815,21447,6,Y,No,13,3,1,80,0,15,5,3,1,0,0,0 +29,No,Travel_Rarely,1370,Research & Development,3,1,Medical,1,1586,2,Male,87,3,1,Laboratory Technician,1,Single,4723,16213,1,Y,Yes,18,3,4,80,0,10,3,3,10,9,1,5 +35,No,Travel_Rarely,670,Research & Development,10,4,Medical,1,1587,1,Female,51,3,2,Healthcare Representative,3,Single,6142,4223,3,Y,Yes,16,3,3,80,0,10,4,3,5,2,0,4 +39,No,Travel_Rarely,1462,Sales,6,3,Medical,1,1588,4,Male,38,4,3,Sales Executive,3,Married,8237,4658,2,Y,No,11,3,1,80,1,11,3,3,7,6,7,6 +29,No,Travel_Frequently,995,Research & Development,2,1,Life Sciences,1,1590,1,Male,87,3,2,Healthcare Representative,4,Divorced,8853,24483,1,Y,No,19,3,4,80,1,6,0,4,6,4,1,3 +50,No,Travel_Rarely,264,Sales,9,3,Marketing,1,1591,3,Male,59,3,5,Manager,3,Married,19331,19519,4,Y,Yes,16,3,3,80,1,27,2,3,1,0,0,0 +23,No,Travel_Rarely,977,Research & Development,10,3,Technical Degree,1,1592,4,Male,45,4,1,Research Scientist,3,Married,2073,12826,2,Y,No,16,3,4,80,1,4,2,3,2,2,2,2 +36,No,Travel_Frequently,1302,Research & Development,6,4,Life Sciences,1,1594,1,Male,80,4,2,Laboratory Technician,1,Married,5562,19711,3,Y,Yes,13,3,4,80,1,9,3,3,3,2,0,2 +42,No,Travel_Rarely,1059,Research & Development,9,2,Other,1,1595,4,Male,93,2,5,Manager,4,Single,19613,26362,8,Y,No,22,4,4,80,0,24,2,3,1,0,0,1 +35,No,Travel_Rarely,750,Research & Development,28,3,Life Sciences,1,1596,2,Male,46,4,2,Laboratory Technician,3,Married,3407,25348,1,Y,No,17,3,4,80,2,10,3,2,10,9,6,8 +34,No,Travel_Frequently,653,Research & Development,10,4,Technical Degree,1,1597,4,Male,92,2,2,Healthcare Representative,3,Married,5063,15332,1,Y,No,14,3,2,80,1,8,3,2,8,2,7,7 +40,No,Travel_Rarely,118,Sales,14,2,Life Sciences,1,1598,4,Female,84,3,2,Sales Executive,1,Married,4639,11262,1,Y,No,15,3,3,80,1,5,2,3,5,4,1,2 +43,No,Travel_Rarely,990,Research & Development,27,3,Technical Degree,1,1599,4,Male,87,4,1,Laboratory Technician,2,Divorced,4876,5855,5,Y,No,12,3,3,80,1,8,0,3,6,4,0,2 +35,No,Travel_Rarely,1349,Research & Development,7,2,Life Sciences,1,1601,3,Male,63,2,1,Laboratory Technician,4,Married,2690,7713,1,Y,No,18,3,4,80,1,1,5,2,1,0,0,1 +46,No,Travel_Rarely,563,Sales,1,4,Life Sciences,1,1602,4,Male,56,4,4,Manager,1,Single,17567,3156,1,Y,No,15,3,2,80,0,27,5,1,26,0,0,12 +28,Yes,Travel_Rarely,329,Research & Development,24,3,Medical,1,1604,3,Male,51,3,1,Laboratory Technician,2,Married,2408,7324,1,Y,Yes,17,3,3,80,3,1,3,3,1,1,0,0 +22,No,Non-Travel,457,Research & Development,26,2,Other,1,1605,2,Female,85,2,1,Research Scientist,3,Married,2814,10293,1,Y,Yes,14,3,2,80,0,4,2,2,4,2,1,3 +50,No,Travel_Frequently,1234,Research & Development,20,5,Medical,1,1606,2,Male,41,3,4,Healthcare Representative,3,Married,11245,20689,2,Y,Yes,15,3,3,80,1,32,3,3,30,8,12,13 +32,No,Travel_Rarely,634,Research & Development,5,4,Other,1,1607,2,Female,35,4,1,Research Scientist,4,Married,3312,18783,3,Y,No,17,3,4,80,2,6,3,3,3,2,0,2 +44,No,Travel_Rarely,1313,Research & Development,7,3,Medical,1,1608,2,Female,31,3,5,Research Director,4,Divorced,19049,3549,0,Y,Yes,14,3,4,80,1,23,4,2,22,7,1,10 +30,No,Travel_Rarely,241,Research & Development,7,3,Medical,1,1609,2,Male,48,2,1,Research Scientist,2,Married,2141,5348,1,Y,No,12,3,2,80,1,6,3,2,6,4,1,1 +45,No,Travel_Rarely,1015,Research & Development,5,5,Medical,1,1611,3,Female,50,1,2,Laboratory Technician,1,Single,5769,23447,1,Y,Yes,14,3,1,80,0,10,3,3,10,7,1,4 +45,No,Non-Travel,336,Sales,26,3,Marketing,1,1612,1,Male,52,2,2,Sales Executive,1,Married,4385,24162,1,Y,No,15,3,1,80,1,10,2,3,10,7,4,5 +31,No,Travel_Frequently,715,Sales,2,4,Other,1,1613,4,Male,54,3,2,Sales Executive,1,Single,5332,21602,7,Y,No,13,3,4,80,0,10,3,3,5,2,0,3 +36,No,Travel_Rarely,559,Research & Development,12,4,Life Sciences,1,1614,3,Female,76,3,2,Manufacturing Director,3,Married,4663,12421,9,Y,Yes,12,3,2,80,2,7,2,3,3,2,1,1 +34,No,Travel_Frequently,426,Research & Development,10,4,Life Sciences,1,1615,3,Male,42,4,2,Manufacturing Director,4,Divorced,4724,17000,1,Y,No,13,3,1,80,1,9,3,3,9,7,7,2 +49,No,Travel_Rarely,722,Research & Development,25,4,Life Sciences,1,1617,3,Female,84,3,1,Laboratory Technician,1,Married,3211,22102,1,Y,No,14,3,4,80,1,10,3,2,9,6,1,4 +39,No,Travel_Rarely,1387,Research & Development,10,5,Medical,1,1618,2,Male,76,3,2,Manufacturing Director,1,Married,5377,3835,2,Y,No,13,3,4,80,3,10,3,3,7,7,7,7 +27,No,Travel_Rarely,1302,Research & Development,19,3,Other,1,1619,4,Male,67,2,1,Laboratory Technician,1,Divorced,4066,16290,1,Y,No,11,3,1,80,2,7,3,3,7,7,0,7 +35,No,Travel_Rarely,819,Research & Development,18,5,Life Sciences,1,1621,2,Male,48,4,2,Research Scientist,1,Married,5208,26312,1,Y,No,11,3,4,80,0,16,2,3,16,15,1,10 +28,No,Travel_Rarely,580,Research & Development,27,3,Medical,1,1622,2,Female,39,1,2,Manufacturing Director,1,Divorced,4877,20460,0,Y,No,21,4,2,80,1,6,5,2,5,3,0,0 +21,No,Travel_Rarely,546,Research & Development,5,1,Medical,1,1623,3,Male,97,3,1,Research Scientist,4,Single,3117,26009,1,Y,No,18,3,3,80,0,3,2,3,2,2,2,2 +18,Yes,Travel_Frequently,544,Sales,3,2,Medical,1,1624,2,Female,70,3,1,Sales Representative,4,Single,1569,18420,1,Y,Yes,12,3,3,80,0,0,2,4,0,0,0,0 +47,No,Travel_Rarely,1176,Human Resources,26,4,Life Sciences,1,1625,4,Female,98,3,5,Manager,3,Married,19658,5220,3,Y,No,11,3,3,80,1,27,2,3,5,2,1,0 +39,No,Travel_Rarely,170,Research & Development,3,2,Medical,1,1627,3,Male,76,2,2,Laboratory Technician,3,Divorced,3069,10302,0,Y,No,15,3,4,80,1,11,3,3,10,8,0,7 +40,No,Travel_Rarely,884,Research & Development,15,3,Life Sciences,1,1628,1,Female,80,2,3,Manufacturing Director,3,Married,10435,25800,1,Y,No,13,3,4,80,2,18,2,3,18,15,14,12 +35,No,Non-Travel,208,Research & Development,8,4,Life Sciences,1,1630,3,Female,52,3,2,Healthcare Representative,3,Married,4148,12250,1,Y,No,12,3,4,80,1,15,5,3,14,11,2,9 +37,No,Travel_Rarely,671,Research & Development,19,3,Life Sciences,1,1631,3,Male,85,3,2,Manufacturing Director,3,Married,5768,26493,3,Y,No,17,3,1,80,3,9,2,2,4,3,0,2 +39,No,Travel_Frequently,711,Research & Development,4,3,Medical,1,1633,1,Female,81,3,2,Manufacturing Director,3,Single,5042,3140,0,Y,No,13,3,4,80,0,10,2,1,9,2,3,8 +45,No,Travel_Rarely,1329,Research & Development,2,2,Other,1,1635,4,Female,59,2,2,Manufacturing Director,4,Divorced,5770,5388,1,Y,No,19,3,1,80,2,10,3,3,10,7,3,9 +38,No,Travel_Rarely,397,Research & Development,2,2,Medical,1,1638,4,Female,54,2,3,Manufacturing Director,3,Married,7756,14199,3,Y,Yes,19,3,4,80,1,10,6,4,5,4,0,2 +35,Yes,Travel_Rarely,737,Sales,10,3,Medical,1,1639,4,Male,55,2,3,Sales Executive,1,Married,10306,21530,9,Y,No,17,3,3,80,0,15,3,3,13,12,6,0 +37,No,Travel_Rarely,1470,Research & Development,10,3,Medical,1,1640,2,Female,71,3,1,Research Scientist,2,Married,3936,9953,1,Y,No,11,3,1,80,1,8,2,1,8,4,7,7 +40,No,Travel_Rarely,448,Research & Development,16,3,Life Sciences,1,1641,3,Female,84,3,3,Manufacturing Director,4,Single,7945,19948,6,Y,Yes,15,3,4,80,0,18,2,2,4,2,3,3 +44,No,Travel_Frequently,602,Human Resources,1,5,Human Resources,1,1642,1,Male,37,3,2,Human Resources,4,Married,5743,10503,4,Y,Yes,11,3,3,80,0,14,3,3,10,7,0,2 +48,No,Travel_Frequently,365,Research & Development,4,5,Medical,1,1644,3,Male,89,2,4,Manager,4,Married,15202,5602,2,Y,No,25,4,2,80,1,23,3,3,2,2,2,2 +35,Yes,Travel_Rarely,763,Sales,15,2,Medical,1,1645,1,Male,59,1,2,Sales Executive,4,Divorced,5440,22098,6,Y,Yes,14,3,4,80,2,7,2,2,2,2,2,2 +24,No,Travel_Frequently,567,Research & Development,2,1,Technical Degree,1,1646,1,Female,32,3,1,Research Scientist,4,Single,3760,17218,1,Y,Yes,13,3,3,80,0,6,2,3,6,3,1,3 +27,No,Travel_Rarely,486,Research & Development,8,3,Medical,1,1647,2,Female,86,4,1,Research Scientist,3,Married,3517,22490,7,Y,No,17,3,1,80,0,5,0,3,3,2,0,2 +27,No,Travel_Frequently,591,Research & Development,2,3,Medical,1,1648,4,Male,87,3,1,Research Scientist,4,Single,2580,6297,2,Y,No,13,3,3,80,0,6,0,2,4,2,1,2 +40,Yes,Travel_Rarely,1329,Research & Development,7,3,Life Sciences,1,1649,1,Male,73,3,1,Laboratory Technician,1,Single,2166,3339,3,Y,Yes,14,3,2,80,0,10,3,1,4,2,0,3 +29,No,Travel_Rarely,469,Sales,10,3,Medical,1,1650,3,Male,42,2,2,Sales Executive,3,Single,5869,23413,9,Y,No,11,3,3,80,0,8,2,3,5,2,1,4 +36,No,Travel_Rarely,711,Research & Development,5,4,Life Sciences,1,1651,2,Female,42,3,3,Healthcare Representative,1,Married,8008,22792,4,Y,No,12,3,3,80,2,9,6,3,3,2,0,2 +25,No,Travel_Frequently,772,Research & Development,2,1,Life Sciences,1,1653,4,Male,77,4,2,Manufacturing Director,3,Divorced,5206,4973,1,Y,No,17,3,3,80,2,7,6,3,7,7,0,7 +39,No,Travel_Rarely,492,Research & Development,12,3,Medical,1,1654,4,Male,66,3,2,Manufacturing Director,2,Married,5295,7693,4,Y,No,21,4,3,80,0,7,3,3,5,4,1,0 +49,No,Travel_Rarely,301,Research & Development,22,4,Other,1,1655,1,Female,72,3,4,Research Director,2,Married,16413,3498,3,Y,No,16,3,2,80,2,27,2,3,4,2,1,2 +50,No,Travel_Rarely,813,Research & Development,17,5,Life Sciences,1,1656,4,Female,50,2,3,Research Director,1,Divorced,13269,21981,5,Y,No,15,3,3,80,3,19,3,3,14,11,1,11 +20,No,Travel_Rarely,1141,Sales,2,3,Medical,1,1657,3,Female,31,3,1,Sales Representative,3,Single,2783,13251,1,Y,No,19,3,1,80,0,2,3,3,2,2,2,2 +34,No,Travel_Rarely,1130,Research & Development,3,3,Life Sciences,1,1658,4,Female,66,3,2,Research Scientist,2,Divorced,5433,19332,1,Y,No,12,3,3,80,1,11,2,3,11,8,7,9 +36,No,Travel_Rarely,311,Research & Development,7,3,Life Sciences,1,1659,1,Male,77,3,1,Laboratory Technician,2,Single,2013,10950,2,Y,No,11,3,3,80,0,15,4,3,4,3,1,3 +49,No,Travel_Rarely,465,Research & Development,6,1,Life Sciences,1,1661,3,Female,41,2,4,Healthcare Representative,3,Married,13966,11652,2,Y,Yes,19,3,2,80,1,30,3,3,15,11,2,12 +36,No,Non-Travel,894,Research & Development,1,4,Medical,1,1662,4,Female,33,2,2,Manufacturing Director,3,Married,4374,15411,0,Y,No,15,3,3,80,0,4,6,3,3,2,1,2 +36,No,Travel_Rarely,1040,Research & Development,3,2,Life Sciences,1,1664,4,Male,79,4,2,Healthcare Representative,1,Divorced,6842,26308,6,Y,No,20,4,1,80,1,13,3,3,5,4,0,4 +54,No,Travel_Rarely,584,Research & Development,22,5,Medical,1,1665,2,Female,91,3,4,Manager,3,Married,17426,18685,3,Y,No,25,4,3,80,1,36,6,3,10,8,4,7 +43,No,Travel_Rarely,1291,Research & Development,15,2,Life Sciences,1,1666,3,Male,65,2,4,Research Director,3,Married,17603,3525,1,Y,No,24,4,1,80,1,14,3,3,14,10,6,11 +35,Yes,Travel_Frequently,880,Sales,12,4,Other,1,1667,4,Male,36,3,2,Sales Executive,4,Single,4581,10414,3,Y,Yes,24,4,1,80,0,13,2,4,11,9,6,7 +38,No,Travel_Frequently,1189,Research & Development,1,3,Life Sciences,1,1668,4,Male,90,3,2,Research Scientist,4,Married,4735,9867,7,Y,No,15,3,4,80,2,19,4,4,13,11,2,9 +29,No,Travel_Rarely,991,Sales,5,3,Medical,1,1669,1,Male,43,2,2,Sales Executive,2,Divorced,4187,3356,1,Y,Yes,13,3,2,80,1,10,3,2,10,0,0,9 +33,No,Travel_Rarely,392,Sales,2,4,Medical,1,1670,4,Male,93,3,2,Sales Executive,4,Divorced,5505,3921,1,Y,No,14,3,3,80,2,6,5,3,6,2,0,4 +32,No,Travel_Rarely,977,Research & Development,2,3,Medical,1,1671,4,Male,45,3,2,Research Scientist,2,Divorced,5470,25518,0,Y,No,13,3,3,80,2,10,4,2,9,5,1,6 +31,No,Travel_Rarely,1112,Sales,5,4,Life Sciences,1,1673,1,Female,67,3,2,Sales Executive,4,Married,5476,22589,1,Y,No,11,3,1,80,2,10,2,3,10,0,0,2 +49,No,Travel_Rarely,464,Research & Development,16,3,Medical,1,1674,4,Female,74,3,1,Laboratory Technician,1,Divorced,2587,24941,4,Y,Yes,16,3,2,80,1,17,2,2,2,2,2,2 +38,No,Travel_Frequently,148,Research & Development,2,3,Medical,1,1675,4,Female,42,2,1,Laboratory Technician,2,Single,2440,23826,1,Y,No,22,4,2,80,0,4,3,3,4,3,3,3 +47,No,Travel_Rarely,1225,Sales,2,4,Life Sciences,1,1676,2,Female,47,4,4,Manager,2,Divorced,15972,21086,6,Y,No,14,3,3,80,3,29,2,3,3,2,1,2 +49,No,Travel_Rarely,809,Research & Development,1,3,Life Sciences,1,1677,3,Male,36,3,4,Manager,3,Single,15379,22384,4,Y,No,14,3,1,80,0,23,2,3,8,7,0,0 +41,No,Travel_Rarely,1206,Sales,23,2,Life Sciences,1,1678,4,Male,80,3,3,Sales Executive,3,Single,7082,11591,3,Y,Yes,16,3,4,80,0,21,2,3,2,0,0,2 +20,No,Travel_Rarely,727,Sales,9,1,Life Sciences,1,1680,4,Male,54,3,1,Sales Representative,1,Single,2728,21082,1,Y,No,11,3,1,80,0,2,3,3,2,2,0,2 +33,No,Non-Travel,530,Sales,16,3,Life Sciences,1,1681,3,Female,36,3,2,Sales Executive,4,Divorced,5368,16130,1,Y,Yes,25,4,3,80,1,7,2,3,6,5,1,2 +36,No,Travel_Rarely,1351,Research & Development,26,4,Life Sciences,1,1682,1,Male,80,3,2,Healthcare Representative,3,Married,5347,7419,6,Y,No,14,3,2,80,2,10,2,2,3,2,0,2 +44,No,Travel_Rarely,528,Human Resources,1,3,Life Sciences,1,1683,3,Female,44,3,1,Human Resources,4,Divorced,3195,4167,4,Y,Yes,18,3,1,80,3,8,2,3,2,2,2,2 +23,Yes,Travel_Rarely,1320,Research & Development,8,1,Medical,1,1684,4,Male,93,2,1,Laboratory Technician,3,Single,3989,20586,1,Y,Yes,11,3,1,80,0,5,2,3,5,4,1,2 +38,No,Travel_Rarely,1495,Research & Development,4,2,Medical,1,1687,4,Female,87,3,1,Laboratory Technician,3,Married,3306,26176,7,Y,No,19,3,4,80,1,7,5,2,0,0,0,0 +53,No,Travel_Rarely,1395,Research & Development,24,4,Medical,1,1689,2,Male,48,4,3,Healthcare Representative,4,Married,7005,3458,3,Y,No,15,3,3,80,0,11,2,3,4,3,1,2 +48,Yes,Travel_Frequently,708,Sales,7,2,Medical,1,1691,4,Female,95,3,1,Sales Representative,3,Married,2655,11740,2,Y,Yes,11,3,3,80,2,19,3,3,9,7,7,7 +32,Yes,Travel_Rarely,1259,Research & Development,2,4,Life Sciences,1,1692,4,Male,95,3,1,Laboratory Technician,2,Single,1393,24852,1,Y,No,12,3,1,80,0,1,2,3,1,0,0,0 +26,No,Non-Travel,786,Research & Development,7,3,Medical,1,1693,4,Male,76,3,1,Laboratory Technician,4,Single,2570,11925,1,Y,No,20,4,3,80,0,7,5,3,7,7,5,7 +55,No,Travel_Rarely,1441,Research & Development,22,3,Technical Degree,1,1694,1,Male,94,2,1,Research Scientist,2,Divorced,3537,23737,5,Y,No,12,3,4,80,1,8,1,3,4,2,1,2 +34,No,Travel_Rarely,1157,Research & Development,5,2,Medical,1,1696,2,Male,57,2,2,Laboratory Technician,4,Married,3986,11912,1,Y,No,14,3,3,80,1,15,3,4,15,10,4,13 +60,No,Travel_Rarely,370,Research & Development,1,4,Medical,1,1697,3,Male,92,1,3,Healthcare Representative,4,Divorced,10883,20467,3,Y,No,20,4,3,80,1,19,2,4,1,0,0,0 +33,No,Travel_Rarely,267,Research & Development,21,3,Medical,1,1698,2,Male,79,4,1,Laboratory Technician,2,Married,2028,13637,1,Y,No,18,3,4,80,3,14,6,3,14,11,2,13 +37,No,Travel_Frequently,1278,Sales,1,4,Medical,1,1700,3,Male,31,1,2,Sales Executive,4,Divorced,9525,7677,1,Y,No,14,3,3,80,2,6,2,2,6,3,1,3 +34,No,Travel_Rarely,678,Research & Development,19,3,Life Sciences,1,1701,2,Female,35,2,1,Research Scientist,4,Married,2929,20338,1,Y,No,12,3,2,80,0,10,3,3,10,9,8,7 +23,Yes,Travel_Rarely,427,Sales,7,3,Life Sciences,1,1702,3,Male,99,3,1,Sales Representative,4,Divorced,2275,25103,1,Y,Yes,21,4,2,80,1,3,2,3,3,2,0,2 +44,No,Travel_Rarely,921,Research & Development,2,3,Life Sciences,1,1703,3,Female,96,4,3,Healthcare Representative,4,Married,7879,14810,1,Y,Yes,19,3,2,80,1,9,2,3,8,7,6,7 +35,No,Travel_Frequently,146,Research & Development,2,4,Medical,1,1704,1,Male,79,2,1,Research Scientist,4,Single,4930,13970,0,Y,Yes,14,3,3,80,0,6,2,4,5,4,1,4 +43,No,Travel_Rarely,1179,Sales,2,3,Medical,1,1706,4,Male,73,3,2,Sales Executive,4,Married,7847,6069,1,Y,Yes,17,3,1,80,1,10,3,3,10,9,8,8 +24,No,Travel_Rarely,581,Research & Development,9,3,Medical,1,1707,3,Male,62,4,1,Research Scientist,3,Married,4401,17616,1,Y,No,16,3,4,80,1,5,1,3,5,3,0,4 +41,No,Travel_Rarely,918,Sales,6,3,Marketing,1,1708,4,Male,35,3,3,Sales Executive,3,Single,9241,15869,1,Y,No,12,3,2,80,0,10,3,3,10,8,8,7 +29,No,Travel_Rarely,1082,Research & Development,9,4,Medical,1,1709,4,Female,43,3,1,Laboratory Technician,3,Married,2974,25412,9,Y,No,17,3,3,80,1,9,2,3,5,3,1,2 +36,No,Travel_Rarely,530,Sales,2,4,Life Sciences,1,1710,3,Female,51,3,2,Sales Representative,4,Single,4502,7439,3,Y,No,15,3,3,80,0,17,2,2,13,7,6,7 +45,No,Non-Travel,1238,Research & Development,1,1,Life Sciences,1,1712,3,Male,74,2,3,Healthcare Representative,3,Married,10748,3395,3,Y,No,23,4,4,80,1,25,3,2,23,15,14,4 +24,Yes,Travel_Rarely,240,Human Resources,22,1,Human Resources,1,1714,4,Male,58,1,1,Human Resources,3,Married,1555,11585,1,Y,No,11,3,3,80,1,1,2,3,1,0,0,0 +47,Yes,Travel_Frequently,1093,Sales,9,3,Life Sciences,1,1716,3,Male,82,1,4,Sales Executive,3,Married,12936,24164,7,Y,No,11,3,3,80,0,25,3,1,23,5,14,10 +26,No,Travel_Rarely,390,Research & Development,17,4,Medical,1,1718,4,Male,62,1,1,Laboratory Technician,3,Married,2305,6217,1,Y,No,15,3,3,80,3,3,3,4,3,2,0,2 +45,No,Travel_Rarely,1005,Research & Development,28,2,Technical Degree,1,1719,4,Female,48,2,4,Research Director,2,Single,16704,17119,1,Y,No,11,3,3,80,0,21,2,3,21,6,8,6 +32,No,Travel_Frequently,585,Research & Development,10,3,Life Sciences,1,1720,1,Male,56,3,1,Research Scientist,3,Married,3433,17360,6,Y,No,13,3,1,80,1,10,3,2,5,2,1,3 +31,No,Travel_Rarely,741,Research & Development,2,4,Life Sciences,1,1721,2,Male,69,3,1,Laboratory Technician,3,Married,3477,18103,1,Y,No,14,3,4,80,1,6,2,4,5,2,0,3 +41,No,Non-Travel,552,Human Resources,4,3,Human Resources,1,1722,3,Male,60,1,2,Human Resources,2,Married,6430,20794,6,Y,No,19,3,2,80,1,10,4,3,3,2,1,2 +40,No,Travel_Rarely,369,Research & Development,8,2,Life Sciences,1,1724,2,Female,92,3,2,Manufacturing Director,1,Married,6516,5041,2,Y,Yes,16,3,2,80,1,18,3,3,1,0,0,0 +24,No,Travel_Rarely,506,Research & Development,29,1,Medical,1,1725,2,Male,91,3,1,Laboratory Technician,1,Divorced,3907,3622,1,Y,No,13,3,2,80,3,6,2,4,6,2,1,2 +46,No,Travel_Rarely,717,Research & Development,13,4,Life Sciences,1,1727,3,Male,34,3,2,Healthcare Representative,2,Single,5562,9697,6,Y,No,14,3,4,80,0,19,3,3,10,7,0,9 +35,No,Travel_Rarely,1370,Research & Development,27,4,Life Sciences,1,1728,4,Male,49,3,2,Manufacturing Director,3,Married,6883,5151,2,Y,No,16,3,2,80,1,17,3,3,7,7,0,7 +30,No,Travel_Rarely,793,Research & Development,16,1,Life Sciences,1,1729,2,Male,33,3,1,Research Scientist,4,Married,2862,3811,1,Y,No,12,3,2,80,1,10,2,2,10,0,0,8 +47,No,Non-Travel,543,Sales,2,4,Marketing,1,1731,3,Male,87,3,2,Sales Executive,2,Married,4978,3536,7,Y,No,11,3,4,80,1,4,3,1,1,0,0,0 +46,No,Travel_Rarely,1277,Sales,2,3,Life Sciences,1,1732,3,Male,74,3,3,Sales Executive,4,Divorced,10368,5596,4,Y,Yes,12,3,2,80,1,13,5,2,10,6,0,3 +36,Yes,Travel_Rarely,1456,Sales,13,5,Marketing,1,1733,2,Male,96,2,2,Sales Executive,1,Divorced,6134,8658,5,Y,Yes,13,3,2,80,3,16,3,3,2,2,2,2 +32,Yes,Travel_Rarely,964,Sales,1,2,Life Sciences,1,1734,1,Male,34,1,2,Sales Executive,2,Single,6735,12147,6,Y,No,15,3,2,80,0,10,2,3,0,0,0,0 +23,No,Travel_Rarely,160,Research & Development,4,1,Medical,1,1735,3,Female,51,3,1,Laboratory Technician,2,Single,3295,12862,1,Y,No,13,3,3,80,0,3,3,1,3,2,1,2 +31,No,Travel_Frequently,163,Research & Development,24,1,Technical Degree,1,1736,4,Female,30,3,2,Manufacturing Director,4,Single,5238,6670,2,Y,No,20,4,4,80,0,9,3,2,5,4,1,4 +39,No,Non-Travel,792,Research & Development,1,3,Life Sciences,1,1737,4,Male,77,3,2,Laboratory Technician,4,Married,6472,8989,1,Y,Yes,15,3,4,80,1,9,2,3,9,8,5,8 +32,No,Travel_Rarely,371,Sales,19,3,Life Sciences,1,1739,4,Male,80,1,3,Sales Executive,3,Married,9610,3840,3,Y,No,13,3,3,80,1,10,2,1,4,3,0,2 +40,No,Travel_Rarely,611,Sales,7,4,Medical,1,1740,2,Male,88,3,5,Manager,2,Single,19833,4349,1,Y,No,14,3,2,80,0,21,3,2,21,8,12,8 +45,No,Travel_Rarely,176,Human Resources,4,3,Life Sciences,1,1744,3,Female,56,1,3,Human Resources,3,Married,9756,6595,4,Y,No,21,4,3,80,2,9,2,4,5,0,0,3 +30,No,Travel_Frequently,1312,Research & Development,2,4,Technical Degree,1,1745,4,Female,78,2,1,Research Scientist,1,Single,4968,26427,0,Y,No,16,3,4,80,0,10,2,3,9,7,0,7 +24,No,Travel_Frequently,897,Human Resources,10,3,Medical,1,1746,1,Male,59,3,1,Human Resources,4,Married,2145,2097,0,Y,No,14,3,4,80,1,3,2,3,2,2,2,1 +30,Yes,Travel_Frequently,600,Human Resources,8,3,Human Resources,1,1747,3,Female,66,2,1,Human Resources,4,Divorced,2180,9732,6,Y,No,11,3,3,80,1,6,0,2,4,2,1,2 +31,No,Travel_Rarely,1003,Sales,5,3,Technical Degree,1,1749,1,Male,51,3,2,Sales Executive,3,Married,8346,20943,1,Y,No,19,3,3,80,1,6,3,3,5,2,0,2 +27,No,Travel_Rarely,1054,Research & Development,8,3,Medical,1,1751,3,Female,67,3,1,Research Scientist,4,Single,3445,6152,1,Y,No,11,3,3,80,0,6,5,2,6,2,1,4 +29,Yes,Travel_Rarely,428,Sales,9,3,Marketing,1,1752,2,Female,52,1,1,Sales Representative,2,Single,2760,14630,1,Y,No,13,3,3,80,0,2,3,3,2,2,2,2 +29,No,Travel_Frequently,461,Research & Development,1,3,Life Sciences,1,1753,4,Male,70,4,2,Healthcare Representative,3,Single,6294,23060,8,Y,Yes,12,3,4,80,0,10,5,4,3,2,0,2 +30,No,Travel_Rarely,979,Sales,15,2,Marketing,1,1754,3,Male,94,2,3,Sales Executive,1,Divorced,7140,3088,2,Y,No,11,3,1,80,1,12,2,3,7,7,1,7 +34,No,Travel_Rarely,181,Research & Development,2,4,Medical,1,1755,4,Male,97,4,1,Research Scientist,4,Married,2932,5586,0,Y,Yes,14,3,1,80,3,6,3,3,5,0,1,2 +33,No,Non-Travel,1283,Sales,2,3,Marketing,1,1756,4,Female,62,3,2,Sales Executive,2,Single,5147,10697,8,Y,No,15,3,4,80,0,13,2,2,11,7,1,7 +49,No,Travel_Rarely,1313,Sales,11,4,Marketing,1,1757,4,Female,80,3,2,Sales Executive,4,Single,4507,8191,3,Y,No,12,3,3,80,0,8,1,4,5,1,0,4 +33,Yes,Travel_Rarely,211,Sales,16,3,Life Sciences,1,1758,1,Female,74,3,3,Sales Executive,1,Single,8564,10092,2,Y,Yes,20,4,3,80,0,11,2,2,0,0,0,0 +38,No,Travel_Frequently,594,Research & Development,2,2,Medical,1,1760,3,Female,75,2,1,Laboratory Technician,2,Married,2468,15963,4,Y,No,14,3,2,80,1,9,4,2,6,1,0,5 +31,Yes,Travel_Rarely,1079,Sales,16,4,Marketing,1,1761,1,Male,70,3,3,Sales Executive,3,Married,8161,19002,2,Y,No,13,3,1,80,3,10,2,3,1,0,0,0 +29,No,Travel_Rarely,590,Research & Development,4,3,Technical Degree,1,1762,4,Female,91,2,1,Research Scientist,1,Divorced,2109,10007,1,Y,No,13,3,3,80,1,1,2,3,1,0,0,0 +30,No,Travel_Rarely,305,Research & Development,16,3,Life Sciences,1,1763,3,Male,58,4,2,Healthcare Representative,3,Married,5294,9128,3,Y,No,16,3,3,80,1,10,3,3,7,0,1,7 +32,No,Non-Travel,953,Research & Development,5,4,Technical Degree,1,1764,2,Male,65,3,1,Research Scientist,2,Single,2718,17674,2,Y,No,14,3,2,80,0,12,3,3,7,7,0,7 +38,No,Travel_Rarely,833,Research & Development,18,3,Medical,1,1766,2,Male,60,1,2,Healthcare Representative,4,Married,5811,24539,3,Y,Yes,16,3,3,80,1,15,2,3,1,0,1,0 +43,Yes,Travel_Frequently,807,Research & Development,17,3,Technical Degree,1,1767,3,Male,38,2,1,Research Scientist,3,Married,2437,15587,9,Y,Yes,16,3,4,80,1,6,4,3,1,0,0,0 +42,No,Travel_Rarely,855,Research & Development,12,3,Medical,1,1768,2,Male,57,3,1,Laboratory Technician,2,Divorced,2766,8952,8,Y,No,22,4,2,80,3,7,6,2,5,3,0,4 +55,No,Travel_Rarely,478,Research & Development,2,3,Medical,1,1770,3,Male,60,2,5,Research Director,1,Married,19038,19805,8,Y,No,12,3,2,80,3,34,2,3,1,0,0,0 +33,No,Non-Travel,775,Research & Development,4,3,Technical Degree,1,1771,4,Male,90,3,2,Research Scientist,2,Divorced,3055,6194,5,Y,No,15,3,4,80,2,11,2,2,9,8,1,7 +41,No,Travel_Rarely,548,Research & Development,9,4,Life Sciences,1,1772,3,Male,94,3,1,Laboratory Technician,1,Divorced,2289,20520,1,Y,No,20,4,2,80,2,5,2,3,5,3,0,4 +34,No,Non-Travel,1375,Sales,10,3,Life Sciences,1,1774,4,Male,87,3,2,Sales Executive,3,Divorced,4001,12313,1,Y,Yes,14,3,3,80,1,15,3,3,15,14,0,7 +53,No,Non-Travel,661,Research & Development,1,4,Medical,1,1775,1,Female,60,2,4,Manufacturing Director,3,Married,12965,22308,4,Y,Yes,20,4,4,80,3,27,2,2,3,2,0,2 +43,No,Travel_Rarely,244,Human Resources,2,3,Life Sciences,1,1778,2,Male,97,3,1,Human Resources,4,Single,3539,5033,0,Y,No,13,3,2,80,0,10,5,3,9,7,1,8 +34,No,Travel_Rarely,511,Sales,3,2,Life Sciences,1,1779,4,Female,32,1,2,Sales Executive,4,Single,6029,25353,5,Y,No,12,3,1,80,0,6,3,3,2,2,2,2 +21,Yes,Travel_Rarely,337,Sales,7,1,Marketing,1,1780,2,Male,31,3,1,Sales Representative,2,Single,2679,4567,1,Y,No,13,3,2,80,0,1,3,3,1,0,1,0 +38,No,Travel_Rarely,1153,Research & Development,6,2,Other,1,1782,4,Female,40,2,1,Laboratory Technician,3,Married,3702,16376,1,Y,No,11,3,2,80,1,5,3,3,5,4,0,4 +22,Yes,Travel_Rarely,1294,Research & Development,8,1,Medical,1,1783,3,Female,79,3,1,Laboratory Technician,1,Married,2398,15999,1,Y,Yes,17,3,3,80,0,1,6,3,1,0,0,0 +31,No,Travel_Rarely,196,Sales,29,4,Marketing,1,1784,1,Female,91,2,2,Sales Executive,4,Married,5468,13402,1,Y,No,14,3,1,80,2,13,3,3,12,7,5,7 +51,No,Travel_Rarely,942,Research & Development,3,3,Technical Degree,1,1786,1,Female,53,3,3,Manager,3,Married,13116,22984,2,Y,No,11,3,4,80,0,15,2,3,2,2,2,2 +37,No,Travel_Rarely,589,Sales,9,2,Marketing,1,1787,2,Male,46,2,2,Sales Executive,2,Married,4189,8800,1,Y,No,14,3,1,80,2,5,2,3,5,2,0,3 +46,No,Travel_Rarely,734,Research & Development,2,4,Medical,1,1789,3,Male,46,3,5,Research Director,4,Divorced,19328,14218,7,Y,Yes,17,3,3,80,1,24,3,3,2,1,2,2 +36,No,Travel_Rarely,1383,Research & Development,10,3,Life Sciences,1,1790,4,Male,90,3,3,Healthcare Representative,1,Married,8321,25949,7,Y,Yes,13,3,4,80,1,15,1,3,12,8,5,7 +44,Yes,Travel_Frequently,429,Research & Development,1,2,Medical,1,1792,3,Male,99,3,1,Research Scientist,2,Divorced,2342,11092,1,Y,Yes,12,3,3,80,3,6,2,2,5,3,2,3 +37,No,Travel_Rarely,1239,Human Resources,8,2,Other,1,1794,3,Male,89,3,2,Human Resources,2,Divorced,4071,12832,2,Y,No,13,3,3,80,0,19,4,2,10,0,4,7 +35,Yes,Travel_Rarely,303,Sales,27,3,Life Sciences,1,1797,3,Male,84,3,2,Sales Executive,4,Single,5813,13492,1,Y,Yes,18,3,4,80,0,10,2,3,10,7,7,7 +33,No,Travel_Rarely,867,Research & Development,8,4,Life Sciences,1,1798,4,Male,90,4,1,Research Scientist,1,Married,3143,6076,6,Y,No,19,3,2,80,1,14,1,3,10,8,7,6 +28,No,Travel_Rarely,1181,Research & Development,1,3,Life Sciences,1,1799,3,Male,82,3,1,Research Scientist,4,Married,2044,5531,1,Y,No,11,3,3,80,1,5,6,4,5,3,0,3 +39,No,Travel_Rarely,1253,Research & Development,10,1,Medical,1,1800,3,Male,65,3,3,Research Director,3,Single,13464,7914,7,Y,No,21,4,3,80,0,9,3,3,4,3,2,2 +46,No,Non-Travel,849,Sales,26,2,Life Sciences,1,1801,2,Male,98,2,2,Sales Executive,2,Single,7991,25166,8,Y,No,15,3,3,80,0,6,3,3,2,2,2,2 +40,No,Travel_Rarely,616,Research & Development,2,2,Life Sciences,1,1802,3,Female,99,3,1,Laboratory Technician,1,Married,3377,25605,4,Y,No,17,3,4,80,1,7,5,2,4,3,0,2 +42,No,Travel_Rarely,1128,Research & Development,13,3,Medical,1,1803,2,Male,95,4,2,Healthcare Representative,1,Married,5538,5696,5,Y,No,18,3,3,80,2,10,2,2,0,0,0,0 +35,No,Non-Travel,1180,Research & Development,2,2,Medical,1,1804,2,Male,90,3,2,Manufacturing Director,4,Divorced,5762,24442,2,Y,No,14,3,3,80,1,15,6,3,7,7,1,7 +38,No,Non-Travel,1336,Human Resources,2,3,Human Resources,1,1805,1,Male,100,3,1,Human Resources,2,Divorced,2592,7129,5,Y,No,13,3,4,80,3,13,3,3,11,10,3,8 +34,Yes,Travel_Frequently,234,Research & Development,9,4,Life Sciences,1,1807,4,Male,93,3,2,Laboratory Technician,1,Married,5346,6208,4,Y,No,17,3,3,80,1,11,3,2,7,1,0,7 +37,Yes,Travel_Rarely,370,Research & Development,10,4,Medical,1,1809,4,Male,58,3,2,Manufacturing Director,1,Single,4213,4992,1,Y,No,15,3,2,80,0,10,4,1,10,3,0,8 +39,No,Travel_Frequently,766,Sales,20,3,Life Sciences,1,1812,3,Male,83,3,2,Sales Executive,4,Divorced,4127,19188,2,Y,No,18,3,4,80,1,7,6,3,2,1,2,2 +43,No,Non-Travel,343,Research & Development,9,3,Life Sciences,1,1813,1,Male,52,3,1,Research Scientist,3,Single,2438,24978,4,Y,No,13,3,3,80,0,7,2,2,3,2,1,2 +41,No,Travel_Rarely,447,Research & Development,5,3,Life Sciences,1,1814,2,Male,85,4,2,Healthcare Representative,2,Single,6870,15530,3,Y,No,12,3,1,80,0,11,3,1,3,2,1,2 +41,No,Travel_Rarely,796,Sales,4,1,Marketing,1,1815,3,Female,81,3,3,Sales Executive,3,Divorced,10447,26458,0,Y,Yes,13,3,4,80,1,23,3,4,22,14,13,5 +30,No,Travel_Rarely,1092,Research & Development,10,3,Medical,1,1816,1,Female,64,3,3,Manufacturing Director,3,Single,9667,2739,9,Y,No,14,3,2,80,0,9,3,3,7,7,0,2 +26,Yes,Travel_Rarely,920,Human Resources,20,2,Medical,1,1818,4,Female,69,3,1,Human Resources,2,Married,2148,6889,0,Y,Yes,11,3,3,80,0,6,3,3,5,1,1,4 +46,Yes,Travel_Rarely,261,Research & Development,21,2,Medical,1,1821,4,Female,66,3,2,Healthcare Representative,2,Married,8926,10842,4,Y,No,22,4,4,80,1,13,2,4,9,7,3,7 +40,No,Travel_Rarely,1194,Research & Development,1,3,Life Sciences,1,1822,3,Female,52,3,2,Healthcare Representative,4,Divorced,6513,9060,4,Y,No,17,3,4,80,1,12,3,3,5,3,0,3 +34,No,Travel_Rarely,810,Sales,8,2,Technical Degree,1,1823,2,Male,92,4,2,Sales Executive,3,Married,6799,22128,1,Y,No,21,4,3,80,2,10,5,3,10,8,4,8 +58,No,Non-Travel,350,Sales,2,3,Medical,1,1824,2,Male,52,3,4,Manager,2,Divorced,16291,22577,4,Y,No,22,4,4,80,1,37,0,2,16,9,14,14 +35,No,Travel_Rarely,185,Research & Development,23,4,Medical,1,1826,2,Male,91,1,1,Laboratory Technician,3,Married,2705,9696,0,Y,No,16,3,2,80,1,6,2,4,5,4,0,3 +47,No,Travel_Rarely,1001,Research & Development,4,3,Life Sciences,1,1827,3,Female,92,2,3,Manufacturing Director,2,Divorced,10333,19271,8,Y,Yes,12,3,3,80,1,28,4,3,22,11,14,10 +40,No,Travel_Rarely,750,Research & Development,12,3,Life Sciences,1,1829,2,Female,47,3,2,Healthcare Representative,1,Divorced,4448,10748,2,Y,No,12,3,2,80,1,15,3,3,7,4,7,7 +54,No,Travel_Rarely,431,Research & Development,7,4,Medical,1,1830,4,Female,68,3,2,Research Scientist,4,Married,6854,15696,4,Y,No,15,3,2,80,1,14,2,2,7,1,1,7 +31,No,Travel_Frequently,1125,Sales,7,4,Marketing,1,1833,1,Female,68,3,3,Sales Executive,1,Married,9637,8277,2,Y,No,14,3,4,80,2,9,3,3,3,2,2,2 +28,No,Travel_Rarely,1217,Research & Development,1,3,Medical,1,1834,3,Female,67,3,1,Research Scientist,1,Married,3591,12719,1,Y,No,25,4,3,80,1,3,3,3,3,2,1,2 +38,No,Travel_Rarely,723,Sales,2,4,Marketing,1,1835,2,Female,77,1,2,Sales Representative,4,Married,5405,4244,2,Y,Yes,20,4,1,80,2,20,4,2,4,2,0,3 +26,No,Travel_Rarely,572,Sales,10,3,Medical,1,1836,3,Male,46,3,2,Sales Executive,4,Single,4684,9125,1,Y,No,13,3,1,80,0,5,4,3,5,3,1,2 +58,No,Travel_Frequently,1216,Research & Development,15,4,Life Sciences,1,1837,1,Male,87,3,4,Research Director,3,Married,15787,21624,2,Y,Yes,14,3,2,80,0,23,3,3,2,2,2,2 +18,No,Non-Travel,1431,Research & Development,14,3,Medical,1,1839,2,Female,33,3,1,Research Scientist,3,Single,1514,8018,1,Y,No,16,3,3,80,0,0,4,1,0,0,0,0 +31,Yes,Travel_Rarely,359,Human Resources,18,5,Human Resources,1,1842,4,Male,89,4,1,Human Resources,1,Married,2956,21495,0,Y,No,17,3,3,80,0,2,4,3,1,0,0,0 +29,Yes,Travel_Rarely,350,Human Resources,13,3,Human Resources,1,1844,1,Male,56,2,1,Human Resources,1,Divorced,2335,3157,4,Y,Yes,15,3,4,80,3,4,3,3,2,2,2,0 +45,No,Non-Travel,589,Sales,2,4,Life Sciences,1,1845,3,Female,67,3,2,Sales Executive,3,Married,5154,19665,4,Y,No,22,4,2,80,2,10,3,4,8,7,5,7 +36,No,Travel_Rarely,430,Research & Development,2,4,Other,1,1847,4,Female,73,3,2,Research Scientist,2,Married,6962,19573,4,Y,Yes,22,4,4,80,1,15,2,3,1,0,0,0 +43,No,Travel_Frequently,1422,Sales,2,4,Life Sciences,1,1849,1,Male,92,3,2,Sales Executive,4,Married,5675,19246,1,Y,No,20,4,3,80,1,7,5,3,7,7,7,7 +27,No,Travel_Frequently,1297,Research & Development,5,2,Life Sciences,1,1850,4,Female,53,3,1,Laboratory Technician,4,Single,2379,19826,0,Y,Yes,14,3,3,80,0,6,3,2,5,4,0,2 +29,No,Travel_Frequently,574,Research & Development,20,1,Medical,1,1852,4,Male,40,3,1,Laboratory Technician,4,Married,3812,7003,1,Y,No,13,3,2,80,0,11,3,4,11,8,3,10 +32,No,Travel_Frequently,1318,Sales,10,4,Marketing,1,1853,4,Male,79,3,2,Sales Executive,4,Single,4648,26075,8,Y,No,13,3,3,80,0,4,2,4,0,0,0,0 +42,No,Non-Travel,355,Research & Development,10,4,Technical Degree,1,1854,3,Male,38,3,1,Research Scientist,3,Married,2936,6161,3,Y,No,22,4,2,80,2,10,1,2,6,3,3,3 +47,No,Travel_Rarely,207,Research & Development,9,4,Life Sciences,1,1856,2,Female,64,3,1,Laboratory Technician,3,Single,2105,5411,4,Y,No,12,3,3,80,0,7,2,3,2,2,2,0 +46,No,Travel_Rarely,706,Research & Development,2,2,Life Sciences,1,1857,4,Male,82,3,3,Manufacturing Director,4,Divorced,8578,19989,3,Y,No,14,3,3,80,1,12,4,2,9,8,4,7 +28,No,Non-Travel,280,Human Resources,1,2,Life Sciences,1,1858,3,Male,43,3,1,Human Resources,4,Divorced,2706,10494,1,Y,No,15,3,2,80,1,3,2,3,3,2,2,2 +29,No,Travel_Rarely,726,Research & Development,29,1,Life Sciences,1,1859,4,Male,93,1,2,Healthcare Representative,3,Divorced,6384,21143,8,Y,No,17,3,4,80,2,11,3,3,7,0,1,6 +42,No,Travel_Rarely,1142,Research & Development,8,3,Life Sciences,1,1860,4,Male,81,3,1,Laboratory Technician,3,Single,3968,13624,4,Y,No,13,3,4,80,0,8,3,3,0,0,0,0 +32,Yes,Travel_Rarely,414,Sales,2,4,Marketing,1,1862,3,Male,82,2,2,Sales Executive,2,Single,9907,26186,7,Y,Yes,12,3,3,80,0,7,3,2,2,2,2,2 +46,No,Travel_Rarely,1319,Sales,3,3,Technical Degree,1,1863,1,Female,45,4,4,Sales Executive,1,Divorced,13225,7739,2,Y,No,12,3,4,80,1,25,5,3,19,17,2,8 +27,No,Travel_Rarely,728,Sales,23,1,Medical,1,1864,2,Female,36,2,2,Sales Representative,3,Married,3540,7018,1,Y,No,21,4,4,80,1,9,5,3,9,8,5,8 +29,No,Travel_Rarely,352,Human Resources,6,1,Medical,1,1865,4,Male,87,2,1,Human Resources,2,Married,2804,15434,1,Y,No,11,3,4,80,0,1,3,3,1,0,0,0 +43,No,Travel_Rarely,823,Research & Development,6,3,Medical,1,1866,1,Female,81,2,5,Manager,3,Married,19392,22539,7,Y,No,13,3,4,80,0,21,2,3,16,12,6,14 +48,No,Travel_Rarely,1224,Research & Development,10,3,Life Sciences,1,1867,4,Male,91,2,5,Research Director,2,Married,19665,13583,4,Y,No,12,3,4,80,0,29,3,3,22,10,12,9 +29,Yes,Travel_Frequently,459,Research & Development,24,2,Life Sciences,1,1868,4,Male,73,2,1,Research Scientist,4,Single,2439,14753,1,Y,Yes,24,4,2,80,0,1,3,2,1,0,1,0 +46,Yes,Travel_Rarely,1254,Sales,10,3,Life Sciences,1,1869,3,Female,64,3,3,Sales Executive,2,Married,7314,14011,5,Y,No,21,4,3,80,3,14,2,3,8,7,0,7 +27,No,Travel_Frequently,1131,Research & Development,15,3,Life Sciences,1,1870,4,Female,77,2,1,Research Scientist,1,Married,4774,23844,0,Y,No,19,3,4,80,1,8,2,2,7,6,7,3 +39,No,Travel_Rarely,835,Research & Development,19,4,Other,1,1871,4,Male,41,3,2,Research Scientist,4,Divorced,3902,5141,8,Y,No,14,3,2,80,3,7,2,3,2,2,2,2 +55,No,Travel_Rarely,836,Research & Development,2,4,Technical Degree,1,1873,2,Male,98,2,1,Research Scientist,4,Married,2662,7975,8,Y,No,20,4,2,80,1,19,2,4,5,2,0,4 +28,No,Travel_Rarely,1172,Sales,3,3,Medical,1,1875,2,Female,78,3,1,Sales Representative,2,Married,2856,3692,1,Y,No,19,3,4,80,1,1,3,3,1,0,0,0 +30,Yes,Travel_Rarely,945,Sales,9,3,Medical,1,1876,2,Male,89,3,1,Sales Representative,4,Single,1081,16019,1,Y,No,13,3,3,80,0,1,3,2,1,0,0,0 +22,Yes,Travel_Rarely,391,Research & Development,7,1,Life Sciences,1,1878,4,Male,75,3,1,Research Scientist,2,Single,2472,26092,1,Y,Yes,23,4,1,80,0,1,2,3,1,0,0,0 +36,No,Travel_Rarely,1266,Sales,10,4,Technical Degree,1,1880,2,Female,63,2,2,Sales Executive,3,Married,5673,6060,1,Y,Yes,13,3,1,80,1,10,4,3,10,9,1,7 +31,No,Travel_Rarely,311,Research & Development,20,3,Life Sciences,1,1881,2,Male,89,3,2,Laboratory Technician,3,Divorced,4197,18624,1,Y,No,11,3,1,80,1,10,2,3,10,8,0,2 +34,No,Travel_Rarely,1480,Sales,4,3,Life Sciences,1,1882,3,Male,64,3,3,Sales Executive,4,Married,9713,24444,2,Y,Yes,13,3,4,80,3,9,3,3,5,3,1,0 +29,No,Travel_Rarely,592,Research & Development,7,3,Life Sciences,1,1883,4,Male,59,3,1,Laboratory Technician,1,Single,2062,19384,3,Y,No,14,3,2,80,0,11,2,3,3,2,1,2 +37,No,Travel_Rarely,783,Research & Development,7,4,Medical,1,1885,4,Male,78,3,2,Research Scientist,1,Married,4284,13588,5,Y,Yes,22,4,3,80,1,16,2,3,5,3,0,4 +35,No,Travel_Rarely,219,Research & Development,16,2,Other,1,1886,4,Female,44,2,2,Manufacturing Director,2,Married,4788,25388,0,Y,Yes,11,3,4,80,0,4,2,3,3,2,0,2 +45,No,Travel_Rarely,556,Research & Development,25,2,Life Sciences,1,1888,2,Female,93,2,2,Manufacturing Director,4,Married,5906,23888,0,Y,No,13,3,4,80,2,10,2,2,9,8,3,8 +36,No,Travel_Frequently,1213,Human Resources,2,1,Human Resources,1,1890,2,Male,94,2,2,Human Resources,4,Single,3886,4223,1,Y,No,21,4,4,80,0,10,2,2,10,1,0,8 +40,No,Travel_Rarely,1137,Research & Development,1,4,Life Sciences,1,1892,1,Male,98,3,4,Manager,1,Divorced,16823,18991,2,Y,No,11,3,1,80,1,22,3,3,19,7,11,16 +26,No,Travel_Rarely,482,Research & Development,1,2,Life Sciences,1,1893,2,Female,90,2,1,Research Scientist,3,Married,2933,14908,1,Y,Yes,13,3,3,80,1,1,3,2,1,0,1,0 +27,No,Travel_Rarely,511,Sales,2,2,Medical,1,1898,1,Female,89,4,2,Sales Executive,3,Single,6500,26997,0,Y,No,14,3,2,80,0,9,5,2,8,7,0,7 +48,No,Travel_Frequently,117,Research & Development,22,3,Medical,1,1900,4,Female,58,3,4,Manager,4,Divorced,17174,2437,3,Y,No,11,3,2,80,1,24,3,3,22,17,4,7 +44,No,Travel_Rarely,170,Research & Development,1,4,Life Sciences,1,1903,2,Male,78,4,2,Healthcare Representative,1,Married,5033,9364,2,Y,No,15,3,4,80,1,10,5,3,2,0,2,2 +34,Yes,Non-Travel,967,Research & Development,16,4,Technical Degree,1,1905,4,Male,85,1,1,Research Scientist,1,Married,2307,14460,1,Y,Yes,23,4,2,80,1,5,2,3,5,2,3,0 +56,Yes,Travel_Rarely,1162,Research & Development,24,2,Life Sciences,1,1907,1,Male,97,3,1,Laboratory Technician,4,Single,2587,10261,1,Y,No,16,3,4,80,0,5,3,3,4,2,1,0 +36,No,Travel_Rarely,335,Sales,17,2,Marketing,1,1908,3,Male,33,2,2,Sales Executive,2,Married,5507,16822,2,Y,No,16,3,3,80,2,12,1,1,4,2,1,3 +41,No,Travel_Rarely,337,Sales,8,3,Marketing,1,1909,3,Female,54,3,2,Sales Executive,2,Married,4393,26841,5,Y,No,21,4,3,80,1,14,3,3,5,4,1,4 +42,No,Travel_Rarely,1396,Research & Development,6,3,Medical,1,1911,3,Male,83,3,3,Research Director,1,Married,13348,14842,9,Y,No,13,3,2,80,1,18,3,4,13,7,5,7 +31,No,Travel_Rarely,1079,Sales,10,2,Medical,1,1912,3,Female,86,3,2,Sales Executive,4,Divorced,6583,20115,2,Y,Yes,11,3,4,80,1,8,2,3,5,2,1,4 +34,No,Travel_Rarely,735,Sales,3,1,Medical,1,1915,4,Female,75,2,2,Sales Executive,4,Married,8103,16495,3,Y,Yes,12,3,3,80,0,9,3,2,4,2,0,1 +31,No,Travel_Rarely,471,Research & Development,4,3,Medical,1,1916,1,Female,62,4,1,Laboratory Technician,3,Divorced,3978,16031,8,Y,No,12,3,2,80,1,4,0,2,2,2,2,2 +26,No,Travel_Frequently,1096,Research & Development,6,3,Other,1,1918,3,Male,61,4,1,Laboratory Technician,4,Married,2544,7102,0,Y,No,18,3,1,80,1,8,3,3,7,7,7,7 +45,No,Travel_Frequently,1297,Research & Development,1,4,Medical,1,1922,2,Male,44,3,2,Healthcare Representative,3,Single,5399,14511,4,Y,No,12,3,3,80,0,12,3,3,4,2,0,3 +33,No,Travel_Rarely,217,Sales,10,4,Marketing,1,1924,2,Male,43,3,2,Sales Executive,3,Single,5487,10410,1,Y,No,14,3,2,80,0,10,2,2,10,4,0,9 +28,No,Travel_Frequently,783,Sales,1,2,Life Sciences,1,1927,3,Male,42,2,2,Sales Executive,4,Married,6834,19255,1,Y,Yes,12,3,3,80,1,7,2,3,7,7,0,7 +29,Yes,Travel_Frequently,746,Sales,24,3,Technical Degree,1,1928,3,Male,45,4,1,Sales Representative,1,Single,1091,10642,1,Y,No,17,3,4,80,0,1,3,3,1,0,0,0 +39,No,Non-Travel,1251,Sales,21,4,Life Sciences,1,1929,1,Female,32,1,2,Sales Executive,3,Married,5736,3987,6,Y,No,19,3,3,80,1,10,1,3,3,2,1,2 +27,No,Travel_Rarely,1354,Research & Development,2,4,Technical Degree,1,1931,2,Male,41,3,1,Research Scientist,2,Married,2226,6073,1,Y,No,11,3,3,80,1,6,3,2,5,3,1,2 +34,No,Travel_Frequently,735,Research & Development,22,4,Other,1,1932,3,Male,86,2,2,Research Scientist,4,Married,5747,26496,1,Y,Yes,15,3,2,80,0,16,3,3,15,10,6,11 +28,Yes,Travel_Rarely,1475,Sales,13,2,Marketing,1,1933,4,Female,84,3,2,Sales Executive,3,Single,9854,23352,3,Y,Yes,11,3,4,80,0,6,0,3,2,0,2,2 +47,No,Non-Travel,1169,Research & Development,14,4,Technical Degree,1,1934,3,Male,64,3,2,Research Scientist,2,Married,5467,2125,8,Y,No,18,3,3,80,1,16,4,4,8,7,1,7 +56,No,Travel_Rarely,1443,Sales,11,5,Marketing,1,1935,4,Female,89,2,2,Sales Executive,1,Married,5380,20328,4,Y,No,16,3,3,80,1,6,3,3,0,0,0,0 +39,No,Travel_Rarely,867,Research & Development,9,2,Medical,1,1936,1,Male,87,3,2,Manufacturing Director,1,Married,5151,12315,1,Y,No,25,4,4,80,1,10,3,3,10,0,7,9 +38,No,Travel_Frequently,1394,Research & Development,8,3,Medical,1,1937,4,Female,58,2,2,Research Scientist,2,Divorced,2133,18115,1,Y,Yes,16,3,3,80,1,20,3,3,20,11,0,7 +58,No,Travel_Rarely,605,Sales,21,3,Life Sciences,1,1938,4,Female,72,3,4,Manager,4,Married,17875,11761,4,Y,Yes,13,3,3,80,1,29,2,2,1,0,0,0 +32,Yes,Travel_Frequently,238,Research & Development,5,2,Life Sciences,1,1939,1,Female,47,4,1,Research Scientist,3,Single,2432,15318,3,Y,Yes,14,3,1,80,0,8,2,3,4,1,0,3 +38,No,Travel_Rarely,1206,Research & Development,9,2,Life Sciences,1,1940,2,Male,71,3,1,Research Scientist,4,Divorced,4771,14293,2,Y,No,19,3,4,80,2,10,0,4,5,2,0,3 +49,No,Travel_Frequently,1064,Research & Development,2,1,Life Sciences,1,1941,2,Male,42,3,5,Research Director,4,Married,19161,13738,3,Y,No,15,3,4,80,0,28,3,3,5,4,4,3 +42,No,Travel_Rarely,419,Sales,12,4,Marketing,1,1943,2,Male,77,3,2,Sales Executive,4,Divorced,5087,2900,3,Y,Yes,12,3,3,80,2,14,4,3,0,0,0,0 +27,Yes,Travel_Frequently,1337,Human Resources,22,3,Human Resources,1,1944,1,Female,58,2,1,Human Resources,2,Married,2863,19555,1,Y,No,12,3,1,80,0,1,2,3,1,0,0,0 +35,No,Travel_Rarely,682,Sales,18,4,Medical,1,1945,2,Male,71,3,2,Sales Executive,1,Married,5561,15975,0,Y,No,16,3,4,80,1,6,2,1,5,3,0,4 +28,No,Non-Travel,1103,Research & Development,16,3,Medical,1,1947,3,Male,49,3,1,Research Scientist,3,Single,2144,2122,1,Y,No,14,3,3,80,0,5,3,2,5,3,1,4 +31,No,Non-Travel,976,Research & Development,3,2,Medical,1,1948,3,Male,48,3,1,Research Scientist,1,Divorced,3065,3995,1,Y,Yes,13,3,4,80,1,4,3,4,4,2,2,3 +36,No,Non-Travel,1351,Research & Development,9,4,Life Sciences,1,1949,1,Male,66,4,1,Laboratory Technician,2,Married,2810,9238,1,Y,No,22,4,2,80,0,5,3,3,5,4,0,2 +34,No,Travel_Rarely,937,Sales,1,3,Marketing,1,1950,1,Male,32,3,3,Sales Executive,4,Single,9888,6770,1,Y,No,21,4,1,80,0,14,3,2,14,8,2,1 +34,No,Travel_Rarely,1239,Sales,13,4,Medical,1,1951,4,Male,39,3,3,Sales Executive,3,Divorced,8628,22914,1,Y,No,18,3,3,80,1,9,2,2,8,7,1,1 +26,No,Travel_Rarely,157,Research & Development,1,3,Medical,1,1952,3,Male,95,3,1,Laboratory Technician,1,Single,2867,20006,0,Y,No,13,3,4,80,0,8,6,2,7,7,7,6 +29,No,Travel_Rarely,136,Research & Development,1,3,Life Sciences,1,1954,1,Male,89,3,2,Healthcare Representative,1,Married,5373,6225,0,Y,No,12,3,1,80,1,6,5,2,5,3,0,2 +32,No,Non-Travel,1146,Research & Development,15,4,Medical,1,1955,3,Female,34,3,2,Healthcare Representative,4,Divorced,6667,16542,5,Y,No,18,3,2,80,1,9,6,3,5,1,1,2 +31,No,Travel_Frequently,1125,Research & Development,1,3,Life Sciences,1,1956,4,Male,48,1,2,Research Scientist,1,Married,5003,5771,1,Y,No,21,4,2,80,0,10,6,3,10,8,8,7 +28,Yes,Travel_Rarely,1404,Research & Development,17,3,Technical Degree,1,1960,3,Male,32,2,1,Laboratory Technician,4,Divorced,2367,18779,5,Y,No,12,3,1,80,1,6,2,2,4,1,0,3 +38,No,Travel_Rarely,1404,Sales,1,3,Life Sciences,1,1961,1,Male,59,2,1,Sales Representative,1,Single,2858,11473,4,Y,No,14,3,1,80,0,20,3,2,1,0,0,0 +35,No,Travel_Rarely,1224,Sales,7,4,Life Sciences,1,1962,3,Female,55,3,2,Sales Executive,4,Married,5204,13586,1,Y,Yes,11,3,4,80,0,10,2,3,10,8,0,9 +27,No,Travel_Rarely,954,Sales,9,3,Marketing,1,1965,4,Male,44,3,2,Sales Executive,4,Single,4105,5099,1,Y,No,14,3,1,80,0,7,5,3,7,7,0,7 +32,No,Travel_Rarely,1373,Research & Development,5,4,Life Sciences,1,1966,4,Male,56,2,2,Manufacturing Director,4,Single,9679,10138,8,Y,No,24,4,2,80,0,8,1,3,1,0,0,0 +31,Yes,Travel_Frequently,754,Sales,26,4,Marketing,1,1967,1,Male,63,3,2,Sales Executive,4,Married,5617,21075,1,Y,Yes,11,3,3,80,0,10,4,3,10,7,0,8 +53,Yes,Travel_Rarely,1168,Sales,24,4,Life Sciences,1,1968,1,Male,66,3,3,Sales Executive,1,Single,10448,5843,6,Y,Yes,13,3,2,80,0,15,2,2,2,2,2,2 +54,No,Travel_Rarely,155,Research & Development,9,2,Life Sciences,1,1969,1,Female,67,3,2,Research Scientist,3,Married,2897,22474,3,Y,No,11,3,3,80,2,9,6,2,4,3,2,3 +33,No,Travel_Frequently,1303,Research & Development,7,2,Life Sciences,1,1970,4,Male,36,3,2,Healthcare Representative,3,Divorced,5968,18079,1,Y,No,20,4,3,80,3,9,2,3,9,7,2,8 +43,No,Travel_Rarely,574,Research & Development,11,3,Life Sciences,1,1971,1,Male,30,3,3,Healthcare Representative,3,Married,7510,16873,1,Y,No,17,3,2,80,1,10,1,3,10,9,0,9 +38,No,Travel_Frequently,1444,Human Resources,1,4,Other,1,1972,4,Male,88,3,1,Human Resources,2,Married,2991,5224,0,Y,Yes,11,3,2,80,1,7,2,3,6,2,1,2 +55,No,Travel_Rarely,189,Human Resources,26,4,Human Resources,1,1973,3,Male,71,4,5,Manager,2,Married,19636,25811,4,Y,Yes,18,3,1,80,1,35,0,3,10,9,1,4 +31,No,Travel_Rarely,1276,Research & Development,2,1,Medical,1,1974,4,Female,59,1,1,Laboratory Technician,4,Divorced,1129,17536,1,Y,Yes,11,3,3,80,3,1,4,3,1,0,0,0 +39,No,Travel_Rarely,119,Sales,15,4,Marketing,1,1975,2,Male,77,3,4,Sales Executive,1,Single,13341,25098,0,Y,No,12,3,1,80,0,21,3,3,20,8,11,10 +42,No,Non-Travel,335,Research & Development,23,2,Life Sciences,1,1976,4,Male,37,2,2,Research Scientist,3,Single,4332,14811,1,Y,No,12,3,4,80,0,20,2,3,20,9,3,7 +31,No,Non-Travel,697,Research & Development,10,3,Medical,1,1979,3,Female,40,3,3,Research Director,3,Married,11031,26862,4,Y,No,20,4,3,80,1,13,2,4,11,7,4,8 +54,No,Travel_Rarely,157,Research & Development,10,3,Medical,1,1980,3,Female,77,3,2,Manufacturing Director,1,Single,4440,25198,6,Y,Yes,19,3,4,80,0,9,3,3,5,2,1,4 +24,No,Travel_Rarely,771,Research & Development,1,2,Life Sciences,1,1981,2,Male,45,2,2,Healthcare Representative,3,Single,4617,14120,1,Y,No,12,3,2,80,0,4,2,2,4,3,1,2 +23,No,Travel_Rarely,571,Research & Development,12,2,Other,1,1982,4,Male,78,3,1,Laboratory Technician,4,Single,2647,13672,1,Y,No,13,3,3,80,0,5,6,4,5,2,1,4 +40,No,Travel_Frequently,692,Research & Development,11,3,Technical Degree,1,1985,4,Female,73,3,2,Laboratory Technician,3,Married,6323,26849,1,Y,No,11,3,1,80,1,10,2,4,10,9,9,4 +40,No,Travel_Rarely,444,Sales,2,2,Marketing,1,1986,2,Female,92,3,2,Sales Executive,2,Married,5677,4258,3,Y,No,14,3,3,80,1,15,4,3,11,8,5,10 +25,No,Travel_Rarely,309,Human Resources,2,3,Human Resources,1,1987,3,Female,82,3,1,Human Resources,2,Married,2187,19655,4,Y,No,14,3,3,80,0,6,3,3,2,0,1,2 +30,No,Travel_Rarely,911,Research & Development,1,2,Medical,1,1989,4,Male,76,3,1,Laboratory Technician,2,Married,3748,4077,1,Y,No,13,3,3,80,0,12,6,2,12,8,1,7 +25,No,Travel_Rarely,977,Research & Development,2,1,Other,1,1992,4,Male,57,3,1,Laboratory Technician,3,Divorced,3977,7298,6,Y,Yes,19,3,3,80,1,7,2,2,2,2,0,2 +47,No,Travel_Rarely,1180,Research & Development,25,3,Medical,1,1993,1,Male,84,3,3,Healthcare Representative,3,Single,8633,13084,2,Y,No,23,4,2,80,0,25,3,3,17,14,12,11 +33,No,Non-Travel,1313,Research & Development,1,2,Medical,1,1994,2,Male,59,2,1,Laboratory Technician,3,Divorced,2008,20439,1,Y,No,12,3,3,80,3,1,2,2,1,1,0,0 +38,No,Travel_Rarely,1321,Sales,1,4,Life Sciences,1,1995,4,Male,86,3,2,Sales Executive,2,Married,4440,7636,0,Y,No,15,3,1,80,2,16,3,3,15,13,5,8 +31,No,Travel_Rarely,1154,Sales,2,2,Life Sciences,1,1996,1,Male,54,3,1,Sales Representative,3,Married,3067,6393,0,Y,No,19,3,3,80,1,3,1,3,2,2,1,2 +38,No,Travel_Frequently,508,Research & Development,6,4,Life Sciences,1,1997,1,Male,72,2,2,Manufacturing Director,3,Married,5321,14284,2,Y,No,11,3,4,80,1,10,1,3,8,3,7,7 +42,No,Travel_Rarely,557,Research & Development,18,4,Life Sciences,1,1998,4,Male,35,3,2,Research Scientist,1,Divorced,5410,11189,6,Y,Yes,17,3,3,80,1,9,3,2,4,3,1,2 +41,No,Travel_Rarely,642,Research & Development,1,3,Life Sciences,1,1999,4,Male,76,3,1,Research Scientist,4,Married,2782,21412,3,Y,No,22,4,1,80,1,12,3,3,5,3,1,0 +47,No,Non-Travel,1162,Research & Development,1,1,Medical,1,2000,3,Female,98,3,3,Research Director,2,Married,11957,17231,0,Y,No,18,3,1,80,2,14,3,1,13,8,5,12 +35,No,Travel_Rarely,1490,Research & Development,11,4,Medical,1,2003,4,Male,43,3,1,Laboratory Technician,3,Married,2660,20232,7,Y,Yes,11,3,3,80,1,5,3,3,2,2,2,2 +22,No,Travel_Rarely,581,Research & Development,1,2,Life Sciences,1,2007,4,Male,63,3,1,Research Scientist,3,Single,3375,17624,0,Y,No,12,3,4,80,0,4,2,4,3,2,1,2 +35,No,Travel_Rarely,1395,Research & Development,9,4,Medical,1,2008,2,Male,48,3,2,Research Scientist,3,Single,5098,18698,1,Y,No,19,3,2,80,0,10,5,3,10,7,0,8 +33,No,Travel_Rarely,501,Research & Development,15,2,Medical,1,2009,2,Female,95,3,2,Healthcare Representative,4,Married,4878,21653,0,Y,Yes,13,3,1,80,1,10,6,3,9,7,8,1 +32,No,Travel_Rarely,267,Research & Development,29,4,Life Sciences,1,2010,3,Female,49,2,1,Laboratory Technician,2,Single,2837,15919,1,Y,No,13,3,3,80,0,6,3,3,6,2,4,1 +40,No,Travel_Rarely,543,Research & Development,1,4,Life Sciences,1,2012,1,Male,83,3,1,Laboratory Technician,4,Married,2406,4060,8,Y,No,19,3,3,80,2,8,3,2,1,0,0,0 +32,No,Travel_Rarely,234,Sales,1,4,Medical,1,2013,2,Male,68,2,1,Sales Representative,2,Married,2269,18024,0,Y,No,14,3,2,80,1,3,2,3,2,2,2,2 +39,No,Travel_Rarely,116,Research & Development,24,1,Life Sciences,1,2014,1,Male,52,3,2,Research Scientist,4,Single,4108,5340,7,Y,No,13,3,1,80,0,18,2,3,7,7,1,7 +38,No,Travel_Rarely,201,Research & Development,10,3,Medical,1,2015,2,Female,99,1,3,Research Director,3,Married,13206,3376,3,Y,No,12,3,1,80,1,20,3,3,18,16,1,11 +32,No,Travel_Rarely,801,Sales,1,4,Marketing,1,2016,3,Female,48,3,3,Sales Executive,4,Married,10422,24032,1,Y,No,19,3,3,80,2,14,3,3,14,10,5,7 +37,No,Travel_Rarely,161,Research & Development,10,3,Life Sciences,1,2017,3,Female,42,4,3,Research Director,4,Married,13744,15471,1,Y,Yes,25,4,1,80,1,16,2,3,16,11,6,8 +25,No,Travel_Rarely,1382,Sales,8,2,Other,1,2018,1,Female,85,3,2,Sales Executive,3,Divorced,4907,13684,0,Y,Yes,22,4,2,80,1,6,3,2,5,3,0,4 +52,No,Non-Travel,585,Sales,29,4,Life Sciences,1,2019,1,Male,40,3,1,Sales Representative,4,Divorced,3482,19788,2,Y,No,15,3,2,80,2,16,3,2,9,8,0,0 +44,No,Travel_Rarely,1037,Research & Development,1,3,Medical,1,2020,2,Male,42,3,1,Research Scientist,4,Single,2436,13422,6,Y,Yes,12,3,3,80,0,6,2,3,4,3,1,2 +21,No,Travel_Rarely,501,Sales,5,1,Medical,1,2021,3,Male,58,3,1,Sales Representative,1,Single,2380,25479,1,Y,Yes,11,3,4,80,0,2,6,3,2,2,1,2 +39,No,Non-Travel,105,Research & Development,9,3,Life Sciences,1,2022,4,Male,87,3,5,Manager,4,Single,19431,15302,2,Y,No,13,3,3,80,0,21,3,2,6,0,1,3 +23,Yes,Travel_Frequently,638,Sales,9,3,Marketing,1,2023,4,Male,33,3,1,Sales Representative,1,Married,1790,26956,1,Y,No,19,3,1,80,1,1,3,2,1,0,1,0 +36,No,Travel_Rarely,557,Sales,3,3,Medical,1,2024,1,Female,94,2,3,Sales Executive,4,Married,7644,12695,0,Y,No,19,3,3,80,2,10,2,3,9,7,3,4 +36,No,Travel_Frequently,688,Research & Development,4,2,Life Sciences,1,2025,4,Female,97,3,2,Manufacturing Director,2,Divorced,5131,9192,7,Y,No,13,3,2,80,3,18,3,3,4,2,0,2 +56,No,Non-Travel,667,Research & Development,1,4,Life Sciences,1,2026,3,Male,57,3,2,Healthcare Representative,3,Divorced,6306,26236,1,Y,No,21,4,1,80,1,13,2,2,13,12,1,9 +29,Yes,Travel_Rarely,1092,Research & Development,1,4,Medical,1,2027,1,Male,36,3,1,Research Scientist,4,Married,4787,26124,9,Y,Yes,14,3,2,80,3,4,3,4,2,2,2,2 +42,No,Travel_Rarely,300,Research & Development,2,3,Life Sciences,1,2031,1,Male,56,3,5,Manager,3,Married,18880,17312,5,Y,No,11,3,1,80,0,24,2,2,22,6,4,14 +56,Yes,Travel_Rarely,310,Research & Development,7,2,Technical Degree,1,2032,4,Male,72,3,1,Laboratory Technician,3,Married,2339,3666,8,Y,No,11,3,4,80,1,14,4,1,10,9,9,8 +41,No,Travel_Rarely,582,Research & Development,28,4,Life Sciences,1,2034,1,Female,60,2,4,Manufacturing Director,2,Married,13570,5640,0,Y,No,23,4,3,80,1,21,3,3,20,7,0,10 +34,No,Travel_Rarely,704,Sales,28,3,Marketing,1,2035,4,Female,95,2,2,Sales Executive,3,Married,6712,8978,1,Y,No,21,4,4,80,2,8,2,3,8,7,1,7 +36,No,Non-Travel,301,Sales,15,4,Marketing,1,2036,4,Male,88,1,2,Sales Executive,4,Divorced,5406,10436,1,Y,No,24,4,1,80,1,15,4,2,15,12,11,11 +41,No,Travel_Rarely,930,Sales,3,3,Life Sciences,1,2037,3,Male,57,2,2,Sales Executive,2,Divorced,8938,12227,2,Y,No,11,3,3,80,1,14,5,3,5,4,0,4 +32,No,Travel_Rarely,529,Research & Development,2,3,Technical Degree,1,2038,4,Male,78,3,1,Research Scientist,1,Single,2439,11288,1,Y,No,14,3,4,80,0,4,4,3,4,2,1,2 +35,No,Travel_Rarely,1146,Human Resources,26,4,Life Sciences,1,2040,3,Female,31,3,3,Human Resources,4,Single,8837,16642,1,Y,Yes,16,3,3,80,0,9,2,3,9,0,1,7 +38,No,Travel_Rarely,345,Sales,10,2,Life Sciences,1,2041,1,Female,100,3,2,Sales Executive,4,Married,5343,5982,1,Y,No,11,3,3,80,1,10,1,3,10,7,1,9 +50,Yes,Travel_Frequently,878,Sales,1,4,Life Sciences,1,2044,2,Male,94,3,2,Sales Executive,3,Divorced,6728,14255,7,Y,No,12,3,4,80,2,12,3,3,6,3,0,1 +36,No,Travel_Rarely,1120,Sales,11,4,Marketing,1,2045,2,Female,100,2,2,Sales Executive,4,Married,6652,14369,4,Y,No,13,3,1,80,1,8,2,2,6,3,0,0 +45,No,Travel_Rarely,374,Sales,20,3,Life Sciences,1,2046,4,Female,50,3,2,Sales Executive,3,Single,4850,23333,8,Y,No,15,3,3,80,0,8,3,3,5,3,0,1 +40,No,Travel_Rarely,1322,Research & Development,2,4,Life Sciences,1,2048,3,Male,52,2,1,Research Scientist,3,Single,2809,2725,2,Y,No,14,3,4,80,0,8,2,3,2,2,2,2 +35,No,Travel_Frequently,1199,Research & Development,18,4,Life Sciences,1,2049,3,Male,80,3,2,Healthcare Representative,3,Married,5689,24594,1,Y,Yes,14,3,4,80,2,10,2,4,10,2,0,2 +40,No,Travel_Rarely,1194,Research & Development,2,4,Medical,1,2051,3,Female,98,3,1,Research Scientist,3,Married,2001,12549,2,Y,No,14,3,2,80,3,20,2,3,5,3,0,2 +35,No,Travel_Rarely,287,Research & Development,1,4,Life Sciences,1,2052,3,Female,62,1,1,Research Scientist,4,Married,2977,8952,1,Y,No,12,3,4,80,1,4,5,3,4,3,1,1 +29,No,Travel_Rarely,1378,Research & Development,13,2,Other,1,2053,4,Male,46,2,2,Laboratory Technician,2,Married,4025,23679,4,Y,Yes,13,3,1,80,1,10,2,3,4,3,0,3 +29,No,Travel_Rarely,468,Research & Development,28,4,Medical,1,2054,4,Female,73,2,1,Research Scientist,1,Single,3785,8489,1,Y,No,14,3,2,80,0,5,3,1,5,4,0,4 +50,Yes,Travel_Rarely,410,Sales,28,3,Marketing,1,2055,4,Male,39,2,3,Sales Executive,1,Divorced,10854,16586,4,Y,Yes,13,3,2,80,1,20,3,3,3,2,2,0 +39,No,Travel_Rarely,722,Sales,24,1,Marketing,1,2056,2,Female,60,2,4,Sales Executive,4,Married,12031,8828,0,Y,No,11,3,1,80,1,21,2,2,20,9,9,6 +31,No,Non-Travel,325,Research & Development,5,3,Medical,1,2057,2,Male,74,3,2,Manufacturing Director,1,Single,9936,3787,0,Y,No,19,3,2,80,0,10,2,3,9,4,1,7 +26,No,Travel_Rarely,1167,Sales,5,3,Other,1,2060,4,Female,30,2,1,Sales Representative,3,Single,2966,21378,0,Y,No,18,3,4,80,0,5,2,3,4,2,0,0 +36,No,Travel_Frequently,884,Research & Development,23,2,Medical,1,2061,3,Male,41,4,2,Laboratory Technician,4,Married,2571,12290,4,Y,No,17,3,3,80,1,17,3,3,5,2,0,3 +39,No,Travel_Rarely,613,Research & Development,6,1,Medical,1,2062,4,Male,42,2,3,Healthcare Representative,1,Married,9991,21457,4,Y,No,15,3,1,80,1,9,5,3,7,7,1,7 +27,No,Travel_Rarely,155,Research & Development,4,3,Life Sciences,1,2064,2,Male,87,4,2,Manufacturing Director,2,Married,6142,5174,1,Y,Yes,20,4,2,80,1,6,0,3,6,2,0,3 +49,No,Travel_Frequently,1023,Sales,2,3,Medical,1,2065,4,Male,63,2,2,Sales Executive,2,Married,5390,13243,2,Y,No,14,3,4,80,0,17,3,2,9,6,0,8 +34,No,Travel_Rarely,628,Research & Development,8,3,Medical,1,2068,2,Male,82,4,2,Laboratory Technician,3,Married,4404,10228,2,Y,No,12,3,1,80,0,6,3,4,4,3,1,2 diff --git a/007/solution/sweetviz_report.html b/007/solution/sweetviz_report.html new file mode 100644 index 00000000..0110ca91 --- /dev/null +++ b/007/solution/sweetviz_report.html @@ -0,0 +1,16573 @@ + + + + + + + + + + + + + + + +
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1470
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22
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CATEGORICAL
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13
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NUMERICAL
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0
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TEXT
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+ 2.1.2
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24.0
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18.0
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42.0
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13.0
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STD
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VAR
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83.5
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KURT.
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0.413
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54,278
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802
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802
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465
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165
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102
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RANGE
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1,397
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IQR
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692
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STD
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404
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VAR
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163k
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KURT.
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SKEW
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29.0
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28.0
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1
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1,064
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602
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362k
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66.0
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5%
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33.0
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MIN
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30.0
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70.0
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35.8
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VAR
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413
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KURT.
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-1.20
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SKEW
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-0.032
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96,860
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+ +
+ +
+
+
+
+
+
+ + +
+ 16 +
+
+ JobRole +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 9 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 17 +
+
+ JobSatisfaction +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 4 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 18 +
+
+ MaritalStatus +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 3 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 19 +
+
+ MonthlyIncome + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 1,349 +
+
+ (92%) +
+
+
+
+
+
ZEROES:
+
+ --- +
+
+ +
+
+
+ +
+
+
MAX
+
19,999
+
+
+
95%
+
17,821
+
+
+
Q3
+
8,379
+
+
+
AVG
+
6,503
+
+
+
MEDIAN
+
4,919
+
+
+
Q1
+
2,911
+
+
+
5%
+
2,098
+
+
+
MIN
+
1,009
+
+
+
+
+
RANGE
+
18,990
+
+
+
IQR
+
5,468
+
+
+
STD
+
4,708
+
+
+
VAR
+
22.2M
+
+
+
+
+
KURT.
+
1.01
+
+
+
SKEW
+
1.37
+
+
+
SUM
+
9.6M
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 20 +
+
+ MonthlyRate + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 1,427 +
+
+ (97%) +
+
+
+
+
+
ZEROES:
+
+ --- +
+
+ +
+
+
+ +
+
+
MAX
+
26,999
+
+
+
95%
+
25,432
+
+
+
Q3
+
20,462
+
+
+
AVG
+
14,313
+
+
+
MEDIAN
+
14,236
+
+
+
Q1
+
8,047
+
+
+
5%
+
3,385
+
+
+
MIN
+
2,094
+
+
+
+
+
RANGE
+
24,905
+
+
+
IQR
+
12,414
+
+
+
STD
+
7,118
+
+
+
VAR
+
50.7M
+
+
+
+
+
KURT.
+
-1.21
+
+
+
SKEW
+
0.019
+
+
+
SUM
+
21.0M
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 21 +
+
+ NumCompaniesWorked +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 10 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 22 +
+
+ Over18 +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 1 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 23 +
+
+ OverTime +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 2 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 24 +
+
+ PercentSalaryHike + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 15 +
+
+ (1%) +
+
+
+
+
+
ZEROES:
+
+ --- +
+
+ +
+
+
+ +
+
+
MAX
+
25.0
+
+
+
95%
+
22.0
+
+
+
Q3
+
18.0
+
+
+
AVG
+
15.2
+
+
+
MEDIAN
+
14.0
+
+
+
Q1
+
12.0
+
+
+
5%
+
11.0
+
+
+
MIN
+
11.0
+
+
+
+
+
RANGE
+
14.0
+
+
+
IQR
+
6.00
+
+
+
STD
+
3.66
+
+
+
VAR
+
13.4
+
+
+
+
+
KURT.
+
-0.301
+
+
+
SKEW
+
0.821
+
+
+
SUM
+
22,358
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 25 +
+
+ PerformanceRating +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 2 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 26 +
+
+ RelationshipSatisfaction +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 4 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 27 +
+
+ StandardHours +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 1 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 28 +
+
+ StockOptionLevel +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 4 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 29 +
+
+ TotalWorkingYears + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 40 +
+
+ (3%) +
+
+
+
+
+
ZEROES:
+
+ 11 +
+
+ (<1%) +
+
+
+ +
+
+
MAX
+
40.0
+
+
+
95%
+
28.0
+
+
+
Q3
+
15.0
+
+
+
AVG
+
11.3
+
+
+
MEDIAN
+
10.0
+
+
+
Q1
+
6.0
+
+
+
5%
+
1.0
+
+
+
MIN
+
0.0
+
+
+
+
+
RANGE
+
40.0
+
+
+
IQR
+
9.00
+
+
+
STD
+
7.78
+
+
+
VAR
+
60.5
+
+
+
+
+
KURT.
+
0.918
+
+
+
SKEW
+
1.12
+
+
+
SUM
+
16,581
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 30 +
+
+ TrainingTimesLastYear +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 7 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 31 +
+
+ WorkLifeBalance +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 4 +
+
+ (<1%) +
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 32 +
+
+ YearsAtCompany + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 37 +
+
+ (3%) +
+
+
+
+
+
ZEROES:
+
+ 44 +
+
+ (3%) +
+
+
+ +
+
+
MAX
+
40.0
+
+
+
95%
+
20.0
+
+
+
Q3
+
9.0
+
+
+
AVG
+
7.0
+
+
+
MEDIAN
+
5.0
+
+
+
Q1
+
3.0
+
+
+
5%
+
1.0
+
+
+
MIN
+
0.0
+
+
+
+
+
RANGE
+
40.0
+
+
+
IQR
+
6.00
+
+
+
STD
+
6.13
+
+
+
VAR
+
37.5
+
+
+
+
+
KURT.
+
3.94
+
+
+
SKEW
+
1.76
+
+
+
SUM
+
10,302
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 33 +
+
+ YearsInCurrentRole + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 19 +
+
+ (1%) +
+
+
+
+
+
ZEROES:
+
+ 244 +
+
+ (17%) +
+
+
+ +
+
+
MAX
+
18.0
+
+
+
95%
+
11.0
+
+
+
Q3
+
7.0
+
+
+
AVG
+
4.2
+
+
+
MEDIAN
+
3.0
+
+
+
Q1
+
2.0
+
+
+
5%
+
0.0
+
+
+
MIN
+
0.0
+
+
+
+
+
RANGE
+
18.0
+
+
+
IQR
+
5.00
+
+
+
STD
+
3.62
+
+
+
VAR
+
13.1
+
+
+
+
+
KURT.
+
0.477
+
+
+
SKEW
+
0.917
+
+
+
SUM
+
6,217
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 34 +
+
+ YearsSinceLastPromotion + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 16 +
+
+ (1%) +
+
+
+
+
+
ZEROES:
+
+ 581 +
+
+ (40%) +
+
+
+ +
+
+
MAX
+
15.0
+
+
+
95%
+
9.0
+
+
+
Q3
+
3.0
+
+
+
AVG
+
2.2
+
+
+
MEDIAN
+
1.0
+
+
+
Q1
+
0.0
+
+
+
5%
+
0.0
+
+
+
MIN
+
0.0
+
+
+
+
+
RANGE
+
15.0
+
+
+
IQR
+
3.00
+
+
+
STD
+
3.22
+
+
+
VAR
+
10.4
+
+
+
+
+
KURT.
+
3.61
+
+
+
SKEW
+
1.98
+
+
+
SUM
+
3,216
+
+
+ +
+ +
+
+
+
+
+
+ + +
+ 35 +
+
+ YearsWithCurrManager + +
+
+
VALUES:
+
+ 1,470 +
+
+ (100%) +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+
+
+
DISTINCT:
+
+ 18 +
+
+ (1%) +
+
+
+
+
+
ZEROES:
+
+ 263 +
+
+ (18%) +
+
+
+ +
+
+
MAX
+
17.0
+
+
+
95%
+
10.0
+
+
+
Q3
+
7.0
+
+
+
AVG
+
4.1
+
+
+
MEDIAN
+
3.0
+
+
+
Q1
+
2.0
+
+
+
5%
+
0.0
+
+
+
MIN
+
0.0
+
+
+
+
+
RANGE
+
17.0
+
+
+
IQR
+
5.00
+
+
+
STD
+
3.57
+
+
+
VAR
+
12.7
+
+
+
+
+
KURT.
+
0.171
+
+
+
SKEW
+
0.833
+
+
+
SUM
+
6,061
+
+
+ +
+ +
+
+
+ + +
+
+
+ + Associations +
+ [Only including dataset "DataFrame"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical, + (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). +

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity. +
+ +
+
+ +
+
+ + Associations +
+ [Only including dataset "None"]
+ ■ Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is assymmetrical, + (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). +

• Circles are the symmetrical numerical correlations (Pearson's) from -1 to 1. The trivial diagonal is intentionally left blank for clarity. +
+ +
+
+ + + +
+
+ +
+
+ Age +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
TotalWorkingYears
+
0.68
+
+
+
MonthlyIncome
+
0.50
+
+
+
YearsAtCompany
+
0.31
+
+
+
YearsSinceLastPromotion
+
0.22
+
+
+
YearsInCurrentRole
+
0.21
+
+
+
YearsWithCurrManager
+
0.20
+
+
+
MonthlyRate
+
0.03
+
+
+
HourlyRate
+
0.02
+
+
+
DailyRate
+
0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
PercentSalaryHike
+
0.00
+
+
+
DistanceFromHome
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.52
+
+
+
NumCompaniesWorked
+
0.44
+
+
+
JobRole
+
0.43
+
+
+
Education
+
0.23
+
+
+
Attrition
+
0.16
+
+
+
MaritalStatus
+
0.12
+
+
+
StockOptionLevel
+
0.11
+
+
+
TrainingTimesLastYear
+
0.09
+
+
+
EducationField
+
0.06
+
+
+
RelationshipSatisfaction
+
0.06
+
+
+
JobInvolvement
+
0.04
+
+
+
Gender
+
0.04
+
+
+
Department
+
0.03
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
35
+
+
78
+
5.3%
+
+
+
+
34
+
+
77
+
5.2%
+
+
+
+
36
+
+
69
+
4.7%
+
+
+
+
31
+
+
69
+
4.7%
+
+
+
+
29
+
+
68
+
4.6%
+
+
+
+
32
+
+
61
+
4.1%
+
+
+
+
30
+
+
60
+
4.1%
+
+
+
+
33
+
+
58
+
3.9%
+
+
+
+
38
+
+
58
+
3.9%
+
+
+
+
40
+
+
57
+
3.9%
+
+
+
+
37
+
+
50
+
3.4%
+
+
+
+
27
+
+
48
+
3.3%
+
+
+
+
28
+
+
48
+
3.3%
+
+
+
+
42
+
+
46
+
3.1%
+
+
+
+
39
+
+
42
+
2.9%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
18
+
+
8
+
0.5%
+
+
+
+
19
+
+
9
+
0.6%
+
+
+
+
20
+
+
11
+
0.7%
+
+
+
+
21
+
+
13
+
0.9%
+
+
+
+
22
+
+
16
+
1.1%
+
+
+
+
23
+
+
14
+
1.0%
+
+
+
+
24
+
+
26
+
1.8%
+
+
+
+
25
+
+
26
+
1.8%
+
+
+
+
26
+
+
39
+
2.7%
+
+
+
+
27
+
+
48
+
3.3%
+
+
+
+
28
+
+
48
+
3.3%
+
+
+
+
29
+
+
68
+
4.6%
+
+
+
+
30
+
+
60
+
4.1%
+
+
+
+
31
+
+
69
+
4.7%
+
+
+
+
32
+
+
61
+
4.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
60
+
+
5
+
0.3%
+
+
+
+
59
+
+
10
+
0.7%
+
+
+
+
58
+
+
14
+
1.0%
+
+
+
+
57
+
+
4
+
0.3%
+
+
+
+
56
+
+
14
+
1.0%
+
+
+
+
55
+
+
22
+
1.5%
+
+
+
+
54
+
+
18
+
1.2%
+
+
+
+
53
+
+
19
+
1.3%
+
+
+
+
52
+
+
18
+
1.2%
+
+
+
+
51
+
+
19
+
1.3%
+
+
+
+
50
+
+
30
+
2.0%
+
+
+
+
49
+
+
24
+
1.6%
+
+
+
+
48
+
+
19
+
1.3%
+
+
+
+
47
+
+
24
+
1.6%
+
+
+
+
46
+
+
33
+
2.2%
+
+
+
+
+ +
+
+
+
+ +
+
+ Attrition +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
No
+ + +
+
1,233
+
84%
+
+ +
+
+ +
Yes
+ + +
+
237
+
16%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
Attrition
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
OverTime
+
0.05
+
+
+
StockOptionLevel
+
0.02
+
+
+
JobLevel
+
0.02
+
+
+
JobRole
+
0.01
+
+
+
MaritalStatus
+
0.01
+
+
+
BusinessTravel
+
0.01
+
+
+
JobInvolvement
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
Department
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON Attrition:
+
+
+
JobRole
+
0.07
+
+
+
OverTime
+
0.06
+
+
+
JobLevel
+
0.06
+
+
+
StockOptionLevel
+
0.05
+
+
+
MaritalStatus
+
0.03
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
JobInvolvement
+
0.02
+
+
+
BusinessTravel
+
0.02
+
+
+
EnvironmentSatisfaction
+
0.02
+
+
+
JobSatisfaction
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
Department
+
0.01
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
Attrition
+ CORRELATION RATIO WITH...
+
+
+
TotalWorkingYears
+
0.17
+
+
+
YearsInCurrentRole
+
0.16
+
+
+
MonthlyIncome
+
0.16
+
+
+
Age
+
0.16
+
+
+
YearsWithCurrManager
+
0.16
+
+
+
YearsAtCompany
+
0.13
+
+
+
DistanceFromHome
+
0.08
+
+
+
DailyRate
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
MonthlyRate
+
0.02
+
+
+
PercentSalaryHike
+
0.01
+
+
+
EmployeeNumber
+
0.01
+
+
+
HourlyRate
+
0.01
+
+
+
+
+
+
+
+ +
+
+ BusinessTravel +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Travel_Rarely
+ + +
+
1,043
+
71%
+
+ +
+
+ +
Travel_Frequently
+ + +
+
277
+
19%
+
+ +
+
+ +
Non-Travel
+ + +
+
150
+
10%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
BusinessTravel
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.02
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
Gender
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
Education
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON BusinessTravel:
+
+
+
Attrition
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
JobLevel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
OverTime
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
BusinessTravel
+ CORRELATION RATIO WITH...
+
+
+
MonthlyIncome
+
0.04
+
+
+
PercentSalaryHike
+
0.04
+
+
+
TotalWorkingYears
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
Age
+
0.03
+
+
+
HourlyRate
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsAtCompany
+
0.02
+
+
+
DailyRate
+
0.02
+
+
+
MonthlyRate
+
0.02
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
+
+
+
+
+ +
+
+ DailyRate +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
EmployeeNumber
+
-0.05
+
+
+
YearsAtCompany
+
-0.03
+
+
+
YearsSinceLastPromotion
+
-0.03
+
+
+
MonthlyRate
+
-0.03
+
+
+
YearsWithCurrManager
+
-0.03
+
+
+
HourlyRate
+
0.02
+
+
+
PercentSalaryHike
+
0.02
+
+
+
TotalWorkingYears
+
0.01
+
+
+
Age
+
0.01
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
MonthlyIncome
+
0.01
+
+
+
DistanceFromHome
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
EducationField
+
0.08
+
+
+
MaritalStatus
+
0.08
+
+
+
JobSatisfaction
+
0.06
+
+
+
Attrition
+
0.06
+
+
+
StockOptionLevel
+
0.05
+
+
+
JobInvolvement
+
0.05
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
JobRole
+
0.05
+
+
+
Education
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
JobLevel
+
0.04
+
+
+
RelationshipSatisfaction
+
0.03
+
+
+
Department
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
691
+
+
6
+
0.4%
+
+
+
+
408
+
+
5
+
0.3%
+
+
+
+
530
+
+
5
+
0.3%
+
+
+
+
1329
+
+
5
+
0.3%
+
+
+
+
1082
+
+
5
+
0.3%
+
+
+
+
329
+
+
5
+
0.3%
+
+
+
+
829
+
+
4
+
0.3%
+
+
+
+
1469
+
+
4
+
0.3%
+
+
+
+
267
+
+
4
+
0.3%
+
+
+
+
217
+
+
4
+
0.3%
+
+
+
+
1283
+
+
4
+
0.3%
+
+
+
+
1225
+
+
4
+
0.3%
+
+
+
+
427
+
+
4
+
0.3%
+
+
+
+
430
+
+
4
+
0.3%
+
+
+
+
465
+
+
4
+
0.3%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
102
+
+
1
+
<0.1%
+
+
+
+
103
+
+
1
+
<0.1%
+
+
+
+
104
+
+
1
+
<0.1%
+
+
+
+
105
+
+
1
+
<0.1%
+
+
+
+
106
+
+
1
+
<0.1%
+
+
+
+
107
+
+
1
+
<0.1%
+
+
+
+
109
+
+
1
+
<0.1%
+
+
+
+
111
+
+
3
+
0.2%
+
+
+
+
115
+
+
1
+
<0.1%
+
+
+
+
116
+
+
2
+
0.1%
+
+
+
+
117
+
+
4
+
0.3%
+
+
+
+
118
+
+
2
+
0.1%
+
+
+
+
119
+
+
2
+
0.1%
+
+
+
+
120
+
+
2
+
0.1%
+
+
+
+
121
+
+
2
+
0.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
1499
+
+
1
+
<0.1%
+
+
+
+
1498
+
+
1
+
<0.1%
+
+
+
+
1496
+
+
2
+
0.1%
+
+
+
+
1495
+
+
3
+
0.2%
+
+
+
+
1492
+
+
1
+
<0.1%
+
+
+
+
1490
+
+
4
+
0.3%
+
+
+
+
1488
+
+
1
+
<0.1%
+
+
+
+
1485
+
+
3
+
0.2%
+
+
+
+
1482
+
+
1
+
<0.1%
+
+
+
+
1480
+
+
2
+
0.1%
+
+
+
+
1479
+
+
2
+
0.1%
+
+
+
+
1476
+
+
3
+
0.2%
+
+
+
+
1475
+
+
2
+
0.1%
+
+
+
+
1474
+
+
2
+
0.1%
+
+
+
+
1473
+
+
1
+
<0.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ Department +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Research & Development
+ + +
+
961
+
65%
+
+ +
+
+ +
Sales
+ + +
+
446
+
30%
+
+ +
+
+ +
Human Resources
+ + +
+
63
+
4%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
Department
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
JobRole
+
0.35
+
+
+
EducationField
+
0.15
+
+
+
JobLevel
+
0.04
+
+
+
Attrition
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON Department:
+
+
+
JobRole
+
0.92
+
+
+
EducationField
+
0.27
+
+
+
JobLevel
+
0.06
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
Attrition
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Gender
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
Department
+ CORRELATION RATIO WITH...
+
+
+
EmployeeNumber
+
0.07
+
+
+
MonthlyIncome
+
0.07
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.04
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
PercentSalaryHike
+
0.04
+
+
+
Age
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
DailyRate
+
0.03
+
+
+
MonthlyRate
+
0.03
+
+
+
HourlyRate
+
0.02
+
+
+
DistanceFromHome
+
0.02
+
+
+
TotalWorkingYears
+
0.02
+
+
+
+
+
+
+
+ +
+
+ DistanceFromHome +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
PercentSalaryHike
+
0.04
+
+
+
EmployeeNumber
+
0.03
+
+
+
HourlyRate
+
0.03
+
+
+
MonthlyRate
+
0.03
+
+
+
YearsInCurrentRole
+
0.02
+
+
+
MonthlyIncome
+
-0.02
+
+
+
YearsWithCurrManager
+
0.01
+
+
+
YearsSinceLastPromotion
+
0.01
+
+
+
YearsAtCompany
+
0.01
+
+
+
DailyRate
+
-0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
Age
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
NumCompaniesWorked
+
0.10
+
+
+
JobLevel
+
0.10
+
+
+
StockOptionLevel
+
0.09
+
+
+
Attrition
+
0.08
+
+
+
JobRole
+
0.07
+
+
+
TrainingTimesLastYear
+
0.06
+
+
+
EducationField
+
0.05
+
+
+
RelationshipSatisfaction
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
Education
+
0.04
+
+
+
JobInvolvement
+
0.03
+
+
+
MaritalStatus
+
0.03
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
PerformanceRating
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
2
+
+
211
+
14.4%
+
+
+
+
1
+
+
208
+
14.1%
+
+
+
+
10
+
+
86
+
5.9%
+
+
+
+
9
+
+
85
+
5.8%
+
+
+
+
3
+
+
84
+
5.7%
+
+
+
+
7
+
+
84
+
5.7%
+
+
+
+
8
+
+
80
+
5.4%
+
+
+
+
5
+
+
65
+
4.4%
+
+
+
+
4
+
+
64
+
4.4%
+
+
+
+
6
+
+
59
+
4.0%
+
+
+
+
16
+
+
32
+
2.2%
+
+
+
+
11
+
+
29
+
2.0%
+
+
+
+
24
+
+
28
+
1.9%
+
+
+
+
23
+
+
27
+
1.8%
+
+
+
+
29
+
+
27
+
1.8%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
1
+
+
208
+
14.1%
+
+
+
+
2
+
+
211
+
14.4%
+
+
+
+
3
+
+
84
+
5.7%
+
+
+
+
4
+
+
64
+
4.4%
+
+
+
+
5
+
+
65
+
4.4%
+
+
+
+
6
+
+
59
+
4.0%
+
+
+
+
7
+
+
84
+
5.7%
+
+
+
+
8
+
+
80
+
5.4%
+
+
+
+
9
+
+
85
+
5.8%
+
+
+
+
10
+
+
86
+
5.9%
+
+
+
+
11
+
+
29
+
2.0%
+
+
+
+
12
+
+
20
+
1.4%
+
+
+
+
13
+
+
19
+
1.3%
+
+
+
+
14
+
+
21
+
1.4%
+
+
+
+
15
+
+
26
+
1.8%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
29
+
+
27
+
1.8%
+
+
+
+
28
+
+
23
+
1.6%
+
+
+
+
27
+
+
12
+
0.8%
+
+
+
+
26
+
+
25
+
1.7%
+
+
+
+
25
+
+
25
+
1.7%
+
+
+
+
24
+
+
28
+
1.9%
+
+
+
+
23
+
+
27
+
1.8%
+
+
+
+
22
+
+
19
+
1.3%
+
+
+
+
21
+
+
18
+
1.2%
+
+
+
+
20
+
+
25
+
1.7%
+
+
+
+
19
+
+
22
+
1.5%
+
+
+
+
18
+
+
26
+
1.8%
+
+
+
+
17
+
+
20
+
1.4%
+
+
+
+
16
+
+
32
+
2.2%
+
+
+
+
15
+
+
26
+
1.8%
+
+
+
+
+ +
+
+
+
+ +
+
+ Education +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
572
+
39%
+
+ +
+
+ +
4
+ + +
+
398
+
27%
+
+ +
+
+ +
2
+ + +
+
282
+
19%
+
+ +
+
+ +
1
+ + +
+
170
+
12%
+
+ +
+
+ +
5
+ + +
+
48
+
3%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
Education
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
JobLevel
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON Education:
+
+
+
NumCompaniesWorked
+
0.03
+
+
+
JobLevel
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
OverTime
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
Education
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.23
+
+
+
TotalWorkingYears
+
0.15
+
+
+
MonthlyIncome
+
0.10
+
+
+
YearsAtCompany
+
0.08
+
+
+
YearsWithCurrManager
+
0.07
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
MonthlyRate
+
0.05
+
+
+
DailyRate
+
0.04
+
+
+
PercentSalaryHike
+
0.04
+
+
+
DistanceFromHome
+
0.04
+
+
+
HourlyRate
+
0.03
+
+
+
+
+
+
+
+ +
+
+ EducationField +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Life Sciences
+ + +
+
606
+
41%
+
+ +
+
+ +
Medical
+ + +
+
464
+
32%
+
+ +
+
+ +
Marketing
+ + +
+
159
+
11%
+
+ +
+
+ +
Technical Degree
+ + +
+
132
+
9%
+
+ +
+
+ +
Other
+ + +
+
82
+
6%
+
+ +
+
+ +
Human Resources
+ + +
+
27
+
2%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
EducationField
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Department
+
0.27
+
+
+
JobRole
+
0.10
+
+
+
JobLevel
+
0.02
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
Attrition
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
Education
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
RelationshipSatisfaction
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON EducationField:
+
+
+
Department
+
0.15
+
+
+
JobRole
+
0.14
+
+
+
JobLevel
+
0.02
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
Education
+
0.01
+
+
+
RelationshipSatisfaction
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
EducationField
+ CORRELATION RATIO WITH...
+
+
+
DailyRate
+
0.08
+
+
+
MonthlyIncome
+
0.08
+
+
+
HourlyRate
+
0.06
+
+
+
Age
+
0.06
+
+
+
PercentSalaryHike
+
0.06
+
+
+
TotalWorkingYears
+
0.06
+
+
+
YearsSinceLastPromotion
+
0.05
+
+
+
YearsAtCompany
+
0.05
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
DistanceFromHome
+
0.05
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
MonthlyRate
+
0.04
+
+
+
EmployeeNumber
+
0.04
+
+
+
+
+
+
+
+ +
+
+ EmployeeCount +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
1
+ + +
+
1,470
+
100%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
EmployeeCount
+ PROVIDES INFORMATION ON...
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON EmployeeCount:
+
+
+
Attrition
+
1.00
+
+
+
BusinessTravel
+
1.00
+
+
+
Department
+
1.00
+
+
+
Education
+
1.00
+
+
+
EducationField
+
1.00
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
Gender
+
1.00
+
+
+
JobInvolvement
+
1.00
+
+
+
JobLevel
+
1.00
+
+
+
JobRole
+
1.00
+
+
+
JobSatisfaction
+
1.00
+
+
+
MaritalStatus
+
1.00
+
+
+
NumCompaniesWorked
+
1.00
+
+
+
Over18
+
1.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
EmployeeCount
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.00
+
+
+
DailyRate
+
0.00
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
HourlyRate
+
0.00
+
+
+
MonthlyIncome
+
0.00
+
+
+
MonthlyRate
+
0.00
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+ +
+
+ EmployeeNumber +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
DailyRate
+
-0.05
+
+
+
HourlyRate
+
0.04
+
+
+
DistanceFromHome
+
0.03
+
+
+
MonthlyIncome
+
-0.01
+
+
+
TotalWorkingYears
+
-0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
MonthlyRate
+
0.01
+
+
+
YearsAtCompany
+
-0.01
+
+
+
Age
+
-0.01
+
+
+
YearsWithCurrManager
+
-0.01
+
+
+
YearsSinceLastPromotion
+
-0.01
+
+
+
YearsInCurrentRole
+
-0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobRole
+
0.09
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
RelationshipSatisfaction
+
0.08
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
Department
+
0.07
+
+
+
StockOptionLevel
+
0.06
+
+
+
JobLevel
+
0.06
+
+
+
MaritalStatus
+
0.05
+
+
+
EnvironmentSatisfaction
+
0.05
+
+
+
JobSatisfaction
+
0.05
+
+
+
Education
+
0.05
+
+
+
EducationField
+
0.04
+
+
+
JobInvolvement
+
0.03
+
+
+
OverTime
+
0.02
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
1
+
+
1
+
<0.1%
+
+
+
+
1391
+
+
1
+
<0.1%
+
+
+
+
1389
+
+
1
+
<0.1%
+
+
+
+
1387
+
+
1
+
<0.1%
+
+
+
+
1383
+
+
1
+
<0.1%
+
+
+
+
1382
+
+
1
+
<0.1%
+
+
+
+
1380
+
+
1
+
<0.1%
+
+
+
+
1379
+
+
1
+
<0.1%
+
+
+
+
1377
+
+
1
+
<0.1%
+
+
+
+
1375
+
+
1
+
<0.1%
+
+
+
+
1374
+
+
1
+
<0.1%
+
+
+
+
1373
+
+
1
+
<0.1%
+
+
+
+
1372
+
+
1
+
<0.1%
+
+
+
+
1371
+
+
1
+
<0.1%
+
+
+
+
1369
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
1
+
+
1
+
<0.1%
+
+
+
+
2
+
+
1
+
<0.1%
+
+
+
+
4
+
+
1
+
<0.1%
+
+
+
+
5
+
+
1
+
<0.1%
+
+
+
+
7
+
+
1
+
<0.1%
+
+
+
+
8
+
+
1
+
<0.1%
+
+
+
+
10
+
+
1
+
<0.1%
+
+
+
+
11
+
+
1
+
<0.1%
+
+
+
+
12
+
+
1
+
<0.1%
+
+
+
+
13
+
+
1
+
<0.1%
+
+
+
+
14
+
+
1
+
<0.1%
+
+
+
+
15
+
+
1
+
<0.1%
+
+
+
+
16
+
+
1
+
<0.1%
+
+
+
+
18
+
+
1
+
<0.1%
+
+
+
+
19
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
2068
+
+
1
+
<0.1%
+
+
+
+
2065
+
+
1
+
<0.1%
+
+
+
+
2064
+
+
1
+
<0.1%
+
+
+
+
2062
+
+
1
+
<0.1%
+
+
+
+
2061
+
+
1
+
<0.1%
+
+
+
+
2060
+
+
1
+
<0.1%
+
+
+
+
2057
+
+
1
+
<0.1%
+
+
+
+
2056
+
+
1
+
<0.1%
+
+
+
+
2055
+
+
1
+
<0.1%
+
+
+
+
2054
+
+
1
+
<0.1%
+
+
+
+
2053
+
+
1
+
<0.1%
+
+
+
+
2052
+
+
1
+
<0.1%
+
+
+
+
2051
+
+
1
+
<0.1%
+
+
+
+
2049
+
+
1
+
<0.1%
+
+
+
+
2048
+
+
1
+
<0.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ EnvironmentSatisfaction +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
453
+
31%
+
+ +
+
+ +
4
+ + +
+
446
+
30%
+
+ +
+
+ +
2
+ + +
+
287
+
20%
+
+ +
+
+ +
1
+ + +
+
284
+
19%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
EnvironmentSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.02
+
+
+
OverTime
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
Education
+
0.00
+
+
+
Department
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON EnvironmentSatisfaction:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
Attrition
+
0.01
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
Department
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
EnvironmentSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
HourlyRate
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
MonthlyRate
+
0.05
+
+
+
PercentSalaryHike
+
0.04
+
+
+
YearsInCurrentRole
+
0.04
+
+
+
TotalWorkingYears
+
0.04
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
Age
+
0.03
+
+
+
MonthlyIncome
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsAtCompany
+
0.02
+
+
+
DailyRate
+
0.02
+
+
+
YearsWithCurrManager
+
0.01
+
+
+
+
+
+
+
+ +
+
+ Gender +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Male
+ + +
+
882
+
60%
+
+ +
+
+ +
Female
+ + +
+
588
+
40%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
Gender
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
JobRole
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
Department
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON Gender:
+
+
+
JobRole
+
0.01
+
+
+
JobLevel
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
Education
+
0.00
+
+
+
Department
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Attrition
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
Gender
+ CORRELATION RATIO WITH...
+
+
+
TotalWorkingYears
+
0.05
+
+
+
YearsInCurrentRole
+
0.04
+
+
+
MonthlyRate
+
0.04
+
+
+
Age
+
0.04
+
+
+
MonthlyIncome
+
0.03
+
+
+
YearsWithCurrManager
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
EmployeeNumber
+
0.02
+
+
+
DailyRate
+
0.01
+
+
+
PercentSalaryHike
+
0.00
+
+
+
DistanceFromHome
+
0.00
+
+
+
HourlyRate
+
0.00
+
+
+
+
+
+
+
+ +
+
+ HourlyRate +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
EmployeeNumber
+
0.04
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsSinceLastPromotion
+
-0.03
+
+
+
Age
+
0.02
+
+
+
YearsInCurrentRole
+
-0.02
+
+
+
DailyRate
+
0.02
+
+
+
YearsWithCurrManager
+
-0.02
+
+
+
YearsAtCompany
+
-0.02
+
+
+
MonthlyIncome
+
-0.02
+
+
+
MonthlyRate
+
-0.02
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
TotalWorkingYears
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobSatisfaction
+
0.08
+
+
+
StockOptionLevel
+
0.07
+
+
+
NumCompaniesWorked
+
0.07
+
+
+
EducationField
+
0.06
+
+
+
EnvironmentSatisfaction
+
0.06
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
JobRole
+
0.05
+
+
+
JobInvolvement
+
0.05
+
+
+
JobLevel
+
0.04
+
+
+
MaritalStatus
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
Education
+
0.03
+
+
+
BusinessTravel
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
66
+
+
29
+
2.0%
+
+
+
+
98
+
+
28
+
1.9%
+
+
+
+
42
+
+
28
+
1.9%
+
+
+
+
48
+
+
28
+
1.9%
+
+
+
+
84
+
+
28
+
1.9%
+
+
+
+
57
+
+
27
+
1.8%
+
+
+
+
79
+
+
27
+
1.8%
+
+
+
+
96
+
+
27
+
1.8%
+
+
+
+
54
+
+
26
+
1.8%
+
+
+
+
52
+
+
26
+
1.8%
+
+
+
+
87
+
+
26
+
1.8%
+
+
+
+
56
+
+
26
+
1.8%
+
+
+
+
46
+
+
25
+
1.7%
+
+
+
+
92
+
+
25
+
1.7%
+
+
+
+
72
+
+
25
+
1.7%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
30
+
+
19
+
1.3%
+
+
+
+
31
+
+
15
+
1.0%
+
+
+
+
32
+
+
24
+
1.6%
+
+
+
+
33
+
+
19
+
1.3%
+
+
+
+
34
+
+
12
+
0.8%
+
+
+
+
35
+
+
18
+
1.2%
+
+
+
+
36
+
+
18
+
1.2%
+
+
+
+
37
+
+
18
+
1.2%
+
+
+
+
38
+
+
13
+
0.9%
+
+
+
+
39
+
+
17
+
1.2%
+
+
+
+
40
+
+
18
+
1.2%
+
+
+
+
41
+
+
21
+
1.4%
+
+
+
+
42
+
+
28
+
1.9%
+
+
+
+
43
+
+
24
+
1.6%
+
+
+
+
44
+
+
18
+
1.2%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
100
+
+
19
+
1.3%
+
+
+
+
99
+
+
20
+
1.4%
+
+
+
+
98
+
+
28
+
1.9%
+
+
+
+
97
+
+
21
+
1.4%
+
+
+
+
96
+
+
27
+
1.8%
+
+
+
+
95
+
+
23
+
1.6%
+
+
+
+
94
+
+
22
+
1.5%
+
+
+
+
93
+
+
16
+
1.1%
+
+
+
+
92
+
+
25
+
1.7%
+
+
+
+
91
+
+
18
+
1.2%
+
+
+
+
90
+
+
19
+
1.3%
+
+
+
+
89
+
+
15
+
1.0%
+
+
+
+
88
+
+
21
+
1.4%
+
+
+
+
87
+
+
26
+
1.8%
+
+
+
+
86
+
+
22
+
1.5%
+
+
+
+
+ +
+
+
+
+ +
+
+ JobInvolvement +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
868
+
59%
+
+ +
+
+ +
2
+ + +
+
375
+
26%
+
+ +
+
+ +
4
+ + +
+
144
+
10%
+
+ +
+
+ +
1
+ + +
+
83
+
6%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
JobInvolvement
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.02
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON JobInvolvement:
+
+
+
Attrition
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
JobInvolvement
+ CORRELATION RATIO WITH...
+
+
+
DailyRate
+
0.05
+
+
+
HourlyRate
+
0.05
+
+
+
PercentSalaryHike
+
0.05
+
+
+
Age
+
0.04
+
+
+
MonthlyRate
+
0.04
+
+
+
DistanceFromHome
+
0.03
+
+
+
EmployeeNumber
+
0.03
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
YearsWithCurrManager
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
MonthlyIncome
+
0.03
+
+
+
YearsInCurrentRole
+
0.01
+
+
+
TotalWorkingYears
+
0.01
+
+
+
+
+
+
+
+ +
+
+ JobLevel +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
1
+ + +
+
543
+
37%
+
+ +
+
+ +
2
+ + +
+
534
+
36%
+
+ +
+
+ +
3
+ + +
+
218
+
15%
+
+ +
+
+ +
4
+ + +
+
106
+
7%
+
+ +
+
+ +
5
+ + +
+
69
+
5%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
JobLevel
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
JobRole
+
0.32
+
+
+
Department
+
0.06
+
+
+
Attrition
+
0.06
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
EducationField
+
0.02
+
+
+
Education
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
MaritalStatus
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Gender
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON JobLevel:
+
+
+
JobRole
+
0.48
+
+
+
Department
+
0.04
+
+
+
NumCompaniesWorked
+
0.03
+
+
+
EducationField
+
0.02
+
+
+
Attrition
+
0.02
+
+
+
Education
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
MaritalStatus
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
JobLevel
+ CORRELATION RATIO WITH...
+
+
+
MonthlyIncome
+
0.96
+
+
+
TotalWorkingYears
+
0.80
+
+
+
YearsAtCompany
+
0.54
+
+
+
Age
+
0.52
+
+
+
YearsInCurrentRole
+
0.41
+
+
+
YearsWithCurrManager
+
0.39
+
+
+
YearsSinceLastPromotion
+
0.36
+
+
+
DistanceFromHome
+
0.10
+
+
+
MonthlyRate
+
0.07
+
+
+
EmployeeNumber
+
0.06
+
+
+
PercentSalaryHike
+
0.05
+
+
+
HourlyRate
+
0.04
+
+
+
DailyRate
+
0.04
+
+
+
+
+
+
+
+ +
+
+ JobRole +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Sales Executive
+ + +
+
326
+
22%
+
+ +
+
+ +
Research Scientist
+ + +
+
292
+
20%
+
+ +
+
+ +
Laboratory Technician
+ + +
+
259
+
18%
+
+ +
+
+ +
Manufacturing Director
+ + +
+
145
+
10%
+
+ +
+
+ +
Healthcare Representative
+ + +
+
131
+
9%
+
+ +
+
+ +
Manager
+ + +
+
102
+
7%
+
+ +
+
+ +
Sales Representative
+ + +
+
83
+
6%
+
+ +
+
+ +
Research Director
+ + +
+
80
+
5%
+
+ +
+
+ +
Human Resources
+ + +
+
52
+
4%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
JobRole
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Department
+
0.92
+
+
+
JobLevel
+
0.48
+
+
+
EducationField
+
0.14
+
+
+
Attrition
+
0.07
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
Education
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
MaritalStatus
+
0.01
+
+
+
Gender
+
0.01
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON JobRole:
+
+
+
Department
+
0.35
+
+
+
JobLevel
+
0.32
+
+
+
EducationField
+
0.10
+
+
+
NumCompaniesWorked
+
0.02
+
+
+
Attrition
+
0.01
+
+
+
Education
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
JobRole
+ CORRELATION RATIO WITH...
+
+
+
MonthlyIncome
+
0.90
+
+
+
TotalWorkingYears
+
0.66
+
+
+
YearsAtCompany
+
0.44
+
+
+
Age
+
0.43
+
+
+
YearsInCurrentRole
+
0.33
+
+
+
YearsWithCurrManager
+
0.32
+
+
+
YearsSinceLastPromotion
+
0.30
+
+
+
EmployeeNumber
+
0.09
+
+
+
PercentSalaryHike
+
0.08
+
+
+
DistanceFromHome
+
0.07
+
+
+
MonthlyRate
+
0.06
+
+
+
HourlyRate
+
0.05
+
+
+
DailyRate
+
0.05
+
+
+
+
+
+
+
+ +
+
+ JobSatisfaction +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
4
+ + +
+
459
+
31%
+
+ +
+
+ +
3
+ + +
+
442
+
30%
+
+ +
+
+ +
1
+ + +
+
289
+
20%
+
+ +
+
+ +
2
+ + +
+
280
+
19%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
JobSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.01
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
Department
+
0.00
+
+
+
PerformanceRating
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON JobSatisfaction:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
JobRole
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
Education
+
0.00
+
+
+
Department
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
PerformanceRating
+
0.00
+
+
+
OverTime
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
JobSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
HourlyRate
+
0.08
+
+
+
DailyRate
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
PercentSalaryHike
+
0.05
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
MonthlyRate
+
0.03
+
+
+
TotalWorkingYears
+
0.02
+
+
+
YearsSinceLastPromotion
+
0.02
+
+
+
DistanceFromHome
+
0.02
+
+
+
YearsInCurrentRole
+
0.02
+
+
+
Age
+
0.01
+
+
+
YearsAtCompany
+
0.01
+
+
+
MonthlyIncome
+
0.01
+
+
+
+
+
+
+
+ +
+
+ MaritalStatus +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Married
+ + +
+
673
+
46%
+
+ +
+
+ +
Single
+ + +
+
470
+
32%
+
+ +
+
+ +
Divorced
+ + +
+
327
+
22%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
MaritalStatus
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
StockOptionLevel
+
0.37
+
+
+
Attrition
+
0.03
+
+
+
JobRole
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
Department
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON MaritalStatus:
+
+
+
StockOptionLevel
+
0.40
+
+
+
Attrition
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
JobLevel
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Department
+
0.00
+
+
+
Education
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
MaritalStatus
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.12
+
+
+
TotalWorkingYears
+
0.09
+
+
+
MonthlyIncome
+
0.09
+
+
+
YearsInCurrentRole
+
0.09
+
+
+
DailyRate
+
0.08
+
+
+
YearsAtCompany
+
0.07
+
+
+
YearsSinceLastPromotion
+
0.06
+
+
+
EmployeeNumber
+
0.05
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
MonthlyRate
+
0.04
+
+
+
HourlyRate
+
0.04
+
+
+
DistanceFromHome
+
0.03
+
+
+
PercentSalaryHike
+
0.03
+
+
+
+
+
+
+
+ +
+
+ MonthlyIncome +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
TotalWorkingYears
+
0.77
+
+
+
YearsAtCompany
+
0.51
+
+
+
Age
+
0.50
+
+
+
YearsInCurrentRole
+
0.36
+
+
+
YearsSinceLastPromotion
+
0.34
+
+
+
YearsWithCurrManager
+
0.34
+
+
+
MonthlyRate
+
0.03
+
+
+
PercentSalaryHike
+
-0.03
+
+
+
DistanceFromHome
+
-0.02
+
+
+
HourlyRate
+
-0.02
+
+
+
EmployeeNumber
+
-0.01
+
+
+
DailyRate
+
0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.96
+
+
+
JobRole
+
0.90
+
+
+
NumCompaniesWorked
+
0.24
+
+
+
Attrition
+
0.16
+
+
+
Education
+
0.10
+
+
+
StockOptionLevel
+
0.09
+
+
+
MaritalStatus
+
0.09
+
+
+
EducationField
+
0.08
+
+
+
Department
+
0.07
+
+
+
BusinessTravel
+
0.04
+
+
+
TrainingTimesLastYear
+
0.04
+
+
+
WorkLifeBalance
+
0.04
+
+
+
RelationshipSatisfaction
+
0.03
+
+
+
Gender
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
2342
+
+
4
+
0.3%
+
+
+
+
6142
+
+
3
+
0.2%
+
+
+
+
2741
+
+
3
+
0.2%
+
+
+
+
2559
+
+
3
+
0.2%
+
+
+
+
2610
+
+
3
+
0.2%
+
+
+
+
2451
+
+
3
+
0.2%
+
+
+
+
5562
+
+
3
+
0.2%
+
+
+
+
3452
+
+
3
+
0.2%
+
+
+
+
2380
+
+
3
+
0.2%
+
+
+
+
6347
+
+
3
+
0.2%
+
+
+
+
2404
+
+
3
+
0.2%
+
+
+
+
2956
+
+
2
+
0.1%
+
+
+
+
2127
+
+
2
+
0.1%
+
+
+
+
2515
+
+
2
+
0.1%
+
+
+
+
2187
+
+
2
+
0.1%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
1009
+
+
1
+
<0.1%
+
+
+
+
1051
+
+
1
+
<0.1%
+
+
+
+
1052
+
+
1
+
<0.1%
+
+
+
+
1081
+
+
1
+
<0.1%
+
+
+
+
1091
+
+
1
+
<0.1%
+
+
+
+
1102
+
+
1
+
<0.1%
+
+
+
+
1118
+
+
1
+
<0.1%
+
+
+
+
1129
+
+
1
+
<0.1%
+
+
+
+
1200
+
+
1
+
<0.1%
+
+
+
+
1223
+
+
1
+
<0.1%
+
+
+
+
1232
+
+
1
+
<0.1%
+
+
+
+
1261
+
+
1
+
<0.1%
+
+
+
+
1274
+
+
1
+
<0.1%
+
+
+
+
1281
+
+
1
+
<0.1%
+
+
+
+
1359
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
19999
+
+
1
+
<0.1%
+
+
+
+
19973
+
+
1
+
<0.1%
+
+
+
+
19943
+
+
1
+
<0.1%
+
+
+
+
19926
+
+
1
+
<0.1%
+
+
+
+
19859
+
+
1
+
<0.1%
+
+
+
+
19847
+
+
1
+
<0.1%
+
+
+
+
19845
+
+
1
+
<0.1%
+
+
+
+
19833
+
+
1
+
<0.1%
+
+
+
+
19740
+
+
1
+
<0.1%
+
+
+
+
19717
+
+
1
+
<0.1%
+
+
+
+
19701
+
+
1
+
<0.1%
+
+
+
+
19665
+
+
1
+
<0.1%
+
+
+
+
19658
+
+
1
+
<0.1%
+
+
+
+
19636
+
+
1
+
<0.1%
+
+
+
+
19627
+
+
1
+
<0.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ MonthlyRate +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
YearsWithCurrManager
+
-0.04
+
+
+
MonthlyIncome
+
0.03
+
+
+
DailyRate
+
-0.03
+
+
+
Age
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
TotalWorkingYears
+
0.03
+
+
+
YearsAtCompany
+
-0.02
+
+
+
HourlyRate
+
-0.02
+
+
+
YearsInCurrentRole
+
-0.01
+
+
+
EmployeeNumber
+
0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
NumCompaniesWorked
+
0.08
+
+
+
JobLevel
+
0.07
+
+
+
TrainingTimesLastYear
+
0.06
+
+
+
JobRole
+
0.06
+
+
+
EnvironmentSatisfaction
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
Education
+
0.05
+
+
+
StockOptionLevel
+
0.05
+
+
+
EducationField
+
0.04
+
+
+
Gender
+
0.04
+
+
+
MaritalStatus
+
0.04
+
+
+
JobInvolvement
+
0.04
+
+
+
JobSatisfaction
+
0.03
+
+
+
WorkLifeBalance
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
4223
+
+
3
+
0.2%
+
+
+
+
9150
+
+
3
+
0.2%
+
+
+
+
9558
+
+
2
+
0.1%
+
+
+
+
12858
+
+
2
+
0.1%
+
+
+
+
22074
+
+
2
+
0.1%
+
+
+
+
25326
+
+
2
+
0.1%
+
+
+
+
9096
+
+
2
+
0.1%
+
+
+
+
13008
+
+
2
+
0.1%
+
+
+
+
12355
+
+
2
+
0.1%
+
+
+
+
7744
+
+
2
+
0.1%
+
+
+
+
6881
+
+
2
+
0.1%
+
+
+
+
11737
+
+
2
+
0.1%
+
+
+
+
22102
+
+
2
+
0.1%
+
+
+
+
3339
+
+
2
+
0.1%
+
+
+
+
11162
+
+
2
+
0.1%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
2094
+
+
1
+
<0.1%
+
+
+
+
2097
+
+
1
+
<0.1%
+
+
+
+
2104
+
+
1
+
<0.1%
+
+
+
+
2112
+
+
1
+
<0.1%
+
+
+
+
2122
+
+
1
+
<0.1%
+
+
+
+
2125
+
+
2
+
0.1%
+
+
+
+
2137
+
+
1
+
<0.1%
+
+
+
+
2227
+
+
1
+
<0.1%
+
+
+
+
2243
+
+
1
+
<0.1%
+
+
+
+
2253
+
+
1
+
<0.1%
+
+
+
+
2261
+
+
1
+
<0.1%
+
+
+
+
2288
+
+
1
+
<0.1%
+
+
+
+
2302
+
+
1
+
<0.1%
+
+
+
+
2323
+
+
1
+
<0.1%
+
+
+
+
2326
+
+
1
+
<0.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
26999
+
+
1
+
<0.1%
+
+
+
+
26997
+
+
1
+
<0.1%
+
+
+
+
26968
+
+
1
+
<0.1%
+
+
+
+
26959
+
+
1
+
<0.1%
+
+
+
+
26956
+
+
1
+
<0.1%
+
+
+
+
26933
+
+
1
+
<0.1%
+
+
+
+
26914
+
+
1
+
<0.1%
+
+
+
+
26897
+
+
1
+
<0.1%
+
+
+
+
26894
+
+
1
+
<0.1%
+
+
+
+
26862
+
+
1
+
<0.1%
+
+
+
+
26849
+
+
1
+
<0.1%
+
+
+
+
26841
+
+
1
+
<0.1%
+
+
+
+
26820
+
+
1
+
<0.1%
+
+
+
+
26767
+
+
1
+
<0.1%
+
+
+
+
26707
+
+
1
+
<0.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ NumCompaniesWorked +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
1
+ + +
+
521
+
35%
+
+ +
+
+ +
0
+ + +
+
197
+
13%
+
+ +
+
+ +
3
+ + +
+
159
+
11%
+
+ +
+
+ +
2
+ + +
+
146
+
10%
+
+ +
+
+ +
4
+ + +
+
139
+
9%
+
+ +
+
+ +
7
+ + +
+
74
+
5%
+
+ +
+
+ +
6
+ + +
+
70
+
5%
+
+ +
+
+ +
5
+ + +
+
63
+
4%
+
+ +
+
+ +
9
+ + +
+
52
+
4%
+
+ +
+
+ +
8
+ + +
+
49
+
3%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
NumCompaniesWorked
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
JobLevel
+
0.03
+
+
+
Education
+
0.03
+
+
+
JobRole
+
0.02
+
+
+
Attrition
+
0.02
+
+
+
EducationField
+
0.02
+
+
+
WorkLifeBalance
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
Department
+
0.01
+
+
+
MaritalStatus
+
0.01
+
+
+
JobInvolvement
+
0.01
+
+
+
PerformanceRating
+
0.01
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON NumCompaniesWorked:
+
+
+
JobRole
+
0.02
+
+
+
JobLevel
+
0.02
+
+
+
Education
+
0.02
+
+
+
EducationField
+
0.01
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
Attrition
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Department
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
NumCompaniesWorked
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.44
+
+
+
TotalWorkingYears
+
0.37
+
+
+
MonthlyIncome
+
0.24
+
+
+
YearsAtCompany
+
0.18
+
+
+
YearsWithCurrManager
+
0.16
+
+
+
YearsInCurrentRole
+
0.16
+
+
+
DistanceFromHome
+
0.10
+
+
+
DailyRate
+
0.09
+
+
+
YearsSinceLastPromotion
+
0.09
+
+
+
EmployeeNumber
+
0.09
+
+
+
MonthlyRate
+
0.08
+
+
+
PercentSalaryHike
+
0.08
+
+
+
HourlyRate
+
0.07
+
+
+
+
+
+
+
+ +
+
+ Over18 +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
Y
+ + +
+
1,470
+
100%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
Over18
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON Over18:
+
+
+
Attrition
+
1.00
+
+
+
BusinessTravel
+
1.00
+
+
+
Department
+
1.00
+
+
+
Education
+
1.00
+
+
+
EducationField
+
1.00
+
+
+
EmployeeCount
+
1.00
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
Gender
+
1.00
+
+
+
JobInvolvement
+
1.00
+
+
+
JobLevel
+
1.00
+
+
+
JobRole
+
1.00
+
+
+
JobSatisfaction
+
1.00
+
+
+
MaritalStatus
+
1.00
+
+
+
NumCompaniesWorked
+
1.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
Over18
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.00
+
+
+
DailyRate
+
0.00
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
HourlyRate
+
0.00
+
+
+
MonthlyIncome
+
0.00
+
+
+
MonthlyRate
+
0.00
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+ +
+
+ OverTime +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
No
+ + +
+
1,054
+
72%
+
+ +
+
+ +
Yes
+ + +
+
416
+
28%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
OverTime
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.06
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON OverTime:
+
+
+
Attrition
+
0.05
+
+
+
TrainingTimesLastYear
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
Education
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
Gender
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
OverTime
+ CORRELATION RATIO WITH...
+
+
+
YearsWithCurrManager
+
0.04
+
+
+
YearsInCurrentRole
+
0.03
+
+
+
Age
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
EmployeeNumber
+
0.02
+
+
+
MonthlyRate
+
0.02
+
+
+
TotalWorkingYears
+
0.01
+
+
+
YearsSinceLastPromotion
+
0.01
+
+
+
YearsAtCompany
+
0.01
+
+
+
DailyRate
+
0.01
+
+
+
HourlyRate
+
0.01
+
+
+
MonthlyIncome
+
0.01
+
+
+
PercentSalaryHike
+
0.01
+
+
+
+
+
+
+
+ +
+
+ PercentSalaryHike +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
DistanceFromHome
+
0.04
+
+
+
YearsAtCompany
+
-0.04
+
+
+
MonthlyIncome
+
-0.03
+
+
+
DailyRate
+
0.02
+
+
+
YearsSinceLastPromotion
+
-0.02
+
+
+
TotalWorkingYears
+
-0.02
+
+
+
EmployeeNumber
+
-0.01
+
+
+
YearsWithCurrManager
+
-0.01
+
+
+
HourlyRate
+
-0.01
+
+
+
MonthlyRate
+
-0.01
+
+
+
Age
+
0.00
+
+
+
YearsInCurrentRole
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
PerformanceRating
+
0.77
+
+
+
NumCompaniesWorked
+
0.08
+
+
+
JobRole
+
0.08
+
+
+
EducationField
+
0.06
+
+
+
StockOptionLevel
+
0.05
+
+
+
JobLevel
+
0.05
+
+
+
WorkLifeBalance
+
0.05
+
+
+
JobSatisfaction
+
0.05
+
+
+
JobInvolvement
+
0.05
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
BusinessTravel
+
0.04
+
+
+
Education
+
0.04
+
+
+
Department
+
0.04
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
11
+
+
210
+
14.3%
+
+
+
+
13
+
+
209
+
14.2%
+
+
+
+
14
+
+
201
+
13.7%
+
+
+
+
12
+
+
198
+
13.5%
+
+
+
+
15
+
+
101
+
6.9%
+
+
+
+
18
+
+
89
+
6.1%
+
+
+
+
17
+
+
82
+
5.6%
+
+
+
+
16
+
+
78
+
5.3%
+
+
+
+
19
+
+
76
+
5.2%
+
+
+
+
22
+
+
56
+
3.8%
+
+
+
+
20
+
+
55
+
3.7%
+
+
+
+
21
+
+
48
+
3.3%
+
+
+
+
23
+
+
28
+
1.9%
+
+
+
+
24
+
+
21
+
1.4%
+
+
+
+
25
+
+
18
+
1.2%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
11
+
+
210
+
14.3%
+
+
+
+
12
+
+
198
+
13.5%
+
+
+
+
13
+
+
209
+
14.2%
+
+
+
+
14
+
+
201
+
13.7%
+
+
+
+
15
+
+
101
+
6.9%
+
+
+
+
16
+
+
78
+
5.3%
+
+
+
+
17
+
+
82
+
5.6%
+
+
+
+
18
+
+
89
+
6.1%
+
+
+
+
19
+
+
76
+
5.2%
+
+
+
+
20
+
+
55
+
3.7%
+
+
+
+
21
+
+
48
+
3.3%
+
+
+
+
22
+
+
56
+
3.8%
+
+
+
+
23
+
+
28
+
1.9%
+
+
+
+
24
+
+
21
+
1.4%
+
+
+
+
25
+
+
18
+
1.2%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
25
+
+
18
+
1.2%
+
+
+
+
24
+
+
21
+
1.4%
+
+
+
+
23
+
+
28
+
1.9%
+
+
+
+
22
+
+
56
+
3.8%
+
+
+
+
21
+
+
48
+
3.3%
+
+
+
+
20
+
+
55
+
3.7%
+
+
+
+
19
+
+
76
+
5.2%
+
+
+
+
18
+
+
89
+
6.1%
+
+
+
+
17
+
+
82
+
5.6%
+
+
+
+
16
+
+
78
+
5.3%
+
+
+
+
15
+
+
101
+
6.9%
+
+
+
+
14
+
+
201
+
13.7%
+
+
+
+
13
+
+
209
+
14.2%
+
+
+
+
12
+
+
198
+
13.5%
+
+
+
+
11
+
+
210
+
14.3%
+
+
+
+
+ +
+
+
+
+ +
+
+ PerformanceRating +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
1,244
+
85%
+
+ +
+
+ +
4
+ + +
+
226
+
15%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
PerformanceRating
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Department
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Education
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON PerformanceRating:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
JobSatisfaction
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
Department
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
PerformanceRating
+ CORRELATION RATIO WITH...
+
+
+
PercentSalaryHike
+
0.77
+
+
+
YearsInCurrentRole
+
0.03
+
+
+
DistanceFromHome
+
0.03
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsSinceLastPromotion
+
0.02
+
+
+
MonthlyIncome
+
0.02
+
+
+
MonthlyRate
+
0.01
+
+
+
TotalWorkingYears
+
0.01
+
+
+
YearsAtCompany
+
0.00
+
+
+
HourlyRate
+
0.00
+
+
+
Age
+
0.00
+
+
+
DailyRate
+
0.00
+
+
+
+
+
+
+
+ +
+
+ RelationshipSatisfaction +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
459
+
31%
+
+ +
+
+ +
4
+ + +
+
432
+
29%
+
+ +
+
+ +
2
+ + +
+
303
+
21%
+
+ +
+
+ +
1
+ + +
+
276
+
19%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
RelationshipSatisfaction
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
EducationField
+
0.01
+
+
+
JobRole
+
0.00
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
Education
+
0.00
+
+
+
Department
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON RelationshipSatisfaction:
+
+
+
JobRole
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
Education
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
Department
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
OverTime
+
0.00
+
+
+
BusinessTravel
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
RelationshipSatisfaction
+ CORRELATION RATIO WITH...
+
+
+
YearsSinceLastPromotion
+
0.09
+
+
+
EmployeeNumber
+
0.08
+
+
+
YearsWithCurrManager
+
0.06
+
+
+
Age
+
0.06
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
HourlyRate
+
0.05
+
+
+
YearsAtCompany
+
0.05
+
+
+
MonthlyRate
+
0.05
+
+
+
PercentSalaryHike
+
0.05
+
+
+
DistanceFromHome
+
0.04
+
+
+
MonthlyIncome
+
0.03
+
+
+
DailyRate
+
0.03
+
+
+
TotalWorkingYears
+
0.02
+
+
+
+
+
+
+
+ +
+
+ StandardHours +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
80
+ + +
+
1,470
+
100%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
StandardHours
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
BusinessTravel
+
0.00
+
+
+
WorkLifeBalance
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
Education
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Gender
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
JobRole
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON StandardHours:
+
+
+
Attrition
+
1.00
+
+
+
BusinessTravel
+
1.00
+
+
+
Department
+
1.00
+
+
+
Education
+
1.00
+
+
+
EducationField
+
1.00
+
+
+
EmployeeCount
+
1.00
+
+
+
EnvironmentSatisfaction
+
1.00
+
+
+
Gender
+
1.00
+
+
+
JobInvolvement
+
1.00
+
+
+
JobLevel
+
1.00
+
+
+
JobRole
+
1.00
+
+
+
JobSatisfaction
+
1.00
+
+
+
MaritalStatus
+
1.00
+
+
+
NumCompaniesWorked
+
1.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
StandardHours
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.00
+
+
+
DailyRate
+
0.00
+
+
+
DistanceFromHome
+
0.00
+
+
+
EmployeeNumber
+
0.00
+
+
+
HourlyRate
+
0.00
+
+
+
MonthlyIncome
+
0.00
+
+
+
MonthlyRate
+
0.00
+
+
+
PercentSalaryHike
+
0.00
+
+
+
TotalWorkingYears
+
0.00
+
+
+
YearsAtCompany
+
0.00
+
+
+
YearsInCurrentRole
+
0.00
+
+
+
YearsSinceLastPromotion
+
0.00
+
+
+
YearsWithCurrManager
+
0.00
+
+
+
+
+
+
+
+ +
+
+ StockOptionLevel +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
0
+ + +
+
631
+
43%
+
+ +
+
+ +
1
+ + +
+
596
+
41%
+
+ +
+
+ +
2
+ + +
+
158
+
11%
+
+ +
+
+ +
3
+ + +
+
85
+
6%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
StockOptionLevel
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
MaritalStatus
+
0.40
+
+
+
Attrition
+
0.05
+
+
+
JobLevel
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
WorkLifeBalance
+
0.01
+
+
+
NumCompaniesWorked
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON StockOptionLevel:
+
+
+
MaritalStatus
+
0.37
+
+
+
Attrition
+
0.02
+
+
+
JobLevel
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
WorkLifeBalance
+
0.00
+
+
+
Education
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Department
+
0.00
+
+
+
OverTime
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
StockOptionLevel
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.11
+
+
+
TotalWorkingYears
+
0.10
+
+
+
MonthlyIncome
+
0.09
+
+
+
DistanceFromHome
+
0.09
+
+
+
YearsAtCompany
+
0.08
+
+
+
YearsInCurrentRole
+
0.08
+
+
+
HourlyRate
+
0.07
+
+
+
YearsWithCurrManager
+
0.06
+
+
+
EmployeeNumber
+
0.06
+
+
+
DailyRate
+
0.05
+
+
+
PercentSalaryHike
+
0.05
+
+
+
MonthlyRate
+
0.05
+
+
+
YearsSinceLastPromotion
+
0.05
+
+
+
+
+
+
+
+ +
+
+ TotalWorkingYears +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
MonthlyIncome
+
0.77
+
+
+
Age
+
0.68
+
+
+
YearsAtCompany
+
0.63
+
+
+
YearsInCurrentRole
+
0.46
+
+
+
YearsWithCurrManager
+
0.46
+
+
+
YearsSinceLastPromotion
+
0.40
+
+
+
MonthlyRate
+
0.03
+
+
+
PercentSalaryHike
+
-0.02
+
+
+
DailyRate
+
0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
DistanceFromHome
+
0.00
+
+
+
HourlyRate
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.80
+
+
+
JobRole
+
0.66
+
+
+
NumCompaniesWorked
+
0.37
+
+
+
Attrition
+
0.17
+
+
+
Education
+
0.15
+
+
+
StockOptionLevel
+
0.10
+
+
+
MaritalStatus
+
0.09
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
EducationField
+
0.06
+
+
+
Gender
+
0.05
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
BusinessTravel
+
0.03
+
+
+
WorkLifeBalance
+
0.03
+
+
+
RelationshipSatisfaction
+
0.02
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
10
+
+
202
+
13.7%
+
+
+
+
6
+
+
125
+
8.5%
+
+
+
+
8
+
+
103
+
7.0%
+
+
+
+
9
+
+
96
+
6.5%
+
+
+
+
5
+
+
88
+
6.0%
+
+
+
+
7
+
+
81
+
5.5%
+
+
+
+
1
+
+
81
+
5.5%
+
+
+
+
4
+
+
63
+
4.3%
+
+
+
+
12
+
+
48
+
3.3%
+
+
+
+
3
+
+
42
+
2.9%
+
+
+
+
15
+
+
40
+
2.7%
+
+
+
+
16
+
+
37
+
2.5%
+
+
+
+
11
+
+
36
+
2.4%
+
+
+
+
13
+
+
36
+
2.4%
+
+
+
+
21
+
+
34
+
2.3%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
0
+
+
11
+
0.7%
+
+
+
+
1
+
+
81
+
5.5%
+
+
+
+
2
+
+
31
+
2.1%
+
+
+
+
3
+
+
42
+
2.9%
+
+
+
+
4
+
+
63
+
4.3%
+
+
+
+
5
+
+
88
+
6.0%
+
+
+
+
6
+
+
125
+
8.5%
+
+
+
+
7
+
+
81
+
5.5%
+
+
+
+
8
+
+
103
+
7.0%
+
+
+
+
9
+
+
96
+
6.5%
+
+
+
+
10
+
+
202
+
13.7%
+
+
+
+
11
+
+
36
+
2.4%
+
+
+
+
12
+
+
48
+
3.3%
+
+
+
+
13
+
+
36
+
2.4%
+
+
+
+
14
+
+
31
+
2.1%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
40
+
+
2
+
0.1%
+
+
+
+
38
+
+
1
+
<0.1%
+
+
+
+
37
+
+
4
+
0.3%
+
+
+
+
36
+
+
6
+
0.4%
+
+
+
+
35
+
+
3
+
0.2%
+
+
+
+
34
+
+
5
+
0.3%
+
+
+
+
33
+
+
7
+
0.5%
+
+
+
+
32
+
+
9
+
0.6%
+
+
+
+
31
+
+
9
+
0.6%
+
+
+
+
30
+
+
7
+
0.5%
+
+
+
+
29
+
+
10
+
0.7%
+
+
+
+
28
+
+
14
+
1.0%
+
+
+
+
27
+
+
7
+
0.5%
+
+
+
+
26
+
+
14
+
1.0%
+
+
+
+
25
+
+
14
+
1.0%
+
+
+
+
+ +
+
+
+
+ +
+
+ TrainingTimesLastYear +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
2
+ + +
+
547
+
37%
+
+ +
+
+ +
3
+ + +
+
491
+
33%
+
+ +
+
+ +
4
+ + +
+
123
+
8%
+
+ +
+
+ +
5
+ + +
+
119
+
8%
+
+ +
+
+ +
1
+ + +
+
71
+
5%
+
+ +
+
+ +
6
+ + +
+
65
+
4%
+
+ +
+
+ +
0
+ + +
+
54
+
4%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
TrainingTimesLastYear
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
OverTime
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
Attrition
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
Education
+
0.01
+
+
+
JobLevel
+
0.01
+
+
+
JobInvolvement
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
Department
+
0.01
+
+
+
JobSatisfaction
+
0.01
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON TrainingTimesLastYear:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
Education
+
0.01
+
+
+
JobLevel
+
0.01
+
+
+
OverTime
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
Attrition
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
Department
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
TrainingTimesLastYear
+ CORRELATION RATIO WITH...
+
+
+
Age
+
0.09
+
+
+
EmployeeNumber
+
0.07
+
+
+
YearsAtCompany
+
0.07
+
+
+
TotalWorkingYears
+
0.07
+
+
+
MonthlyRate
+
0.06
+
+
+
DistanceFromHome
+
0.06
+
+
+
HourlyRate
+
0.05
+
+
+
DailyRate
+
0.05
+
+
+
YearsWithCurrManager
+
0.05
+
+
+
YearsInCurrentRole
+
0.05
+
+
+
MonthlyIncome
+
0.04
+
+
+
YearsSinceLastPromotion
+
0.04
+
+
+
PercentSalaryHike
+
0.03
+
+
+
+
+
+
+
+ +
+
+ WorkLifeBalance +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + + +
+ +
+
TOP CATEGORIES
+
+
+ +
+ +
+
+ + +
+ +
3
+ + +
+
893
+
61%
+
+ +
+
+ +
2
+ + +
+
344
+
23%
+
+ +
+
+ +
4
+ + +
+
153
+
10%
+
+ +
+
+ +
1
+ + +
+
80
+
5%
+
+ +
+
+
+
+ +
ALL
+ + +
+
1,470
+
100%
+
+ +
+
+
+ + +
+ + +
+ +
+ CATEGORICAL ASSOCIATIONS
+ (UNCERTAINTY COEFFICIENT, 0 to 1) +
+
WorkLifeBalance
+ PROVIDES INFORMATION ON...
+
+
+
EmployeeCount
+
1.00
+
+
+
Over18
+
1.00
+
+
+
StandardHours
+
1.00
+
+
+
Attrition
+
0.01
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
Department
+
0.01
+
+
+
JobRole
+
0.00
+
+
+
StockOptionLevel
+
0.00
+
+
+
EducationField
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
THESE FEATURES
GIVE INFORMATION
+ ON WorkLifeBalance:
+
+
+
NumCompaniesWorked
+
0.01
+
+
+
JobRole
+
0.01
+
+
+
EducationField
+
0.01
+
+
+
StockOptionLevel
+
0.01
+
+
+
Attrition
+
0.00
+
+
+
Department
+
0.00
+
+
+
JobLevel
+
0.00
+
+
+
Education
+
0.00
+
+
+
JobSatisfaction
+
0.00
+
+
+
TrainingTimesLastYear
+
0.00
+
+
+
EnvironmentSatisfaction
+
0.00
+
+
+
RelationshipSatisfaction
+
0.00
+
+
+
JobInvolvement
+
0.00
+
+
+
MaritalStatus
+
0.00
+
+
+ +
+ NUMERICAL ASSOCIATIONS
+ (CORRELATION RATIO, 0 to 1) +
+
WorkLifeBalance
+ CORRELATION RATIO WITH...
+
+
+
YearsInCurrentRole
+
0.06
+
+
+
PercentSalaryHike
+
0.05
+
+
+
DailyRate
+
0.04
+
+
+
DistanceFromHome
+
0.04
+
+
+
HourlyRate
+
0.04
+
+
+
MonthlyIncome
+
0.04
+
+
+
YearsSinceLastPromotion
+
0.03
+
+
+
MonthlyRate
+
0.03
+
+
+
YearsAtCompany
+
0.03
+
+
+
TotalWorkingYears
+
0.03
+
+
+
Age
+
0.02
+
+
+
EmployeeNumber
+
0.02
+
+
+
YearsWithCurrManager
+
0.02
+
+
+
+
+
+
+
+ +
+
+ YearsAtCompany +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
YearsWithCurrManager
+
0.77
+
+
+
YearsInCurrentRole
+
0.76
+
+
+
TotalWorkingYears
+
0.63
+
+
+
YearsSinceLastPromotion
+
0.62
+
+
+
MonthlyIncome
+
0.51
+
+
+
Age
+
0.31
+
+
+
PercentSalaryHike
+
-0.04
+
+
+
DailyRate
+
-0.03
+
+
+
MonthlyRate
+
-0.02
+
+
+
HourlyRate
+
-0.02
+
+
+
EmployeeNumber
+
-0.01
+
+
+
DistanceFromHome
+
0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.54
+
+
+
JobRole
+
0.44
+
+
+
NumCompaniesWorked
+
0.18
+
+
+
Attrition
+
0.13
+
+
+
StockOptionLevel
+
0.08
+
+
+
Education
+
0.08
+
+
+
MaritalStatus
+
0.07
+
+
+
TrainingTimesLastYear
+
0.07
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
Department
+
0.03
+
+
+
JobInvolvement
+
0.03
+
+
+
Gender
+
0.03
+
+
+
WorkLifeBalance
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
5
+
+
196
+
13.3%
+
+
+
+
1
+
+
171
+
11.6%
+
+
+
+
3
+
+
128
+
8.7%
+
+
+
+
2
+
+
127
+
8.6%
+
+
+
+
10
+
+
120
+
8.2%
+
+
+
+
4
+
+
110
+
7.5%
+
+
+
+
7
+
+
90
+
6.1%
+
+
+
+
9
+
+
82
+
5.6%
+
+
+
+
8
+
+
80
+
5.4%
+
+
+
+
6
+
+
76
+
5.2%
+
+
+
+
0
+
+
44
+
3.0%
+
+
+
+
11
+
+
32
+
2.2%
+
+
+
+
20
+
+
27
+
1.8%
+
+
+
+
13
+
+
24
+
1.6%
+
+
+
+
15
+
+
20
+
1.4%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
0
+
+
44
+
3.0%
+
+
+
+
1
+
+
171
+
11.6%
+
+
+
+
2
+
+
127
+
8.6%
+
+
+
+
3
+
+
128
+
8.7%
+
+
+
+
4
+
+
110
+
7.5%
+
+
+
+
5
+
+
196
+
13.3%
+
+
+
+
6
+
+
76
+
5.2%
+
+
+
+
7
+
+
90
+
6.1%
+
+
+
+
8
+
+
80
+
5.4%
+
+
+
+
9
+
+
82
+
5.6%
+
+
+
+
10
+
+
120
+
8.2%
+
+
+
+
11
+
+
32
+
2.2%
+
+
+
+
12
+
+
14
+
1.0%
+
+
+
+
13
+
+
24
+
1.6%
+
+
+
+
14
+
+
18
+
1.2%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
40
+
+
1
+
<0.1%
+
+
+
+
37
+
+
1
+
<0.1%
+
+
+
+
36
+
+
2
+
0.1%
+
+
+
+
34
+
+
1
+
<0.1%
+
+
+
+
33
+
+
5
+
0.3%
+
+
+
+
32
+
+
3
+
0.2%
+
+
+
+
31
+
+
3
+
0.2%
+
+
+
+
30
+
+
1
+
<0.1%
+
+
+
+
29
+
+
2
+
0.1%
+
+
+
+
27
+
+
2
+
0.1%
+
+
+
+
26
+
+
4
+
0.3%
+
+
+
+
25
+
+
4
+
0.3%
+
+
+
+
24
+
+
6
+
0.4%
+
+
+
+
23
+
+
2
+
0.1%
+
+
+
+
22
+
+
15
+
1.0%
+
+
+
+
+ +
+
+
+
+ +
+
+ YearsInCurrentRole +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
YearsAtCompany
+
0.76
+
+
+
YearsWithCurrManager
+
0.71
+
+
+
YearsSinceLastPromotion
+
0.55
+
+
+
TotalWorkingYears
+
0.46
+
+
+
MonthlyIncome
+
0.36
+
+
+
Age
+
0.21
+
+
+
HourlyRate
+
-0.02
+
+
+
DistanceFromHome
+
0.02
+
+
+
MonthlyRate
+
-0.01
+
+
+
DailyRate
+
0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
PercentSalaryHike
+
-0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.41
+
+
+
JobRole
+
0.33
+
+
+
Attrition
+
0.16
+
+
+
NumCompaniesWorked
+
0.16
+
+
+
MaritalStatus
+
0.09
+
+
+
StockOptionLevel
+
0.08
+
+
+
Education
+
0.06
+
+
+
Department
+
0.06
+
+
+
WorkLifeBalance
+
0.06
+
+
+
RelationshipSatisfaction
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
Gender
+
0.04
+
+
+
EnvironmentSatisfaction
+
0.04
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
2
+
+
372
+
25.3%
+
+
+
+
0
+
+
244
+
16.6%
+
+
+
+
7
+
+
222
+
15.1%
+
+
+
+
3
+
+
135
+
9.2%
+
+
+
+
4
+
+
104
+
7.1%
+
+
+
+
8
+
+
89
+
6.1%
+
+
+
+
9
+
+
67
+
4.6%
+
+
+
+
1
+
+
57
+
3.9%
+
+
+
+
6
+
+
37
+
2.5%
+
+
+
+
5
+
+
36
+
2.4%
+
+
+
+
10
+
+
29
+
2.0%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
14
+
+
11
+
0.7%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
0
+
+
244
+
16.6%
+
+
+
+
1
+
+
57
+
3.9%
+
+
+
+
2
+
+
372
+
25.3%
+
+
+
+
3
+
+
135
+
9.2%
+
+
+
+
4
+
+
104
+
7.1%
+
+
+
+
5
+
+
36
+
2.4%
+
+
+
+
6
+
+
37
+
2.5%
+
+
+
+
7
+
+
222
+
15.1%
+
+
+
+
8
+
+
89
+
6.1%
+
+
+
+
9
+
+
67
+
4.6%
+
+
+
+
10
+
+
29
+
2.0%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
14
+
+
11
+
0.7%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
18
+
+
2
+
0.1%
+
+
+
+
17
+
+
4
+
0.3%
+
+
+
+
16
+
+
7
+
0.5%
+
+
+
+
15
+
+
8
+
0.5%
+
+
+
+
14
+
+
11
+
0.7%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
10
+
+
29
+
2.0%
+
+
+
+
9
+
+
67
+
4.6%
+
+
+
+
8
+
+
89
+
6.1%
+
+
+
+
7
+
+
222
+
15.1%
+
+
+
+
6
+
+
37
+
2.5%
+
+
+
+
5
+
+
36
+
2.4%
+
+
+
+
4
+
+
104
+
7.1%
+
+
+
+
+ +
+
+
+
+ +
+
+ YearsSinceLastPromotion +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
YearsAtCompany
+
0.62
+
+
+
YearsInCurrentRole
+
0.55
+
+
+
YearsWithCurrManager
+
0.51
+
+
+
TotalWorkingYears
+
0.40
+
+
+
MonthlyIncome
+
0.34
+
+
+
Age
+
0.22
+
+
+
DailyRate
+
-0.03
+
+
+
HourlyRate
+
-0.03
+
+
+
PercentSalaryHike
+
-0.02
+
+
+
DistanceFromHome
+
0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
MonthlyRate
+
0.00
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.36
+
+
+
JobRole
+
0.30
+
+
+
NumCompaniesWorked
+
0.09
+
+
+
RelationshipSatisfaction
+
0.09
+
+
+
MaritalStatus
+
0.06
+
+
+
Education
+
0.06
+
+
+
EducationField
+
0.05
+
+
+
StockOptionLevel
+
0.05
+
+
+
Department
+
0.04
+
+
+
TrainingTimesLastYear
+
0.04
+
+
+
EnvironmentSatisfaction
+
0.03
+
+
+
BusinessTravel
+
0.03
+
+
+
Attrition
+
0.03
+
+
+
JobInvolvement
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
0
+
+
581
+
39.5%
+
+
+
+
1
+
+
357
+
24.3%
+
+
+
+
2
+
+
159
+
10.8%
+
+
+
+
7
+
+
76
+
5.2%
+
+
+
+
4
+
+
61
+
4.1%
+
+
+
+
3
+
+
52
+
3.5%
+
+
+
+
5
+
+
45
+
3.1%
+
+
+
+
6
+
+
32
+
2.2%
+
+
+
+
11
+
+
24
+
1.6%
+
+
+
+
8
+
+
18
+
1.2%
+
+
+
+
9
+
+
17
+
1.2%
+
+
+
+
15
+
+
13
+
0.9%
+
+
+
+
13
+
+
10
+
0.7%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
14
+
+
9
+
0.6%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
0
+
+
581
+
39.5%
+
+
+
+
1
+
+
357
+
24.3%
+
+
+
+
2
+
+
159
+
10.8%
+
+
+
+
3
+
+
52
+
3.5%
+
+
+
+
4
+
+
61
+
4.1%
+
+
+
+
5
+
+
45
+
3.1%
+
+
+
+
6
+
+
32
+
2.2%
+
+
+
+
7
+
+
76
+
5.2%
+
+
+
+
8
+
+
18
+
1.2%
+
+
+
+
9
+
+
17
+
1.2%
+
+
+
+
10
+
+
6
+
0.4%
+
+
+
+
11
+
+
24
+
1.6%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
13
+
+
10
+
0.7%
+
+
+
+
14
+
+
9
+
0.6%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
15
+
+
13
+
0.9%
+
+
+
+
14
+
+
9
+
0.6%
+
+
+
+
13
+
+
10
+
0.7%
+
+
+
+
12
+
+
10
+
0.7%
+
+
+
+
11
+
+
24
+
1.6%
+
+
+
+
10
+
+
6
+
0.4%
+
+
+
+
9
+
+
17
+
1.2%
+
+
+
+
8
+
+
18
+
1.2%
+
+
+
+
7
+
+
76
+
5.2%
+
+
+
+
6
+
+
32
+
2.2%
+
+
+
+
5
+
+
45
+
3.1%
+
+
+
+
4
+
+
61
+
4.1%
+
+
+
+
3
+
+
52
+
3.5%
+
+
+
+
2
+
+
159
+
10.8%
+
+
+
+
1
+
+
357
+
24.3%
+
+
+
+
+ +
+
+
+
+ +
+
+ YearsWithCurrManager +
+
+
+
MISSING:
+
+ --- +
+
+ +
+
+
+ + +
+ + + + +
+ + + + + +
+ + > +
+
NUMERICAL ASSOCIATIONS
+
+ (PEARSON, -1 to 1) +
+ +
+
+
YearsAtCompany
+
0.77
+
+
+
YearsInCurrentRole
+
0.71
+
+
+
YearsSinceLastPromotion
+
0.51
+
+
+
TotalWorkingYears
+
0.46
+
+
+
MonthlyIncome
+
0.34
+
+
+
Age
+
0.20
+
+
+
MonthlyRate
+
-0.04
+
+
+
DailyRate
+
-0.03
+
+
+
HourlyRate
+
-0.02
+
+
+
DistanceFromHome
+
0.01
+
+
+
PercentSalaryHike
+
-0.01
+
+
+
EmployeeNumber
+
-0.01
+
+
+
CATEGORICAL ASSOCIATIONS
+
+ (CORRELATION RATIO, 0 to 1) +
+
+
+
JobLevel
+
0.39
+
+
+
JobRole
+
0.32
+
+
+
NumCompaniesWorked
+
0.16
+
+
+
Attrition
+
0.16
+
+
+
Education
+
0.07
+
+
+
StockOptionLevel
+
0.06
+
+
+
RelationshipSatisfaction
+
0.06
+
+
+
MaritalStatus
+
0.05
+
+
+
TrainingTimesLastYear
+
0.05
+
+
+
EducationField
+
0.05
+
+
+
OverTime
+
0.04
+
+
+
JobSatisfaction
+
0.04
+
+
+
Department
+
0.04
+
+
+
JobInvolvement
+
0.03
+
+
+
+ +
+ +
+
MOST FREQUENT VALUES
+
+
+
2
+
+
344
+
23.4%
+
+
+
+
0
+
+
263
+
17.9%
+
+
+
+
7
+
+
216
+
14.7%
+
+
+
+
3
+
+
142
+
9.7%
+
+
+
+
8
+
+
107
+
7.3%
+
+
+
+
4
+
+
98
+
6.7%
+
+
+
+
1
+
+
76
+
5.2%
+
+
+
+
9
+
+
64
+
4.4%
+
+
+
+
5
+
+
31
+
2.1%
+
+
+
+
6
+
+
29
+
2.0%
+
+
+
+
10
+
+
27
+
1.8%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
12
+
+
18
+
1.2%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
17
+
+
7
+
0.5%
+
+
+
+
+ +
+ +
+
SMALLEST VALUES
+
+
+
0
+
+
263
+
17.9%
+
+
+
+
1
+
+
76
+
5.2%
+
+
+
+
2
+
+
344
+
23.4%
+
+
+
+
3
+
+
142
+
9.7%
+
+
+
+
4
+
+
98
+
6.7%
+
+
+
+
5
+
+
31
+
2.1%
+
+
+
+
6
+
+
29
+
2.0%
+
+
+
+
7
+
+
216
+
14.7%
+
+
+
+
8
+
+
107
+
7.3%
+
+
+
+
9
+
+
64
+
4.4%
+
+
+
+
10
+
+
27
+
1.8%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
12
+
+
18
+
1.2%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
14
+
+
5
+
0.3%
+
+
+
+
+ +
+ +
+
LARGEST VALUES
+
+
+
17
+
+
7
+
0.5%
+
+
+
+
16
+
+
2
+
0.1%
+
+
+
+
15
+
+
5
+
0.3%
+
+
+
+
14
+
+
5
+
0.3%
+
+
+
+
13
+
+
14
+
1.0%
+
+
+
+
12
+
+
18
+
1.2%
+
+
+
+
11
+
+
22
+
1.5%
+
+
+
+
10
+
+
27
+
1.8%
+
+
+
+
9
+
+
64
+
4.4%
+
+
+
+
8
+
+
107
+
7.3%
+
+
+
+
7
+
+
216
+
14.7%
+
+
+
+
6
+
+
29
+
2.0%
+
+
+
+
5
+
+
31
+
2.1%
+
+
+
+
4
+
+
98
+
6.7%
+
+
+
+
3
+
+
142
+
9.7%
+
+
+
+
+ +
+
+
+
+ + \ No newline at end of file From 54941e124e8d1947aeef96c583c2d8151007739d Mon Sep 17 00:00:00 2001 From: Abhishek Raut Date: Sun, 29 Aug 2021 07:20:52 +0530 Subject: [PATCH 2/4] Re-Arranging Files --- 007/solution/employee_attrition.csv | 1471 --------------------------- 1 file changed, 1471 deletions(-) delete mode 100644 007/solution/employee_attrition.csv diff --git a/007/solution/employee_attrition.csv b/007/solution/employee_attrition.csv deleted file mode 100644 index 7838fe03..00000000 --- a/007/solution/employee_attrition.csv +++ /dev/null @@ -1,1471 +0,0 @@ -Age,Attrition,BusinessTravel,DailyRate,Department,DistanceFromHome,Education,EducationField,EmployeeCount,EmployeeNumber,EnvironmentSatisfaction,Gender,HourlyRate,JobInvolvement,JobLevel,JobRole,JobSatisfaction,MaritalStatus,MonthlyIncome,MonthlyRate,NumCompaniesWorked,Over18,OverTime,PercentSalaryHike,PerformanceRating,RelationshipSatisfaction,StandardHours,StockOptionLevel,TotalWorkingYears,TrainingTimesLastYear,WorkLifeBalance,YearsAtCompany,YearsInCurrentRole,YearsSinceLastPromotion,YearsWithCurrManager -41,Yes,Travel_Rarely,1102,Sales,1,2,Life Sciences,1,1,2,Female,94,3,2,Sales Executive,4,Single,5993,19479,8,Y,Yes,11,3,1,80,0,8,0,1,6,4,0,5 -49,No,Travel_Frequently,279,Research & Development,8,1,Life Sciences,1,2,3,Male,61,2,2,Research Scientist,2,Married,5130,24907,1,Y,No,23,4,4,80,1,10,3,3,10,7,1,7 -37,Yes,Travel_Rarely,1373,Research & Development,2,2,Other,1,4,4,Male,92,2,1,Laboratory Technician,3,Single,2090,2396,6,Y,Yes,15,3,2,80,0,7,3,3,0,0,0,0 -33,No,Travel_Frequently,1392,Research & Development,3,4,Life Sciences,1,5,4,Female,56,3,1,Research Scientist,3,Married,2909,23159,1,Y,Yes,11,3,3,80,0,8,3,3,8,7,3,0 -27,No,Travel_Rarely,591,Research & Development,2,1,Medical,1,7,1,Male,40,3,1,Laboratory Technician,2,Married,3468,16632,9,Y,No,12,3,4,80,1,6,3,3,2,2,2,2 -32,No,Travel_Frequently,1005,Research & Development,2,2,Life Sciences,1,8,4,Male,79,3,1,Laboratory Technician,4,Single,3068,11864,0,Y,No,13,3,3,80,0,8,2,2,7,7,3,6 -59,No,Travel_Rarely,1324,Research & Development,3,3,Medical,1,10,3,Female,81,4,1,Laboratory Technician,1,Married,2670,9964,4,Y,Yes,20,4,1,80,3,12,3,2,1,0,0,0 -30,No,Travel_Rarely,1358,Research & Development,24,1,Life Sciences,1,11,4,Male,67,3,1,Laboratory Technician,3,Divorced,2693,13335,1,Y,No,22,4,2,80,1,1,2,3,1,0,0,0 -38,No,Travel_Frequently,216,Research & Development,23,3,Life Sciences,1,12,4,Male,44,2,3,Manufacturing Director,3,Single,9526,8787,0,Y,No,21,4,2,80,0,10,2,3,9,7,1,8 -36,No,Travel_Rarely,1299,Research & Development,27,3,Medical,1,13,3,Male,94,3,2,Healthcare Representative,3,Married,5237,16577,6,Y,No,13,3,2,80,2,17,3,2,7,7,7,7 -35,No,Travel_Rarely,809,Research & Development,16,3,Medical,1,14,1,Male,84,4,1,Laboratory Technician,2,Married,2426,16479,0,Y,No,13,3,3,80,1,6,5,3,5,4,0,3 -29,No,Travel_Rarely,153,Research & Development,15,2,Life Sciences,1,15,4,Female,49,2,2,Laboratory Technician,3,Single,4193,12682,0,Y,Yes,12,3,4,80,0,10,3,3,9,5,0,8 -31,No,Travel_Rarely,670,Research & Development,26,1,Life Sciences,1,16,1,Male,31,3,1,Research Scientist,3,Divorced,2911,15170,1,Y,No,17,3,4,80,1,5,1,2,5,2,4,3 -34,No,Travel_Rarely,1346,Research & Development,19,2,Medical,1,18,2,Male,93,3,1,Laboratory Technician,4,Divorced,2661,8758,0,Y,No,11,3,3,80,1,3,2,3,2,2,1,2 -28,Yes,Travel_Rarely,103,Research & Development,24,3,Life Sciences,1,19,3,Male,50,2,1,Laboratory Technician,3,Single,2028,12947,5,Y,Yes,14,3,2,80,0,6,4,3,4,2,0,3 -29,No,Travel_Rarely,1389,Research & Development,21,4,Life Sciences,1,20,2,Female,51,4,3,Manufacturing Director,1,Divorced,9980,10195,1,Y,No,11,3,3,80,1,10,1,3,10,9,8,8 -32,No,Travel_Rarely,334,Research & Development,5,2,Life Sciences,1,21,1,Male,80,4,1,Research Scientist,2,Divorced,3298,15053,0,Y,Yes,12,3,4,80,2,7,5,2,6,2,0,5 -22,No,Non-Travel,1123,Research & Development,16,2,Medical,1,22,4,Male,96,4,1,Laboratory Technician,4,Divorced,2935,7324,1,Y,Yes,13,3,2,80,2,1,2,2,1,0,0,0 -53,No,Travel_Rarely,1219,Sales,2,4,Life Sciences,1,23,1,Female,78,2,4,Manager,4,Married,15427,22021,2,Y,No,16,3,3,80,0,31,3,3,25,8,3,7 -38,No,Travel_Rarely,371,Research & Development,2,3,Life Sciences,1,24,4,Male,45,3,1,Research Scientist,4,Single,3944,4306,5,Y,Yes,11,3,3,80,0,6,3,3,3,2,1,2 -24,No,Non-Travel,673,Research & Development,11,2,Other,1,26,1,Female,96,4,2,Manufacturing Director,3,Divorced,4011,8232,0,Y,No,18,3,4,80,1,5,5,2,4,2,1,3 -36,Yes,Travel_Rarely,1218,Sales,9,4,Life Sciences,1,27,3,Male,82,2,1,Sales Representative,1,Single,3407,6986,7,Y,No,23,4,2,80,0,10,4,3,5,3,0,3 -34,No,Travel_Rarely,419,Research & Development,7,4,Life Sciences,1,28,1,Female,53,3,3,Research Director,2,Single,11994,21293,0,Y,No,11,3,3,80,0,13,4,3,12,6,2,11 -21,No,Travel_Rarely,391,Research & Development,15,2,Life Sciences,1,30,3,Male,96,3,1,Research Scientist,4,Single,1232,19281,1,Y,No,14,3,4,80,0,0,6,3,0,0,0,0 -34,Yes,Travel_Rarely,699,Research & Development,6,1,Medical,1,31,2,Male,83,3,1,Research Scientist,1,Single,2960,17102,2,Y,No,11,3,3,80,0,8,2,3,4,2,1,3 -53,No,Travel_Rarely,1282,Research & Development,5,3,Other,1,32,3,Female,58,3,5,Manager,3,Divorced,19094,10735,4,Y,No,11,3,4,80,1,26,3,2,14,13,4,8 -32,Yes,Travel_Frequently,1125,Research & Development,16,1,Life Sciences,1,33,2,Female,72,1,1,Research Scientist,1,Single,3919,4681,1,Y,Yes,22,4,2,80,0,10,5,3,10,2,6,7 -42,No,Travel_Rarely,691,Sales,8,4,Marketing,1,35,3,Male,48,3,2,Sales Executive,2,Married,6825,21173,0,Y,No,11,3,4,80,1,10,2,3,9,7,4,2 -44,No,Travel_Rarely,477,Research & Development,7,4,Medical,1,36,1,Female,42,2,3,Healthcare Representative,4,Married,10248,2094,3,Y,No,14,3,4,80,1,24,4,3,22,6,5,17 -46,No,Travel_Rarely,705,Sales,2,4,Marketing,1,38,2,Female,83,3,5,Manager,1,Single,18947,22822,3,Y,No,12,3,4,80,0,22,2,2,2,2,2,1 -33,No,Travel_Rarely,924,Research & Development,2,3,Medical,1,39,3,Male,78,3,1,Laboratory Technician,4,Single,2496,6670,4,Y,No,11,3,4,80,0,7,3,3,1,1,0,0 -44,No,Travel_Rarely,1459,Research & Development,10,4,Other,1,40,4,Male,41,3,2,Healthcare Representative,4,Married,6465,19121,2,Y,Yes,13,3,4,80,0,9,5,4,4,2,1,3 -30,No,Travel_Rarely,125,Research & Development,9,2,Medical,1,41,4,Male,83,2,1,Laboratory Technician,3,Single,2206,16117,1,Y,No,13,3,1,80,0,10,5,3,10,0,1,8 -39,Yes,Travel_Rarely,895,Sales,5,3,Technical Degree,1,42,4,Male,56,3,2,Sales Representative,4,Married,2086,3335,3,Y,No,14,3,3,80,1,19,6,4,1,0,0,0 -24,Yes,Travel_Rarely,813,Research & Development,1,3,Medical,1,45,2,Male,61,3,1,Research Scientist,4,Married,2293,3020,2,Y,Yes,16,3,1,80,1,6,2,2,2,0,2,0 -43,No,Travel_Rarely,1273,Research & Development,2,2,Medical,1,46,4,Female,72,4,1,Research Scientist,3,Divorced,2645,21923,1,Y,No,12,3,4,80,2,6,3,2,5,3,1,4 -50,Yes,Travel_Rarely,869,Sales,3,2,Marketing,1,47,1,Male,86,2,1,Sales Representative,3,Married,2683,3810,1,Y,Yes,14,3,3,80,0,3,2,3,3,2,0,2 -35,No,Travel_Rarely,890,Sales,2,3,Marketing,1,49,4,Female,97,3,1,Sales Representative,4,Married,2014,9687,1,Y,No,13,3,1,80,0,2,3,3,2,2,2,2 -36,No,Travel_Rarely,852,Research & Development,5,4,Life Sciences,1,51,2,Female,82,2,1,Research Scientist,1,Married,3419,13072,9,Y,Yes,14,3,4,80,1,6,3,4,1,1,0,0 -33,No,Travel_Frequently,1141,Sales,1,3,Life Sciences,1,52,3,Female,42,4,2,Sales Executive,1,Married,5376,3193,2,Y,No,19,3,1,80,2,10,3,3,5,3,1,3 -35,No,Travel_Rarely,464,Research & Development,4,2,Other,1,53,3,Male,75,3,1,Laboratory Technician,4,Divorced,1951,10910,1,Y,No,12,3,3,80,1,1,3,3,1,0,0,0 -27,No,Travel_Rarely,1240,Research & Development,2,4,Life Sciences,1,54,4,Female,33,3,1,Laboratory Technician,1,Divorced,2341,19715,1,Y,No,13,3,4,80,1,1,6,3,1,0,0,0 -26,Yes,Travel_Rarely,1357,Research & Development,25,3,Life Sciences,1,55,1,Male,48,1,1,Laboratory Technician,3,Single,2293,10558,1,Y,No,12,3,3,80,0,1,2,2,1,0,0,1 -27,No,Travel_Frequently,994,Sales,8,3,Life Sciences,1,56,4,Male,37,3,3,Sales Executive,3,Single,8726,2975,1,Y,No,15,3,4,80,0,9,0,3,9,8,1,7 -30,No,Travel_Frequently,721,Research & Development,1,2,Medical,1,57,3,Female,58,3,2,Laboratory Technician,4,Single,4011,10781,1,Y,No,23,4,4,80,0,12,2,3,12,8,3,7 -41,Yes,Travel_Rarely,1360,Research & Development,12,3,Technical Degree,1,58,2,Female,49,3,5,Research Director,3,Married,19545,16280,1,Y,No,12,3,4,80,0,23,0,3,22,15,15,8 -34,No,Non-Travel,1065,Sales,23,4,Marketing,1,60,2,Male,72,3,2,Sales Executive,3,Single,4568,10034,0,Y,No,20,4,3,80,0,10,2,3,9,5,8,7 -37,No,Travel_Rarely,408,Research & Development,19,2,Life Sciences,1,61,2,Male,73,3,1,Research Scientist,2,Married,3022,10227,4,Y,No,21,4,1,80,0,8,1,3,1,0,0,0 -46,No,Travel_Frequently,1211,Sales,5,4,Marketing,1,62,1,Male,98,3,2,Sales Executive,4,Single,5772,20445,4,Y,Yes,21,4,3,80,0,14,4,3,9,6,0,8 -35,No,Travel_Rarely,1229,Research & Development,8,1,Life Sciences,1,63,4,Male,36,4,1,Laboratory Technician,4,Married,2269,4892,1,Y,No,19,3,4,80,0,1,2,3,1,0,0,1 -48,Yes,Travel_Rarely,626,Research & Development,1,2,Life Sciences,1,64,1,Male,98,2,3,Laboratory Technician,3,Single,5381,19294,9,Y,Yes,13,3,4,80,0,23,2,3,1,0,0,0 -28,Yes,Travel_Rarely,1434,Research & Development,5,4,Technical Degree,1,65,3,Male,50,3,1,Laboratory Technician,3,Single,3441,11179,1,Y,Yes,13,3,3,80,0,2,3,2,2,2,2,2 -44,No,Travel_Rarely,1488,Sales,1,5,Marketing,1,68,2,Female,75,3,2,Sales Executive,1,Divorced,5454,4009,5,Y,Yes,21,4,3,80,1,9,2,2,4,3,1,3 -35,No,Non-Travel,1097,Research & Development,11,2,Medical,1,70,3,Male,79,2,3,Healthcare Representative,1,Married,9884,8302,2,Y,Yes,13,3,3,80,1,10,3,3,4,0,2,3 -26,No,Travel_Rarely,1443,Sales,23,3,Marketing,1,72,3,Female,47,2,2,Sales Executive,4,Married,4157,21436,7,Y,Yes,19,3,3,80,1,5,2,2,2,2,0,0 -33,No,Travel_Frequently,515,Research & Development,1,2,Life Sciences,1,73,1,Female,98,3,3,Research Director,4,Single,13458,15146,1,Y,Yes,12,3,3,80,0,15,1,3,15,14,8,12 -35,No,Travel_Frequently,853,Sales,18,5,Life Sciences,1,74,2,Male,71,3,3,Sales Executive,1,Married,9069,11031,1,Y,No,22,4,4,80,1,9,3,2,9,8,1,8 -35,No,Travel_Rarely,1142,Research & Development,23,4,Medical,1,75,3,Female,30,3,1,Laboratory Technician,1,Married,4014,16002,3,Y,Yes,15,3,3,80,1,4,3,3,2,2,2,2 -31,No,Travel_Rarely,655,Research & Development,7,4,Life Sciences,1,76,4,Male,48,3,2,Laboratory Technician,4,Divorced,5915,9528,3,Y,No,22,4,4,80,1,10,3,2,7,7,1,7 -37,No,Travel_Rarely,1115,Research & Development,1,4,Life Sciences,1,77,1,Male,51,2,2,Manufacturing Director,3,Divorced,5993,2689,1,Y,No,18,3,3,80,1,7,2,4,7,5,0,7 -32,No,Travel_Rarely,427,Research & Development,1,3,Medical,1,78,1,Male,33,3,2,Manufacturing Director,4,Married,6162,10877,1,Y,Yes,22,4,2,80,1,9,3,3,9,8,7,8 -38,No,Travel_Frequently,653,Research & Development,29,5,Life Sciences,1,79,4,Female,50,3,2,Laboratory Technician,4,Single,2406,5456,1,Y,No,11,3,4,80,0,10,2,3,10,3,9,9 -50,No,Travel_Rarely,989,Research & Development,7,2,Medical,1,80,2,Female,43,2,5,Research Director,3,Divorced,18740,16701,5,Y,Yes,12,3,4,80,1,29,2,2,27,3,13,8 -59,No,Travel_Rarely,1435,Sales,25,3,Life Sciences,1,81,1,Female,99,3,3,Sales Executive,1,Single,7637,2354,7,Y,No,11,3,4,80,0,28,3,2,21,16,7,9 -36,No,Travel_Rarely,1223,Research & Development,8,3,Technical Degree,1,83,3,Female,59,3,3,Healthcare Representative,3,Divorced,10096,8202,1,Y,No,13,3,2,80,3,17,2,3,17,14,12,8 -55,No,Travel_Rarely,836,Research & Development,8,3,Medical,1,84,4,Female,33,3,4,Manager,3,Divorced,14756,19730,2,Y,Yes,14,3,3,80,3,21,2,3,5,0,0,2 -36,No,Travel_Frequently,1195,Research & Development,11,3,Life Sciences,1,85,2,Male,95,2,2,Manufacturing Director,2,Single,6499,22656,1,Y,No,13,3,3,80,0,6,3,3,6,5,0,3 -45,No,Travel_Rarely,1339,Research & Development,7,3,Life Sciences,1,86,2,Male,59,3,3,Research Scientist,1,Divorced,9724,18787,2,Y,No,17,3,3,80,1,25,2,3,1,0,0,0 -35,No,Travel_Frequently,664,Research & Development,1,3,Medical,1,88,2,Male,79,3,1,Research Scientist,1,Married,2194,5868,4,Y,No,13,3,4,80,1,5,2,2,3,2,1,2 -36,Yes,Travel_Rarely,318,Research & Development,9,3,Medical,1,90,4,Male,79,2,1,Research Scientist,3,Married,3388,21777,0,Y,Yes,17,3,1,80,1,2,0,2,1,0,0,0 -59,No,Travel_Frequently,1225,Sales,1,1,Life Sciences,1,91,1,Female,57,2,2,Sales Executive,3,Single,5473,24668,7,Y,No,11,3,4,80,0,20,2,2,4,3,1,3 -29,No,Travel_Rarely,1328,Research & Development,2,3,Life Sciences,1,94,3,Male,76,3,1,Research Scientist,2,Married,2703,4956,0,Y,No,23,4,4,80,1,6,3,3,5,4,0,4 -31,No,Travel_Rarely,1082,Research & Development,1,4,Medical,1,95,3,Male,87,3,1,Research Scientist,2,Single,2501,18775,1,Y,No,17,3,2,80,0,1,4,3,1,1,1,0 -32,No,Travel_Rarely,548,Research & Development,1,3,Life Sciences,1,96,2,Male,66,3,2,Research Scientist,2,Married,6220,7346,1,Y,No,17,3,2,80,2,10,3,3,10,4,0,9 -36,No,Travel_Rarely,132,Research & Development,6,3,Life Sciences,1,97,2,Female,55,4,1,Laboratory Technician,4,Married,3038,22002,3,Y,No,12,3,2,80,0,5,3,3,1,0,0,0 -31,No,Travel_Rarely,746,Research & Development,8,4,Life Sciences,1,98,3,Female,61,3,2,Manufacturing Director,4,Single,4424,20682,1,Y,No,23,4,4,80,0,11,2,3,11,7,1,8 -35,No,Travel_Rarely,776,Sales,1,4,Marketing,1,100,3,Male,32,2,2,Sales Executive,1,Single,4312,23016,0,Y,No,14,3,2,80,0,16,2,3,15,13,2,8 -45,No,Travel_Rarely,193,Research & Development,6,4,Other,1,101,4,Male,52,3,3,Research Director,1,Married,13245,15067,4,Y,Yes,14,3,2,80,0,17,3,4,0,0,0,0 -37,No,Travel_Rarely,397,Research & Development,7,4,Medical,1,102,1,Male,30,3,3,Research Director,3,Single,13664,25258,4,Y,No,13,3,1,80,0,16,3,4,5,2,0,2 -46,No,Travel_Rarely,945,Human Resources,5,2,Medical,1,103,2,Male,80,3,2,Human Resources,2,Divorced,5021,10425,8,Y,Yes,22,4,4,80,1,16,2,3,4,2,0,2 -30,No,Travel_Rarely,852,Research & Development,1,1,Life Sciences,1,104,4,Male,55,2,2,Laboratory Technician,4,Married,5126,15998,1,Y,Yes,12,3,3,80,2,10,1,2,10,8,3,0 -35,No,Travel_Rarely,1214,Research & Development,1,3,Medical,1,105,2,Male,30,2,1,Research Scientist,3,Single,2859,26278,1,Y,No,18,3,1,80,0,6,3,3,6,4,0,4 -55,No,Travel_Rarely,111,Sales,1,2,Life Sciences,1,106,1,Male,70,3,3,Sales Executive,4,Married,10239,18092,3,Y,No,14,3,4,80,1,24,4,3,1,0,1,0 -38,No,Non-Travel,573,Research & Development,6,3,Medical,1,107,2,Female,79,1,2,Research Scientist,4,Divorced,5329,15717,7,Y,Yes,12,3,4,80,3,17,3,3,13,11,1,9 -34,No,Travel_Rarely,1153,Research & Development,1,2,Medical,1,110,1,Male,94,3,2,Manufacturing Director,2,Married,4325,17736,1,Y,No,15,3,3,80,0,5,2,3,5,2,1,3 -56,No,Travel_Rarely,1400,Research & Development,7,3,Life Sciences,1,112,4,Male,49,1,3,Manufacturing Director,4,Single,7260,21698,4,Y,No,11,3,1,80,0,37,3,2,6,4,0,2 -23,No,Travel_Rarely,541,Sales,2,1,Technical Degree,1,113,3,Male,62,3,1,Sales Representative,1,Divorced,2322,9518,3,Y,No,13,3,3,80,1,3,3,3,0,0,0,0 -51,No,Travel_Rarely,432,Research & Development,9,4,Life Sciences,1,116,4,Male,96,3,1,Laboratory Technician,4,Married,2075,18725,3,Y,No,23,4,2,80,2,10,4,3,4,2,0,3 -30,No,Travel_Rarely,288,Research & Development,2,3,Life Sciences,1,117,3,Male,99,2,2,Healthcare Representative,4,Married,4152,15830,1,Y,No,19,3,1,80,3,11,3,3,11,10,10,8 -46,Yes,Travel_Rarely,669,Sales,9,2,Medical,1,118,3,Male,64,2,3,Sales Executive,4,Single,9619,13596,1,Y,No,16,3,4,80,0,9,3,3,9,8,4,7 -40,No,Travel_Frequently,530,Research & Development,1,4,Life Sciences,1,119,3,Male,78,2,4,Healthcare Representative,2,Married,13503,14115,1,Y,No,22,4,4,80,1,22,3,2,22,3,11,11 -51,No,Travel_Rarely,632,Sales,21,4,Marketing,1,120,3,Male,71,3,2,Sales Executive,4,Single,5441,8423,0,Y,Yes,22,4,4,80,0,11,2,1,10,7,1,0 -30,No,Travel_Rarely,1334,Sales,4,2,Medical,1,121,3,Female,63,2,2,Sales Executive,2,Divorced,5209,19760,1,Y,Yes,12,3,2,80,3,11,4,2,11,8,2,7 -46,No,Travel_Frequently,638,Research & Development,1,3,Medical,1,124,3,Male,40,2,3,Healthcare Representative,1,Married,10673,3142,2,Y,Yes,13,3,3,80,1,21,5,2,10,9,9,5 -32,No,Travel_Rarely,1093,Sales,6,4,Medical,1,125,2,Male,87,3,2,Sales Executive,3,Single,5010,24301,1,Y,No,16,3,1,80,0,12,0,3,11,8,5,7 -54,No,Travel_Rarely,1217,Research & Development,2,4,Technical Degree,1,126,1,Female,60,3,3,Research Director,3,Married,13549,24001,9,Y,No,12,3,1,80,1,16,5,1,4,3,0,3 -24,No,Travel_Rarely,1353,Sales,3,2,Other,1,128,1,Female,33,3,2,Sales Executive,3,Married,4999,17519,0,Y,No,21,4,1,80,1,4,2,2,3,2,0,2 -28,No,Non-Travel,120,Sales,4,3,Medical,1,129,2,Male,43,3,2,Sales Executive,3,Married,4221,8863,1,Y,No,15,3,2,80,0,5,3,4,5,4,0,4 -58,No,Travel_Rarely,682,Sales,10,4,Medical,1,131,4,Male,37,3,4,Sales Executive,3,Single,13872,24409,0,Y,No,13,3,3,80,0,38,1,2,37,10,1,8 -44,No,Non-Travel,489,Research & Development,23,3,Medical,1,132,2,Male,67,3,2,Laboratory Technician,2,Married,2042,25043,4,Y,No,12,3,3,80,1,17,3,4,3,2,1,2 -37,Yes,Travel_Rarely,807,Human Resources,6,4,Human Resources,1,133,3,Male,63,3,1,Human Resources,1,Divorced,2073,23648,4,Y,Yes,22,4,4,80,0,7,3,3,3,2,0,2 -32,No,Travel_Rarely,827,Research & Development,1,1,Life Sciences,1,134,4,Male,71,3,1,Research Scientist,1,Single,2956,15178,1,Y,No,13,3,4,80,0,1,2,3,1,0,0,0 -20,Yes,Travel_Frequently,871,Research & Development,6,3,Life Sciences,1,137,4,Female,66,2,1,Laboratory Technician,4,Single,2926,19783,1,Y,Yes,18,3,2,80,0,1,5,3,1,0,1,0 -34,No,Travel_Rarely,665,Research & Development,6,4,Other,1,138,1,Female,41,3,2,Research Scientist,3,Single,4809,12482,1,Y,No,14,3,3,80,0,16,3,3,16,13,2,10 -37,No,Non-Travel,1040,Research & Development,2,2,Life Sciences,1,139,3,Male,100,2,2,Healthcare Representative,4,Divorced,5163,15850,5,Y,No,14,3,4,80,1,17,2,4,1,0,0,0 -59,No,Non-Travel,1420,Human Resources,2,4,Human Resources,1,140,3,Female,32,2,5,Manager,4,Married,18844,21922,9,Y,No,21,4,4,80,1,30,3,3,3,2,2,2 -50,No,Travel_Frequently,1115,Research & Development,1,3,Life Sciences,1,141,1,Female,73,3,5,Research Director,2,Married,18172,9755,3,Y,Yes,19,3,1,80,0,28,1,2,8,3,0,7 -25,Yes,Travel_Rarely,240,Sales,5,3,Marketing,1,142,3,Male,46,2,2,Sales Executive,3,Single,5744,26959,1,Y,Yes,11,3,4,80,0,6,1,3,6,4,0,3 -25,No,Travel_Rarely,1280,Research & Development,7,1,Medical,1,143,4,Male,64,2,1,Research Scientist,4,Married,2889,26897,1,Y,No,11,3,3,80,2,2,2,3,2,2,2,1 -22,No,Travel_Rarely,534,Research & Development,15,3,Medical,1,144,2,Female,59,3,1,Laboratory Technician,4,Single,2871,23785,1,Y,No,15,3,3,80,0,1,5,3,0,0,0,0 -51,No,Travel_Frequently,1456,Research & Development,1,4,Medical,1,145,1,Female,30,2,3,Healthcare Representative,1,Single,7484,25796,3,Y,No,20,4,3,80,0,23,1,2,13,12,12,8 -34,Yes,Travel_Frequently,658,Research & Development,7,3,Life Sciences,1,147,1,Male,66,1,2,Laboratory Technician,3,Single,6074,22887,1,Y,Yes,24,4,4,80,0,9,3,3,9,7,0,6 -54,No,Non-Travel,142,Human Resources,26,3,Human Resources,1,148,4,Female,30,4,4,Manager,4,Single,17328,13871,2,Y,Yes,12,3,3,80,0,23,3,3,5,3,4,4 -24,No,Travel_Rarely,1127,Research & Development,18,1,Life Sciences,1,150,2,Male,52,3,1,Laboratory Technician,3,Married,2774,13257,0,Y,No,12,3,3,80,1,6,2,3,5,3,1,2 -34,No,Travel_Rarely,1031,Research & Development,6,4,Life Sciences,1,151,3,Female,45,2,2,Research Scientist,2,Divorced,4505,15000,6,Y,No,15,3,3,80,1,12,3,3,1,0,0,0 -37,No,Travel_Rarely,1189,Sales,3,3,Life Sciences,1,152,3,Male,87,3,3,Sales Executive,4,Single,7428,14506,2,Y,No,12,3,1,80,0,12,3,3,5,3,1,3 -34,No,Travel_Rarely,1354,Research & Development,5,3,Medical,1,153,3,Female,45,2,3,Manager,1,Single,11631,5615,2,Y,No,12,3,4,80,0,14,6,3,11,10,5,8 -36,No,Travel_Frequently,1467,Sales,11,2,Technical Degree,1,154,2,Female,92,3,3,Sales Executive,4,Married,9738,22952,0,Y,No,14,3,3,80,1,10,6,3,9,7,2,8 -36,No,Travel_Rarely,922,Research & Development,3,2,Life Sciences,1,155,1,Female,39,3,1,Laboratory Technician,4,Divorced,2835,2561,5,Y,No,22,4,1,80,1,7,2,3,1,0,0,0 -43,No,Travel_Frequently,394,Sales,26,2,Life Sciences,1,158,3,Male,92,3,4,Manager,4,Married,16959,19494,1,Y,Yes,12,3,4,80,2,25,3,4,25,12,4,12 -30,No,Travel_Frequently,1312,Research & Development,23,3,Life Sciences,1,159,1,Male,96,1,1,Research Scientist,3,Divorced,2613,22310,1,Y,No,25,4,3,80,3,10,2,2,10,7,0,9 -33,No,Non-Travel,750,Sales,22,2,Marketing,1,160,3,Male,95,3,2,Sales Executive,2,Married,6146,15480,0,Y,No,13,3,1,80,1,8,2,4,7,7,0,7 -56,Yes,Travel_Rarely,441,Research & Development,14,4,Life Sciences,1,161,2,Female,72,3,1,Research Scientist,2,Married,4963,4510,9,Y,Yes,18,3,1,80,3,7,2,3,5,4,4,3 -51,No,Travel_Rarely,684,Research & Development,6,3,Life Sciences,1,162,1,Male,51,3,5,Research Director,3,Single,19537,6462,7,Y,No,13,3,3,80,0,23,5,3,20,18,15,15 -31,Yes,Travel_Rarely,249,Sales,6,4,Life Sciences,1,163,2,Male,76,1,2,Sales Executive,3,Married,6172,20739,4,Y,Yes,18,3,2,80,0,12,3,2,7,7,7,7 -26,No,Travel_Rarely,841,Research & Development,6,3,Other,1,164,3,Female,46,2,1,Research Scientist,2,Married,2368,23300,1,Y,No,19,3,3,80,0,5,3,2,5,4,4,3 -58,Yes,Travel_Rarely,147,Research & Development,23,4,Medical,1,165,4,Female,94,3,3,Healthcare Representative,4,Married,10312,3465,1,Y,No,12,3,4,80,1,40,3,2,40,10,15,6 -19,Yes,Travel_Rarely,528,Sales,22,1,Marketing,1,167,4,Male,50,3,1,Sales Representative,3,Single,1675,26820,1,Y,Yes,19,3,4,80,0,0,2,2,0,0,0,0 -22,No,Travel_Rarely,594,Research & Development,2,1,Technical Degree,1,169,3,Male,100,3,1,Laboratory Technician,4,Married,2523,19299,0,Y,No,14,3,3,80,1,3,2,3,2,1,2,1 -49,No,Travel_Rarely,470,Research & Development,20,4,Medical,1,170,3,Female,96,3,2,Manufacturing Director,1,Married,6567,5549,1,Y,No,14,3,3,80,0,16,2,2,15,11,5,11 -43,No,Travel_Frequently,957,Research & Development,28,3,Medical,1,171,2,Female,72,4,1,Research Scientist,3,Single,4739,16090,4,Y,No,12,3,4,80,0,18,2,3,3,2,1,2 -50,No,Travel_Frequently,809,Sales,12,3,Marketing,1,174,3,Female,77,3,3,Sales Executive,4,Single,9208,6645,4,Y,No,11,3,4,80,0,16,3,3,2,2,2,1 -31,Yes,Travel_Rarely,542,Sales,20,3,Life Sciences,1,175,2,Female,71,1,2,Sales Executive,3,Married,4559,24788,3,Y,Yes,11,3,3,80,1,4,2,3,2,2,2,2 -41,No,Travel_Rarely,802,Sales,9,1,Life Sciences,1,176,3,Male,96,3,3,Sales Executive,3,Divorced,8189,21196,3,Y,Yes,13,3,3,80,1,12,2,3,9,7,0,7 -26,No,Travel_Rarely,1355,Human Resources,25,1,Life Sciences,1,177,3,Female,61,3,1,Human Resources,3,Married,2942,8916,1,Y,No,23,4,4,80,1,8,3,3,8,7,5,7 -36,No,Travel_Rarely,216,Research & Development,6,2,Medical,1,178,2,Male,84,3,2,Manufacturing Director,2,Divorced,4941,2819,6,Y,No,20,4,4,80,2,7,0,3,3,2,0,1 -51,Yes,Travel_Frequently,1150,Research & Development,8,4,Life Sciences,1,179,1,Male,53,1,3,Manufacturing Director,4,Single,10650,25150,2,Y,No,15,3,4,80,0,18,2,3,4,2,0,3 -39,No,Travel_Rarely,1329,Sales,4,4,Life Sciences,1,182,4,Female,47,2,2,Sales Executive,3,Married,5902,14590,4,Y,No,14,3,3,80,1,17,1,4,15,11,5,9 -25,No,Travel_Rarely,959,Sales,28,3,Life Sciences,1,183,1,Male,41,2,2,Sales Executive,3,Married,8639,24835,2,Y,No,18,3,4,80,0,6,3,3,2,2,2,2 -30,No,Travel_Rarely,1240,Human Resources,9,3,Human Resources,1,184,3,Male,48,3,2,Human Resources,4,Married,6347,13982,0,Y,Yes,19,3,4,80,0,12,2,1,11,9,4,7 -32,Yes,Travel_Rarely,1033,Research & Development,9,3,Medical,1,190,1,Female,41,3,1,Laboratory Technician,1,Single,4200,10224,7,Y,No,22,4,1,80,0,10,2,4,5,4,0,4 -45,No,Travel_Rarely,1316,Research & Development,29,3,Medical,1,192,3,Male,83,3,1,Research Scientist,4,Single,3452,9752,5,Y,No,13,3,2,80,0,9,2,2,6,5,0,3 -38,No,Travel_Rarely,364,Research & Development,3,5,Technical Degree,1,193,4,Female,32,3,2,Research Scientist,3,Single,4317,2302,3,Y,Yes,20,4,2,80,0,19,2,3,3,2,2,2 -30,No,Travel_Rarely,438,Research & Development,18,3,Life Sciences,1,194,1,Female,75,3,1,Research Scientist,3,Single,2632,23910,1,Y,No,14,3,3,80,0,5,4,2,5,4,0,4 -32,No,Travel_Frequently,689,Sales,9,2,Medical,1,195,4,Male,35,1,2,Sales Executive,4,Divorced,4668,22812,0,Y,No,17,3,4,80,3,9,2,4,8,7,0,7 -30,No,Travel_Rarely,201,Research & Development,5,3,Technical Degree,1,197,4,Female,84,3,1,Research Scientist,1,Divorced,3204,10415,5,Y,No,14,3,4,80,1,8,3,3,3,2,2,2 -30,No,Travel_Rarely,1427,Research & Development,2,1,Medical,1,198,2,Male,35,2,1,Laboratory Technician,4,Single,2720,11162,0,Y,No,13,3,4,80,0,6,3,3,5,3,1,2 -41,No,Travel_Frequently,857,Research & Development,10,3,Life Sciences,1,199,4,Male,91,2,4,Manager,1,Divorced,17181,12888,4,Y,No,13,3,2,80,1,21,2,2,7,6,7,7 -41,No,Travel_Rarely,933,Research & Development,9,4,Life Sciences,1,200,3,Male,94,3,1,Laboratory Technician,1,Married,2238,6961,2,Y,No,21,4,4,80,1,7,2,3,5,0,1,4 -19,No,Travel_Rarely,1181,Research & Development,3,1,Medical,1,201,2,Female,79,3,1,Laboratory Technician,2,Single,1483,16102,1,Y,No,14,3,4,80,0,1,3,3,1,0,0,0 -40,No,Travel_Frequently,1395,Research & Development,26,3,Medical,1,202,2,Female,54,3,2,Research Scientist,2,Divorced,5605,8504,1,Y,No,11,3,1,80,1,20,2,3,20,7,2,13 -35,No,Travel_Rarely,662,Sales,1,5,Marketing,1,204,3,Male,94,3,3,Sales Executive,2,Married,7295,11439,1,Y,No,13,3,1,80,2,10,3,3,10,8,0,6 -53,No,Travel_Rarely,1436,Sales,6,2,Marketing,1,205,2,Male,34,3,2,Sales Representative,3,Married,2306,16047,2,Y,Yes,20,4,4,80,1,13,3,1,7,7,4,5 -45,No,Travel_Rarely,194,Research & Development,9,3,Life Sciences,1,206,2,Male,60,3,2,Laboratory Technician,2,Divorced,2348,10901,8,Y,No,18,3,3,80,1,20,2,1,17,9,0,15 -32,No,Travel_Frequently,967,Sales,8,3,Marketing,1,207,2,Female,43,3,3,Sales Executive,4,Single,8998,15589,1,Y,No,14,3,4,80,0,9,2,3,9,8,3,7 -29,No,Non-Travel,1496,Research & Development,1,1,Technical Degree,1,208,4,Male,41,3,2,Manufacturing Director,3,Married,4319,26283,1,Y,No,13,3,1,80,1,10,1,3,10,7,0,9 -51,No,Travel_Rarely,1169,Research & Development,7,4,Medical,1,211,2,Male,34,2,2,Manufacturing Director,3,Married,6132,13983,2,Y,No,17,3,3,80,0,10,2,3,1,0,0,0 -58,No,Travel_Rarely,1145,Research & Development,9,3,Medical,1,214,2,Female,75,2,1,Research Scientist,2,Married,3346,11873,4,Y,Yes,20,4,2,80,1,9,3,2,1,0,0,0 -40,No,Travel_Rarely,630,Sales,4,4,Marketing,1,215,3,Male,67,2,3,Sales Executive,4,Married,10855,8552,7,Y,No,11,3,1,80,1,15,2,2,12,11,2,11 -34,No,Travel_Frequently,303,Sales,2,4,Marketing,1,216,3,Female,75,3,1,Sales Representative,3,Married,2231,11314,6,Y,No,18,3,4,80,1,6,3,3,4,3,1,2 -22,No,Travel_Rarely,1256,Research & Development,19,1,Medical,1,217,3,Male,80,3,1,Research Scientist,4,Married,2323,11992,1,Y,No,24,4,1,80,2,2,6,3,2,2,2,2 -27,No,Non-Travel,691,Research & Development,9,3,Medical,1,218,4,Male,57,3,1,Research Scientist,2,Divorced,2024,5970,6,Y,No,18,3,4,80,1,6,1,1,2,2,2,2 -28,No,Travel_Rarely,440,Research & Development,21,3,Medical,1,221,3,Male,42,3,1,Research Scientist,4,Married,2713,6672,1,Y,No,11,3,3,80,1,5,2,1,5,2,0,2 -57,No,Travel_Rarely,334,Research & Development,24,2,Life Sciences,1,223,3,Male,83,4,3,Healthcare Representative,4,Divorced,9439,23402,3,Y,Yes,16,3,2,80,1,12,2,1,5,3,1,4 -27,No,Non-Travel,1450,Research & Development,3,3,Medical,1,224,3,Male,79,2,1,Research Scientist,3,Divorced,2566,25326,1,Y,Yes,15,3,4,80,1,1,2,2,1,1,0,1 -50,No,Travel_Rarely,1452,Research & Development,11,3,Life Sciences,1,226,3,Female,53,3,5,Manager,2,Single,19926,17053,3,Y,No,15,3,2,80,0,21,5,3,5,4,4,4 -41,No,Travel_Rarely,465,Research & Development,14,3,Life Sciences,1,227,1,Male,56,3,1,Research Scientist,3,Divorced,2451,4609,4,Y,No,12,3,1,80,1,13,2,3,9,8,1,8 -30,No,Travel_Rarely,1339,Sales,5,3,Life Sciences,1,228,2,Female,41,3,3,Sales Executive,4,Married,9419,8053,2,Y,No,12,3,3,80,1,12,2,3,10,9,7,4 -38,No,Travel_Rarely,702,Sales,1,4,Life Sciences,1,230,1,Female,59,2,2,Sales Executive,4,Single,8686,12930,4,Y,No,22,4,3,80,0,12,2,4,8,3,0,7 -32,No,Travel_Rarely,120,Research & Development,6,5,Life Sciences,1,231,3,Male,43,3,1,Research Scientist,3,Single,3038,12430,3,Y,No,20,4,1,80,0,8,2,3,5,4,1,4 -27,No,Travel_Rarely,1157,Research & Development,17,3,Technical Degree,1,233,3,Male,51,3,1,Research Scientist,2,Married,3058,13364,0,Y,Yes,16,3,4,80,1,6,3,2,5,2,1,1 -19,Yes,Travel_Frequently,602,Sales,1,1,Technical Degree,1,235,3,Female,100,1,1,Sales Representative,1,Single,2325,20989,0,Y,No,21,4,1,80,0,1,5,4,0,0,0,0 -36,No,Travel_Frequently,1480,Research & Development,3,2,Medical,1,238,4,Male,30,3,1,Laboratory Technician,2,Single,2088,15062,4,Y,No,12,3,3,80,0,13,3,2,8,7,7,2 -30,No,Non-Travel,111,Research & Development,9,3,Medical,1,239,3,Male,66,3,2,Laboratory Technician,1,Divorced,3072,11012,1,Y,No,11,3,3,80,2,12,4,3,12,9,6,10 -45,No,Travel_Rarely,1268,Sales,4,2,Life Sciences,1,240,3,Female,30,3,2,Sales Executive,1,Divorced,5006,6319,4,Y,Yes,11,3,1,80,1,9,3,4,5,4,0,3 -56,No,Travel_Rarely,713,Research & Development,8,3,Life Sciences,1,241,3,Female,67,3,1,Research Scientist,1,Divorced,4257,13939,4,Y,Yes,18,3,3,80,1,19,3,3,2,2,2,2 -33,No,Travel_Rarely,134,Research & Development,2,3,Life Sciences,1,242,3,Male,90,3,1,Research Scientist,4,Single,2500,10515,0,Y,No,14,3,1,80,0,4,2,4,3,1,0,2 -19,Yes,Travel_Rarely,303,Research & Development,2,3,Life Sciences,1,243,2,Male,47,2,1,Laboratory Technician,4,Single,1102,9241,1,Y,No,22,4,3,80,0,1,3,2,1,0,1,0 -46,No,Travel_Rarely,526,Sales,1,2,Marketing,1,244,2,Female,92,3,3,Sales Executive,1,Divorced,10453,2137,1,Y,No,25,4,3,80,3,24,2,3,24,13,15,7 -38,No,Travel_Rarely,1380,Research & Development,9,2,Life Sciences,1,245,3,Female,75,3,1,Laboratory Technician,4,Single,2288,6319,1,Y,No,12,3,3,80,0,2,3,3,2,2,2,1 -31,No,Travel_Rarely,140,Research & Development,12,1,Medical,1,246,3,Female,95,3,1,Research Scientist,4,Married,3929,6984,8,Y,Yes,23,4,3,80,1,7,0,3,4,2,0,2 -34,No,Travel_Rarely,629,Research & Development,27,2,Medical,1,247,4,Female,95,3,1,Research Scientist,2,Single,2311,5711,2,Y,No,15,3,4,80,0,9,3,3,3,2,1,2 -41,Yes,Travel_Rarely,1356,Sales,20,2,Marketing,1,248,2,Female,70,3,1,Sales Representative,2,Single,3140,21728,1,Y,Yes,22,4,4,80,0,4,5,2,4,3,0,2 -50,No,Travel_Rarely,328,Research & Development,1,3,Medical,1,249,3,Male,86,2,1,Laboratory Technician,3,Married,3690,3425,2,Y,No,15,3,4,80,1,5,2,2,3,2,0,2 -53,No,Travel_Rarely,1084,Research & Development,13,2,Medical,1,250,4,Female,57,4,2,Manufacturing Director,1,Divorced,4450,26250,1,Y,No,11,3,3,80,2,5,3,3,4,2,1,3 -33,No,Travel_Rarely,931,Research & Development,14,3,Medical,1,252,4,Female,72,3,1,Research Scientist,2,Married,2756,4673,1,Y,No,13,3,4,80,1,8,5,3,8,7,1,6 -40,No,Travel_Rarely,989,Research & Development,4,1,Medical,1,253,4,Female,46,3,5,Manager,3,Married,19033,6499,1,Y,No,14,3,2,80,1,21,2,3,20,8,9,9 -55,No,Travel_Rarely,692,Research & Development,14,4,Medical,1,254,3,Male,61,4,5,Research Director,2,Single,18722,13339,8,Y,No,11,3,4,80,0,36,3,3,24,15,2,15 -34,No,Travel_Frequently,1069,Research & Development,2,1,Life Sciences,1,256,4,Male,45,2,2,Manufacturing Director,3,Married,9547,14074,1,Y,No,17,3,3,80,0,10,2,2,10,9,1,9 -51,No,Travel_Rarely,313,Research & Development,3,3,Medical,1,258,4,Female,98,3,4,Healthcare Representative,2,Single,13734,7192,3,Y,No,18,3,3,80,0,21,6,3,7,7,1,0 -52,No,Travel_Rarely,699,Research & Development,1,4,Life Sciences,1,259,3,Male,65,2,5,Manager,3,Married,19999,5678,0,Y,No,14,3,1,80,1,34,5,3,33,18,11,9 -27,No,Travel_Rarely,894,Research & Development,9,3,Medical,1,260,4,Female,99,3,1,Research Scientist,2,Single,2279,11781,1,Y,No,16,3,4,80,0,7,2,2,7,7,0,3 -35,Yes,Travel_Rarely,556,Research & Development,23,2,Life Sciences,1,261,2,Male,50,2,2,Manufacturing Director,3,Married,5916,15497,3,Y,Yes,13,3,1,80,0,8,1,3,1,0,0,1 -43,No,Non-Travel,1344,Research & Development,7,3,Medical,1,262,4,Male,37,4,1,Research Scientist,4,Divorced,2089,5228,4,Y,No,14,3,4,80,3,7,3,4,5,4,2,2 -45,No,Non-Travel,1195,Research & Development,2,2,Medical,1,264,1,Male,65,2,4,Manager,4,Married,16792,20462,9,Y,No,23,4,4,80,1,22,1,3,20,8,11,8 -37,No,Travel_Rarely,290,Research & Development,21,3,Life Sciences,1,267,2,Male,65,4,1,Research Scientist,1,Married,3564,22977,1,Y,Yes,12,3,1,80,1,8,3,2,8,7,1,7 -35,No,Travel_Frequently,138,Research & Development,2,3,Medical,1,269,2,Female,37,3,2,Laboratory Technician,2,Single,4425,15986,5,Y,No,11,3,4,80,0,10,5,3,6,2,1,2 -42,No,Non-Travel,926,Research & Development,21,2,Medical,1,270,3,Female,36,3,2,Manufacturing Director,3,Divorced,5265,16439,2,Y,No,16,3,2,80,1,11,5,3,5,3,0,2 -38,No,Travel_Rarely,1261,Research & Development,2,4,Life Sciences,1,271,4,Male,88,3,2,Manufacturing Director,3,Married,6553,7259,9,Y,No,14,3,2,80,0,14,3,3,1,0,0,0 -38,No,Travel_Rarely,1084,Research & Development,29,3,Technical Degree,1,273,4,Male,54,3,2,Manufacturing Director,4,Married,6261,4185,3,Y,No,18,3,1,80,1,9,3,1,7,7,1,7 -27,No,Travel_Frequently,472,Research & Development,1,1,Technical Degree,1,274,3,Male,60,2,2,Manufacturing Director,1,Married,4298,9679,5,Y,No,19,3,3,80,1,6,1,3,2,2,2,0 -49,No,Non-Travel,1002,Research & Development,18,4,Life Sciences,1,275,4,Male,92,3,2,Manufacturing Director,4,Divorced,6804,23793,1,Y,Yes,15,3,1,80,2,7,0,3,7,7,1,7 -34,No,Travel_Frequently,878,Research & Development,10,4,Medical,1,277,4,Male,43,3,1,Research Scientist,3,Divorced,3815,5972,1,Y,Yes,17,3,4,80,1,5,4,4,5,3,2,0 -40,No,Travel_Rarely,905,Research & Development,19,2,Medical,1,281,3,Male,99,3,2,Laboratory Technician,4,Married,2741,16523,8,Y,Yes,15,3,3,80,1,15,2,4,7,2,3,7 -38,Yes,Travel_Rarely,1180,Research & Development,29,1,Medical,1,282,2,Male,70,3,2,Healthcare Representative,1,Married,6673,11354,7,Y,Yes,19,3,2,80,0,17,2,3,1,0,0,0 -29,Yes,Travel_Rarely,121,Sales,27,3,Marketing,1,283,2,Female,35,3,3,Sales Executive,4,Married,7639,24525,1,Y,No,22,4,4,80,3,10,3,2,10,4,1,9 -22,No,Travel_Rarely,1136,Research & Development,5,3,Life Sciences,1,284,4,Male,60,4,1,Research Scientist,2,Divorced,2328,12392,1,Y,Yes,16,3,1,80,1,4,2,2,4,2,2,2 -36,No,Travel_Frequently,635,Research & Development,18,1,Medical,1,286,2,Female,73,3,1,Laboratory Technician,4,Single,2153,7703,1,Y,No,13,3,1,80,0,8,2,3,8,1,1,7 -40,No,Non-Travel,1151,Research & Development,9,5,Life Sciences,1,287,4,Male,63,2,2,Healthcare Representative,4,Married,4876,14242,9,Y,No,14,3,4,80,1,5,5,1,3,2,0,2 -46,No,Travel_Rarely,644,Research & Development,1,4,Medical,1,288,4,Male,97,3,3,Healthcare Representative,1,Divorced,9396,12368,7,Y,No,16,3,3,80,1,17,3,3,4,2,0,3 -32,Yes,Travel_Rarely,1045,Sales,4,4,Medical,1,291,4,Male,32,1,3,Sales Executive,4,Married,10400,25812,1,Y,No,11,3,3,80,0,14,2,2,14,8,9,8 -30,No,Non-Travel,829,Research & Development,1,1,Life Sciences,1,292,3,Male,88,2,3,Manufacturing Director,3,Single,8474,20925,1,Y,No,22,4,3,80,0,12,2,3,11,8,5,8 -27,No,Travel_Frequently,1242,Sales,20,3,Life Sciences,1,293,4,Female,90,3,2,Sales Executive,3,Single,9981,12916,1,Y,No,14,3,4,80,0,7,2,3,7,7,0,7 -51,No,Travel_Rarely,1469,Research & Development,8,4,Life Sciences,1,296,2,Male,81,2,3,Research Director,2,Married,12490,15736,5,Y,No,16,3,4,80,2,16,5,1,10,9,4,7 -30,Yes,Travel_Rarely,1005,Research & Development,3,3,Technical Degree,1,297,4,Female,88,3,1,Research Scientist,1,Single,2657,8556,5,Y,Yes,11,3,3,80,0,8,5,3,5,2,0,4 -41,No,Travel_Rarely,896,Sales,6,3,Life Sciences,1,298,4,Female,75,3,3,Manager,4,Single,13591,14674,3,Y,Yes,18,3,3,80,0,16,3,3,1,0,0,0 -30,Yes,Travel_Frequently,334,Sales,26,4,Marketing,1,299,3,Female,52,2,2,Sales Executive,1,Single,6696,22967,5,Y,No,15,3,3,80,0,9,5,2,6,3,0,1 -29,Yes,Travel_Rarely,992,Research & Development,1,3,Technical Degree,1,300,3,Male,85,3,1,Research Scientist,3,Single,2058,19757,0,Y,No,14,3,4,80,0,7,1,2,6,2,1,5 -45,No,Non-Travel,1052,Sales,6,3,Medical,1,302,4,Female,57,2,3,Sales Executive,4,Single,8865,16840,6,Y,No,12,3,4,80,0,23,2,3,19,7,12,8 -54,No,Travel_Rarely,1147,Sales,3,3,Marketing,1,303,4,Female,52,3,2,Sales Executive,1,Married,5940,17011,2,Y,No,14,3,4,80,1,16,4,3,6,2,0,5 -36,No,Travel_Rarely,1396,Research & Development,5,2,Life Sciences,1,304,4,Male,62,3,2,Laboratory Technician,2,Single,5914,9945,8,Y,No,16,3,4,80,0,16,3,4,13,11,3,7 -33,No,Travel_Rarely,147,Research & Development,4,4,Medical,1,305,3,Female,47,2,1,Research Scientist,2,Married,2622,13248,6,Y,No,21,4,4,80,0,7,3,3,3,2,1,1 -37,No,Travel_Frequently,663,Research & Development,11,3,Other,1,306,2,Male,47,3,3,Research Director,4,Divorced,12185,10056,1,Y,Yes,14,3,3,80,3,10,1,3,10,8,0,7 -38,No,Travel_Rarely,119,Sales,3,3,Life Sciences,1,307,1,Male,76,3,3,Sales Executive,3,Divorced,10609,9647,0,Y,No,12,3,3,80,2,17,6,2,16,10,5,13 -31,No,Non-Travel,979,Research & Development,1,4,Medical,1,308,3,Male,90,1,2,Manufacturing Director,3,Married,4345,4381,0,Y,No,12,3,4,80,1,6,2,3,5,4,1,4 -59,No,Travel_Rarely,142,Research & Development,3,3,Life Sciences,1,309,3,Male,70,2,1,Research Scientist,4,Married,2177,8456,3,Y,No,17,3,1,80,1,7,6,3,1,0,0,0 -37,No,Travel_Frequently,319,Sales,4,4,Marketing,1,311,1,Male,41,3,1,Sales Representative,4,Divorced,2793,2539,4,Y,No,17,3,3,80,1,13,2,3,9,8,5,8 -29,No,Travel_Frequently,1413,Sales,1,1,Medical,1,312,2,Female,42,3,3,Sales Executive,4,Married,7918,6599,1,Y,No,14,3,4,80,1,11,5,3,11,10,4,1 -35,No,Travel_Frequently,944,Sales,1,3,Marketing,1,314,3,Female,92,3,3,Sales Executive,3,Single,8789,9096,1,Y,No,14,3,1,80,0,10,3,4,10,7,0,8 -29,Yes,Travel_Rarely,896,Research & Development,18,1,Medical,1,315,3,Male,86,2,1,Research Scientist,4,Single,2389,14961,1,Y,Yes,13,3,3,80,0,4,3,2,4,3,0,1 -52,No,Travel_Rarely,1323,Research & Development,2,3,Life Sciences,1,316,3,Female,89,2,1,Laboratory Technician,4,Single,3212,3300,7,Y,No,15,3,2,80,0,6,3,2,2,2,2,2 -42,No,Travel_Rarely,532,Research & Development,4,2,Technical Degree,1,319,3,Male,58,3,5,Manager,4,Married,19232,4933,1,Y,No,11,3,4,80,0,22,3,3,22,17,11,15 -59,No,Travel_Rarely,818,Human Resources,6,2,Medical,1,321,2,Male,52,3,1,Human Resources,3,Married,2267,25657,8,Y,No,17,3,4,80,0,7,2,2,2,2,2,2 -50,No,Travel_Rarely,854,Sales,1,4,Medical,1,323,4,Female,68,3,5,Manager,4,Divorced,19517,24118,3,Y,No,11,3,3,80,1,32,3,2,7,0,0,6 -33,Yes,Travel_Rarely,813,Research & Development,14,3,Medical,1,325,3,Male,58,3,1,Laboratory Technician,4,Married,2436,22149,5,Y,Yes,13,3,3,80,1,8,2,1,5,4,0,4 -43,No,Travel_Rarely,1034,Sales,16,3,Marketing,1,327,4,Female,80,3,4,Manager,4,Married,16064,7744,5,Y,Yes,22,4,3,80,1,22,3,3,17,13,1,9 -33,Yes,Travel_Rarely,465,Research & Development,2,2,Life Sciences,1,328,1,Female,39,3,1,Laboratory Technician,1,Married,2707,21509,7,Y,No,20,4,1,80,0,13,3,4,9,7,1,7 -52,No,Non-Travel,771,Sales,2,4,Life Sciences,1,329,1,Male,79,2,5,Manager,3,Single,19068,21030,1,Y,Yes,18,3,4,80,0,33,2,4,33,7,15,12 -32,No,Travel_Rarely,1401,Sales,4,2,Life Sciences,1,330,3,Female,56,3,1,Sales Representative,2,Married,3931,20990,2,Y,No,11,3,1,80,1,6,5,3,4,3,1,2 -32,Yes,Travel_Rarely,515,Research & Development,1,3,Life Sciences,1,331,4,Male,62,2,1,Laboratory Technician,3,Single,3730,9571,0,Y,Yes,14,3,4,80,0,4,2,1,3,2,1,2 -39,No,Travel_Rarely,1431,Research & Development,1,4,Medical,1,332,3,Female,96,3,1,Laboratory Technician,3,Divorced,2232,15417,7,Y,No,14,3,3,80,3,7,1,3,3,2,1,2 -32,No,Non-Travel,976,Sales,26,4,Marketing,1,333,3,Male,100,3,2,Sales Executive,4,Married,4465,12069,0,Y,No,18,3,1,80,0,4,2,3,3,2,2,2 -41,No,Travel_Rarely,1411,Research & Development,19,2,Life Sciences,1,334,3,Male,36,3,2,Research Scientist,1,Divorced,3072,19877,2,Y,No,16,3,1,80,2,17,2,2,1,0,0,0 -40,No,Travel_Rarely,1300,Research & Development,24,2,Technical Degree,1,335,1,Male,62,3,2,Research Scientist,4,Divorced,3319,24447,1,Y,No,17,3,1,80,2,9,3,3,9,8,4,7 -45,No,Travel_Rarely,252,Research & Development,1,3,Other,1,336,3,Male,70,4,5,Manager,4,Married,19202,15970,0,Y,No,11,3,3,80,1,25,2,3,24,0,1,7 -31,No,Travel_Frequently,1327,Research & Development,3,4,Medical,1,337,2,Male,73,3,3,Research Director,3,Divorced,13675,13523,9,Y,No,12,3,1,80,1,9,3,3,2,2,2,2 -33,No,Travel_Rarely,832,Research & Development,5,4,Life Sciences,1,338,3,Female,63,2,1,Research Scientist,4,Married,2911,14776,1,Y,No,13,3,3,80,1,2,2,2,2,2,0,2 -34,No,Travel_Rarely,470,Research & Development,2,4,Life Sciences,1,339,4,Male,84,2,2,Manufacturing Director,1,Married,5957,23687,6,Y,No,13,3,2,80,1,13,3,3,11,9,5,9 -37,No,Travel_Rarely,1017,Research & Development,1,2,Medical,1,340,3,Female,83,2,1,Research Scientist,1,Married,3920,18697,2,Y,No,14,3,1,80,1,17,2,2,3,1,0,2 -45,No,Travel_Frequently,1199,Research & Development,7,4,Life Sciences,1,341,1,Male,77,4,2,Manufacturing Director,3,Married,6434,5118,4,Y,No,17,3,4,80,1,9,1,3,3,2,0,2 -37,Yes,Travel_Frequently,504,Research & Development,10,3,Medical,1,342,1,Male,61,3,3,Manufacturing Director,3,Divorced,10048,22573,6,Y,No,11,3,2,80,2,17,5,3,1,0,0,0 -39,No,Travel_Frequently,505,Research & Development,2,4,Technical Degree,1,343,3,Female,64,3,3,Healthcare Representative,3,Single,10938,6420,0,Y,No,25,4,4,80,0,20,1,3,19,6,11,8 -29,No,Travel_Rarely,665,Research & Development,15,3,Life Sciences,1,346,3,Male,60,3,1,Research Scientist,4,Single,2340,22673,1,Y,No,19,3,1,80,0,6,1,3,6,5,1,5 -42,No,Travel_Rarely,916,Research & Development,17,2,Life Sciences,1,347,4,Female,82,4,2,Research Scientist,1,Single,6545,23016,3,Y,Yes,13,3,3,80,0,10,1,3,3,2,0,2 -29,No,Travel_Rarely,1247,Sales,20,2,Marketing,1,349,4,Male,45,3,2,Sales Executive,4,Divorced,6931,10732,2,Y,No,14,3,4,80,1,10,2,3,3,2,0,2 -25,No,Travel_Rarely,685,Research & Development,1,3,Life Sciences,1,350,1,Female,62,3,2,Manufacturing Director,3,Married,4898,7505,0,Y,No,12,3,4,80,2,5,3,3,4,2,1,2 -42,No,Travel_Rarely,269,Research & Development,2,3,Medical,1,351,4,Female,56,2,1,Laboratory Technician,1,Divorced,2593,8007,0,Y,Yes,11,3,3,80,1,10,4,3,9,6,7,8 -40,No,Travel_Rarely,1416,Research & Development,2,2,Medical,1,352,1,Male,49,3,5,Research Director,3,Divorced,19436,5949,0,Y,No,19,3,4,80,1,22,5,3,21,7,3,9 -51,No,Travel_Rarely,833,Research & Development,1,3,Life Sciences,1,353,3,Male,96,3,1,Research Scientist,4,Married,2723,23231,1,Y,No,11,3,2,80,0,1,0,2,1,0,0,0 -31,Yes,Travel_Frequently,307,Research & Development,29,2,Medical,1,355,3,Male,71,2,1,Laboratory Technician,2,Single,3479,11652,0,Y,No,11,3,2,80,0,6,2,4,5,4,1,4 -32,No,Travel_Frequently,1311,Research & Development,7,3,Life Sciences,1,359,2,Male,100,4,1,Laboratory Technician,2,Married,2794,26062,1,Y,No,20,4,3,80,0,5,3,1,5,1,0,3 -38,No,Non-Travel,1327,Sales,2,2,Life Sciences,1,361,4,Male,39,2,2,Sales Executive,4,Married,5249,19682,3,Y,No,18,3,4,80,1,13,0,3,8,7,7,5 -32,No,Travel_Rarely,128,Research & Development,2,1,Technical Degree,1,362,4,Male,84,2,2,Laboratory Technician,1,Single,2176,19737,4,Y,No,13,3,4,80,0,9,5,3,6,2,0,4 -46,No,Travel_Rarely,488,Sales,2,3,Technical Degree,1,363,3,Female,75,1,4,Manager,2,Married,16872,14977,3,Y,Yes,12,3,2,80,1,28,2,2,7,7,7,7 -28,Yes,Travel_Rarely,529,Research & Development,2,4,Life Sciences,1,364,1,Male,79,3,1,Laboratory Technician,3,Single,3485,14935,2,Y,No,11,3,3,80,0,5,5,1,0,0,0,0 -29,No,Travel_Rarely,1210,Sales,2,3,Medical,1,366,1,Male,78,2,2,Sales Executive,2,Married,6644,3687,2,Y,No,19,3,2,80,2,10,2,3,0,0,0,0 -31,No,Travel_Rarely,1463,Research & Development,23,3,Medical,1,367,2,Male,64,2,2,Healthcare Representative,4,Married,5582,14408,0,Y,No,21,4,2,80,1,10,2,3,9,0,7,8 -25,No,Non-Travel,675,Research & Development,5,2,Life Sciences,1,369,2,Male,85,4,2,Healthcare Representative,1,Divorced,4000,18384,1,Y,No,12,3,4,80,2,6,2,3,6,3,1,5 -45,No,Travel_Rarely,1385,Research & Development,20,2,Medical,1,372,3,Male,79,3,4,Healthcare Representative,4,Married,13496,7501,0,Y,Yes,14,3,2,80,0,21,2,3,20,7,4,10 -36,No,Travel_Rarely,1403,Research & Development,6,3,Life Sciences,1,373,4,Male,47,3,1,Laboratory Technician,4,Married,3210,20251,0,Y,No,11,3,3,80,1,16,4,3,15,13,10,11 -55,No,Travel_Rarely,452,Research & Development,1,3,Medical,1,374,4,Male,81,3,5,Manager,1,Single,19045,18938,0,Y,Yes,14,3,3,80,0,37,2,3,36,10,4,13 -47,Yes,Non-Travel,666,Research & Development,29,4,Life Sciences,1,376,1,Male,88,3,3,Manager,2,Married,11849,10268,1,Y,Yes,12,3,4,80,1,10,2,2,10,7,9,9 -28,No,Travel_Rarely,1158,Research & Development,9,3,Medical,1,377,4,Male,94,3,1,Research Scientist,4,Married,2070,2613,1,Y,No,23,4,4,80,1,5,3,2,5,2,0,4 -37,No,Travel_Rarely,228,Sales,6,4,Medical,1,378,3,Male,98,3,2,Sales Executive,4,Married,6502,22825,4,Y,No,14,3,2,80,1,7,5,4,5,4,0,1 -21,No,Travel_Rarely,996,Research & Development,3,2,Medical,1,379,4,Male,100,2,1,Research Scientist,3,Single,3230,10531,1,Y,No,17,3,1,80,0,3,4,4,3,2,1,0 -37,No,Non-Travel,728,Research & Development,1,4,Medical,1,380,1,Female,80,3,3,Research Director,4,Divorced,13603,11677,2,Y,Yes,18,3,1,80,2,15,2,3,5,2,0,2 -35,No,Travel_Rarely,1315,Research & Development,22,3,Life Sciences,1,381,2,Female,71,4,3,Manager,2,Divorced,11996,19100,7,Y,No,18,3,2,80,1,10,6,2,7,7,6,2 -38,No,Travel_Rarely,322,Sales,7,2,Medical,1,382,1,Female,44,4,2,Sales Executive,1,Divorced,5605,19191,1,Y,Yes,24,4,3,80,1,8,3,3,8,0,7,7 -26,No,Travel_Frequently,1479,Research & Development,1,3,Life Sciences,1,384,3,Female,84,3,2,Manufacturing Director,2,Divorced,6397,26767,1,Y,No,20,4,1,80,1,6,6,1,6,5,1,4 -50,No,Travel_Rarely,797,Research & Development,4,1,Life Sciences,1,385,1,Male,96,3,5,Research Director,2,Divorced,19144,15815,3,Y,No,14,3,1,80,2,28,4,2,10,4,1,6 -53,No,Travel_Rarely,1070,Research & Development,3,4,Medical,1,386,3,Male,45,3,4,Research Director,3,Married,17584,21016,3,Y,Yes,16,3,4,80,3,21,5,2,5,3,1,3 -42,No,Travel_Rarely,635,Sales,1,1,Life Sciences,1,387,2,Male,99,3,2,Sales Executive,3,Married,4907,24532,1,Y,No,25,4,3,80,0,20,3,3,20,16,11,6 -29,No,Travel_Frequently,442,Sales,2,2,Life Sciences,1,388,2,Male,44,3,2,Sales Executive,4,Single,4554,20260,1,Y,No,18,3,1,80,0,10,3,2,10,7,0,9 -55,No,Travel_Rarely,147,Research & Development,20,2,Technical Degree,1,389,2,Male,37,3,2,Laboratory Technician,4,Married,5415,15972,3,Y,Yes,19,3,4,80,1,12,4,3,10,7,0,8 -26,No,Travel_Frequently,496,Research & Development,11,2,Medical,1,390,1,Male,60,3,2,Healthcare Representative,1,Married,4741,22722,1,Y,Yes,13,3,3,80,1,5,3,3,5,3,3,3 -37,No,Travel_Rarely,1372,Research & Development,1,3,Life Sciences,1,391,4,Female,42,3,1,Research Scientist,4,Single,2115,15881,1,Y,No,12,3,2,80,0,17,3,3,17,12,5,7 -44,Yes,Travel_Frequently,920,Research & Development,24,3,Life Sciences,1,392,4,Male,43,3,1,Laboratory Technician,3,Divorced,3161,19920,3,Y,Yes,22,4,4,80,1,19,0,1,1,0,0,0 -38,No,Travel_Rarely,688,Research & Development,23,4,Life Sciences,1,393,4,Male,82,3,2,Healthcare Representative,4,Divorced,5745,18899,9,Y,No,14,3,2,80,1,10,2,3,2,2,1,2 -26,Yes,Travel_Rarely,1449,Research & Development,16,4,Medical,1,394,1,Male,45,3,1,Laboratory Technician,2,Divorced,2373,14180,2,Y,Yes,13,3,4,80,1,5,2,3,3,2,0,2 -28,No,Travel_Rarely,1117,Research & Development,8,2,Life Sciences,1,395,4,Female,66,3,1,Research Scientist,4,Single,3310,4488,1,Y,No,21,4,4,80,0,5,3,3,5,3,0,2 -49,No,Travel_Frequently,636,Research & Development,10,4,Life Sciences,1,396,3,Female,35,3,5,Research Director,1,Single,18665,25594,9,Y,Yes,11,3,4,80,0,22,4,3,3,2,1,2 -36,No,Travel_Rarely,506,Research & Development,3,3,Technical Degree,1,397,3,Male,30,3,2,Research Scientist,2,Single,4485,26285,4,Y,No,12,3,4,80,0,10,2,3,8,0,7,7 -31,No,Travel_Frequently,444,Sales,5,3,Marketing,1,399,4,Female,84,3,1,Sales Representative,2,Divorced,2789,3909,1,Y,No,11,3,3,80,1,2,5,2,2,2,2,2 -26,Yes,Travel_Rarely,950,Sales,4,4,Marketing,1,401,4,Male,48,2,2,Sales Executive,4,Single,5828,8450,1,Y,Yes,12,3,2,80,0,8,0,3,8,7,7,4 -37,No,Travel_Frequently,889,Research & Development,9,3,Medical,1,403,2,Male,53,3,1,Research Scientist,4,Married,2326,11411,1,Y,Yes,12,3,3,80,3,4,3,2,4,2,1,2 -42,No,Travel_Frequently,555,Sales,26,3,Marketing,1,404,3,Female,77,3,4,Sales Executive,2,Married,13525,14864,5,Y,No,14,3,4,80,1,23,2,4,20,4,4,8 -18,Yes,Travel_Rarely,230,Research & Development,3,3,Life Sciences,1,405,3,Male,54,3,1,Laboratory Technician,3,Single,1420,25233,1,Y,No,13,3,3,80,0,0,2,3,0,0,0,0 -35,No,Travel_Rarely,1232,Sales,16,3,Marketing,1,406,3,Male,96,3,3,Sales Executive,2,Married,8020,5100,0,Y,No,15,3,3,80,2,12,3,2,11,9,6,9 -36,No,Travel_Frequently,566,Research & Development,18,4,Life Sciences,1,407,3,Male,81,4,1,Laboratory Technician,4,Married,3688,7122,4,Y,No,18,3,4,80,2,4,2,3,1,0,0,0 -51,No,Travel_Rarely,1302,Research & Development,2,3,Medical,1,408,4,Male,84,1,2,Manufacturing Director,2,Divorced,5482,16321,5,Y,No,18,3,4,80,1,13,3,3,4,1,1,2 -41,No,Travel_Rarely,334,Sales,2,4,Life Sciences,1,410,4,Male,88,3,4,Manager,2,Single,16015,15896,1,Y,No,19,3,2,80,0,22,2,3,22,10,0,4 -18,No,Travel_Rarely,812,Sales,10,3,Medical,1,411,4,Female,69,2,1,Sales Representative,3,Single,1200,9724,1,Y,No,12,3,1,80,0,0,2,3,0,0,0,0 -28,No,Travel_Rarely,1476,Research & Development,16,2,Medical,1,412,2,Male,68,4,2,Healthcare Representative,1,Single,5661,4824,0,Y,No,19,3,3,80,0,9,2,3,8,3,0,7 -31,No,Travel_Rarely,218,Sales,7,3,Technical Degree,1,416,2,Male,100,4,2,Sales Executive,4,Married,6929,12241,4,Y,No,11,3,2,80,1,10,3,2,8,7,7,7 -39,No,Travel_Rarely,1132,Research & Development,1,3,Medical,1,417,3,Male,48,4,3,Healthcare Representative,4,Divorced,9613,10942,0,Y,No,17,3,1,80,3,19,5,2,18,10,3,7 -36,No,Non-Travel,1105,Research & Development,24,4,Life Sciences,1,419,2,Female,47,3,2,Laboratory Technician,2,Married,5674,6927,7,Y,No,15,3,3,80,1,11,3,3,9,8,0,8 -32,No,Travel_Rarely,906,Sales,7,3,Life Sciences,1,420,4,Male,91,2,2,Sales Executive,3,Married,5484,16985,1,Y,No,14,3,3,80,1,13,3,2,13,8,4,8 -38,No,Travel_Rarely,849,Research & Development,25,2,Life Sciences,1,421,1,Female,81,2,3,Research Director,2,Married,12061,26707,3,Y,No,17,3,3,80,1,19,2,3,10,8,0,1 -58,No,Non-Travel,390,Research & Development,1,4,Life Sciences,1,422,4,Male,32,1,2,Healthcare Representative,3,Divorced,5660,17056,2,Y,Yes,13,3,4,80,1,12,2,3,5,3,1,2 -31,No,Travel_Rarely,691,Research & Development,5,4,Technical Degree,1,423,3,Male,86,3,1,Research Scientist,4,Married,4821,10077,0,Y,Yes,12,3,3,80,1,6,4,3,5,2,0,3 -31,No,Travel_Rarely,106,Human Resources,2,3,Human Resources,1,424,1,Male,62,2,2,Human Resources,1,Married,6410,17822,3,Y,No,12,3,4,80,0,9,1,3,2,2,1,0 -45,No,Travel_Frequently,1249,Research & Development,7,3,Life Sciences,1,425,1,Male,97,3,3,Laboratory Technician,1,Divorced,5210,20308,1,Y,No,18,3,1,80,1,24,2,3,24,9,9,11 -31,No,Travel_Rarely,192,Research & Development,2,4,Life Sciences,1,426,3,Male,32,3,1,Research Scientist,4,Divorced,2695,7747,0,Y,Yes,18,3,2,80,1,3,2,1,2,2,2,2 -33,No,Travel_Frequently,553,Research & Development,5,4,Life Sciences,1,428,4,Female,74,3,3,Manager,2,Married,11878,23364,6,Y,No,11,3,2,80,2,12,2,3,10,6,8,8 -39,No,Travel_Rarely,117,Research & Development,10,1,Medical,1,429,3,Male,99,3,4,Manager,1,Married,17068,5355,1,Y,Yes,14,3,4,80,0,21,3,3,21,9,11,10 -43,No,Travel_Frequently,185,Research & Development,10,4,Life Sciences,1,430,3,Female,33,3,1,Laboratory Technician,4,Single,2455,10675,0,Y,No,19,3,1,80,0,9,5,3,8,7,1,7 -49,No,Travel_Rarely,1091,Research & Development,1,2,Technical Degree,1,431,3,Female,90,2,4,Healthcare Representative,3,Single,13964,17810,7,Y,Yes,12,3,4,80,0,25,2,3,7,1,0,7 -52,Yes,Travel_Rarely,723,Research & Development,8,4,Medical,1,433,3,Male,85,2,2,Research Scientist,2,Married,4941,17747,2,Y,No,15,3,1,80,0,11,3,2,8,2,7,7 -27,No,Travel_Rarely,1220,Research & Development,5,3,Life Sciences,1,434,3,Female,85,3,1,Research Scientist,2,Single,2478,20938,1,Y,Yes,12,3,2,80,0,4,2,2,4,3,1,2 -32,No,Travel_Rarely,588,Sales,8,2,Technical Degree,1,436,3,Female,65,2,2,Sales Executive,2,Married,5228,24624,1,Y,Yes,11,3,4,80,0,13,2,3,13,12,11,9 -27,No,Travel_Rarely,1377,Sales,2,3,Life Sciences,1,437,4,Male,74,3,2,Sales Executive,3,Single,4478,5242,1,Y,Yes,11,3,1,80,0,5,3,3,5,4,0,4 -31,No,Travel_Rarely,691,Sales,7,3,Marketing,1,438,4,Male,73,3,2,Sales Executive,4,Divorced,7547,7143,4,Y,No,12,3,4,80,3,13,3,3,7,7,1,7 -32,No,Travel_Rarely,1018,Research & Development,2,4,Medical,1,439,1,Female,74,4,2,Research Scientist,4,Single,5055,10557,7,Y,No,16,3,3,80,0,10,0,2,7,7,0,7 -28,Yes,Travel_Rarely,1157,Research & Development,2,4,Medical,1,440,1,Male,84,1,1,Research Scientist,4,Married,3464,24737,5,Y,Yes,13,3,4,80,0,5,4,2,3,2,2,2 -30,No,Travel_Rarely,1275,Research & Development,28,2,Medical,1,441,4,Female,64,3,2,Research Scientist,4,Married,5775,11934,1,Y,No,13,3,4,80,2,11,2,3,10,8,1,9 -31,No,Travel_Frequently,798,Research & Development,7,2,Life Sciences,1,442,3,Female,48,2,3,Manufacturing Director,3,Married,8943,14034,1,Y,No,24,4,1,80,1,10,2,3,10,9,8,9 -39,No,Travel_Frequently,672,Research & Development,7,2,Medical,1,444,3,Male,54,2,5,Manager,4,Married,19272,21141,1,Y,No,15,3,1,80,1,21,2,3,21,9,13,3 -39,Yes,Travel_Rarely,1162,Sales,3,2,Medical,1,445,4,Female,41,3,2,Sales Executive,3,Married,5238,17778,4,Y,Yes,18,3,1,80,0,12,3,2,1,0,0,0 -33,No,Travel_Frequently,508,Sales,10,3,Marketing,1,446,2,Male,46,2,2,Sales Executive,4,Single,4682,4317,3,Y,No,14,3,3,80,0,9,6,2,7,7,0,1 -47,No,Travel_Rarely,1482,Research & Development,5,5,Life Sciences,1,447,4,Male,42,3,5,Research Director,3,Married,18300,16375,4,Y,No,11,3,2,80,1,21,2,3,3,2,1,1 -43,No,Travel_Frequently,559,Research & Development,10,4,Life Sciences,1,448,3,Female,82,2,2,Laboratory Technician,3,Divorced,5257,6227,1,Y,No,11,3,2,80,1,9,3,4,9,7,0,0 -27,No,Non-Travel,210,Sales,1,1,Marketing,1,449,3,Male,73,3,2,Sales Executive,2,Married,6349,22107,0,Y,Yes,13,3,4,80,1,6,0,3,5,4,1,4 -54,No,Travel_Frequently,928,Research & Development,20,4,Life Sciences,1,450,4,Female,31,3,2,Research Scientist,3,Single,4869,16885,3,Y,No,12,3,4,80,0,20,4,2,4,3,0,3 -43,No,Travel_Rarely,1001,Research & Development,7,3,Life Sciences,1,451,3,Female,43,3,3,Healthcare Representative,1,Married,9985,9262,8,Y,No,16,3,1,80,1,10,1,2,1,0,0,0 -45,No,Travel_Rarely,549,Research & Development,8,4,Other,1,452,4,Male,75,3,2,Research Scientist,4,Married,3697,9278,9,Y,No,14,3,1,80,2,12,3,3,10,9,9,8 -40,No,Travel_Rarely,1124,Sales,1,2,Medical,1,453,2,Male,57,1,2,Sales Executive,4,Married,7457,13273,2,Y,Yes,22,4,3,80,3,6,2,2,4,3,0,2 -29,Yes,Travel_Rarely,318,Research & Development,8,4,Other,1,454,2,Male,77,1,1,Laboratory Technician,1,Married,2119,4759,1,Y,Yes,11,3,4,80,0,7,4,2,7,7,0,7 -29,No,Travel_Rarely,738,Research & Development,9,5,Other,1,455,2,Male,30,2,1,Laboratory Technician,4,Single,3983,7621,0,Y,No,17,3,3,80,0,4,2,3,3,2,2,2 -30,No,Travel_Rarely,570,Sales,5,3,Marketing,1,456,4,Female,30,2,2,Sales Executive,3,Divorced,6118,5431,1,Y,No,13,3,3,80,3,10,2,3,10,9,1,2 -27,No,Travel_Rarely,1130,Sales,8,4,Marketing,1,458,2,Female,56,3,2,Sales Executive,2,Married,6214,3415,1,Y,No,18,3,1,80,1,8,3,3,8,7,0,7 -37,No,Travel_Rarely,1192,Research & Development,5,2,Medical,1,460,4,Male,61,3,2,Manufacturing Director,4,Divorced,6347,23177,7,Y,No,16,3,3,80,2,8,2,2,6,2,0,4 -38,No,Travel_Rarely,343,Research & Development,15,2,Life Sciences,1,461,3,Male,92,2,3,Research Director,4,Divorced,11510,15682,0,Y,Yes,14,3,2,80,1,12,3,3,11,10,2,9 -31,No,Travel_Rarely,1232,Research & Development,7,4,Medical,1,462,3,Female,39,3,3,Manufacturing Director,4,Single,7143,25713,1,Y,Yes,14,3,3,80,0,11,2,2,11,9,4,10 -29,No,Travel_Rarely,144,Sales,10,1,Marketing,1,463,4,Female,39,2,2,Sales Executive,2,Divorced,8268,11866,1,Y,Yes,14,3,1,80,2,7,2,3,7,7,1,7 -35,No,Travel_Rarely,1296,Research & Development,5,4,Technical Degree,1,464,3,Male,62,3,3,Manufacturing Director,2,Single,8095,18264,0,Y,No,13,3,4,80,0,17,5,3,16,6,0,13 -23,No,Travel_Rarely,1309,Research & Development,26,1,Life Sciences,1,465,3,Male,83,3,1,Research Scientist,4,Divorced,2904,16092,1,Y,No,12,3,3,80,2,4,2,2,4,2,0,2 -41,No,Travel_Rarely,483,Research & Development,6,3,Medical,1,466,4,Male,95,2,2,Manufacturing Director,2,Single,6032,10110,6,Y,Yes,15,3,4,80,0,8,3,3,5,4,1,2 -47,No,Travel_Frequently,1309,Sales,4,1,Medical,1,467,2,Male,99,3,2,Sales Representative,3,Single,2976,25751,3,Y,No,19,3,1,80,0,5,3,3,0,0,0,0 -42,No,Travel_Rarely,810,Research & Development,23,5,Life Sciences,1,468,1,Female,44,3,4,Research Director,4,Single,15992,15901,2,Y,No,14,3,2,80,0,16,2,3,1,0,0,0 -29,No,Non-Travel,746,Sales,2,3,Life Sciences,1,469,4,Male,61,3,2,Sales Executive,3,Married,4649,16928,1,Y,No,14,3,1,80,1,4,3,2,4,3,0,2 -42,No,Travel_Rarely,544,Human Resources,2,1,Technical Degree,1,470,3,Male,52,3,1,Human Resources,3,Divorced,2696,24017,0,Y,Yes,11,3,3,80,1,4,5,3,3,2,1,0 -32,No,Travel_Rarely,1062,Research & Development,2,3,Medical,1,471,3,Female,75,3,1,Laboratory Technician,2,Married,2370,3956,1,Y,No,13,3,3,80,1,8,4,3,8,0,0,7 -48,No,Travel_Rarely,530,Sales,29,1,Medical,1,473,1,Female,91,3,3,Manager,3,Married,12504,23978,3,Y,No,21,4,2,80,1,15,3,1,0,0,0,0 -37,No,Travel_Rarely,1319,Research & Development,6,3,Medical,1,474,3,Male,51,4,2,Research Scientist,1,Divorced,5974,17001,4,Y,Yes,13,3,1,80,2,13,2,3,7,7,6,7 -30,No,Non-Travel,641,Sales,25,2,Technical Degree,1,475,4,Female,85,3,2,Sales Executive,3,Married,4736,6069,7,Y,Yes,12,3,2,80,1,4,2,4,2,2,2,2 -26,No,Travel_Rarely,933,Sales,1,3,Life Sciences,1,476,3,Male,57,3,2,Sales Executive,3,Married,5296,20156,1,Y,No,17,3,2,80,1,8,3,3,8,7,7,7 -42,No,Travel_Rarely,1332,Research & Development,2,4,Other,1,477,1,Male,98,2,2,Healthcare Representative,4,Single,6781,17078,3,Y,No,23,4,2,80,0,14,6,3,1,0,0,0 -21,Yes,Travel_Frequently,756,Sales,1,1,Technical Degree,1,478,1,Female,99,2,1,Sales Representative,2,Single,2174,9150,1,Y,Yes,11,3,3,80,0,3,3,3,3,2,1,2 -36,No,Non-Travel,845,Sales,1,5,Medical,1,479,4,Female,45,3,2,Sales Executive,4,Single,6653,15276,4,Y,No,15,3,2,80,0,7,6,3,1,0,0,0 -36,No,Travel_Frequently,541,Sales,3,4,Medical,1,481,1,Male,48,2,3,Sales Executive,4,Married,9699,7246,4,Y,No,11,3,1,80,1,16,2,3,13,9,1,12 -57,No,Travel_Rarely,593,Research & Development,1,4,Medical,1,482,4,Male,88,3,2,Healthcare Representative,3,Married,6755,2967,2,Y,No,11,3,3,80,0,15,2,3,3,2,1,2 -40,No,Travel_Rarely,1171,Research & Development,10,4,Life Sciences,1,483,4,Female,46,4,1,Laboratory Technician,3,Married,2213,22495,3,Y,Yes,13,3,3,80,1,10,3,3,7,7,1,7 -21,No,Non-Travel,895,Sales,9,2,Medical,1,484,1,Male,39,3,1,Sales Representative,4,Single,2610,2851,1,Y,No,24,4,3,80,0,3,3,2,3,2,2,2 -33,Yes,Travel_Rarely,350,Sales,5,3,Marketing,1,485,4,Female,34,3,1,Sales Representative,3,Single,2851,9150,1,Y,Yes,13,3,2,80,0,1,2,3,1,0,0,0 -37,No,Travel_Rarely,921,Research & Development,10,3,Medical,1,486,3,Female,98,3,1,Laboratory Technician,1,Married,3452,17663,6,Y,No,20,4,2,80,1,17,3,3,5,4,0,3 -46,No,Non-Travel,1144,Research & Development,7,4,Medical,1,487,3,Female,30,3,2,Manufacturing Director,3,Married,5258,16044,2,Y,No,14,3,3,80,0,7,2,4,1,0,0,0 -41,Yes,Travel_Frequently,143,Sales,4,3,Marketing,1,488,1,Male,56,3,2,Sales Executive,2,Single,9355,9558,1,Y,No,18,3,3,80,0,8,5,3,8,7,7,7 -50,No,Travel_Rarely,1046,Research & Development,10,3,Technical Degree,1,491,4,Male,100,2,3,Healthcare Representative,4,Single,10496,2755,6,Y,No,15,3,4,80,0,20,2,3,4,3,1,3 -40,Yes,Travel_Rarely,575,Sales,22,2,Marketing,1,492,3,Male,68,2,2,Sales Executive,3,Married,6380,6110,2,Y,Yes,12,3,1,80,2,8,6,3,6,4,1,0 -31,No,Travel_Rarely,408,Research & Development,9,4,Life Sciences,1,493,3,Male,42,2,1,Research Scientist,2,Single,2657,7551,0,Y,Yes,16,3,4,80,0,3,5,3,2,2,2,2 -21,Yes,Travel_Rarely,156,Sales,12,3,Life Sciences,1,494,3,Female,90,4,1,Sales Representative,2,Single,2716,25422,1,Y,No,15,3,4,80,0,1,0,3,1,0,0,0 -29,No,Travel_Rarely,1283,Research & Development,23,3,Life Sciences,1,495,4,Male,54,3,1,Research Scientist,4,Single,2201,18168,9,Y,No,16,3,4,80,0,6,4,3,3,2,1,2 -35,No,Travel_Rarely,755,Research & Development,9,4,Life Sciences,1,496,3,Male,97,2,2,Healthcare Representative,2,Single,6540,19394,9,Y,No,19,3,3,80,0,10,5,3,1,1,0,0 -27,No,Travel_Rarely,1469,Research & Development,1,2,Medical,1,497,4,Male,82,3,1,Laboratory Technician,2,Divorced,3816,17881,1,Y,No,11,3,2,80,1,5,2,3,5,2,0,4 -28,No,Travel_Rarely,304,Sales,9,4,Life Sciences,1,498,2,Male,92,3,2,Sales Executive,4,Single,5253,20750,1,Y,No,16,3,4,80,0,7,1,3,7,5,0,7 -49,No,Travel_Rarely,1261,Research & Development,7,3,Other,1,499,2,Male,31,2,3,Healthcare Representative,3,Single,10965,12066,8,Y,No,24,4,3,80,0,26,2,3,5,2,0,0 -51,No,Travel_Rarely,1178,Sales,14,2,Life Sciences,1,500,3,Female,87,3,2,Sales Executive,4,Married,4936,14862,4,Y,No,11,3,3,80,1,18,2,2,7,7,0,7 -36,No,Travel_Rarely,329,Research & Development,2,3,Life Sciences,1,501,4,Female,96,3,1,Research Scientist,3,Married,2543,11868,4,Y,No,13,3,2,80,1,6,3,3,2,2,2,2 -34,Yes,Non-Travel,1362,Sales,19,3,Marketing,1,502,1,Male,67,4,2,Sales Executive,4,Single,5304,4652,8,Y,Yes,13,3,2,80,0,9,3,2,5,2,0,4 -55,No,Travel_Rarely,1311,Research & Development,2,3,Life Sciences,1,505,3,Female,97,3,4,Manager,4,Single,16659,23258,2,Y,Yes,13,3,3,80,0,30,2,3,5,4,1,2 -24,No,Travel_Rarely,1371,Sales,10,4,Marketing,1,507,4,Female,77,3,2,Sales Executive,3,Divorced,4260,5915,1,Y,Yes,12,3,4,80,1,5,2,4,5,2,0,3 -30,No,Travel_Rarely,202,Sales,2,1,Technical Degree,1,508,3,Male,72,3,1,Sales Representative,2,Married,2476,17434,1,Y,No,18,3,1,80,1,1,3,3,1,0,0,0 -26,Yes,Travel_Frequently,575,Research & Development,3,1,Technical Degree,1,510,3,Male,73,3,1,Research Scientist,1,Single,3102,6582,0,Y,No,22,4,3,80,0,7,2,3,6,4,0,4 -22,No,Travel_Rarely,253,Research & Development,11,3,Medical,1,511,1,Female,43,3,1,Research Scientist,2,Married,2244,24440,1,Y,No,13,3,4,80,1,2,1,3,2,1,1,2 -36,No,Travel_Rarely,164,Sales,2,2,Medical,1,513,2,Male,61,2,3,Sales Executive,3,Married,7596,3809,1,Y,No,13,3,2,80,2,10,2,3,10,9,9,0 -30,Yes,Travel_Frequently,464,Research & Development,4,3,Technical Degree,1,514,3,Male,40,3,1,Research Scientist,4,Single,2285,3427,9,Y,Yes,23,4,3,80,0,3,4,3,1,0,0,0 -37,No,Travel_Rarely,1107,Research & Development,14,3,Life Sciences,1,515,4,Female,95,3,1,Laboratory Technician,1,Divorced,3034,26914,1,Y,No,12,3,3,80,1,18,2,2,18,7,12,17 -40,No,Travel_Rarely,759,Sales,2,2,Marketing,1,516,4,Female,46,3,2,Sales Executive,2,Divorced,5715,22553,7,Y,No,12,3,3,80,2,8,5,3,5,4,1,3 -42,No,Travel_Rarely,201,Research & Development,1,4,Life Sciences,1,517,2,Female,95,3,1,Laboratory Technician,1,Divorced,2576,20490,3,Y,No,16,3,2,80,1,8,5,3,5,2,1,2 -37,No,Travel_Rarely,1305,Research & Development,10,4,Life Sciences,1,518,3,Male,49,3,2,Manufacturing Director,2,Single,4197,21123,2,Y,Yes,12,3,4,80,0,18,2,2,1,0,0,1 -43,No,Travel_Rarely,982,Research & Development,12,3,Life Sciences,1,520,1,Male,59,2,4,Research Director,2,Divorced,14336,4345,1,Y,No,11,3,3,80,1,25,3,3,25,10,3,9 -40,No,Travel_Rarely,555,Research & Development,2,3,Medical,1,521,2,Female,78,2,2,Laboratory Technician,3,Married,3448,13436,6,Y,No,22,4,2,80,1,20,3,3,1,0,0,0 -54,No,Travel_Rarely,821,Research & Development,5,2,Medical,1,522,1,Male,86,3,5,Research Director,1,Married,19406,8509,4,Y,No,11,3,3,80,1,24,4,2,4,2,1,2 -34,No,Non-Travel,1381,Sales,4,4,Marketing,1,523,3,Female,72,3,2,Sales Executive,3,Married,6538,12740,9,Y,No,15,3,1,80,1,6,3,3,3,2,1,2 -31,No,Travel_Rarely,480,Research & Development,7,2,Medical,1,524,2,Female,31,3,2,Manufacturing Director,1,Married,4306,4156,1,Y,No,12,3,2,80,1,13,5,1,13,10,3,12 -43,No,Travel_Frequently,313,Research & Development,21,3,Medical,1,525,4,Male,61,3,1,Laboratory Technician,4,Married,2258,15238,7,Y,No,20,4,1,80,1,8,1,3,3,2,1,2 -43,No,Travel_Rarely,1473,Research & Development,8,4,Other,1,526,3,Female,74,3,2,Healthcare Representative,3,Divorced,4522,2227,4,Y,Yes,14,3,4,80,0,8,3,3,5,2,0,2 -25,No,Travel_Rarely,891,Sales,4,2,Life Sciences,1,527,2,Female,99,2,2,Sales Executive,4,Single,4487,12090,1,Y,Yes,11,3,2,80,0,5,3,3,5,4,1,3 -37,No,Non-Travel,1063,Research & Development,25,5,Medical,1,529,2,Female,72,3,2,Research Scientist,3,Married,4449,23866,3,Y,Yes,15,3,1,80,2,15,2,3,13,11,10,7 -31,No,Travel_Rarely,329,Research & Development,1,2,Life Sciences,1,530,4,Male,98,2,1,Laboratory Technician,1,Married,2218,16193,1,Y,No,12,3,3,80,1,4,3,3,4,2,3,2 -39,No,Travel_Frequently,1218,Research & Development,1,1,Life Sciences,1,531,2,Male,52,3,5,Manager,3,Divorced,19197,8213,1,Y,Yes,14,3,3,80,1,21,3,3,21,8,1,6 -56,No,Travel_Frequently,906,Sales,6,3,Life Sciences,1,532,3,Female,86,4,4,Sales Executive,1,Married,13212,18256,9,Y,No,11,3,4,80,3,36,0,2,7,7,7,7 -30,No,Travel_Rarely,1082,Sales,12,3,Technical Degree,1,533,2,Female,83,3,2,Sales Executive,3,Single,6577,19558,0,Y,No,11,3,2,80,0,6,6,3,5,4,4,4 -41,No,Travel_Rarely,645,Sales,1,3,Marketing,1,534,2,Male,49,4,3,Sales Executive,1,Married,8392,19566,1,Y,No,16,3,3,80,1,10,2,3,10,7,0,7 -28,No,Travel_Rarely,1300,Research & Development,17,2,Medical,1,536,3,Male,79,3,2,Laboratory Technician,1,Divorced,4558,13535,1,Y,No,12,3,4,80,1,10,2,3,10,0,1,8 -25,Yes,Travel_Rarely,688,Research & Development,3,3,Medical,1,538,1,Male,91,3,1,Laboratory Technician,1,Married,4031,9396,5,Y,No,13,3,3,80,1,6,5,3,2,2,0,2 -52,No,Travel_Rarely,319,Research & Development,3,3,Medical,1,543,4,Male,39,2,3,Manufacturing Director,3,Married,7969,19609,2,Y,Yes,14,3,3,80,0,28,4,3,5,4,0,4 -45,No,Travel_Rarely,192,Research & Development,10,2,Life Sciences,1,544,1,Male,69,3,1,Research Scientist,4,Married,2654,9655,3,Y,No,21,4,4,80,2,8,3,2,2,2,0,2 -52,No,Travel_Rarely,1490,Research & Development,4,2,Life Sciences,1,546,4,Female,30,3,4,Manager,4,Married,16555,10310,2,Y,No,13,3,4,80,0,31,2,1,5,2,1,4 -42,No,Travel_Frequently,532,Research & Development,29,2,Life Sciences,1,547,1,Female,92,3,2,Research Scientist,3,Divorced,4556,12932,2,Y,No,11,3,2,80,1,19,3,3,5,4,0,2 -30,No,Travel_Rarely,317,Research & Development,2,3,Life Sciences,1,548,3,Female,43,1,2,Manufacturing Director,4,Single,6091,24793,2,Y,No,20,4,3,80,0,11,2,3,5,4,0,2 -60,No,Travel_Rarely,422,Research & Development,7,3,Life Sciences,1,549,1,Female,41,3,5,Manager,1,Married,19566,3854,5,Y,No,11,3,4,80,0,33,5,1,29,8,11,10 -46,No,Travel_Rarely,1485,Research & Development,18,3,Medical,1,550,3,Female,87,3,2,Manufacturing Director,3,Divorced,4810,26314,2,Y,No,14,3,3,80,1,19,5,2,10,7,0,8 -42,No,Travel_Frequently,1368,Research & Development,28,4,Technical Degree,1,551,4,Female,88,2,2,Healthcare Representative,4,Married,4523,4386,0,Y,No,11,3,4,80,3,7,4,4,6,5,0,4 -24,Yes,Travel_Rarely,1448,Sales,1,1,Technical Degree,1,554,1,Female,62,3,1,Sales Representative,2,Single,3202,21972,1,Y,Yes,16,3,2,80,0,6,4,3,5,3,1,4 -34,Yes,Travel_Frequently,296,Sales,6,2,Marketing,1,555,4,Female,33,1,1,Sales Representative,3,Divorced,2351,12253,0,Y,No,16,3,4,80,1,3,3,2,2,2,1,0 -38,No,Travel_Frequently,1490,Research & Development,2,2,Life Sciences,1,556,4,Male,42,3,1,Laboratory Technician,4,Married,1702,12106,1,Y,Yes,23,4,3,80,1,1,3,3,1,0,0,0 -40,No,Travel_Rarely,1398,Sales,2,4,Life Sciences,1,558,3,Female,79,3,5,Manager,3,Married,18041,13022,0,Y,No,14,3,4,80,0,21,2,3,20,15,1,12 -26,No,Travel_Rarely,1349,Research & Development,23,3,Life Sciences,1,560,1,Female,90,3,1,Research Scientist,4,Divorced,2886,3032,1,Y,No,22,4,2,80,2,3,3,1,3,2,0,2 -30,No,Non-Travel,1400,Research & Development,3,3,Life Sciences,1,562,3,Male,53,3,1,Laboratory Technician,4,Married,2097,16734,4,Y,No,15,3,3,80,1,9,3,1,5,3,1,4 -29,No,Travel_Rarely,986,Research & Development,3,4,Medical,1,564,2,Male,93,2,3,Research Director,3,Married,11935,21526,1,Y,No,18,3,3,80,0,10,2,3,10,2,0,7 -29,Yes,Travel_Rarely,408,Research & Development,25,5,Technical Degree,1,565,3,Female,71,2,1,Research Scientist,2,Married,2546,18300,5,Y,No,16,3,2,80,0,6,2,4,2,2,1,1 -19,Yes,Travel_Rarely,489,Human Resources,2,2,Technical Degree,1,566,1,Male,52,2,1,Human Resources,4,Single,2564,18437,1,Y,No,12,3,3,80,0,1,3,4,1,0,0,0 -30,No,Non-Travel,1398,Sales,22,4,Other,1,567,3,Female,69,3,3,Sales Executive,1,Married,8412,2890,0,Y,No,11,3,3,80,0,10,3,3,9,8,7,8 -57,No,Travel_Rarely,210,Sales,29,3,Marketing,1,568,1,Male,56,2,4,Manager,4,Divorced,14118,22102,3,Y,No,12,3,3,80,1,32,3,2,1,0,0,0 -50,No,Travel_Rarely,1099,Research & Development,29,4,Life Sciences,1,569,2,Male,88,2,4,Manager,3,Married,17046,9314,0,Y,No,15,3,2,80,1,28,2,3,27,10,15,7 -30,No,Non-Travel,1116,Research & Development,2,3,Medical,1,571,3,Female,49,3,1,Laboratory Technician,4,Single,2564,7181,0,Y,No,14,3,3,80,0,12,2,2,11,7,6,7 -60,No,Travel_Frequently,1499,Sales,28,3,Marketing,1,573,3,Female,80,2,3,Sales Executive,1,Married,10266,2845,4,Y,No,19,3,4,80,0,22,5,4,18,13,13,11 -47,No,Travel_Rarely,983,Research & Development,2,2,Medical,1,574,1,Female,65,3,2,Manufacturing Director,4,Divorced,5070,7389,5,Y,No,13,3,3,80,3,20,2,3,5,0,0,4 -46,No,Travel_Rarely,1009,Research & Development,2,3,Life Sciences,1,575,1,Male,51,3,4,Research Director,3,Married,17861,2288,6,Y,No,13,3,3,80,0,26,2,1,3,2,0,1 -35,No,Travel_Rarely,144,Research & Development,22,3,Life Sciences,1,577,4,Male,46,1,1,Laboratory Technician,3,Single,4230,19225,0,Y,No,15,3,3,80,0,6,2,3,5,4,4,3 -54,No,Travel_Rarely,548,Research & Development,8,4,Life Sciences,1,578,3,Female,42,3,2,Laboratory Technician,3,Single,3780,23428,7,Y,No,11,3,3,80,0,19,3,3,1,0,0,0 -34,No,Travel_Rarely,1303,Research & Development,2,4,Life Sciences,1,579,4,Male,62,2,1,Research Scientist,3,Divorced,2768,8416,3,Y,No,12,3,3,80,1,14,3,3,7,3,5,7 -46,No,Travel_Rarely,1125,Sales,10,3,Marketing,1,580,3,Female,94,2,3,Sales Executive,4,Married,9071,11563,2,Y,Yes,19,3,3,80,1,15,3,3,3,2,1,2 -31,No,Travel_Rarely,1274,Research & Development,9,1,Life Sciences,1,581,3,Male,33,3,3,Manufacturing Director,2,Divorced,10648,14394,1,Y,No,25,4,4,80,1,13,6,4,13,8,0,8 -33,Yes,Travel_Rarely,1277,Research & Development,15,1,Medical,1,582,2,Male,56,3,3,Manager,3,Married,13610,24619,7,Y,Yes,12,3,4,80,0,15,2,4,7,6,7,7 -33,Yes,Travel_Rarely,587,Research & Development,10,1,Medical,1,584,1,Male,38,1,1,Laboratory Technician,4,Divorced,3408,6705,7,Y,No,13,3,1,80,3,8,2,3,4,3,1,3 -30,No,Travel_Rarely,413,Sales,7,1,Marketing,1,585,4,Male,57,3,1,Sales Representative,2,Single,2983,18398,0,Y,No,14,3,1,80,0,4,3,3,3,2,1,2 -35,No,Travel_Rarely,1276,Research & Development,16,3,Life Sciences,1,586,4,Male,72,3,3,Healthcare Representative,3,Married,7632,14295,4,Y,Yes,12,3,3,80,0,10,2,3,8,7,0,0 -31,Yes,Travel_Frequently,534,Research & Development,20,3,Life Sciences,1,587,1,Male,66,3,3,Healthcare Representative,3,Married,9824,22908,3,Y,No,12,3,1,80,0,12,2,3,1,0,0,0 -34,Yes,Travel_Frequently,988,Human Resources,23,3,Human Resources,1,590,2,Female,43,3,3,Human Resources,1,Divorced,9950,11533,9,Y,Yes,15,3,3,80,3,11,2,3,3,2,0,2 -42,No,Travel_Frequently,1474,Research & Development,5,2,Other,1,591,2,Male,97,3,1,Laboratory Technician,3,Married,2093,9260,4,Y,No,17,3,4,80,1,8,4,3,2,2,2,0 -36,No,Non-Travel,635,Sales,10,4,Medical,1,592,2,Male,32,3,3,Sales Executive,4,Single,9980,15318,1,Y,No,14,3,4,80,0,10,3,2,10,3,9,7 -22,Yes,Travel_Frequently,1368,Research & Development,4,1,Technical Degree,1,593,3,Male,99,2,1,Laboratory Technician,3,Single,3894,9129,5,Y,No,16,3,3,80,0,4,3,3,2,2,1,2 -48,No,Travel_Rarely,163,Sales,2,5,Marketing,1,595,2,Female,37,3,2,Sales Executive,4,Married,4051,19658,2,Y,No,14,3,1,80,1,14,2,3,9,7,6,7 -55,No,Travel_Rarely,1117,Sales,18,5,Life Sciences,1,597,1,Female,83,3,4,Manager,2,Single,16835,9873,3,Y,No,23,4,4,80,0,37,2,3,10,9,7,7 -41,No,Non-Travel,267,Sales,10,2,Life Sciences,1,599,4,Male,56,3,2,Sales Executive,4,Single,6230,13430,7,Y,No,14,3,4,80,0,16,3,3,14,3,1,10 -35,No,Travel_Rarely,619,Sales,1,3,Marketing,1,600,2,Male,85,3,2,Sales Executive,3,Married,4717,18659,9,Y,No,11,3,3,80,0,15,2,3,11,9,6,9 -40,No,Travel_Rarely,302,Research & Development,6,3,Life Sciences,1,601,2,Female,75,3,4,Manufacturing Director,3,Single,13237,20364,7,Y,No,15,3,3,80,0,22,3,3,20,6,5,13 -39,No,Travel_Frequently,443,Research & Development,8,1,Life Sciences,1,602,3,Female,48,3,1,Laboratory Technician,3,Married,3755,17872,1,Y,No,11,3,1,80,1,8,3,3,8,3,0,7 -31,No,Travel_Rarely,828,Sales,2,1,Life Sciences,1,604,2,Male,77,3,2,Sales Executive,4,Single,6582,8346,4,Y,Yes,13,3,3,80,0,10,2,4,6,5,0,5 -42,No,Travel_Rarely,319,Research & Development,24,3,Medical,1,605,4,Male,56,3,3,Manufacturing Director,1,Married,7406,6950,1,Y,Yes,21,4,4,80,1,10,5,2,10,9,5,8 -45,No,Travel_Rarely,561,Sales,2,3,Other,1,606,4,Male,61,3,2,Sales Executive,2,Married,4805,16177,0,Y,No,19,3,2,80,1,9,3,4,8,7,3,7 -26,Yes,Travel_Frequently,426,Human Resources,17,4,Life Sciences,1,608,2,Female,58,3,1,Human Resources,3,Divorced,2741,22808,0,Y,Yes,11,3,2,80,1,8,2,2,7,7,1,0 -29,No,Travel_Rarely,232,Research & Development,19,3,Technical Degree,1,611,4,Male,34,3,2,Manufacturing Director,4,Divorced,4262,22645,4,Y,No,12,3,2,80,2,8,2,4,3,2,1,2 -33,No,Travel_Rarely,922,Research & Development,1,5,Medical,1,612,1,Female,95,4,4,Research Director,3,Divorced,16184,22578,4,Y,No,19,3,3,80,1,10,2,3,6,1,0,5 -31,No,Travel_Rarely,688,Sales,7,3,Life Sciences,1,613,3,Male,44,2,3,Manager,4,Divorced,11557,25291,9,Y,No,21,4,3,80,1,10,3,2,5,4,0,1 -18,Yes,Travel_Frequently,1306,Sales,5,3,Marketing,1,614,2,Male,69,3,1,Sales Representative,2,Single,1878,8059,1,Y,Yes,14,3,4,80,0,0,3,3,0,0,0,0 -40,No,Non-Travel,1094,Sales,28,3,Other,1,615,3,Male,58,1,3,Sales Executive,1,Divorced,10932,11373,3,Y,No,15,3,3,80,1,20,2,3,1,0,0,1 -41,No,Non-Travel,509,Research & Development,2,4,Other,1,616,1,Female,62,2,2,Healthcare Representative,3,Single,6811,2112,2,Y,Yes,17,3,1,80,0,10,3,3,8,7,0,7 -26,No,Travel_Rarely,775,Sales,29,2,Medical,1,618,1,Male,45,3,2,Sales Executive,3,Divorced,4306,4267,5,Y,No,12,3,1,80,2,8,5,3,0,0,0,0 -35,No,Travel_Rarely,195,Sales,1,3,Medical,1,620,1,Female,80,3,2,Sales Executive,3,Single,4859,6698,1,Y,No,16,3,4,80,0,5,3,3,5,4,0,3 -34,No,Travel_Rarely,258,Sales,21,4,Life Sciences,1,621,4,Male,74,4,2,Sales Executive,4,Single,5337,19921,1,Y,No,12,3,4,80,0,10,3,3,10,7,5,7 -26,Yes,Travel_Rarely,471,Research & Development,24,3,Technical Degree,1,622,3,Male,66,1,1,Laboratory Technician,4,Single,2340,23213,1,Y,Yes,18,3,2,80,0,1,3,1,1,0,0,0 -37,No,Travel_Rarely,799,Research & Development,1,3,Technical Degree,1,623,2,Female,59,3,3,Manufacturing Director,4,Single,7491,23848,4,Y,No,17,3,4,80,0,12,3,4,6,5,1,2 -46,No,Travel_Frequently,1034,Research & Development,18,1,Medical,1,624,1,Female,86,3,3,Healthcare Representative,3,Married,10527,8984,5,Y,No,11,3,4,80,0,28,3,2,2,2,1,2 -41,No,Travel_Rarely,1276,Sales,2,5,Life Sciences,1,625,2,Female,91,3,4,Manager,1,Married,16595,5626,7,Y,No,16,3,2,80,1,22,2,3,18,16,11,8 -37,No,Non-Travel,142,Sales,9,4,Medical,1,626,1,Male,69,3,3,Sales Executive,2,Divorced,8834,24666,1,Y,No,13,3,4,80,1,9,6,3,9,5,7,7 -52,No,Travel_Rarely,956,Research & Development,6,2,Technical Degree,1,630,4,Male,78,3,2,Research Scientist,1,Divorced,5577,22087,3,Y,Yes,12,3,2,80,2,18,3,3,10,9,6,9 -32,Yes,Non-Travel,1474,Sales,11,4,Other,1,631,4,Male,60,4,2,Sales Executive,3,Married,4707,23914,8,Y,No,12,3,4,80,0,6,2,3,4,2,1,2 -24,No,Travel_Frequently,535,Sales,24,3,Medical,1,632,4,Male,38,3,1,Sales Representative,4,Married,2400,5530,0,Y,No,13,3,3,80,2,3,3,3,2,2,2,1 -38,No,Travel_Rarely,1495,Research & Development,10,3,Medical,1,634,3,Female,76,3,2,Healthcare Representative,3,Married,9824,22174,3,Y,No,19,3,3,80,1,18,4,3,1,0,0,0 -37,No,Travel_Rarely,446,Research & Development,1,4,Life Sciences,1,635,2,Female,65,3,2,Manufacturing Director,2,Married,6447,15701,6,Y,No,12,3,2,80,1,8,2,2,6,5,4,3 -49,No,Travel_Rarely,1245,Research & Development,18,4,Life Sciences,1,638,4,Male,58,2,5,Research Director,3,Divorced,19502,2125,1,Y,Yes,17,3,3,80,1,31,5,3,31,9,0,9 -24,No,Travel_Rarely,691,Research & Development,23,3,Medical,1,639,2,Male,89,4,1,Research Scientist,4,Married,2725,21630,1,Y,Yes,11,3,2,80,2,6,3,3,6,5,1,4 -26,No,Travel_Rarely,703,Sales,28,2,Marketing,1,641,1,Male,66,3,2,Sales Executive,2,Married,6272,7428,1,Y,No,20,4,4,80,2,6,5,4,5,3,1,4 -24,No,Travel_Rarely,823,Research & Development,17,2,Other,1,643,4,Male,94,2,1,Laboratory Technician,2,Married,2127,9100,1,Y,No,21,4,4,80,1,1,2,3,1,0,0,0 -50,No,Travel_Frequently,1246,Human Resources,3,3,Medical,1,644,1,Male,99,3,5,Manager,2,Married,18200,7999,1,Y,No,11,3,3,80,1,32,2,3,32,5,10,7 -25,No,Travel_Rarely,622,Sales,13,1,Medical,1,645,2,Male,40,3,1,Sales Representative,3,Married,2096,26376,1,Y,No,11,3,3,80,0,7,1,3,7,4,0,6 -24,Yes,Travel_Frequently,1287,Research & Development,7,3,Life Sciences,1,647,1,Female,55,3,1,Laboratory Technician,3,Married,2886,14168,1,Y,Yes,16,3,4,80,1,6,4,3,6,3,1,2 -30,Yes,Travel_Frequently,448,Sales,12,4,Life Sciences,1,648,2,Male,74,2,1,Sales Representative,1,Married,2033,14470,1,Y,No,18,3,3,80,1,1,2,4,1,0,0,0 -34,No,Travel_Rarely,254,Research & Development,1,2,Life Sciences,1,649,2,Male,83,2,1,Research Scientist,4,Married,3622,22794,1,Y,Yes,13,3,4,80,1,6,3,3,6,5,1,3 -31,Yes,Travel_Rarely,1365,Sales,13,4,Medical,1,650,2,Male,46,3,2,Sales Executive,1,Divorced,4233,11512,2,Y,No,17,3,3,80,0,9,2,1,3,1,1,2 -35,No,Travel_Rarely,538,Research & Development,25,2,Other,1,652,1,Male,54,2,2,Laboratory Technician,4,Single,3681,14004,4,Y,No,14,3,4,80,0,9,3,3,3,2,0,2 -31,No,Travel_Rarely,525,Sales,6,4,Medical,1,653,1,Male,66,4,2,Sales Executive,4,Divorced,5460,6219,4,Y,No,22,4,4,80,2,13,4,4,7,7,5,7 -27,No,Travel_Rarely,798,Research & Development,6,4,Medical,1,655,1,Female,66,2,1,Research Scientist,3,Divorced,2187,5013,0,Y,No,12,3,3,80,2,6,5,2,5,3,0,3 -37,No,Travel_Rarely,558,Sales,2,3,Marketing,1,656,4,Male,75,3,2,Sales Executive,3,Married,9602,3010,4,Y,Yes,11,3,3,80,1,17,3,2,3,0,1,0 -20,No,Travel_Rarely,959,Research & Development,1,3,Life Sciences,1,657,4,Female,83,2,1,Research Scientist,2,Single,2836,11757,1,Y,No,13,3,4,80,0,1,0,4,1,0,0,0 -42,No,Travel_Rarely,622,Research & Development,2,4,Life Sciences,1,659,3,Female,81,3,2,Healthcare Representative,4,Married,4089,5718,1,Y,No,13,3,2,80,2,10,4,3,10,2,2,2 -43,No,Travel_Rarely,782,Research & Development,6,4,Other,1,661,2,Male,50,2,4,Research Director,4,Divorced,16627,2671,4,Y,Yes,14,3,3,80,1,21,3,2,1,0,0,0 -38,No,Travel_Rarely,362,Research & Development,1,1,Life Sciences,1,662,3,Female,43,3,1,Research Scientist,1,Single,2619,14561,3,Y,No,17,3,4,80,0,8,3,2,0,0,0,0 -43,No,Travel_Frequently,1001,Research & Development,9,5,Medical,1,663,4,Male,72,3,2,Laboratory Technician,3,Divorced,5679,19627,3,Y,Yes,13,3,2,80,1,10,3,3,8,7,4,7 -48,No,Travel_Rarely,1236,Research & Development,1,4,Life Sciences,1,664,4,Female,40,2,4,Manager,1,Married,15402,17997,7,Y,No,11,3,1,80,1,21,3,1,3,2,0,2 -44,No,Travel_Rarely,1112,Human Resources,1,4,Life Sciences,1,665,1,Female,50,2,2,Human Resources,3,Single,5985,26894,4,Y,No,11,3,2,80,0,10,1,4,2,2,0,2 -34,No,Travel_Rarely,204,Sales,14,3,Technical Degree,1,666,3,Female,31,3,1,Sales Representative,3,Divorced,2579,2912,1,Y,Yes,18,3,4,80,2,8,3,3,8,2,0,6 -27,Yes,Travel_Rarely,1420,Sales,2,1,Marketing,1,667,3,Male,85,3,1,Sales Representative,1,Divorced,3041,16346,0,Y,No,11,3,2,80,1,5,3,3,4,3,0,2 -21,No,Travel_Rarely,1343,Sales,22,1,Technical Degree,1,669,3,Male,49,3,1,Sales Representative,3,Single,3447,24444,1,Y,No,11,3,3,80,0,3,2,3,3,2,1,2 -44,No,Travel_Rarely,1315,Research & Development,3,4,Other,1,671,4,Male,35,3,5,Manager,4,Married,19513,9358,4,Y,Yes,12,3,1,80,1,26,2,4,2,2,0,1 -22,No,Travel_Rarely,604,Research & Development,6,1,Medical,1,675,1,Male,69,3,1,Research Scientist,3,Married,2773,12145,0,Y,No,20,4,4,80,0,3,3,3,2,2,2,2 -33,No,Travel_Rarely,1216,Sales,8,4,Marketing,1,677,3,Male,39,3,2,Sales Executive,3,Divorced,7104,20431,0,Y,No,12,3,4,80,0,6,3,3,5,0,1,2 -32,No,Travel_Rarely,646,Research & Development,9,4,Life Sciences,1,679,1,Female,92,3,2,Research Scientist,4,Married,6322,18089,1,Y,Yes,12,3,4,80,1,6,2,2,6,4,0,5 -30,No,Travel_Frequently,160,Research & Development,3,3,Medical,1,680,3,Female,71,3,1,Research Scientist,3,Divorced,2083,22653,1,Y,No,20,4,3,80,1,1,2,3,1,0,0,0 -53,No,Travel_Rarely,238,Sales,1,1,Medical,1,682,4,Female,34,3,2,Sales Executive,1,Single,8381,7507,7,Y,No,20,4,4,80,0,18,2,4,14,7,8,10 -34,No,Travel_Rarely,1397,Research & Development,1,5,Life Sciences,1,683,2,Male,42,3,1,Research Scientist,4,Married,2691,7660,1,Y,No,12,3,4,80,1,10,4,2,10,9,8,8 -45,Yes,Travel_Frequently,306,Sales,26,4,Life Sciences,1,684,1,Female,100,3,2,Sales Executive,1,Married,4286,5630,2,Y,No,14,3,4,80,2,5,4,3,1,1,0,0 -26,No,Travel_Rarely,991,Research & Development,6,3,Life Sciences,1,686,3,Female,71,3,1,Laboratory Technician,4,Married,2659,17759,1,Y,Yes,13,3,3,80,1,3,2,3,3,2,0,2 -37,No,Travel_Rarely,482,Research & Development,3,3,Other,1,689,3,Male,36,3,3,Manufacturing Director,3,Married,9434,9606,1,Y,No,15,3,3,80,1,10,2,3,10,7,7,8 -29,No,Travel_Rarely,1176,Sales,3,2,Medical,1,690,2,Female,62,3,2,Sales Executive,3,Married,5561,3487,1,Y,No,14,3,1,80,1,6,5,2,6,0,1,2 -35,No,Travel_Rarely,1017,Research & Development,6,4,Life Sciences,1,691,2,Male,82,1,2,Research Scientist,4,Single,6646,19368,1,Y,No,13,3,2,80,0,17,3,3,17,11,11,8 -33,No,Travel_Frequently,1296,Research & Development,6,3,Life Sciences,1,692,3,Male,30,3,2,Healthcare Representative,4,Divorced,7725,5335,3,Y,No,23,4,3,80,1,15,2,1,13,11,4,7 -54,No,Travel_Rarely,397,Human Resources,19,4,Medical,1,698,3,Male,88,3,3,Human Resources,2,Married,10725,6729,2,Y,No,15,3,3,80,1,16,1,4,9,7,7,1 -36,No,Travel_Rarely,913,Research & Development,9,2,Medical,1,699,2,Male,48,2,2,Manufacturing Director,2,Divorced,8847,13934,2,Y,Yes,11,3,3,80,1,13,2,3,3,2,0,2 -27,No,Travel_Rarely,1115,Research & Development,3,4,Medical,1,700,1,Male,54,2,1,Research Scientist,4,Single,2045,15174,0,Y,No,13,3,4,80,0,5,0,3,4,2,1,1 -20,Yes,Travel_Rarely,1362,Research & Development,10,1,Medical,1,701,4,Male,32,3,1,Research Scientist,3,Single,1009,26999,1,Y,Yes,11,3,4,80,0,1,5,3,1,0,1,1 -33,Yes,Travel_Frequently,1076,Research & Development,3,3,Life Sciences,1,702,1,Male,70,3,1,Research Scientist,1,Single,3348,3164,1,Y,Yes,11,3,1,80,0,10,3,3,10,8,9,7 -35,No,Non-Travel,727,Research & Development,3,3,Life Sciences,1,704,3,Male,41,2,1,Laboratory Technician,3,Married,1281,16900,1,Y,No,18,3,3,80,2,1,3,3,1,0,0,0 -23,No,Travel_Rarely,885,Research & Development,4,3,Medical,1,705,1,Male,58,4,1,Research Scientist,1,Married,2819,8544,2,Y,No,16,3,1,80,1,5,3,4,3,2,0,2 -25,No,Travel_Rarely,810,Sales,8,3,Life Sciences,1,707,4,Male,57,4,2,Sales Executive,2,Married,4851,15678,0,Y,No,22,4,3,80,1,4,4,3,3,2,1,2 -38,No,Travel_Rarely,243,Sales,7,4,Marketing,1,709,4,Female,46,2,2,Sales Executive,4,Single,4028,7791,0,Y,No,20,4,1,80,0,8,2,3,7,7,0,5 -29,No,Travel_Frequently,806,Research & Development,1,4,Life Sciences,1,710,2,Male,76,1,1,Research Scientist,4,Divorced,2720,18959,1,Y,No,18,3,4,80,1,10,5,3,10,7,2,8 -48,No,Travel_Rarely,817,Sales,2,1,Marketing,1,712,2,Male,56,4,2,Sales Executive,2,Married,8120,18597,3,Y,No,12,3,4,80,0,12,3,3,2,2,2,2 -27,No,Travel_Frequently,1410,Sales,3,1,Medical,1,714,4,Female,71,4,2,Sales Executive,4,Divorced,4647,16673,1,Y,Yes,20,4,2,80,2,6,3,3,6,5,0,4 -37,No,Travel_Rarely,1225,Research & Development,10,2,Life Sciences,1,715,4,Male,80,4,1,Research Scientist,4,Single,4680,15232,3,Y,No,17,3,1,80,0,4,2,3,1,0,0,0 -50,No,Travel_Rarely,1207,Research & Development,28,1,Medical,1,716,4,Male,74,4,1,Laboratory Technician,3,Married,3221,3297,1,Y,Yes,11,3,3,80,3,20,3,3,20,8,3,8 -34,No,Travel_Rarely,1442,Research & Development,9,3,Medical,1,717,4,Female,46,2,3,Healthcare Representative,2,Single,8621,17654,1,Y,No,14,3,2,80,0,9,3,4,8,7,7,7 -24,Yes,Travel_Rarely,693,Sales,3,2,Life Sciences,1,720,1,Female,65,3,2,Sales Executive,3,Single,4577,24785,9,Y,No,14,3,1,80,0,4,3,3,2,2,2,0 -39,No,Travel_Rarely,408,Research & Development,2,4,Technical Degree,1,721,4,Female,80,2,2,Healthcare Representative,3,Single,4553,20978,1,Y,No,11,3,1,80,0,20,4,3,20,7,11,10 -32,No,Travel_Rarely,929,Sales,10,3,Marketing,1,722,4,Male,55,3,2,Sales Executive,4,Single,5396,21703,1,Y,No,12,3,4,80,0,10,2,2,10,7,0,8 -50,Yes,Travel_Frequently,562,Sales,8,2,Technical Degree,1,723,2,Male,50,3,2,Sales Executive,3,Married,6796,23452,3,Y,Yes,14,3,1,80,1,18,4,3,4,3,1,3 -38,No,Travel_Rarely,827,Research & Development,1,4,Life Sciences,1,724,2,Female,33,4,2,Healthcare Representative,4,Single,7625,19383,0,Y,No,13,3,3,80,0,10,4,2,9,7,1,8 -27,No,Travel_Rarely,608,Research & Development,1,2,Life Sciences,1,725,3,Female,68,3,3,Manufacturing Director,1,Married,7412,6009,1,Y,No,11,3,4,80,0,9,3,3,9,7,0,7 -32,No,Travel_Rarely,1018,Research & Development,3,2,Life Sciences,1,727,3,Female,39,3,3,Research Director,4,Single,11159,19373,3,Y,No,15,3,4,80,0,10,6,3,7,7,7,7 -47,No,Travel_Rarely,703,Sales,14,4,Marketing,1,728,4,Male,42,3,2,Sales Executive,1,Single,4960,11825,2,Y,No,12,3,4,80,0,20,2,3,7,7,1,7 -40,No,Travel_Frequently,580,Sales,5,4,Life Sciences,1,729,4,Male,48,2,3,Sales Executive,1,Married,10475,23772,5,Y,Yes,21,4,3,80,1,20,2,3,18,13,1,12 -53,No,Travel_Rarely,970,Research & Development,7,3,Life Sciences,1,730,3,Male,59,4,4,Research Director,3,Married,14814,13514,3,Y,No,19,3,3,80,0,32,3,3,5,1,1,3 -41,No,Travel_Rarely,427,Human Resources,10,4,Human Resources,1,731,2,Male,73,2,5,Manager,4,Divorced,19141,8861,3,Y,No,15,3,2,80,3,23,2,2,21,6,12,6 -60,No,Travel_Rarely,1179,Sales,16,4,Marketing,1,732,1,Male,84,3,2,Sales Executive,1,Single,5405,11924,8,Y,No,14,3,4,80,0,10,1,3,2,2,2,2 -27,No,Travel_Frequently,294,Research & Development,10,2,Life Sciences,1,733,4,Male,32,3,3,Manufacturing Director,1,Divorced,8793,4809,1,Y,No,21,4,3,80,2,9,4,2,9,7,1,7 -41,No,Travel_Rarely,314,Human Resources,1,3,Human Resources,1,734,4,Male,59,2,5,Manager,3,Married,19189,19562,1,Y,No,12,3,2,80,1,22,3,3,22,7,2,10 -50,No,Travel_Rarely,316,Sales,8,4,Marketing,1,738,4,Male,54,3,1,Sales Representative,2,Married,3875,9983,7,Y,No,15,3,4,80,1,4,2,3,2,2,2,2 -28,Yes,Travel_Rarely,654,Research & Development,1,2,Life Sciences,1,741,1,Female,67,1,1,Research Scientist,2,Single,2216,3872,7,Y,Yes,13,3,4,80,0,10,4,3,7,7,3,7 -36,No,Non-Travel,427,Research & Development,8,3,Life Sciences,1,742,1,Female,63,4,3,Research Director,1,Married,11713,20335,9,Y,No,14,3,1,80,1,10,2,3,8,7,0,5 -38,No,Travel_Rarely,168,Research & Development,1,3,Life Sciences,1,743,3,Female,81,3,3,Manufacturing Director,3,Single,7861,15397,4,Y,Yes,14,3,4,80,0,10,4,4,1,0,0,0 -44,No,Non-Travel,381,Research & Development,24,3,Medical,1,744,1,Male,49,1,1,Laboratory Technician,3,Single,3708,2104,2,Y,No,14,3,3,80,0,9,5,3,5,2,1,4 -47,No,Travel_Frequently,217,Sales,3,3,Medical,1,746,4,Female,49,3,4,Sales Executive,3,Divorced,13770,10225,9,Y,Yes,12,3,4,80,2,28,2,2,22,2,11,13 -30,No,Travel_Rarely,501,Sales,27,5,Marketing,1,747,3,Male,99,3,2,Sales Executive,4,Divorced,5304,25275,7,Y,No,23,4,4,80,1,10,2,2,8,7,7,7 -29,No,Travel_Rarely,1396,Sales,10,3,Life Sciences,1,749,3,Male,99,3,1,Sales Representative,3,Single,2642,2755,1,Y,No,11,3,3,80,0,1,6,3,1,0,0,0 -42,Yes,Travel_Frequently,933,Research & Development,19,3,Medical,1,752,3,Male,57,4,1,Research Scientist,3,Divorced,2759,20366,6,Y,Yes,12,3,4,80,0,7,2,3,2,2,2,2 -43,No,Travel_Frequently,775,Sales,15,3,Life Sciences,1,754,4,Male,47,2,2,Sales Executive,4,Married,6804,23683,3,Y,No,18,3,3,80,1,7,5,3,2,2,2,2 -34,No,Travel_Rarely,970,Research & Development,8,2,Medical,1,757,2,Female,96,3,2,Healthcare Representative,3,Single,6142,7360,3,Y,No,11,3,4,80,0,10,2,3,5,1,4,3 -23,No,Travel_Rarely,650,Research & Development,9,1,Medical,1,758,2,Male,37,3,1,Laboratory Technician,1,Married,2500,4344,1,Y,No,14,3,4,80,1,5,2,4,4,3,0,2 -39,No,Travel_Rarely,141,Human Resources,3,3,Human Resources,1,760,3,Female,44,4,2,Human Resources,2,Married,6389,18767,9,Y,No,15,3,3,80,1,12,3,1,8,3,3,6 -56,No,Travel_Rarely,832,Research & Development,9,3,Medical,1,762,3,Male,81,3,4,Healthcare Representative,4,Married,11103,20420,7,Y,No,11,3,3,80,0,30,1,2,10,7,1,1 -40,No,Travel_Rarely,804,Research & Development,2,1,Medical,1,763,4,Female,86,2,1,Research Scientist,4,Single,2342,22929,0,Y,Yes,20,4,4,80,0,5,2,2,4,2,2,3 -27,No,Travel_Rarely,975,Research & Development,7,3,Medical,1,764,4,Female,55,2,2,Healthcare Representative,1,Single,6811,23398,8,Y,No,19,3,1,80,0,9,2,1,7,6,0,7 -29,No,Travel_Rarely,1090,Sales,10,3,Marketing,1,766,4,Male,83,3,1,Sales Representative,2,Divorced,2297,17967,1,Y,No,14,3,4,80,2,2,2,3,2,2,2,2 -53,No,Travel_Rarely,346,Research & Development,6,3,Life Sciences,1,769,4,Male,86,3,2,Laboratory Technician,4,Single,2450,10919,2,Y,No,17,3,4,80,0,19,4,3,2,2,2,2 -35,No,Non-Travel,1225,Research & Development,2,4,Life Sciences,1,771,4,Female,61,3,2,Healthcare Representative,1,Divorced,5093,4761,2,Y,No,11,3,1,80,1,16,2,4,1,0,0,0 -32,No,Travel_Frequently,430,Research & Development,24,4,Life Sciences,1,772,1,Male,80,3,2,Laboratory Technician,4,Married,5309,21146,1,Y,No,15,3,4,80,2,10,2,3,10,8,4,7 -38,No,Travel_Rarely,268,Research & Development,2,5,Medical,1,773,4,Male,92,3,1,Research Scientist,3,Married,3057,20471,6,Y,Yes,13,3,2,80,1,6,0,1,1,0,0,1 -34,No,Travel_Rarely,167,Research & Development,8,5,Life Sciences,1,775,2,Female,32,3,2,Manufacturing Director,1,Divorced,5121,4187,3,Y,No,14,3,3,80,1,7,3,3,0,0,0,0 -52,No,Travel_Rarely,621,Sales,3,4,Marketing,1,776,3,Male,31,2,4,Manager,1,Married,16856,10084,1,Y,No,11,3,1,80,0,34,3,4,34,6,1,16 -33,Yes,Travel_Rarely,527,Research & Development,1,4,Other,1,780,4,Male,63,3,1,Research Scientist,4,Single,2686,5207,1,Y,Yes,13,3,3,80,0,10,2,2,10,9,7,8 -25,No,Travel_Rarely,883,Sales,26,1,Medical,1,781,3,Female,32,3,2,Sales Executive,4,Single,6180,22807,1,Y,No,23,4,2,80,0,6,5,2,6,5,1,4 -45,No,Travel_Rarely,954,Sales,2,2,Technical Degree,1,783,2,Male,46,1,2,Sales Representative,3,Single,6632,12388,0,Y,No,13,3,1,80,0,9,3,3,8,7,3,1 -23,No,Travel_Rarely,310,Research & Development,10,1,Medical,1,784,1,Male,79,4,1,Research Scientist,3,Single,3505,19630,1,Y,No,18,3,4,80,0,2,3,3,2,2,0,2 -47,Yes,Travel_Frequently,719,Sales,27,2,Life Sciences,1,785,2,Female,77,4,2,Sales Executive,3,Single,6397,10339,4,Y,Yes,12,3,4,80,0,8,2,3,5,4,1,3 -34,No,Travel_Rarely,304,Sales,2,3,Other,1,786,4,Male,60,3,2,Sales Executive,4,Single,6274,18686,1,Y,No,22,4,3,80,0,6,5,3,6,5,1,4 -55,Yes,Travel_Rarely,725,Research & Development,2,3,Medical,1,787,4,Male,78,3,5,Manager,1,Married,19859,21199,5,Y,Yes,13,3,4,80,1,24,2,3,5,2,1,4 -36,No,Non-Travel,1434,Sales,8,4,Life Sciences,1,789,1,Male,76,2,3,Sales Executive,1,Single,7587,14229,1,Y,No,15,3,2,80,0,10,1,3,10,7,0,9 -52,No,Non-Travel,715,Research & Development,19,4,Medical,1,791,4,Male,41,3,1,Research Scientist,4,Married,4258,26589,0,Y,No,18,3,1,80,1,5,3,3,4,3,1,2 -26,No,Travel_Frequently,575,Research & Development,1,2,Life Sciences,1,792,1,Female,71,1,1,Laboratory Technician,4,Divorced,4364,5288,3,Y,No,14,3,1,80,1,5,2,3,2,2,2,0 -29,No,Travel_Rarely,657,Research & Development,27,3,Medical,1,793,2,Female,66,3,2,Healthcare Representative,3,Married,4335,25549,4,Y,No,12,3,1,80,1,11,3,2,8,7,1,1 -26,Yes,Travel_Rarely,1146,Sales,8,3,Technical Degree,1,796,4,Male,38,2,2,Sales Executive,1,Single,5326,3064,6,Y,No,17,3,3,80,0,6,2,2,4,3,1,2 -34,No,Travel_Rarely,182,Research & Development,1,4,Life Sciences,1,797,2,Female,72,4,1,Research Scientist,4,Single,3280,13551,2,Y,No,16,3,3,80,0,10,2,3,4,2,1,3 -54,No,Travel_Rarely,376,Research & Development,19,4,Medical,1,799,4,Female,95,3,2,Manufacturing Director,1,Divorced,5485,22670,9,Y,Yes,11,3,2,80,2,9,4,3,5,3,1,4 -27,No,Travel_Frequently,829,Sales,8,1,Marketing,1,800,3,Male,84,3,2,Sales Executive,4,Married,4342,24008,0,Y,No,19,3,2,80,1,5,3,3,4,2,1,1 -37,No,Travel_Rarely,571,Research & Development,10,1,Life Sciences,1,802,4,Female,82,3,1,Research Scientist,1,Divorced,2782,19905,0,Y,Yes,13,3,2,80,2,6,3,2,5,3,4,3 -38,No,Travel_Frequently,240,Research & Development,2,4,Life Sciences,1,803,1,Female,75,4,2,Manufacturing Director,1,Single,5980,26085,6,Y,Yes,12,3,4,80,0,17,2,3,15,7,4,12 -34,No,Travel_Rarely,121,Research & Development,2,4,Medical,1,804,3,Female,86,2,1,Research Scientist,1,Single,4381,7530,1,Y,No,11,3,3,80,0,6,3,3,6,5,1,3 -35,No,Travel_Rarely,384,Sales,8,4,Life Sciences,1,805,1,Female,72,3,1,Sales Representative,4,Married,2572,20317,1,Y,No,16,3,2,80,1,3,1,2,3,2,0,2 -30,No,Travel_Rarely,921,Research & Development,1,3,Life Sciences,1,806,4,Male,38,1,1,Laboratory Technician,3,Married,3833,24375,3,Y,No,21,4,3,80,2,7,2,3,2,2,0,2 -40,No,Travel_Frequently,791,Research & Development,2,2,Medical,1,807,3,Female,38,4,2,Healthcare Representative,2,Married,4244,9931,1,Y,No,24,4,4,80,1,8,2,3,8,7,3,7 -34,No,Travel_Rarely,1111,Sales,8,2,Life Sciences,1,808,3,Female,93,3,2,Sales Executive,1,Married,6500,13305,5,Y,No,17,3,2,80,1,6,1,3,3,2,1,2 -42,No,Travel_Frequently,570,Research & Development,8,3,Life Sciences,1,809,2,Male,66,3,5,Manager,4,Divorced,18430,16225,1,Y,No,13,3,2,80,1,24,4,2,24,7,14,9 -23,Yes,Travel_Rarely,1243,Research & Development,6,3,Life Sciences,1,811,3,Male,63,4,1,Laboratory Technician,1,Married,1601,3445,1,Y,Yes,21,4,3,80,2,1,2,3,0,0,0,0 -24,No,Non-Travel,1092,Research & Development,9,3,Life Sciences,1,812,3,Male,60,2,1,Laboratory Technician,2,Divorced,2694,26551,1,Y,No,11,3,3,80,3,1,4,3,1,0,0,0 -52,No,Travel_Rarely,1325,Research & Development,11,4,Life Sciences,1,813,4,Female,82,3,2,Laboratory Technician,3,Married,3149,21821,8,Y,No,20,4,2,80,1,9,3,3,5,2,1,4 -50,No,Travel_Rarely,691,Research & Development,2,3,Medical,1,815,3,Male,64,3,4,Research Director,3,Married,17639,6881,5,Y,No,16,3,4,80,0,30,3,3,4,3,0,3 -29,Yes,Travel_Rarely,805,Research & Development,1,2,Life Sciences,1,816,2,Female,36,2,1,Laboratory Technician,1,Married,2319,6689,1,Y,Yes,11,3,4,80,1,1,1,3,1,0,0,0 -33,No,Travel_Rarely,213,Research & Development,7,3,Medical,1,817,3,Male,49,3,3,Research Director,3,Married,11691,25995,0,Y,No,11,3,4,80,0,14,3,4,13,9,3,7 -33,Yes,Travel_Rarely,118,Sales,16,3,Marketing,1,819,1,Female,69,3,2,Sales Executive,1,Single,5324,26507,5,Y,No,15,3,3,80,0,6,3,3,3,2,0,2 -47,No,Travel_Rarely,202,Research & Development,2,2,Other,1,820,3,Female,33,3,4,Manager,4,Married,16752,12982,1,Y,Yes,11,3,3,80,1,26,3,2,26,14,3,0 -36,No,Travel_Rarely,676,Research & Development,1,3,Other,1,823,3,Female,35,3,2,Manufacturing Director,2,Married,5228,23361,0,Y,No,15,3,1,80,1,10,2,3,9,7,0,5 -29,No,Travel_Rarely,1252,Research & Development,23,2,Life Sciences,1,824,3,Male,81,4,1,Research Scientist,3,Married,2700,23779,1,Y,No,24,4,3,80,1,10,3,3,10,7,0,7 -58,Yes,Travel_Rarely,286,Research & Development,2,4,Life Sciences,1,825,4,Male,31,3,5,Research Director,2,Single,19246,25761,7,Y,Yes,12,3,4,80,0,40,2,3,31,15,13,8 -35,No,Travel_Rarely,1258,Research & Development,1,4,Life Sciences,1,826,4,Female,40,4,1,Research Scientist,3,Single,2506,13301,3,Y,No,13,3,3,80,0,7,0,3,2,2,2,2 -42,No,Travel_Rarely,932,Research & Development,1,2,Life Sciences,1,827,4,Female,43,2,2,Manufacturing Director,4,Married,6062,4051,9,Y,Yes,13,3,4,80,1,8,4,3,4,3,0,2 -28,Yes,Travel_Rarely,890,Research & Development,2,4,Medical,1,828,3,Male,46,3,1,Research Scientist,3,Single,4382,16374,6,Y,No,17,3,4,80,0,5,3,2,2,2,2,1 -36,No,Travel_Rarely,1041,Human Resources,13,3,Human Resources,1,829,3,Male,36,3,1,Human Resources,2,Married,2143,25527,4,Y,No,13,3,2,80,1,8,2,3,5,2,0,4 -32,No,Travel_Rarely,859,Research & Development,4,3,Life Sciences,1,830,3,Female,98,2,2,Manufacturing Director,3,Married,6162,19124,1,Y,No,12,3,3,80,1,14,3,3,14,13,6,8 -40,No,Travel_Frequently,720,Research & Development,16,4,Medical,1,832,1,Male,51,2,2,Laboratory Technician,3,Single,5094,11983,6,Y,No,14,3,4,80,0,10,6,3,1,0,0,0 -30,No,Travel_Rarely,946,Research & Development,2,3,Medical,1,833,3,Female,52,2,2,Manufacturing Director,4,Single,6877,20234,5,Y,Yes,24,4,2,80,0,12,4,2,0,0,0,0 -45,No,Travel_Rarely,252,Research & Development,2,3,Life Sciences,1,834,2,Female,95,2,1,Research Scientist,3,Single,2274,6153,1,Y,No,14,3,4,80,0,1,3,3,1,0,0,0 -42,No,Travel_Rarely,933,Research & Development,29,3,Life Sciences,1,836,2,Male,98,3,2,Manufacturing Director,2,Married,4434,11806,1,Y,No,13,3,4,80,1,10,3,2,9,8,7,8 -38,No,Travel_Frequently,471,Research & Development,12,3,Life Sciences,1,837,1,Male,45,2,2,Healthcare Representative,1,Divorced,6288,4284,2,Y,No,15,3,3,80,1,13,3,2,4,3,1,2 -34,No,Travel_Frequently,702,Research & Development,16,4,Life Sciences,1,838,3,Female,100,2,1,Research Scientist,4,Single,2553,8306,1,Y,No,16,3,3,80,0,6,3,3,5,2,1,3 -49,Yes,Travel_Rarely,1184,Sales,11,3,Marketing,1,840,3,Female,43,3,3,Sales Executive,4,Married,7654,5860,1,Y,No,18,3,1,80,2,9,3,4,9,8,7,7 -55,Yes,Travel_Rarely,436,Sales,2,1,Medical,1,842,3,Male,37,3,2,Sales Executive,4,Single,5160,21519,4,Y,No,16,3,3,80,0,12,3,2,9,7,7,3 -43,No,Travel_Rarely,589,Research & Development,14,2,Life Sciences,1,843,2,Male,94,3,4,Research Director,1,Married,17159,5200,6,Y,No,24,4,3,80,1,22,3,3,4,1,1,0 -27,No,Travel_Rarely,269,Research & Development,5,1,Technical Degree,1,844,3,Male,42,2,3,Research Director,4,Divorced,12808,8842,1,Y,Yes,16,3,2,80,1,9,3,3,9,8,0,8 -35,No,Travel_Rarely,950,Research & Development,7,3,Other,1,845,3,Male,59,3,3,Manufacturing Director,3,Single,10221,18869,3,Y,No,21,4,2,80,0,17,3,4,8,5,1,6 -28,No,Travel_Rarely,760,Sales,2,4,Marketing,1,846,2,Female,81,3,2,Sales Executive,2,Married,4779,3698,1,Y,Yes,20,4,1,80,0,8,2,3,8,7,7,5 -34,No,Travel_Rarely,829,Human Resources,3,2,Human Resources,1,847,3,Male,88,3,1,Human Resources,4,Married,3737,2243,0,Y,No,19,3,3,80,1,4,1,1,3,2,0,2 -26,Yes,Travel_Frequently,887,Research & Development,5,2,Medical,1,848,3,Female,88,2,1,Research Scientist,3,Married,2366,20898,1,Y,Yes,14,3,1,80,1,8,2,3,8,7,1,7 -27,No,Non-Travel,443,Research & Development,3,3,Medical,1,850,4,Male,50,3,1,Research Scientist,4,Married,1706,16571,1,Y,No,11,3,3,80,3,0,6,2,0,0,0,0 -51,No,Travel_Rarely,1318,Sales,26,4,Marketing,1,851,1,Female,66,3,4,Manager,3,Married,16307,5594,2,Y,No,14,3,3,80,1,29,2,2,20,6,4,17 -44,No,Travel_Rarely,625,Research & Development,4,3,Medical,1,852,4,Male,50,3,2,Healthcare Representative,2,Single,5933,5197,9,Y,No,12,3,4,80,0,10,2,2,5,2,2,3 -25,No,Travel_Rarely,180,Research & Development,2,1,Medical,1,854,1,Male,65,4,1,Research Scientist,1,Single,3424,21632,7,Y,No,13,3,3,80,0,6,3,2,4,3,0,1 -33,No,Travel_Rarely,586,Sales,1,3,Medical,1,855,1,Male,48,4,2,Sales Executive,1,Divorced,4037,21816,1,Y,No,22,4,1,80,1,9,5,3,9,8,0,8 -35,No,Travel_Rarely,1343,Research & Development,27,1,Medical,1,856,3,Female,53,2,1,Research Scientist,1,Single,2559,17852,1,Y,No,11,3,4,80,0,6,3,2,6,5,1,1 -36,No,Travel_Rarely,928,Sales,1,2,Life Sciences,1,857,2,Male,56,3,2,Sales Executive,4,Married,6201,2823,1,Y,Yes,14,3,4,80,1,18,1,2,18,14,4,11 -32,No,Travel_Rarely,117,Sales,13,4,Life Sciences,1,859,2,Male,73,3,2,Sales Executive,4,Divorced,4403,9250,2,Y,No,11,3,3,80,1,8,3,2,5,2,0,3 -30,No,Travel_Frequently,1012,Research & Development,5,4,Life Sciences,1,861,2,Male,75,2,1,Research Scientist,4,Divorced,3761,2373,9,Y,No,12,3,2,80,1,10,3,2,5,4,0,3 -53,No,Travel_Rarely,661,Sales,7,2,Marketing,1,862,1,Female,78,2,3,Sales Executive,4,Married,10934,20715,7,Y,Yes,18,3,4,80,1,35,3,3,5,2,0,4 -45,No,Travel_Rarely,930,Sales,9,3,Marketing,1,864,4,Male,74,3,3,Sales Executive,1,Divorced,10761,19239,4,Y,Yes,12,3,3,80,1,18,2,3,5,4,0,2 -32,No,Travel_Rarely,638,Research & Development,8,2,Medical,1,865,3,Female,91,4,2,Research Scientist,3,Married,5175,22162,5,Y,No,12,3,3,80,1,9,3,2,5,3,1,3 -52,No,Travel_Frequently,890,Research & Development,25,4,Medical,1,867,3,Female,81,2,4,Manufacturing Director,4,Married,13826,19028,3,Y,No,22,4,3,80,0,31,3,3,9,8,0,0 -37,No,Travel_Rarely,342,Sales,16,4,Marketing,1,868,4,Male,66,2,2,Sales Executive,3,Divorced,6334,24558,4,Y,No,19,3,4,80,2,9,2,3,1,0,0,0 -28,No,Travel_Rarely,1169,Human Resources,8,2,Medical,1,869,2,Male,63,2,1,Human Resources,4,Divorced,4936,23965,1,Y,No,13,3,4,80,1,6,6,3,5,1,0,4 -22,No,Travel_Rarely,1230,Research & Development,1,2,Life Sciences,1,872,4,Male,33,2,2,Manufacturing Director,4,Married,4775,19146,6,Y,No,22,4,1,80,2,4,2,1,2,2,2,2 -44,No,Travel_Rarely,986,Research & Development,8,4,Life Sciences,1,874,1,Male,62,4,1,Laboratory Technician,4,Married,2818,5044,2,Y,Yes,24,4,3,80,1,10,2,2,3,2,0,2 -42,No,Travel_Frequently,1271,Research & Development,2,1,Medical,1,875,2,Male,35,3,1,Research Scientist,4,Single,2515,9068,5,Y,Yes,14,3,4,80,0,8,2,3,2,1,2,2 -36,No,Travel_Rarely,1278,Human Resources,8,3,Life Sciences,1,878,1,Male,77,2,1,Human Resources,1,Married,2342,8635,0,Y,No,21,4,3,80,0,6,3,3,5,4,0,3 -25,No,Travel_Rarely,141,Sales,3,1,Other,1,879,3,Male,98,3,2,Sales Executive,1,Married,4194,14363,1,Y,Yes,18,3,4,80,0,5,3,3,5,3,0,3 -35,No,Travel_Rarely,607,Research & Development,9,3,Life Sciences,1,880,4,Female,66,2,3,Manufacturing Director,3,Married,10685,23457,1,Y,Yes,20,4,2,80,1,17,2,3,17,14,5,15 -35,Yes,Travel_Frequently,130,Research & Development,25,4,Life Sciences,1,881,4,Female,96,3,1,Research Scientist,2,Divorced,2022,16612,1,Y,Yes,19,3,1,80,1,10,3,2,10,2,7,8 -32,No,Non-Travel,300,Research & Development,1,3,Life Sciences,1,882,4,Male,61,3,1,Laboratory Technician,4,Divorced,2314,9148,0,Y,No,12,3,2,80,1,4,2,3,3,0,0,2 -25,No,Travel_Rarely,583,Sales,4,1,Marketing,1,885,3,Male,87,2,2,Sales Executive,1,Married,4256,18154,1,Y,No,12,3,1,80,0,5,1,4,5,2,0,3 -49,No,Travel_Rarely,1418,Research & Development,1,3,Technical Degree,1,887,3,Female,36,3,1,Research Scientist,1,Married,3580,10554,2,Y,No,16,3,2,80,1,7,2,3,4,2,0,2 -24,No,Non-Travel,1269,Research & Development,4,1,Life Sciences,1,888,1,Male,46,2,1,Laboratory Technician,4,Married,3162,10778,0,Y,No,17,3,4,80,0,6,2,2,5,2,3,4 -32,No,Travel_Frequently,379,Sales,5,2,Life Sciences,1,889,2,Male,48,3,2,Sales Executive,2,Married,6524,8891,1,Y,No,14,3,4,80,1,10,3,3,10,8,5,3 -38,No,Travel_Rarely,395,Sales,9,3,Marketing,1,893,2,Male,98,2,1,Sales Representative,2,Married,2899,12102,0,Y,No,19,3,4,80,1,3,3,3,2,2,1,2 -42,No,Travel_Rarely,1265,Research & Development,3,3,Life Sciences,1,894,3,Female,95,4,2,Laboratory Technician,4,Married,5231,23726,2,Y,Yes,13,3,2,80,1,17,1,2,5,3,1,3 -31,No,Travel_Rarely,1222,Research & Development,11,4,Life Sciences,1,895,4,Male,48,3,1,Research Scientist,4,Married,2356,14871,3,Y,Yes,19,3,2,80,1,8,2,3,6,4,0,2 -29,Yes,Travel_Rarely,341,Sales,1,3,Medical,1,896,2,Female,48,2,1,Sales Representative,3,Divorced,2800,23522,6,Y,Yes,19,3,3,80,3,5,3,3,3,2,0,2 -53,No,Travel_Rarely,868,Sales,8,3,Marketing,1,897,1,Male,73,3,4,Sales Executive,4,Married,11836,22789,5,Y,No,14,3,3,80,1,28,3,3,2,0,2,2 -35,No,Travel_Rarely,672,Research & Development,25,3,Technical Degree,1,899,4,Male,78,2,3,Manufacturing Director,2,Married,10903,9129,3,Y,No,16,3,1,80,0,16,2,3,13,10,4,8 -37,No,Travel_Frequently,1231,Sales,21,2,Medical,1,900,3,Female,54,3,1,Sales Representative,4,Married,2973,21222,5,Y,No,15,3,2,80,1,10,3,3,5,4,0,0 -53,No,Travel_Rarely,102,Research & Development,23,4,Life Sciences,1,901,4,Female,72,3,4,Research Director,4,Single,14275,20206,6,Y,No,18,3,3,80,0,33,0,3,12,9,3,8 -43,No,Travel_Frequently,422,Research & Development,1,3,Life Sciences,1,902,4,Female,33,3,2,Healthcare Representative,4,Married,5562,21782,4,Y,No,13,3,2,80,1,12,2,2,5,2,2,2 -47,No,Travel_Rarely,249,Sales,2,2,Marketing,1,903,3,Female,35,3,2,Sales Executive,4,Married,4537,17783,0,Y,Yes,22,4,1,80,1,8,2,3,7,6,7,7 -37,No,Non-Travel,1252,Sales,19,2,Medical,1,904,1,Male,32,3,3,Sales Executive,2,Single,7642,4814,1,Y,Yes,13,3,4,80,0,10,2,3,10,0,0,9 -50,No,Non-Travel,881,Research & Development,2,4,Life Sciences,1,905,1,Male,98,3,4,Manager,1,Divorced,17924,4544,1,Y,No,11,3,4,80,1,31,3,3,31,6,14,7 -39,No,Travel_Rarely,1383,Human Resources,2,3,Life Sciences,1,909,4,Female,42,2,2,Human Resources,4,Married,5204,7790,8,Y,No,11,3,3,80,2,13,2,3,5,4,0,4 -33,No,Travel_Rarely,1075,Human Resources,3,2,Human Resources,1,910,4,Male,57,3,1,Human Resources,2,Divorced,2277,22650,3,Y,Yes,11,3,3,80,1,7,4,4,4,3,0,3 -32,Yes,Travel_Rarely,374,Research & Development,25,4,Life Sciences,1,911,1,Male,87,3,1,Laboratory Technician,4,Single,2795,18016,1,Y,Yes,24,4,3,80,0,1,2,1,1,0,0,1 -29,No,Travel_Rarely,1086,Research & Development,7,1,Medical,1,912,1,Female,62,2,1,Laboratory Technician,4,Divorced,2532,6054,6,Y,No,14,3,3,80,3,8,5,3,4,3,0,3 -44,No,Travel_Rarely,661,Research & Development,9,2,Life Sciences,1,913,2,Male,61,3,1,Research Scientist,1,Married,2559,7508,1,Y,Yes,13,3,4,80,0,8,0,3,8,7,7,1 -28,No,Travel_Rarely,821,Sales,5,4,Medical,1,916,1,Male,98,3,2,Sales Executive,4,Single,4908,24252,1,Y,No,14,3,2,80,0,4,3,3,4,2,0,2 -58,Yes,Travel_Frequently,781,Research & Development,2,1,Life Sciences,1,918,4,Male,57,2,1,Laboratory Technician,4,Divorced,2380,13384,9,Y,Yes,14,3,4,80,1,3,3,2,1,0,0,0 -43,No,Travel_Rarely,177,Research & Development,8,3,Life Sciences,1,920,1,Female,55,3,2,Manufacturing Director,2,Divorced,4765,23814,4,Y,No,21,4,3,80,1,4,2,4,1,0,0,0 -20,Yes,Travel_Rarely,500,Sales,2,3,Medical,1,922,3,Female,49,2,1,Sales Representative,3,Single,2044,22052,1,Y,No,13,3,4,80,0,2,3,2,2,2,0,2 -21,Yes,Travel_Rarely,1427,Research & Development,18,1,Other,1,923,4,Female,65,3,1,Research Scientist,4,Single,2693,8870,1,Y,No,19,3,1,80,0,1,3,2,1,0,0,0 -36,No,Travel_Rarely,1425,Research & Development,14,1,Life Sciences,1,924,3,Male,68,3,2,Healthcare Representative,4,Married,6586,4821,0,Y,Yes,17,3,1,80,1,17,2,2,16,8,4,11 -47,No,Travel_Rarely,1454,Sales,2,4,Life Sciences,1,925,4,Female,65,2,1,Sales Representative,4,Single,3294,13137,1,Y,Yes,18,3,1,80,0,3,3,2,3,2,1,2 -22,Yes,Travel_Rarely,617,Research & Development,3,1,Life Sciences,1,926,2,Female,34,3,2,Manufacturing Director,3,Married,4171,10022,0,Y,Yes,19,3,1,80,1,4,3,4,3,2,0,2 -41,Yes,Travel_Rarely,1085,Research & Development,2,4,Life Sciences,1,927,2,Female,57,1,1,Laboratory Technician,4,Divorced,2778,17725,4,Y,Yes,13,3,3,80,1,10,1,2,7,7,1,0 -28,No,Travel_Rarely,995,Research & Development,9,3,Medical,1,930,3,Female,77,3,1,Research Scientist,3,Divorced,2377,9834,5,Y,No,18,3,2,80,1,6,2,3,2,2,2,2 -39,Yes,Travel_Rarely,1122,Research & Development,6,3,Medical,1,932,4,Male,70,3,1,Laboratory Technician,1,Married,2404,4303,7,Y,Yes,21,4,4,80,0,8,2,1,2,2,2,2 -27,No,Travel_Rarely,618,Research & Development,4,3,Life Sciences,1,933,2,Female,76,3,1,Research Scientist,3,Single,2318,17808,1,Y,No,19,3,3,80,0,1,2,3,1,1,0,0 -34,No,Travel_Rarely,546,Research & Development,10,3,Life Sciences,1,934,2,Male,83,3,1,Laboratory Technician,2,Divorced,2008,6896,1,Y,No,14,3,2,80,2,1,3,3,1,0,1,0 -42,No,Travel_Rarely,462,Sales,14,2,Medical,1,936,3,Female,68,2,2,Sales Executive,3,Single,6244,7824,7,Y,No,17,3,1,80,0,10,6,3,5,4,0,3 -33,No,Travel_Rarely,1198,Research & Development,1,4,Other,1,939,3,Male,100,2,1,Research Scientist,1,Single,2799,3339,3,Y,Yes,11,3,2,80,0,6,1,3,3,2,0,2 -58,No,Travel_Rarely,1272,Research & Development,5,3,Technical Degree,1,940,3,Female,37,2,3,Healthcare Representative,2,Divorced,10552,9255,2,Y,Yes,13,3,4,80,1,24,3,3,6,0,0,4 -31,No,Travel_Rarely,154,Sales,7,4,Life Sciences,1,941,2,Male,41,2,1,Sales Representative,3,Married,2329,11737,3,Y,No,15,3,2,80,0,13,2,4,7,7,5,2 -35,No,Travel_Rarely,1137,Research & Development,21,1,Life Sciences,1,942,4,Female,51,3,2,Healthcare Representative,4,Married,4014,19170,1,Y,Yes,25,4,4,80,1,10,2,1,10,6,0,7 -49,No,Travel_Rarely,527,Research & Development,8,2,Other,1,944,1,Female,51,3,3,Laboratory Technician,2,Married,7403,22477,4,Y,No,11,3,3,80,1,29,3,2,26,9,1,7 -48,No,Travel_Rarely,1469,Research & Development,20,4,Medical,1,945,4,Male,51,3,1,Research Scientist,3,Married,2259,5543,4,Y,No,17,3,1,80,2,13,2,2,0,0,0,0 -31,No,Non-Travel,1188,Sales,20,2,Marketing,1,947,4,Female,45,3,2,Sales Executive,3,Married,6932,24406,1,Y,No,13,3,4,80,1,9,2,2,9,8,0,0 -36,No,Travel_Rarely,188,Research & Development,7,4,Other,1,949,2,Male,65,3,1,Research Scientist,4,Single,4678,23293,2,Y,No,18,3,3,80,0,8,6,3,6,2,0,1 -38,No,Travel_Rarely,1333,Research & Development,1,3,Technical Degree,1,950,4,Female,80,3,3,Research Director,1,Married,13582,16292,1,Y,No,13,3,2,80,1,15,3,3,15,12,5,11 -32,No,Non-Travel,1184,Research & Development,1,3,Life Sciences,1,951,3,Female,70,2,1,Laboratory Technician,2,Married,2332,3974,6,Y,No,20,4,3,80,0,5,3,3,3,0,0,2 -25,Yes,Travel_Rarely,867,Sales,19,2,Marketing,1,952,3,Male,36,2,1,Sales Representative,2,Married,2413,18798,1,Y,Yes,18,3,3,80,3,1,2,3,1,0,0,0 -40,No,Travel_Rarely,658,Sales,10,4,Marketing,1,954,1,Male,67,2,3,Sales Executive,2,Divorced,9705,20652,2,Y,No,12,3,2,80,1,11,2,2,1,0,0,0 -26,No,Travel_Frequently,1283,Sales,1,3,Medical,1,956,3,Male,52,2,2,Sales Executive,1,Single,4294,11148,1,Y,No,12,3,2,80,0,7,2,3,7,7,0,7 -41,No,Travel_Rarely,263,Research & Development,6,3,Medical,1,957,4,Male,59,3,1,Laboratory Technician,1,Single,4721,3119,2,Y,Yes,13,3,3,80,0,20,3,3,18,13,2,17 -36,No,Travel_Rarely,938,Research & Development,2,4,Medical,1,958,3,Male,79,3,1,Laboratory Technician,3,Single,2519,12287,4,Y,No,21,4,3,80,0,16,6,3,11,8,3,9 -19,Yes,Travel_Rarely,419,Sales,21,3,Other,1,959,4,Male,37,2,1,Sales Representative,2,Single,2121,9947,1,Y,Yes,13,3,2,80,0,1,3,4,1,0,0,0 -20,Yes,Travel_Rarely,129,Research & Development,4,3,Technical Degree,1,960,1,Male,84,3,1,Laboratory Technician,1,Single,2973,13008,1,Y,No,19,3,2,80,0,1,2,3,1,0,0,0 -31,No,Travel_Rarely,616,Research & Development,12,3,Medical,1,961,4,Female,41,3,2,Healthcare Representative,4,Married,5855,17369,0,Y,Yes,11,3,3,80,2,10,2,1,9,7,8,5 -40,No,Travel_Frequently,1469,Research & Development,9,4,Medical,1,964,4,Male,35,3,1,Research Scientist,2,Divorced,3617,25063,8,Y,Yes,14,3,4,80,1,3,2,3,1,1,0,0 -32,No,Travel_Rarely,498,Research & Development,3,4,Medical,1,966,3,Female,93,3,2,Manufacturing Director,1,Married,6725,13554,1,Y,No,12,3,3,80,1,8,2,4,8,7,6,3 -36,Yes,Travel_Rarely,530,Sales,3,1,Life Sciences,1,967,3,Male,51,2,3,Sales Executive,4,Married,10325,5518,1,Y,Yes,11,3,1,80,1,16,6,3,16,7,3,7 -33,No,Travel_Rarely,1069,Research & Development,1,3,Life Sciences,1,969,2,Female,42,2,2,Healthcare Representative,4,Single,6949,12291,0,Y,No,14,3,1,80,0,6,3,3,5,0,1,4 -37,Yes,Travel_Rarely,625,Sales,1,4,Life Sciences,1,970,1,Male,46,2,3,Sales Executive,3,Married,10609,14922,5,Y,No,11,3,3,80,0,17,2,1,14,1,11,7 -45,No,Non-Travel,805,Research & Development,4,2,Life Sciences,1,972,3,Male,57,3,2,Laboratory Technician,2,Married,4447,23163,1,Y,No,12,3,2,80,0,9,5,2,9,7,0,8 -29,No,Travel_Frequently,1404,Sales,20,3,Technical Degree,1,974,3,Female,84,3,1,Sales Representative,4,Married,2157,18203,1,Y,No,15,3,2,80,1,3,5,3,3,1,0,2 -35,No,Travel_Rarely,1219,Sales,18,3,Medical,1,975,3,Female,86,3,2,Sales Executive,3,Married,4601,6179,1,Y,No,16,3,2,80,0,5,3,3,5,2,1,0 -52,No,Travel_Rarely,1053,Research & Development,1,2,Life Sciences,1,976,4,Male,70,3,4,Manager,4,Married,17099,13829,2,Y,No,15,3,2,80,1,26,2,2,9,8,7,8 -58,Yes,Travel_Rarely,289,Research & Development,2,3,Technical Degree,1,977,4,Male,51,3,1,Research Scientist,3,Single,2479,26227,4,Y,No,24,4,1,80,0,7,4,3,1,0,0,0 -53,No,Travel_Rarely,1376,Sales,2,2,Medical,1,981,3,Male,45,3,4,Manager,3,Divorced,14852,13938,6,Y,No,13,3,3,80,1,22,3,4,17,13,15,2 -30,No,Travel_Rarely,231,Sales,8,2,Other,1,982,3,Male,62,3,3,Sales Executive,3,Divorced,7264,9977,5,Y,No,11,3,1,80,1,10,2,4,8,4,7,7 -38,No,Non-Travel,152,Sales,10,3,Technical Degree,1,983,3,Female,85,3,2,Sales Executive,4,Single,5666,19899,1,Y,Yes,13,3,2,80,0,6,1,3,5,3,1,3 -35,No,Travel_Rarely,882,Sales,3,4,Life Sciences,1,984,4,Male,92,3,3,Sales Executive,4,Divorced,7823,6812,6,Y,No,13,3,2,80,1,12,2,3,10,9,0,8 -39,No,Travel_Rarely,903,Sales,2,5,Life Sciences,1,985,1,Male,41,4,3,Sales Executive,3,Single,7880,2560,0,Y,No,18,3,4,80,0,9,3,3,8,7,0,7 -40,Yes,Non-Travel,1479,Sales,24,3,Life Sciences,1,986,2,Female,100,4,4,Sales Executive,2,Single,13194,17071,4,Y,Yes,16,3,4,80,0,22,2,2,1,0,0,0 -47,No,Travel_Frequently,1379,Research & Development,16,4,Medical,1,987,3,Male,64,4,2,Manufacturing Director,3,Divorced,5067,6759,1,Y,Yes,19,3,3,80,0,20,3,4,19,10,2,7 -36,No,Non-Travel,1229,Sales,8,4,Technical Degree,1,990,1,Male,84,3,2,Sales Executive,4,Divorced,5079,25952,4,Y,No,13,3,4,80,2,12,3,3,7,7,0,7 -31,Yes,Non-Travel,335,Research & Development,9,2,Medical,1,991,3,Male,46,2,1,Research Scientist,1,Single,2321,10322,0,Y,Yes,22,4,1,80,0,4,0,3,3,2,1,2 -33,No,Non-Travel,722,Sales,17,3,Life Sciences,1,992,4,Male,38,3,4,Manager,3,Single,17444,20489,1,Y,No,11,3,4,80,0,10,2,3,10,8,6,0 -29,Yes,Travel_Rarely,906,Research & Development,10,3,Life Sciences,1,994,4,Female,92,2,1,Research Scientist,1,Single,2404,11479,6,Y,Yes,20,4,3,80,0,3,5,3,0,0,0,0 -33,No,Travel_Rarely,461,Research & Development,13,1,Life Sciences,1,995,2,Female,53,3,1,Research Scientist,4,Single,3452,17241,3,Y,No,18,3,1,80,0,5,4,3,3,2,0,2 -45,No,Travel_Rarely,974,Research & Development,1,4,Medical,1,996,4,Female,91,3,1,Laboratory Technician,4,Divorced,2270,11005,3,Y,No,14,3,4,80,2,8,2,3,5,3,0,2 -50,No,Travel_Rarely,1126,Research & Development,1,2,Medical,1,997,4,Male,66,3,4,Research Director,4,Divorced,17399,6615,9,Y,No,22,4,3,80,1,32,1,2,5,4,1,3 -33,No,Travel_Frequently,827,Research & Development,1,4,Other,1,998,3,Female,84,4,2,Healthcare Representative,2,Married,5488,20161,1,Y,Yes,13,3,1,80,1,6,2,3,6,5,1,2 -41,No,Travel_Frequently,840,Research & Development,9,3,Medical,1,999,1,Male,64,3,5,Research Director,3,Divorced,19419,3735,2,Y,No,17,3,2,80,1,21,2,4,18,16,0,11 -27,No,Travel_Rarely,1134,Research & Development,16,4,Technical Degree,1,1001,3,Female,37,3,1,Laboratory Technician,2,Married,2811,12086,9,Y,No,14,3,2,80,1,4,2,3,2,2,2,2 -45,No,Non-Travel,248,Research & Development,23,2,Life Sciences,1,1002,4,Male,42,3,2,Laboratory Technician,1,Married,3633,14039,1,Y,Yes,15,3,3,80,1,9,2,3,9,8,0,8 -47,No,Travel_Rarely,955,Sales,4,2,Life Sciences,1,1003,4,Female,83,3,2,Sales Executive,4,Single,4163,8571,1,Y,Yes,17,3,3,80,0,9,0,3,9,0,0,7 -30,Yes,Travel_Rarely,138,Research & Development,22,3,Life Sciences,1,1004,1,Female,48,3,1,Research Scientist,3,Married,2132,11539,4,Y,Yes,11,3,2,80,0,7,2,3,5,2,0,1 -50,No,Travel_Rarely,939,Research & Development,24,3,Life Sciences,1,1005,4,Male,95,3,4,Manufacturing Director,3,Married,13973,4161,3,Y,Yes,18,3,4,80,1,22,2,3,12,11,1,5 -38,No,Travel_Frequently,1391,Research & Development,10,1,Medical,1,1006,3,Male,66,3,1,Research Scientist,3,Married,2684,12127,0,Y,No,17,3,2,80,1,3,0,2,2,1,0,2 -46,No,Travel_Rarely,566,Research & Development,7,2,Medical,1,1007,4,Male,75,3,3,Manufacturing Director,3,Divorced,10845,24208,6,Y,No,13,3,2,80,1,13,3,3,8,7,0,7 -24,No,Travel_Rarely,1206,Research & Development,17,1,Medical,1,1009,4,Female,41,2,2,Manufacturing Director,3,Divorced,4377,24117,1,Y,No,15,3,2,80,2,5,6,3,4,2,3,2 -35,Yes,Travel_Rarely,622,Research & Development,14,4,Other,1,1010,3,Male,39,2,1,Laboratory Technician,2,Divorced,3743,10074,1,Y,Yes,24,4,4,80,1,5,2,1,4,2,0,2 -31,No,Travel_Frequently,853,Research & Development,1,1,Life Sciences,1,1011,3,Female,96,3,2,Manufacturing Director,1,Married,4148,11275,1,Y,No,12,3,3,80,1,4,1,3,4,3,0,3 -18,No,Non-Travel,287,Research & Development,5,2,Life Sciences,1,1012,2,Male,73,3,1,Research Scientist,4,Single,1051,13493,1,Y,No,15,3,4,80,0,0,2,3,0,0,0,0 -54,No,Travel_Rarely,1441,Research & Development,17,3,Technical Degree,1,1013,3,Female,56,3,3,Manufacturing Director,3,Married,10739,13943,8,Y,No,11,3,3,80,1,22,2,3,10,7,0,8 -35,No,Travel_Rarely,583,Research & Development,25,4,Medical,1,1014,3,Female,57,3,3,Healthcare Representative,3,Divorced,10388,6975,1,Y,Yes,11,3,3,80,1,16,3,2,16,10,10,1 -30,No,Travel_Rarely,153,Research & Development,8,2,Life Sciences,1,1015,2,Female,73,4,3,Research Director,1,Married,11416,17802,0,Y,Yes,12,3,3,80,3,9,4,2,8,7,1,7 -20,Yes,Travel_Rarely,1097,Research & Development,11,3,Medical,1,1016,4,Female,98,2,1,Research Scientist,1,Single,2600,18275,1,Y,Yes,15,3,1,80,0,1,2,3,1,0,0,0 -30,Yes,Travel_Frequently,109,Research & Development,5,3,Medical,1,1017,2,Female,60,3,1,Laboratory Technician,2,Single,2422,25725,0,Y,No,17,3,1,80,0,4,3,3,3,2,1,2 -26,No,Travel_Rarely,1066,Research & Development,2,2,Medical,1,1018,4,Male,32,4,2,Manufacturing Director,4,Married,5472,3334,1,Y,No,12,3,2,80,0,8,2,3,8,7,1,3 -22,No,Travel_Rarely,217,Research & Development,8,1,Life Sciences,1,1019,2,Male,94,1,1,Laboratory Technician,1,Married,2451,6881,1,Y,No,15,3,1,80,1,4,3,2,4,3,1,1 -48,No,Travel_Rarely,277,Research & Development,6,3,Life Sciences,1,1022,1,Male,97,2,2,Healthcare Representative,3,Single,4240,13119,2,Y,No,13,3,4,80,0,19,0,3,2,2,2,2 -48,No,Travel_Rarely,1355,Research & Development,4,4,Life Sciences,1,1024,3,Male,78,2,3,Healthcare Representative,3,Single,10999,22245,7,Y,No,14,3,2,80,0,27,3,3,15,11,4,8 -41,No,Travel_Rarely,549,Research & Development,7,2,Medical,1,1025,4,Female,42,3,2,Manufacturing Director,3,Single,5003,23371,6,Y,No,14,3,2,80,0,8,6,3,2,2,2,1 -39,No,Travel_Rarely,466,Research & Development,1,1,Life Sciences,1,1026,4,Female,65,2,4,Manufacturing Director,4,Married,12742,7060,1,Y,No,16,3,3,80,1,21,3,3,21,6,11,8 -27,No,Travel_Rarely,1055,Research & Development,2,4,Life Sciences,1,1027,1,Female,47,3,2,Manufacturing Director,4,Married,4227,4658,0,Y,No,18,3,2,80,1,4,2,3,3,2,2,2 -35,No,Travel_Rarely,802,Research & Development,10,3,Other,1,1028,2,Male,45,3,1,Laboratory Technician,4,Divorced,3917,9541,1,Y,No,20,4,1,80,1,3,4,2,3,2,1,2 -42,No,Travel_Rarely,265,Sales,5,2,Marketing,1,1029,4,Male,90,3,5,Manager,3,Married,18303,7770,6,Y,No,13,3,2,80,0,21,3,4,1,0,0,0 -50,No,Travel_Rarely,804,Research & Development,9,3,Life Sciences,1,1030,1,Male,64,3,1,Laboratory Technician,4,Married,2380,20165,4,Y,No,18,3,2,80,0,8,5,3,1,0,0,0 -59,No,Travel_Rarely,715,Research & Development,2,3,Life Sciences,1,1032,3,Female,69,2,4,Manufacturing Director,4,Single,13726,21829,3,Y,Yes,13,3,1,80,0,30,4,3,5,3,4,3 -37,Yes,Travel_Rarely,1141,Research & Development,11,2,Medical,1,1033,1,Female,61,1,2,Healthcare Representative,2,Married,4777,14382,5,Y,No,15,3,1,80,0,15,2,1,1,0,0,0 -55,No,Travel_Frequently,135,Research & Development,18,4,Medical,1,1034,3,Male,62,3,2,Healthcare Representative,2,Married,6385,12992,3,Y,Yes,14,3,4,80,2,17,3,3,8,7,6,7 -41,No,Non-Travel,247,Research & Development,7,1,Life Sciences,1,1035,2,Female,55,1,5,Research Director,3,Divorced,19973,20284,1,Y,No,22,4,2,80,2,21,3,3,21,16,5,10 -38,No,Travel_Rarely,1035,Sales,3,4,Life Sciences,1,1036,2,Male,42,3,2,Sales Executive,4,Single,6861,4981,8,Y,Yes,12,3,3,80,0,19,1,3,1,0,0,0 -26,Yes,Non-Travel,265,Sales,29,2,Medical,1,1037,2,Male,79,1,2,Sales Executive,1,Single,4969,21813,8,Y,No,18,3,4,80,0,7,6,3,2,2,2,2 -52,Yes,Travel_Rarely,266,Sales,2,1,Marketing,1,1038,1,Female,57,1,5,Manager,4,Married,19845,25846,1,Y,No,15,3,4,80,1,33,3,3,32,14,6,9 -44,No,Travel_Rarely,1448,Sales,28,3,Medical,1,1039,4,Female,53,4,4,Sales Executive,4,Married,13320,11737,3,Y,Yes,18,3,3,80,1,23,2,3,12,11,11,11 -50,No,Non-Travel,145,Sales,1,3,Life Sciences,1,1040,4,Female,95,3,2,Sales Executive,3,Married,6347,24920,0,Y,No,12,3,1,80,1,19,3,3,18,7,0,13 -36,Yes,Travel_Rarely,885,Research & Development,16,4,Life Sciences,1,1042,3,Female,43,4,1,Laboratory Technician,1,Single,2743,8269,1,Y,No,16,3,3,80,0,18,1,3,17,13,15,14 -39,No,Travel_Frequently,945,Research & Development,22,3,Medical,1,1043,4,Female,82,3,3,Manufacturing Director,1,Single,10880,5083,1,Y,Yes,13,3,3,80,0,21,2,3,21,6,2,8 -33,No,Non-Travel,1038,Sales,8,1,Life Sciences,1,1044,2,Female,88,2,1,Sales Representative,4,Single,2342,21437,0,Y,No,19,3,4,80,0,3,2,2,2,2,2,2 -45,No,Travel_Rarely,1234,Sales,11,2,Life Sciences,1,1045,4,Female,90,3,4,Manager,4,Married,17650,5404,3,Y,No,13,3,2,80,1,26,4,4,9,3,1,1 -32,No,Non-Travel,1109,Research & Development,29,4,Medical,1,1046,4,Female,69,3,1,Laboratory Technician,3,Single,4025,11135,9,Y,No,12,3,2,80,0,10,2,3,8,7,7,7 -34,No,Travel_Rarely,216,Sales,1,4,Marketing,1,1047,2,Male,75,4,2,Sales Executive,4,Divorced,9725,12278,0,Y,No,11,3,4,80,1,16,2,2,15,1,0,9 -59,No,Travel_Rarely,1089,Sales,1,2,Technical Degree,1,1048,2,Male,66,3,3,Manager,4,Married,11904,11038,3,Y,Yes,14,3,3,80,1,14,1,1,6,4,0,4 -45,No,Travel_Rarely,788,Human Resources,24,4,Medical,1,1049,2,Male,36,3,1,Human Resources,2,Single,2177,8318,1,Y,No,16,3,1,80,0,6,3,3,6,3,0,4 -53,No,Travel_Frequently,124,Sales,2,3,Marketing,1,1050,3,Female,38,2,3,Sales Executive,2,Married,7525,23537,2,Y,No,12,3,1,80,1,30,2,3,15,7,6,12 -36,Yes,Travel_Rarely,660,Research & Development,15,3,Other,1,1052,1,Male,81,3,2,Laboratory Technician,3,Divorced,4834,7858,7,Y,No,14,3,2,80,1,9,3,2,1,0,0,0 -26,Yes,Travel_Frequently,342,Research & Development,2,3,Life Sciences,1,1053,1,Male,57,3,1,Research Scientist,1,Married,2042,15346,6,Y,Yes,14,3,2,80,1,6,2,3,3,2,1,2 -34,No,Travel_Rarely,1333,Sales,10,4,Life Sciences,1,1055,3,Female,87,3,1,Sales Representative,3,Married,2220,18410,1,Y,Yes,19,3,4,80,1,1,2,3,1,1,0,0 -28,No,Travel_Rarely,1144,Sales,10,1,Medical,1,1056,4,Male,74,3,1,Sales Representative,2,Married,1052,23384,1,Y,No,22,4,2,80,0,1,5,3,1,0,0,0 -38,No,Travel_Frequently,1186,Research & Development,3,4,Other,1,1060,3,Male,44,3,1,Research Scientist,3,Married,2821,2997,3,Y,No,16,3,1,80,1,8,2,3,2,2,2,2 -50,No,Travel_Rarely,1464,Research & Development,2,4,Medical,1,1061,2,Male,62,3,5,Research Director,3,Married,19237,12853,2,Y,Yes,11,3,4,80,1,29,2,2,8,1,7,7 -37,No,Travel_Rarely,124,Research & Development,3,3,Other,1,1062,4,Female,35,3,2,Healthcare Representative,2,Single,4107,13848,3,Y,No,15,3,1,80,0,8,3,2,4,3,0,1 -40,No,Travel_Rarely,300,Sales,26,3,Marketing,1,1066,3,Male,74,3,2,Sales Executive,1,Married,8396,22217,1,Y,No,14,3,2,80,1,8,3,2,7,7,7,5 -26,No,Travel_Frequently,921,Research & Development,1,1,Medical,1,1068,1,Female,66,2,1,Research Scientist,3,Divorced,2007,25265,1,Y,No,13,3,3,80,2,5,5,3,5,3,1,3 -46,No,Travel_Rarely,430,Research & Development,1,4,Medical,1,1069,4,Male,40,3,5,Research Director,4,Divorced,19627,21445,9,Y,No,17,3,4,80,2,23,0,3,2,2,2,2 -54,No,Travel_Rarely,1082,Sales,2,4,Life Sciences,1,1070,3,Female,41,2,3,Sales Executive,3,Married,10686,8392,6,Y,No,11,3,2,80,1,13,4,3,9,4,7,0 -56,No,Travel_Frequently,1240,Research & Development,9,3,Medical,1,1071,1,Female,63,3,1,Research Scientist,3,Married,2942,12154,2,Y,No,19,3,2,80,1,18,4,3,5,4,0,3 -36,No,Travel_Rarely,796,Research & Development,12,5,Medical,1,1073,4,Female,51,2,3,Manufacturing Director,4,Single,8858,15669,0,Y,No,11,3,2,80,0,15,2,2,14,8,7,8 -55,No,Non-Travel,444,Research & Development,2,1,Medical,1,1074,3,Male,40,2,4,Manager,1,Single,16756,17323,7,Y,No,15,3,2,80,0,31,3,4,9,7,6,2 -43,No,Travel_Rarely,415,Sales,25,3,Medical,1,1076,3,Male,79,2,3,Sales Executive,4,Divorced,10798,5268,5,Y,No,13,3,3,80,1,18,5,3,1,0,0,0 -20,Yes,Travel_Frequently,769,Sales,9,3,Marketing,1,1077,4,Female,54,3,1,Sales Representative,4,Single,2323,17205,1,Y,Yes,14,3,2,80,0,2,3,3,2,2,0,2 -21,Yes,Travel_Rarely,1334,Research & Development,10,3,Life Sciences,1,1079,3,Female,36,2,1,Laboratory Technician,1,Single,1416,17258,1,Y,No,13,3,1,80,0,1,6,2,1,0,1,0 -46,No,Travel_Rarely,1003,Research & Development,8,4,Life Sciences,1,1080,4,Female,74,2,2,Research Scientist,1,Divorced,4615,21029,8,Y,Yes,23,4,1,80,3,19,2,3,16,13,1,7 -51,Yes,Travel_Rarely,1323,Research & Development,4,4,Life Sciences,1,1081,1,Male,34,3,1,Research Scientist,3,Married,2461,10332,9,Y,Yes,12,3,3,80,3,18,2,4,10,0,2,7 -28,Yes,Non-Travel,1366,Research & Development,24,2,Technical Degree,1,1082,2,Male,72,2,3,Healthcare Representative,1,Single,8722,12355,1,Y,No,12,3,1,80,0,10,2,2,10,7,1,9 -26,No,Travel_Rarely,192,Research & Development,1,2,Medical,1,1083,1,Male,59,2,1,Laboratory Technician,1,Married,3955,11141,1,Y,No,16,3,1,80,2,6,2,3,5,3,1,3 -30,No,Travel_Rarely,1176,Research & Development,20,3,Other,1,1084,3,Male,85,3,2,Manufacturing Director,1,Married,9957,9096,0,Y,No,15,3,3,80,1,7,1,2,6,2,0,2 -41,No,Travel_Rarely,509,Research & Development,7,2,Technical Degree,1,1085,2,Female,43,4,1,Research Scientist,3,Married,3376,18863,1,Y,No,13,3,3,80,0,10,3,3,10,6,0,8 -38,No,Travel_Rarely,330,Research & Development,17,1,Life Sciences,1,1088,3,Female,65,2,3,Healthcare Representative,3,Married,8823,24608,0,Y,No,18,3,1,80,1,20,4,2,19,9,1,9 -40,No,Travel_Rarely,1492,Research & Development,20,4,Technical Degree,1,1092,1,Male,61,3,3,Healthcare Representative,4,Married,10322,26542,4,Y,No,20,4,4,80,1,14,6,3,11,10,11,1 -27,No,Non-Travel,1277,Research & Development,8,5,Life Sciences,1,1094,1,Male,87,1,1,Laboratory Technician,3,Married,4621,5869,1,Y,No,19,3,4,80,3,3,4,3,3,2,1,2 -55,No,Travel_Frequently,1091,Research & Development,2,1,Life Sciences,1,1096,4,Male,65,3,3,Manufacturing Director,2,Married,10976,15813,3,Y,No,18,3,2,80,1,23,4,3,3,2,1,2 -28,No,Travel_Rarely,857,Research & Development,10,3,Other,1,1097,3,Female,59,3,2,Research Scientist,3,Single,3660,7909,3,Y,No,13,3,4,80,0,10,4,4,8,7,1,7 -44,Yes,Travel_Rarely,1376,Human Resources,1,2,Medical,1,1098,2,Male,91,2,3,Human Resources,1,Married,10482,2326,9,Y,No,14,3,4,80,1,24,1,3,20,6,3,6 -33,No,Travel_Rarely,654,Research & Development,5,3,Life Sciences,1,1099,4,Male,34,2,3,Healthcare Representative,4,Divorced,7119,21214,4,Y,No,15,3,3,80,1,9,2,3,3,2,1,2 -35,Yes,Travel_Rarely,1204,Sales,4,3,Technical Degree,1,1100,4,Male,86,3,3,Sales Executive,1,Single,9582,10333,0,Y,Yes,22,4,1,80,0,9,2,3,8,7,4,7 -33,Yes,Travel_Frequently,827,Research & Development,29,4,Medical,1,1101,1,Female,54,2,2,Research Scientist,3,Single,4508,3129,1,Y,No,22,4,2,80,0,14,4,3,13,7,3,8 -28,No,Travel_Rarely,895,Research & Development,15,2,Life Sciences,1,1102,1,Male,50,3,1,Laboratory Technician,3,Divorced,2207,22482,1,Y,No,16,3,4,80,1,4,5,2,4,2,2,2 -34,No,Travel_Frequently,618,Research & Development,3,1,Life Sciences,1,1103,1,Male,45,3,2,Healthcare Representative,4,Single,7756,22266,0,Y,No,17,3,3,80,0,7,1,2,6,2,0,4 -37,No,Travel_Rarely,309,Sales,10,4,Life Sciences,1,1105,4,Female,88,2,2,Sales Executive,4,Divorced,6694,24223,2,Y,Yes,14,3,3,80,3,8,5,3,1,0,0,0 -25,Yes,Travel_Rarely,1219,Research & Development,4,1,Technical Degree,1,1106,4,Male,32,3,1,Laboratory Technician,4,Married,3691,4605,1,Y,Yes,15,3,2,80,1,7,3,4,7,7,5,6 -26,Yes,Travel_Rarely,1330,Research & Development,21,3,Medical,1,1107,1,Male,37,3,1,Laboratory Technician,3,Divorced,2377,19373,1,Y,No,20,4,3,80,1,1,0,2,1,1,0,0 -33,Yes,Travel_Rarely,1017,Research & Development,25,3,Medical,1,1108,1,Male,55,2,1,Research Scientist,2,Single,2313,2993,4,Y,Yes,20,4,2,80,0,5,0,3,2,2,2,2 -42,No,Travel_Rarely,469,Research & Development,2,2,Medical,1,1109,4,Male,35,3,4,Manager,1,Married,17665,14399,0,Y,No,17,3,4,80,1,23,3,3,22,6,13,7 -28,Yes,Travel_Frequently,1009,Research & Development,1,3,Medical,1,1111,1,Male,45,2,1,Laboratory Technician,2,Divorced,2596,7160,1,Y,No,15,3,1,80,2,1,2,3,1,0,0,0 -50,Yes,Travel_Frequently,959,Sales,1,4,Other,1,1113,4,Male,81,3,2,Sales Executive,3,Single,4728,17251,3,Y,Yes,14,3,4,80,0,5,4,3,0,0,0,0 -33,No,Travel_Frequently,970,Sales,7,3,Life Sciences,1,1114,4,Female,30,3,2,Sales Executive,2,Married,4302,13401,0,Y,No,17,3,3,80,1,4,3,3,3,2,0,2 -34,No,Non-Travel,697,Research & Development,3,4,Life Sciences,1,1115,3,Male,40,2,1,Research Scientist,4,Married,2979,22478,3,Y,No,17,3,4,80,3,6,2,3,0,0,0,0 -48,No,Non-Travel,1262,Research & Development,1,4,Medical,1,1116,1,Male,35,4,4,Manager,4,Single,16885,16154,2,Y,No,22,4,3,80,0,27,3,2,5,4,2,1 -45,No,Non-Travel,1050,Sales,9,4,Life Sciences,1,1117,2,Female,65,2,2,Sales Executive,3,Married,5593,17970,1,Y,No,13,3,4,80,1,15,2,3,15,10,4,12 -52,No,Travel_Rarely,994,Research & Development,7,4,Life Sciences,1,1118,2,Male,87,3,3,Healthcare Representative,2,Single,10445,15322,7,Y,No,19,3,4,80,0,18,4,3,8,6,4,0 -38,No,Travel_Rarely,770,Sales,10,4,Marketing,1,1119,3,Male,73,2,3,Sales Executive,3,Divorced,8740,5569,0,Y,Yes,14,3,2,80,2,9,2,3,8,7,2,7 -29,No,Travel_Rarely,1107,Research & Development,28,4,Life Sciences,1,1120,3,Female,93,3,1,Research Scientist,4,Divorced,2514,26968,4,Y,No,22,4,1,80,1,11,1,3,7,5,1,7 -28,No,Travel_Rarely,950,Research & Development,3,3,Medical,1,1121,4,Female,93,3,3,Manufacturing Director,2,Divorced,7655,8039,0,Y,No,17,3,2,80,3,10,3,2,9,7,1,7 -46,No,Travel_Rarely,406,Sales,3,1,Marketing,1,1124,1,Male,52,3,4,Manager,3,Married,17465,15596,3,Y,No,12,3,4,80,1,23,3,3,12,9,4,9 -38,No,Travel_Rarely,130,Sales,2,2,Marketing,1,1125,4,Male,32,3,3,Sales Executive,2,Single,7351,20619,7,Y,No,16,3,3,80,0,10,2,3,1,0,0,0 -43,No,Travel_Frequently,1082,Research & Development,27,3,Life Sciences,1,1126,3,Female,83,3,3,Manufacturing Director,1,Married,10820,11535,8,Y,No,11,3,3,80,1,18,1,3,8,7,0,1 -39,Yes,Travel_Frequently,203,Research & Development,2,3,Life Sciences,1,1127,1,Male,84,3,4,Healthcare Representative,4,Divorced,12169,13547,7,Y,No,11,3,4,80,3,21,4,3,18,7,11,5 -40,No,Travel_Rarely,1308,Research & Development,14,3,Medical,1,1128,3,Male,44,2,5,Research Director,3,Single,19626,17544,1,Y,No,14,3,1,80,0,21,2,4,20,7,4,9 -21,No,Travel_Rarely,984,Research & Development,1,1,Technical Degree,1,1131,4,Female,70,2,1,Research Scientist,2,Single,2070,25326,1,Y,Yes,11,3,3,80,0,2,6,4,2,2,2,2 -39,No,Non-Travel,439,Research & Development,9,3,Life Sciences,1,1132,3,Male,70,3,2,Laboratory Technician,2,Single,6782,8770,9,Y,No,15,3,3,80,0,9,2,2,5,4,0,3 -36,No,Non-Travel,217,Research & Development,18,4,Life Sciences,1,1133,1,Male,78,3,2,Manufacturing Director,4,Single,7779,23238,2,Y,No,20,4,1,80,0,18,0,3,11,9,0,9 -31,No,Travel_Frequently,793,Sales,20,3,Life Sciences,1,1135,3,Male,67,4,1,Sales Representative,4,Married,2791,21981,0,Y,No,12,3,1,80,1,3,4,3,2,2,2,2 -28,No,Travel_Rarely,1451,Research & Development,2,1,Life Sciences,1,1136,1,Male,67,2,1,Research Scientist,2,Married,3201,19911,0,Y,No,17,3,1,80,0,6,2,1,5,3,0,4 -35,No,Travel_Frequently,1182,Sales,11,2,Marketing,1,1137,4,Male,54,3,2,Sales Executive,4,Divorced,4968,18500,1,Y,No,11,3,4,80,1,5,3,3,5,2,0,2 -49,No,Travel_Rarely,174,Sales,8,4,Technical Degree,1,1138,4,Male,56,2,4,Sales Executive,2,Married,13120,11879,6,Y,No,17,3,2,80,1,22,3,3,9,8,2,3 -34,No,Travel_Frequently,1003,Research & Development,2,2,Life Sciences,1,1140,4,Male,95,3,2,Manufacturing Director,3,Single,4033,15834,2,Y,No,11,3,4,80,0,5,3,2,3,2,0,2 -29,No,Travel_Frequently,490,Research & Development,10,3,Life Sciences,1,1143,4,Female,61,3,1,Research Scientist,2,Divorced,3291,17940,0,Y,No,14,3,4,80,2,8,2,2,7,5,1,1 -42,No,Travel_Rarely,188,Research & Development,29,3,Medical,1,1148,2,Male,56,1,2,Laboratory Technician,4,Single,4272,9558,4,Y,No,19,3,1,80,0,16,3,3,1,0,0,0 -29,No,Travel_Rarely,718,Research & Development,8,1,Medical,1,1150,2,Male,79,2,2,Manufacturing Director,4,Married,5056,17689,1,Y,Yes,15,3,3,80,1,10,2,2,10,7,1,2 -38,No,Travel_Rarely,433,Human Resources,1,3,Human Resources,1,1152,3,Male,37,4,1,Human Resources,3,Married,2844,6004,1,Y,No,13,3,4,80,1,7,2,4,7,6,5,0 -28,No,Travel_Frequently,773,Research & Development,6,3,Life Sciences,1,1154,3,Male,39,2,1,Research Scientist,3,Divorced,2703,22088,1,Y,Yes,14,3,4,80,1,3,2,3,3,1,0,2 -18,Yes,Non-Travel,247,Research & Development,8,1,Medical,1,1156,3,Male,80,3,1,Laboratory Technician,3,Single,1904,13556,1,Y,No,12,3,4,80,0,0,0,3,0,0,0,0 -33,Yes,Travel_Rarely,603,Sales,9,4,Marketing,1,1157,1,Female,77,3,2,Sales Executive,1,Single,8224,18385,0,Y,Yes,17,3,1,80,0,6,3,3,5,2,0,3 -41,No,Travel_Rarely,167,Research & Development,12,4,Life Sciences,1,1158,2,Male,46,3,1,Laboratory Technician,4,Married,4766,9051,3,Y,Yes,11,3,1,80,1,6,4,3,1,0,0,0 -31,Yes,Travel_Frequently,874,Research & Development,15,3,Medical,1,1160,3,Male,72,3,1,Laboratory Technician,3,Married,2610,6233,1,Y,No,12,3,3,80,1,2,5,2,2,2,2,2 -37,No,Travel_Rarely,367,Research & Development,25,2,Medical,1,1161,3,Female,52,2,2,Healthcare Representative,4,Divorced,5731,17171,7,Y,No,13,3,3,80,2,9,2,3,6,2,1,3 -27,No,Travel_Rarely,199,Research & Development,6,3,Life Sciences,1,1162,4,Male,55,2,1,Research Scientist,3,Married,2539,7950,1,Y,No,13,3,3,80,1,4,0,3,4,2,2,2 -34,No,Travel_Rarely,1400,Sales,9,1,Life Sciences,1,1163,2,Female,70,3,2,Sales Executive,3,Married,5714,5829,1,Y,No,20,4,1,80,0,6,3,2,6,5,1,3 -35,No,Travel_Rarely,528,Human Resources,8,4,Technical Degree,1,1164,3,Male,100,3,1,Human Resources,3,Single,4323,7108,1,Y,No,17,3,2,80,0,6,2,1,5,4,1,4 -29,Yes,Travel_Rarely,408,Sales,23,1,Life Sciences,1,1165,4,Female,45,2,3,Sales Executive,1,Married,7336,11162,1,Y,No,13,3,1,80,1,11,3,1,11,8,3,10 -40,No,Travel_Frequently,593,Research & Development,9,4,Medical,1,1166,2,Female,88,3,3,Research Director,3,Single,13499,13782,9,Y,No,17,3,3,80,0,20,3,2,18,7,2,13 -42,Yes,Travel_Frequently,481,Sales,12,3,Life Sciences,1,1167,3,Male,44,3,4,Sales Executive,1,Single,13758,2447,0,Y,Yes,12,3,2,80,0,22,2,2,21,9,13,14 -42,No,Travel_Rarely,647,Sales,4,4,Marketing,1,1171,2,Male,45,3,2,Sales Executive,1,Single,5155,2253,7,Y,No,13,3,4,80,0,9,3,4,6,4,1,5 -35,No,Travel_Rarely,982,Research & Development,1,4,Medical,1,1172,4,Male,58,2,1,Laboratory Technician,3,Married,2258,16340,6,Y,No,12,3,2,80,1,10,2,3,8,0,1,7 -24,No,Travel_Rarely,477,Research & Development,24,3,Medical,1,1173,4,Male,49,3,1,Laboratory Technician,2,Single,3597,6409,8,Y,No,22,4,4,80,0,6,2,3,4,3,1,2 -28,Yes,Travel_Rarely,1485,Research & Development,12,1,Life Sciences,1,1175,3,Female,79,3,1,Laboratory Technician,4,Married,2515,22955,1,Y,Yes,11,3,4,80,0,1,4,2,1,1,0,0 -26,No,Travel_Rarely,1384,Research & Development,3,4,Medical,1,1177,1,Male,82,4,1,Laboratory Technician,4,Married,4420,13421,1,Y,No,22,4,2,80,1,8,2,3,8,7,0,7 -30,No,Travel_Rarely,852,Sales,10,3,Marketing,1,1179,3,Male,72,2,2,Sales Executive,3,Married,6578,2706,1,Y,No,18,3,1,80,1,10,3,3,10,3,1,4 -40,No,Travel_Frequently,902,Research & Development,26,2,Medical,1,1180,3,Female,92,2,2,Research Scientist,4,Married,4422,21203,3,Y,Yes,13,3,4,80,1,16,3,1,1,1,0,0 -35,No,Travel_Rarely,819,Research & Development,2,3,Life Sciences,1,1182,3,Male,44,2,3,Manufacturing Director,2,Divorced,10274,19588,2,Y,No,18,3,2,80,1,15,2,4,7,7,6,4 -34,No,Travel_Frequently,669,Research & Development,1,3,Medical,1,1184,4,Male,97,2,2,Healthcare Representative,1,Single,5343,25755,0,Y,No,20,4,3,80,0,14,3,3,13,9,4,9 -35,No,Travel_Frequently,636,Research & Development,4,4,Other,1,1185,4,Male,47,2,1,Laboratory Technician,4,Married,2376,26537,1,Y,No,13,3,2,80,1,2,2,4,2,2,2,2 -43,Yes,Travel_Rarely,1372,Sales,9,3,Marketing,1,1188,1,Female,85,1,2,Sales Executive,3,Single,5346,9489,8,Y,No,13,3,2,80,0,7,2,2,4,3,1,3 -32,No,Non-Travel,862,Sales,2,1,Life Sciences,1,1190,3,Female,76,3,1,Sales Representative,1,Divorced,2827,14947,1,Y,No,12,3,3,80,3,1,3,3,1,0,0,0 -56,No,Travel_Rarely,718,Research & Development,4,4,Technical Degree,1,1191,4,Female,92,3,5,Manager,1,Divorced,19943,18575,4,Y,No,13,3,4,80,1,28,2,3,5,2,4,2 -29,No,Travel_Rarely,1401,Research & Development,6,1,Medical,1,1192,2,Female,54,3,1,Laboratory Technician,4,Married,3131,26342,1,Y,No,13,3,1,80,1,10,5,3,10,8,0,8 -19,No,Travel_Rarely,645,Research & Development,9,2,Life Sciences,1,1193,3,Male,54,3,1,Research Scientist,1,Single,2552,7172,1,Y,No,25,4,3,80,0,1,4,3,1,1,0,0 -45,No,Travel_Rarely,1457,Research & Development,7,3,Medical,1,1195,1,Female,83,3,1,Research Scientist,3,Married,4477,20100,4,Y,Yes,19,3,3,80,1,7,2,2,3,2,0,2 -37,No,Travel_Rarely,977,Research & Development,1,3,Life Sciences,1,1196,4,Female,56,2,2,Manufacturing Director,4,Married,6474,9961,1,Y,No,13,3,2,80,1,14,2,2,14,8,3,11 -20,No,Travel_Rarely,805,Research & Development,3,3,Life Sciences,1,1198,1,Male,87,2,1,Laboratory Technician,3,Single,3033,12828,1,Y,No,12,3,1,80,0,2,2,2,2,2,1,2 -44,Yes,Travel_Rarely,1097,Research & Development,10,4,Life Sciences,1,1200,3,Male,96,3,1,Research Scientist,3,Single,2936,10826,1,Y,Yes,11,3,3,80,0,6,4,3,6,4,0,2 -53,No,Travel_Rarely,1223,Research & Development,7,2,Medical,1,1201,4,Female,50,3,5,Manager,3,Divorced,18606,18640,3,Y,No,18,3,2,80,1,26,6,3,7,7,4,7 -29,No,Travel_Rarely,942,Research & Development,15,1,Life Sciences,1,1202,2,Female,69,1,1,Research Scientist,4,Married,2168,26933,0,Y,Yes,18,3,1,80,1,6,2,2,5,4,1,3 -22,Yes,Travel_Frequently,1256,Research & Development,3,4,Life Sciences,1,1203,3,Male,48,2,1,Research Scientist,4,Married,2853,4223,0,Y,Yes,11,3,2,80,1,1,5,3,0,0,0,0 -46,No,Travel_Rarely,1402,Sales,2,3,Marketing,1,1204,3,Female,69,3,4,Manager,1,Married,17048,24097,8,Y,No,23,4,1,80,0,28,2,3,26,15,15,9 -44,No,Non-Travel,111,Research & Development,17,3,Life Sciences,1,1206,4,Male,74,1,1,Research Scientist,3,Single,2290,4279,2,Y,No,13,3,4,80,0,6,3,3,0,0,0,0 -33,No,Travel_Rarely,147,Human Resources,2,3,Human Resources,1,1207,2,Male,99,3,1,Human Resources,3,Married,3600,8429,1,Y,No,13,3,4,80,1,5,2,3,5,4,1,4 -41,Yes,Non-Travel,906,Research & Development,5,2,Life Sciences,1,1210,1,Male,95,2,1,Research Scientist,1,Divorced,2107,20293,6,Y,No,17,3,1,80,1,5,2,1,1,0,0,0 -30,No,Travel_Rarely,1329,Sales,29,4,Life Sciences,1,1211,3,Male,61,3,2,Sales Executive,1,Divorced,4115,13192,8,Y,No,19,3,3,80,3,8,3,3,4,3,0,3 -40,No,Travel_Frequently,1184,Sales,2,4,Medical,1,1212,2,Male,62,3,2,Sales Executive,2,Married,4327,25440,5,Y,No,12,3,4,80,3,5,2,3,0,0,0,0 -50,No,Travel_Frequently,1421,Research & Development,2,3,Medical,1,1215,4,Female,30,3,4,Manager,1,Married,17856,9490,2,Y,No,22,4,3,80,1,32,3,3,2,2,2,2 -28,No,Travel_Rarely,1179,Research & Development,19,4,Medical,1,1216,4,Male,78,2,1,Laboratory Technician,1,Married,3196,12449,1,Y,No,12,3,3,80,3,6,2,3,6,5,3,3 -46,No,Travel_Rarely,1450,Research & Development,15,2,Life Sciences,1,1217,4,Male,52,3,5,Research Director,2,Married,19081,10849,5,Y,No,11,3,1,80,1,25,2,3,4,2,0,3 -35,No,Travel_Rarely,1361,Sales,17,4,Life Sciences,1,1218,3,Male,94,3,2,Sales Executive,1,Married,8966,21026,3,Y,Yes,15,3,4,80,3,15,2,3,7,7,1,7 -24,Yes,Travel_Rarely,984,Research & Development,17,2,Life Sciences,1,1219,4,Female,97,3,1,Laboratory Technician,2,Married,2210,3372,1,Y,No,13,3,1,80,1,1,3,1,1,0,0,0 -33,No,Travel_Frequently,1146,Sales,25,3,Medical,1,1220,2,Female,82,3,2,Sales Executive,3,Married,4539,4905,1,Y,No,12,3,1,80,1,10,3,2,10,7,0,1 -36,No,Travel_Rarely,917,Research & Development,6,4,Life Sciences,1,1221,3,Male,60,1,1,Laboratory Technician,3,Divorced,2741,6865,1,Y,No,14,3,3,80,1,7,4,3,7,7,1,7 -30,No,Travel_Rarely,853,Research & Development,7,4,Life Sciences,1,1224,3,Male,49,3,2,Laboratory Technician,3,Divorced,3491,11309,1,Y,No,13,3,1,80,3,10,4,2,10,7,8,9 -44,No,Travel_Rarely,200,Research & Development,29,4,Other,1,1225,4,Male,32,3,2,Research Scientist,4,Single,4541,7744,1,Y,No,25,4,2,80,0,20,3,3,20,11,13,17 -20,No,Travel_Rarely,654,Sales,21,3,Marketing,1,1226,3,Male,43,4,1,Sales Representative,4,Single,2678,5050,1,Y,No,17,3,4,80,0,2,2,3,2,1,2,2 -46,No,Travel_Rarely,150,Research & Development,2,4,Technical Degree,1,1228,4,Male,60,3,2,Manufacturing Director,4,Divorced,7379,17433,2,Y,No,11,3,3,80,1,12,3,2,6,3,1,4 -42,No,Non-Travel,179,Human Resources,2,5,Medical,1,1231,4,Male,79,4,2,Human Resources,1,Married,6272,12858,7,Y,No,16,3,1,80,1,10,3,4,4,3,0,3 -60,No,Travel_Rarely,696,Sales,7,4,Marketing,1,1233,2,Male,52,4,2,Sales Executive,4,Divorced,5220,10893,0,Y,Yes,18,3,2,80,1,12,3,3,11,7,1,9 -32,No,Travel_Frequently,116,Research & Development,13,3,Other,1,1234,3,Female,77,2,1,Laboratory Technician,2,Married,2743,7331,1,Y,No,20,4,3,80,1,2,2,3,2,2,2,2 -32,No,Travel_Frequently,1316,Research & Development,2,2,Life Sciences,1,1235,4,Female,38,3,2,Research Scientist,3,Single,4998,2338,4,Y,Yes,14,3,4,80,0,10,2,3,8,7,0,7 -36,No,Travel_Rarely,363,Research & Development,1,3,Technical Degree,1,1237,3,Female,77,1,3,Manufacturing Director,1,Divorced,10252,4235,2,Y,Yes,21,4,3,80,1,17,2,3,7,7,7,7 -33,No,Travel_Rarely,117,Research & Development,9,3,Medical,1,1238,1,Male,60,3,1,Research Scientist,4,Married,2781,6311,0,Y,No,13,3,2,80,1,15,5,3,14,10,4,10 -40,No,Travel_Rarely,107,Sales,10,3,Technical Degree,1,1239,2,Female,84,2,2,Sales Executive,2,Divorced,6852,11591,7,Y,No,12,3,2,80,1,7,2,4,5,1,1,3 -25,No,Travel_Rarely,1356,Sales,10,4,Life Sciences,1,1240,3,Male,57,3,2,Sales Executive,4,Single,4950,20623,0,Y,No,14,3,2,80,0,5,4,3,4,3,1,1 -30,No,Travel_Rarely,1465,Research & Development,1,3,Medical,1,1241,4,Male,63,3,1,Research Scientist,2,Married,3579,9369,0,Y,Yes,21,4,1,80,1,12,2,3,11,9,5,7 -42,No,Travel_Frequently,458,Research & Development,26,5,Medical,1,1242,1,Female,60,3,3,Research Director,1,Married,13191,23281,3,Y,Yes,17,3,3,80,0,20,6,3,1,0,0,0 -35,No,Non-Travel,1212,Sales,8,2,Marketing,1,1243,3,Female,78,2,3,Sales Executive,4,Married,10377,13755,4,Y,Yes,11,3,2,80,1,16,6,2,13,2,4,12 -27,No,Travel_Rarely,1103,Research & Development,14,3,Life Sciences,1,1244,1,Male,42,3,1,Research Scientist,1,Married,2235,14377,1,Y,Yes,14,3,4,80,2,9,3,2,9,7,6,8 -54,No,Travel_Frequently,966,Research & Development,1,4,Life Sciences,1,1245,4,Female,53,3,3,Manufacturing Director,3,Divorced,10502,9659,7,Y,No,17,3,1,80,1,33,2,1,5,4,1,4 -44,No,Travel_Rarely,1117,Research & Development,2,1,Life Sciences,1,1246,1,Female,72,4,1,Research Scientist,4,Married,2011,19982,1,Y,No,13,3,4,80,1,10,5,3,10,5,7,7 -19,Yes,Non-Travel,504,Research & Development,10,3,Medical,1,1248,1,Female,96,2,1,Research Scientist,2,Single,1859,6148,1,Y,Yes,25,4,2,80,0,1,2,4,1,1,0,0 -29,No,Travel_Rarely,1010,Research & Development,1,3,Life Sciences,1,1249,1,Female,97,3,1,Research Scientist,4,Divorced,3760,5598,1,Y,No,15,3,1,80,3,3,5,3,3,2,1,2 -54,No,Travel_Rarely,685,Research & Development,3,3,Life Sciences,1,1250,4,Male,85,3,4,Research Director,4,Married,17779,23474,3,Y,No,14,3,1,80,0,36,2,3,10,9,0,9 -31,No,Travel_Rarely,1332,Research & Development,11,2,Medical,1,1251,3,Male,80,3,2,Healthcare Representative,1,Married,6833,17089,1,Y,Yes,12,3,4,80,0,6,2,2,6,5,0,1 -31,No,Travel_Rarely,1062,Research & Development,24,3,Medical,1,1252,3,Female,96,2,2,Healthcare Representative,1,Single,6812,17198,1,Y,No,19,3,2,80,0,10,2,3,10,9,1,8 -59,No,Travel_Rarely,326,Sales,3,3,Life Sciences,1,1254,3,Female,48,2,2,Sales Executive,4,Single,5171,16490,5,Y,No,17,3,4,80,0,13,2,3,6,1,0,5 -43,No,Travel_Rarely,920,Research & Development,3,3,Life Sciences,1,1255,3,Male,96,1,5,Research Director,4,Married,19740,18625,3,Y,No,14,3,2,80,1,25,2,3,8,7,0,7 -49,No,Travel_Rarely,1098,Research & Development,4,2,Medical,1,1256,1,Male,85,2,5,Manager,3,Married,18711,12124,2,Y,No,13,3,3,80,1,23,2,4,1,0,0,0 -36,No,Travel_Frequently,469,Research & Development,3,3,Technical Degree,1,1257,3,Male,46,3,1,Research Scientist,2,Married,3692,9256,1,Y,No,12,3,3,80,0,12,2,2,11,10,0,7 -48,No,Travel_Rarely,969,Research & Development,2,2,Technical Degree,1,1258,4,Male,76,4,1,Laboratory Technician,2,Single,2559,16620,5,Y,No,11,3,3,80,0,7,4,2,1,0,0,0 -27,No,Travel_Rarely,1167,Research & Development,4,2,Life Sciences,1,1259,1,Male,76,3,1,Research Scientist,3,Divorced,2517,3208,1,Y,No,11,3,2,80,3,5,2,3,5,3,0,3 -29,No,Travel_Rarely,1329,Research & Development,7,3,Life Sciences,1,1260,3,Male,82,3,2,Healthcare Representative,4,Divorced,6623,4204,1,Y,Yes,11,3,2,80,2,6,2,3,6,0,1,0 -48,No,Travel_Rarely,715,Research & Development,1,3,Life Sciences,1,1263,4,Male,76,2,5,Research Director,4,Single,18265,8733,6,Y,No,12,3,3,80,0,25,3,4,1,0,0,0 -29,No,Travel_Rarely,694,Research & Development,1,3,Life Sciences,1,1264,4,Female,87,2,4,Research Director,4,Divorced,16124,3423,3,Y,No,14,3,2,80,2,9,2,2,7,7,1,7 -34,No,Travel_Rarely,1320,Research & Development,20,3,Technical Degree,1,1265,3,Female,89,4,1,Research Scientist,3,Married,2585,21643,0,Y,No,17,3,4,80,0,2,5,2,1,0,0,0 -44,No,Travel_Rarely,1099,Sales,5,3,Marketing,1,1267,2,Male,88,3,5,Manager,2,Married,18213,8751,7,Y,No,11,3,3,80,1,26,5,3,22,9,3,10 -33,No,Travel_Rarely,536,Sales,10,5,Marketing,1,1268,4,Male,82,4,3,Sales Executive,3,Divorced,8380,21708,0,Y,Yes,14,3,4,80,2,10,3,3,9,8,0,8 -19,No,Travel_Rarely,265,Research & Development,25,3,Life Sciences,1,1269,2,Female,57,4,1,Research Scientist,4,Single,2994,21221,1,Y,Yes,12,3,4,80,0,1,2,3,1,0,0,1 -23,No,Travel_Rarely,373,Research & Development,1,2,Life Sciences,1,1270,4,Male,47,3,1,Research Scientist,3,Married,1223,16901,1,Y,No,22,4,4,80,1,1,2,3,1,0,0,1 -25,Yes,Travel_Frequently,599,Sales,24,1,Life Sciences,1,1273,3,Male,73,1,1,Sales Representative,4,Single,1118,8040,1,Y,Yes,14,3,4,80,0,1,4,3,1,0,1,0 -26,No,Travel_Rarely,583,Research & Development,4,2,Life Sciences,1,1275,3,Male,53,3,1,Research Scientist,4,Single,2875,9973,1,Y,Yes,20,4,2,80,0,8,2,2,8,5,2,2 -45,Yes,Travel_Rarely,1449,Sales,2,3,Marketing,1,1277,1,Female,94,1,5,Manager,2,Single,18824,2493,2,Y,Yes,16,3,1,80,0,26,2,3,24,10,1,11 -55,No,Non-Travel,177,Research & Development,8,1,Medical,1,1278,4,Male,37,2,4,Healthcare Representative,2,Divorced,13577,25592,1,Y,Yes,15,3,4,80,1,34,3,3,33,9,15,0 -21,Yes,Travel_Frequently,251,Research & Development,10,2,Life Sciences,1,1279,1,Female,45,2,1,Laboratory Technician,3,Single,2625,25308,1,Y,No,20,4,3,80,0,2,2,1,2,2,2,2 -46,No,Travel_Rarely,168,Sales,4,2,Marketing,1,1280,4,Female,33,2,5,Manager,2,Married,18789,9946,2,Y,No,14,3,3,80,1,26,2,3,11,4,0,8 -34,No,Travel_Rarely,131,Sales,2,3,Marketing,1,1281,3,Female,86,3,2,Sales Executive,1,Single,4538,6039,0,Y,Yes,12,3,4,80,0,4,3,3,3,2,0,2 -51,No,Travel_Frequently,237,Sales,9,3,Life Sciences,1,1282,4,Male,83,3,5,Manager,2,Divorced,19847,19196,4,Y,Yes,24,4,1,80,1,31,5,2,29,10,11,10 -59,No,Travel_Rarely,1429,Research & Development,18,4,Medical,1,1283,4,Male,67,3,3,Manufacturing Director,4,Single,10512,20002,6,Y,No,12,3,4,80,0,25,6,2,9,7,5,4 -34,No,Travel_Frequently,135,Research & Development,19,3,Medical,1,1285,3,Female,46,3,2,Laboratory Technician,2,Divorced,4444,22534,4,Y,No,13,3,3,80,2,15,2,4,11,8,5,10 -28,No,Travel_Frequently,791,Research & Development,1,4,Medical,1,1286,4,Male,44,3,1,Laboratory Technician,3,Single,2154,6842,0,Y,Yes,11,3,3,80,0,5,2,2,4,2,0,2 -44,No,Travel_Rarely,1199,Research & Development,4,2,Life Sciences,1,1288,3,Male,92,4,5,Manager,1,Divorced,19190,17477,1,Y,No,14,3,4,80,2,26,4,2,25,9,14,13 -34,No,Travel_Frequently,648,Human Resources,11,3,Life Sciences,1,1289,3,Male,56,2,2,Human Resources,2,Married,4490,21833,4,Y,No,11,3,4,80,2,14,5,4,10,9,1,8 -35,No,Travel_Rarely,735,Research & Development,6,1,Life Sciences,1,1291,3,Male,66,3,1,Research Scientist,3,Married,3506,6020,0,Y,Yes,14,3,4,80,0,4,3,3,3,2,2,2 -42,No,Travel_Rarely,603,Research & Development,7,4,Medical,1,1292,2,Female,78,4,2,Research Scientist,2,Married,2372,5628,6,Y,Yes,16,3,4,80,0,18,2,3,1,0,0,0 -43,No,Travel_Rarely,531,Sales,4,4,Marketing,1,1293,4,Female,56,2,3,Sales Executive,4,Single,10231,20364,3,Y,No,14,3,4,80,0,23,3,4,21,7,15,17 -36,No,Travel_Rarely,429,Research & Development,2,4,Life Sciences,1,1294,3,Female,53,3,2,Manufacturing Director,2,Single,5410,2323,9,Y,Yes,11,3,4,80,0,18,2,3,16,14,5,12 -44,Yes,Travel_Rarely,621,Research & Development,15,3,Medical,1,1295,1,Female,73,3,3,Healthcare Representative,4,Married,7978,14075,1,Y,No,11,3,4,80,1,10,2,3,10,7,0,5 -28,No,Travel_Frequently,193,Research & Development,2,3,Life Sciences,1,1296,4,Male,52,2,1,Laboratory Technician,4,Married,3867,14222,1,Y,Yes,12,3,2,80,1,2,2,3,2,2,2,2 -51,No,Travel_Frequently,968,Research & Development,6,2,Medical,1,1297,2,Female,40,2,1,Laboratory Technician,3,Single,2838,4257,0,Y,No,14,3,2,80,0,8,6,2,7,0,7,7 -30,No,Non-Travel,879,Research & Development,9,2,Medical,1,1298,3,Female,72,3,2,Manufacturing Director,3,Single,4695,12858,7,Y,Yes,18,3,3,80,0,10,3,3,8,4,1,7 -29,Yes,Travel_Rarely,806,Research & Development,7,3,Technical Degree,1,1299,2,Female,39,3,1,Laboratory Technician,3,Divorced,3339,17285,3,Y,Yes,13,3,1,80,2,10,2,3,7,7,7,7 -28,No,Travel_Rarely,640,Research & Development,1,3,Technical Degree,1,1301,4,Male,84,3,1,Research Scientist,1,Single,2080,4732,2,Y,No,11,3,2,80,0,5,2,2,3,2,1,2 -25,No,Travel_Rarely,266,Research & Development,1,3,Medical,1,1303,4,Female,40,3,1,Research Scientist,2,Single,2096,18830,1,Y,No,18,3,4,80,0,2,3,2,2,2,2,1 -32,No,Travel_Rarely,604,Sales,8,3,Medical,1,1304,3,Male,56,4,2,Sales Executive,4,Married,6209,11693,1,Y,No,15,3,3,80,2,10,4,4,10,7,0,8 -45,No,Travel_Frequently,364,Research & Development,25,3,Medical,1,1306,2,Female,83,3,5,Manager,2,Single,18061,13035,3,Y,No,22,4,3,80,0,22,4,3,0,0,0,0 -39,No,Travel_Rarely,412,Research & Development,13,4,Medical,1,1307,3,Female,94,2,4,Manager,2,Divorced,17123,17334,6,Y,Yes,13,3,4,80,2,21,4,3,19,9,15,2 -58,No,Travel_Rarely,848,Research & Development,23,4,Life Sciences,1,1308,1,Male,88,3,1,Research Scientist,3,Divorced,2372,26076,1,Y,No,12,3,4,80,2,2,3,3,2,2,2,2 -32,Yes,Travel_Rarely,1089,Research & Development,7,2,Life Sciences,1,1309,4,Male,79,3,2,Laboratory Technician,3,Married,4883,22845,1,Y,No,18,3,1,80,1,10,3,3,10,4,1,1 -39,Yes,Travel_Rarely,360,Research & Development,23,3,Medical,1,1310,3,Male,93,3,1,Research Scientist,1,Single,3904,22154,0,Y,No,13,3,1,80,0,6,2,3,5,2,0,3 -30,No,Travel_Rarely,1138,Research & Development,6,3,Technical Degree,1,1311,1,Female,48,2,2,Laboratory Technician,4,Married,4627,23631,0,Y,No,12,3,1,80,1,10,6,3,9,2,6,7 -36,No,Travel_Rarely,325,Research & Development,10,4,Technical Degree,1,1312,4,Female,63,3,3,Healthcare Representative,3,Married,7094,5747,3,Y,No,12,3,1,80,0,10,0,3,7,7,1,7 -46,No,Travel_Rarely,991,Human Resources,1,2,Life Sciences,1,1314,4,Female,44,3,1,Human Resources,1,Single,3423,22957,6,Y,No,12,3,3,80,0,10,3,4,7,6,5,7 -28,No,Non-Travel,1476,Research & Development,1,3,Life Sciences,1,1315,3,Female,55,1,2,Laboratory Technician,4,Married,6674,16392,0,Y,No,11,3,1,80,3,10,6,3,9,8,7,5 -50,No,Travel_Rarely,1322,Research & Development,28,3,Life Sciences,1,1317,4,Female,43,3,4,Research Director,1,Married,16880,22422,4,Y,Yes,11,3,2,80,0,25,2,3,3,2,1,2 -40,Yes,Travel_Rarely,299,Sales,25,4,Marketing,1,1318,4,Male,57,2,3,Sales Executive,2,Single,9094,17235,2,Y,Yes,12,3,3,80,0,9,2,3,5,4,1,0 -52,Yes,Travel_Rarely,1030,Sales,5,3,Life Sciences,1,1319,2,Male,64,3,3,Sales Executive,2,Single,8446,21534,9,Y,Yes,19,3,3,80,0,10,2,2,8,7,7,7 -30,No,Travel_Rarely,634,Research & Development,17,4,Medical,1,1321,2,Female,95,3,3,Manager,1,Married,11916,25927,1,Y,Yes,23,4,4,80,2,9,2,3,9,1,0,8 -39,No,Travel_Rarely,524,Research & Development,18,2,Life Sciences,1,1322,1,Male,32,3,2,Manufacturing Director,3,Single,4534,13352,0,Y,No,11,3,1,80,0,9,6,3,8,7,1,7 -31,No,Non-Travel,587,Sales,2,4,Life Sciences,1,1324,4,Female,57,3,3,Sales Executive,3,Divorced,9852,8935,1,Y,Yes,19,3,1,80,1,10,5,2,10,8,9,6 -41,No,Non-Travel,256,Sales,10,2,Medical,1,1329,3,Male,40,1,2,Sales Executive,2,Single,6151,22074,1,Y,No,13,3,1,80,0,19,4,3,19,2,11,9 -31,Yes,Travel_Frequently,1060,Sales,1,3,Life Sciences,1,1331,4,Female,54,3,1,Sales Representative,2,Single,2302,8319,1,Y,Yes,11,3,1,80,0,3,2,4,3,2,2,2 -44,Yes,Travel_Rarely,935,Research & Development,3,3,Life Sciences,1,1333,1,Male,89,3,1,Laboratory Technician,1,Married,2362,14669,4,Y,No,12,3,3,80,0,10,4,4,3,2,1,2 -42,No,Non-Travel,495,Research & Development,2,1,Life Sciences,1,1334,3,Male,37,3,4,Manager,3,Married,17861,26582,0,Y,Yes,13,3,4,80,0,21,3,2,20,8,2,10 -55,No,Travel_Rarely,282,Research & Development,2,2,Medical,1,1336,4,Female,58,1,5,Manager,3,Married,19187,6992,4,Y,No,14,3,4,80,1,23,5,3,19,9,9,11 -56,No,Travel_Rarely,206,Human Resources,8,4,Life Sciences,1,1338,4,Male,99,3,5,Manager,2,Single,19717,4022,6,Y,No,14,3,1,80,0,36,4,3,7,3,7,7 -40,No,Non-Travel,458,Research & Development,16,2,Life Sciences,1,1340,3,Male,74,3,1,Research Scientist,3,Divorced,3544,8532,9,Y,No,16,3,2,80,1,6,0,3,4,2,0,0 -34,No,Travel_Rarely,943,Research & Development,9,3,Life Sciences,1,1344,4,Male,86,3,3,Healthcare Representative,4,Divorced,8500,5494,0,Y,No,11,3,4,80,1,10,0,2,9,7,1,6 -40,No,Travel_Rarely,523,Research & Development,2,3,Life Sciences,1,1346,3,Male,98,3,2,Research Scientist,4,Single,4661,22455,1,Y,No,13,3,3,80,0,9,4,3,9,8,8,8 -41,No,Travel_Frequently,1018,Sales,1,3,Marketing,1,1349,3,Female,66,3,2,Sales Executive,1,Divorced,4103,4297,0,Y,No,17,3,4,80,1,10,2,3,9,3,1,7 -35,No,Travel_Frequently,482,Research & Development,4,4,Life Sciences,1,1350,3,Male,87,3,2,Research Scientist,3,Single,4249,2690,1,Y,Yes,11,3,2,80,0,9,3,3,9,6,1,1 -51,No,Travel_Rarely,770,Human Resources,5,3,Life Sciences,1,1352,3,Male,84,3,4,Manager,2,Divorced,14026,17588,1,Y,Yes,11,3,2,80,1,33,2,3,33,9,0,10 -38,No,Travel_Rarely,1009,Sales,2,2,Life Sciences,1,1355,2,Female,31,3,2,Sales Executive,1,Divorced,6893,19461,3,Y,No,15,3,4,80,1,11,3,3,7,7,1,7 -34,No,Travel_Rarely,507,Sales,15,2,Medical,1,1356,3,Female,66,3,2,Sales Executive,1,Single,6125,23553,1,Y,No,12,3,4,80,0,10,6,4,10,8,9,6 -25,No,Travel_Rarely,882,Research & Development,19,1,Medical,1,1358,4,Male,67,3,1,Laboratory Technician,4,Married,3669,9075,3,Y,No,11,3,3,80,3,7,6,2,3,2,1,2 -58,Yes,Travel_Rarely,601,Research & Development,7,4,Medical,1,1360,3,Female,53,2,3,Manufacturing Director,1,Married,10008,12023,7,Y,Yes,14,3,4,80,0,31,0,2,10,9,5,9 -40,No,Travel_Rarely,329,Research & Development,1,4,Life Sciences,1,1361,2,Male,88,3,1,Laboratory Technician,2,Married,2387,6762,3,Y,No,22,4,3,80,1,7,3,3,4,2,0,3 -36,No,Travel_Frequently,607,Sales,7,3,Marketing,1,1362,1,Female,83,4,2,Sales Executive,1,Married,4639,2261,2,Y,No,16,3,4,80,1,17,2,2,15,7,6,13 -48,No,Travel_Rarely,855,Research & Development,4,3,Life Sciences,1,1363,4,Male,54,3,3,Manufacturing Director,4,Single,7898,18706,1,Y,No,11,3,3,80,0,11,2,3,10,9,0,8 -27,No,Travel_Rarely,1291,Sales,11,3,Medical,1,1364,3,Female,98,4,1,Sales Representative,4,Married,2534,6527,8,Y,No,14,3,2,80,1,5,4,3,1,0,0,0 -51,No,Travel_Rarely,1405,Research & Development,11,2,Technical Degree,1,1367,4,Female,82,2,4,Manufacturing Director,2,Single,13142,24439,3,Y,No,16,3,2,80,0,29,1,2,5,2,0,3 -18,No,Non-Travel,1124,Research & Development,1,3,Life Sciences,1,1368,4,Female,97,3,1,Laboratory Technician,4,Single,1611,19305,1,Y,No,15,3,3,80,0,0,5,4,0,0,0,0 -35,No,Travel_Rarely,817,Research & Development,1,3,Medical,1,1369,4,Female,60,2,2,Laboratory Technician,4,Married,5363,10846,0,Y,No,12,3,2,80,1,10,0,3,9,7,0,0 -27,No,Travel_Frequently,793,Sales,2,1,Life Sciences,1,1371,4,Male,43,1,2,Sales Executive,4,Single,5071,20392,3,Y,No,20,4,2,80,0,8,3,3,6,2,0,0 -55,Yes,Travel_Rarely,267,Sales,13,4,Marketing,1,1372,1,Male,85,4,4,Sales Executive,3,Single,13695,9277,6,Y,Yes,17,3,3,80,0,24,2,2,19,7,3,8 -56,No,Travel_Rarely,1369,Research & Development,23,3,Life Sciences,1,1373,4,Male,68,3,4,Manufacturing Director,2,Married,13402,18235,4,Y,Yes,12,3,1,80,1,33,0,3,19,16,15,9 -34,No,Non-Travel,999,Research & Development,26,1,Technical Degree,1,1374,1,Female,92,2,1,Research Scientist,3,Divorced,2029,15891,1,Y,No,20,4,3,80,3,5,2,3,5,4,0,0 -40,No,Travel_Rarely,1202,Research & Development,2,1,Medical,1,1375,2,Female,89,4,2,Healthcare Representative,3,Divorced,6377,13888,5,Y,No,20,4,2,80,3,15,0,3,12,11,11,8 -34,No,Travel_Rarely,285,Research & Development,29,3,Medical,1,1377,2,Male,86,3,2,Laboratory Technician,3,Married,5429,17491,4,Y,No,13,3,1,80,2,10,1,3,8,7,7,7 -31,Yes,Travel_Frequently,703,Sales,2,3,Life Sciences,1,1379,3,Female,90,2,1,Sales Representative,4,Single,2785,11882,7,Y,No,14,3,3,80,0,3,3,4,1,0,0,0 -35,Yes,Travel_Frequently,662,Sales,18,4,Marketing,1,1380,4,Female,67,3,2,Sales Executive,3,Married,4614,23288,0,Y,Yes,18,3,3,80,1,5,0,2,4,2,3,2 -38,No,Travel_Frequently,693,Research & Development,7,3,Life Sciences,1,1382,4,Male,57,4,1,Research Scientist,3,Divorced,2610,15748,1,Y,No,11,3,4,80,3,4,2,3,4,2,0,3 -34,No,Travel_Rarely,404,Research & Development,2,4,Technical Degree,1,1383,3,Female,98,3,2,Healthcare Representative,4,Single,6687,6163,1,Y,No,11,3,4,80,0,14,2,4,14,11,4,11 -28,No,Travel_Rarely,736,Sales,26,3,Life Sciences,1,1387,3,Male,48,2,2,Sales Executive,1,Married,4724,24232,1,Y,No,11,3,3,80,1,5,0,3,5,3,0,4 -31,Yes,Travel_Rarely,330,Research & Development,22,4,Medical,1,1389,4,Male,98,3,2,Manufacturing Director,3,Married,6179,21057,1,Y,Yes,15,3,4,80,2,10,3,2,10,2,6,7 -39,No,Travel_Rarely,1498,Sales,21,4,Life Sciences,1,1390,1,Male,44,2,2,Sales Executive,4,Married,6120,3567,3,Y,Yes,12,3,4,80,2,8,2,4,5,4,1,4 -51,No,Travel_Frequently,541,Sales,2,3,Marketing,1,1391,2,Male,52,3,3,Sales Executive,2,Married,10596,15395,2,Y,No,11,3,2,80,0,14,5,3,4,2,3,2 -41,No,Travel_Frequently,1200,Research & Development,22,3,Life Sciences,1,1392,4,Female,75,3,2,Research Scientist,4,Divorced,5467,13953,3,Y,Yes,14,3,1,80,2,12,4,2,6,2,3,3 -37,No,Travel_Rarely,1439,Research & Development,4,1,Life Sciences,1,1394,3,Male,54,3,1,Research Scientist,3,Married,2996,5182,7,Y,Yes,15,3,4,80,0,8,2,3,6,4,1,3 -33,No,Travel_Frequently,1111,Sales,5,1,Life Sciences,1,1395,2,Male,61,3,2,Sales Executive,4,Married,9998,19293,6,Y,No,13,3,1,80,0,8,2,4,5,4,1,2 -32,No,Travel_Rarely,499,Sales,2,1,Marketing,1,1396,3,Male,36,3,2,Sales Executive,2,Married,4078,20497,0,Y,Yes,13,3,1,80,3,4,3,2,3,2,1,2 -39,No,Non-Travel,1485,Research & Development,25,2,Life Sciences,1,1397,3,Male,71,3,3,Healthcare Representative,3,Married,10920,3449,3,Y,No,21,4,2,80,1,13,2,3,6,4,0,5 -25,No,Travel_Rarely,1372,Sales,18,1,Life Sciences,1,1399,1,Male,93,4,2,Sales Executive,3,Married,6232,12477,2,Y,No,11,3,2,80,0,6,3,2,3,2,1,2 -52,No,Travel_Frequently,322,Research & Development,28,2,Medical,1,1401,4,Female,59,4,4,Manufacturing Director,3,Married,13247,9731,2,Y,Yes,11,3,2,80,1,24,3,2,5,3,0,2 -43,No,Travel_Rarely,930,Research & Development,6,3,Medical,1,1402,1,Female,73,2,2,Research Scientist,3,Single,4081,20003,1,Y,Yes,14,3,1,80,0,20,3,1,20,7,1,8 -27,No,Travel_Rarely,205,Sales,10,3,Marketing,1,1403,4,Female,98,2,2,Sales Executive,4,Married,5769,7100,1,Y,Yes,11,3,4,80,0,6,3,3,6,2,4,4 -27,Yes,Travel_Rarely,135,Research & Development,17,4,Life Sciences,1,1405,4,Female,51,3,1,Research Scientist,3,Single,2394,25681,1,Y,Yes,13,3,4,80,0,8,2,3,8,2,7,7 -26,No,Travel_Rarely,683,Research & Development,2,1,Medical,1,1407,1,Male,36,2,1,Research Scientist,4,Single,3904,4050,0,Y,No,12,3,4,80,0,5,2,3,4,3,1,1 -42,No,Travel_Rarely,1147,Human Resources,10,3,Human Resources,1,1408,3,Female,31,3,4,Manager,1,Married,16799,16616,0,Y,No,14,3,3,80,1,21,5,3,20,7,0,9 -52,No,Travel_Rarely,258,Research & Development,8,4,Other,1,1409,3,Female,54,3,1,Laboratory Technician,1,Married,2950,17363,9,Y,No,13,3,3,80,0,12,2,1,5,4,0,4 -37,No,Travel_Rarely,1462,Research & Development,11,3,Medical,1,1411,1,Female,94,3,1,Laboratory Technician,3,Single,3629,19106,4,Y,No,18,3,1,80,0,8,6,3,3,2,0,2 -35,No,Travel_Frequently,200,Research & Development,18,2,Life Sciences,1,1412,3,Male,60,3,3,Manufacturing Director,4,Single,9362,19944,2,Y,No,11,3,3,80,0,10,2,3,2,2,2,2 -25,No,Travel_Rarely,949,Research & Development,1,3,Technical Degree,1,1415,1,Male,81,3,1,Laboratory Technician,4,Married,3229,4910,4,Y,No,11,3,2,80,1,7,2,2,3,2,0,2 -26,No,Travel_Rarely,652,Research & Development,7,3,Other,1,1417,3,Male,100,4,1,Laboratory Technician,1,Single,3578,23577,0,Y,No,12,3,4,80,0,8,2,3,7,7,0,7 -29,No,Travel_Rarely,332,Human Resources,17,3,Other,1,1419,2,Male,51,2,3,Human Resources,1,Single,7988,9769,1,Y,No,13,3,1,80,0,10,3,2,10,9,0,9 -49,Yes,Travel_Frequently,1475,Research & Development,28,2,Life Sciences,1,1420,1,Male,97,2,2,Laboratory Technician,1,Single,4284,22710,3,Y,No,20,4,1,80,0,20,2,3,4,3,1,3 -29,Yes,Travel_Frequently,337,Research & Development,14,1,Other,1,1421,3,Female,84,3,3,Healthcare Representative,4,Single,7553,22930,0,Y,Yes,12,3,1,80,0,9,1,3,8,7,7,7 -54,No,Travel_Rarely,971,Research & Development,1,3,Medical,1,1422,4,Female,54,3,4,Research Director,4,Single,17328,5652,6,Y,No,19,3,4,80,0,29,3,2,20,7,12,7 -58,No,Travel_Rarely,1055,Research & Development,1,3,Medical,1,1423,4,Female,76,3,5,Research Director,1,Married,19701,22456,3,Y,Yes,21,4,3,80,1,32,3,3,9,8,1,5 -55,No,Travel_Rarely,1136,Research & Development,1,4,Medical,1,1424,2,Male,81,4,4,Research Director,4,Divorced,14732,12414,2,Y,No,13,3,4,80,2,31,4,4,7,7,0,0 -36,No,Travel_Rarely,1174,Sales,3,4,Marketing,1,1425,1,Female,99,3,2,Sales Executive,2,Single,9278,20763,3,Y,Yes,16,3,4,80,0,15,3,3,5,4,0,1 -31,Yes,Travel_Frequently,667,Sales,1,4,Life Sciences,1,1427,2,Female,50,1,1,Sales Representative,3,Single,1359,16154,1,Y,No,12,3,2,80,0,1,3,3,1,0,0,0 -30,No,Travel_Rarely,855,Sales,7,4,Marketing,1,1428,4,Female,73,3,2,Sales Executive,1,Divorced,4779,12761,7,Y,No,14,3,2,80,2,8,3,3,3,2,0,2 -31,No,Travel_Rarely,182,Research & Development,8,5,Life Sciences,1,1430,1,Female,93,3,4,Research Director,2,Single,16422,8847,3,Y,No,11,3,3,80,0,9,3,4,3,2,1,0 -34,No,Travel_Frequently,560,Research & Development,1,4,Other,1,1431,4,Male,91,3,1,Research Scientist,1,Divorced,2996,20284,5,Y,No,14,3,3,80,2,10,2,3,4,3,1,3 -31,Yes,Travel_Rarely,202,Research & Development,8,3,Life Sciences,1,1433,1,Female,34,2,1,Research Scientist,2,Single,1261,22262,1,Y,No,12,3,3,80,0,1,3,4,1,0,0,0 -27,No,Travel_Rarely,1377,Research & Development,11,1,Life Sciences,1,1434,2,Male,91,3,1,Laboratory Technician,1,Married,2099,7679,0,Y,No,14,3,2,80,0,6,3,4,5,0,1,4 -36,No,Travel_Rarely,172,Research & Development,4,4,Life Sciences,1,1435,1,Male,37,2,2,Laboratory Technician,4,Single,5810,22604,1,Y,No,16,3,3,80,0,10,2,2,10,4,1,8 -36,No,Travel_Rarely,329,Sales,16,4,Marketing,1,1436,3,Female,98,2,2,Sales Executive,1,Married,5647,13494,4,Y,No,13,3,1,80,2,11,3,2,3,2,0,2 -47,No,Travel_Rarely,465,Research & Development,1,3,Technical Degree,1,1438,1,Male,74,3,1,Research Scientist,4,Married,3420,10205,7,Y,No,12,3,3,80,1,17,2,2,6,5,1,2 -25,Yes,Travel_Rarely,383,Sales,9,2,Life Sciences,1,1439,1,Male,68,2,1,Sales Representative,1,Married,4400,15182,3,Y,No,12,3,1,80,0,6,2,3,3,2,2,2 -37,No,Non-Travel,1413,Research & Development,5,2,Technical Degree,1,1440,3,Male,84,4,1,Laboratory Technician,3,Single,3500,25470,0,Y,No,14,3,1,80,0,7,2,1,6,5,1,3 -56,No,Travel_Rarely,1255,Research & Development,1,2,Life Sciences,1,1441,1,Female,90,3,1,Research Scientist,1,Married,2066,10494,2,Y,No,22,4,4,80,1,5,3,4,3,2,1,0 -47,No,Travel_Rarely,359,Research & Development,2,4,Medical,1,1443,1,Female,82,3,4,Research Director,3,Married,17169,26703,3,Y,No,19,3,2,80,2,26,2,4,20,17,5,6 -24,No,Travel_Rarely,1476,Sales,4,1,Medical,1,1445,4,Female,42,3,2,Sales Executive,3,Married,4162,15211,1,Y,Yes,12,3,3,80,2,5,3,3,5,4,0,3 -32,No,Travel_Rarely,601,Sales,7,5,Marketing,1,1446,4,Male,97,3,2,Sales Executive,4,Married,9204,23343,4,Y,No,12,3,3,80,1,7,3,2,4,3,0,3 -34,No,Travel_Rarely,401,Research & Development,1,3,Life Sciences,1,1447,4,Female,86,2,1,Laboratory Technician,2,Married,3294,3708,5,Y,No,17,3,1,80,1,7,2,2,5,4,0,2 -41,No,Travel_Rarely,1283,Research & Development,5,5,Medical,1,1448,2,Male,90,4,1,Research Scientist,3,Married,2127,5561,2,Y,Yes,12,3,1,80,0,7,5,2,4,2,0,3 -40,No,Non-Travel,663,Research & Development,9,4,Other,1,1449,3,Male,81,3,2,Laboratory Technician,3,Divorced,3975,23099,3,Y,No,11,3,3,80,2,11,2,4,8,7,0,7 -31,No,Travel_Rarely,326,Sales,8,2,Life Sciences,1,1453,1,Male,31,3,3,Sales Executive,4,Divorced,10793,8386,1,Y,No,18,3,1,80,1,13,5,3,13,7,9,9 -46,Yes,Travel_Rarely,377,Sales,9,3,Marketing,1,1457,1,Male,52,3,3,Sales Executive,4,Divorced,10096,15986,4,Y,No,11,3,1,80,1,28,1,4,7,7,4,3 -39,Yes,Non-Travel,592,Research & Development,2,3,Life Sciences,1,1458,1,Female,54,2,1,Laboratory Technician,1,Single,3646,17181,2,Y,Yes,23,4,2,80,0,11,2,4,1,0,0,0 -31,Yes,Travel_Frequently,1445,Research & Development,1,5,Life Sciences,1,1459,3,Female,100,4,3,Manufacturing Director,2,Single,7446,8931,1,Y,No,11,3,1,80,0,10,2,3,10,8,4,7 -45,No,Travel_Rarely,1038,Research & Development,20,3,Medical,1,1460,2,Male,95,1,3,Healthcare Representative,1,Divorced,10851,19863,2,Y,Yes,18,3,2,80,1,24,2,3,7,7,0,7 -31,No,Travel_Rarely,1398,Human Resources,8,2,Medical,1,1461,4,Female,96,4,1,Human Resources,2,Single,2109,24609,9,Y,No,18,3,4,80,0,8,3,3,3,2,0,2 -31,Yes,Travel_Frequently,523,Research & Development,2,3,Life Sciences,1,1464,2,Male,94,3,1,Laboratory Technician,4,Married,3722,21081,6,Y,Yes,13,3,3,80,1,7,2,1,2,2,2,2 -45,No,Travel_Rarely,1448,Research & Development,29,3,Technical Degree,1,1465,2,Male,55,3,3,Manufacturing Director,4,Married,9380,14720,4,Y,Yes,18,3,4,80,2,10,4,4,3,1,1,2 -48,No,Travel_Rarely,1221,Sales,7,3,Marketing,1,1466,3,Male,96,3,2,Sales Executive,1,Divorced,5486,24795,4,Y,No,11,3,1,80,3,15,3,3,2,2,2,2 -34,Yes,Travel_Rarely,1107,Human Resources,9,4,Technical Degree,1,1467,1,Female,52,3,1,Human Resources,3,Married,2742,3072,1,Y,No,15,3,4,80,0,2,0,3,2,2,2,2 -40,No,Non-Travel,218,Research & Development,8,1,Medical,1,1468,4,Male,55,2,3,Research Director,2,Divorced,13757,25178,2,Y,No,11,3,3,80,1,16,5,3,9,8,4,8 -28,No,Travel_Rarely,866,Sales,5,3,Medical,1,1469,4,Male,84,3,2,Sales Executive,1,Single,8463,23490,0,Y,No,18,3,4,80,0,6,4,3,5,4,1,3 -44,No,Non-Travel,981,Research & Development,5,3,Life Sciences,1,1471,3,Male,90,2,1,Laboratory Technician,3,Single,3162,7973,3,Y,No,14,3,4,80,0,7,5,3,5,2,0,3 -53,No,Travel_Rarely,447,Research & Development,2,3,Medical,1,1472,4,Male,39,4,4,Research Director,2,Single,16598,19764,4,Y,No,12,3,2,80,0,35,2,2,9,8,8,8 -49,No,Travel_Rarely,1495,Research & Development,5,4,Technical Degree,1,1473,1,Male,96,3,2,Healthcare Representative,3,Married,6651,21534,2,Y,No,14,3,2,80,1,20,0,2,3,2,1,2 -40,No,Travel_Rarely,896,Research & Development,2,3,Medical,1,1474,3,Male,68,3,1,Research Scientist,3,Divorced,2345,8045,2,Y,No,14,3,3,80,1,8,3,4,3,1,1,2 -44,No,Travel_Rarely,1467,Research & Development,20,3,Life Sciences,1,1475,4,Male,49,3,1,Research Scientist,2,Single,3420,21158,1,Y,No,13,3,3,80,0,6,3,2,5,2,1,3 -33,No,Travel_Frequently,430,Sales,7,3,Medical,1,1477,4,Male,54,3,2,Sales Executive,1,Married,4373,17456,0,Y,No,14,3,1,80,2,5,2,3,4,3,0,3 -34,No,Travel_Rarely,1326,Sales,3,3,Other,1,1478,4,Male,81,1,2,Sales Executive,1,Single,4759,15891,3,Y,No,18,3,4,80,0,15,2,3,13,9,3,12 -30,No,Travel_Rarely,1358,Sales,16,1,Life Sciences,1,1479,4,Male,96,3,2,Sales Executive,3,Married,5301,2939,8,Y,No,15,3,3,80,2,4,2,2,2,1,2,2 -42,No,Travel_Frequently,748,Research & Development,9,2,Medical,1,1480,1,Female,74,3,1,Laboratory Technician,4,Single,3673,16458,1,Y,No,13,3,3,80,0,12,3,3,12,9,5,8 -44,No,Travel_Frequently,383,Sales,1,5,Marketing,1,1481,1,Female,79,3,2,Sales Executive,3,Married,4768,9282,7,Y,No,12,3,3,80,1,11,4,2,1,0,0,0 -30,No,Non-Travel,990,Research & Development,7,3,Technical Degree,1,1482,3,Male,64,3,1,Research Scientist,3,Divorced,1274,7152,1,Y,No,13,3,2,80,2,1,2,2,1,0,0,0 -57,No,Travel_Rarely,405,Research & Development,1,2,Life Sciences,1,1483,2,Male,93,4,2,Research Scientist,3,Married,4900,2721,0,Y,No,24,4,1,80,1,13,2,2,12,9,2,8 -49,No,Travel_Rarely,1490,Research & Development,7,4,Life Sciences,1,1484,3,Male,35,3,3,Healthcare Representative,2,Divorced,10466,20948,3,Y,No,14,3,2,80,2,29,3,3,8,7,0,7 -34,No,Travel_Frequently,829,Research & Development,15,3,Medical,1,1485,2,Male,71,3,4,Research Director,1,Divorced,17007,11929,7,Y,No,14,3,4,80,2,16,3,2,14,8,6,9 -28,Yes,Travel_Frequently,1496,Sales,1,3,Technical Degree,1,1486,1,Male,92,3,1,Sales Representative,3,Married,2909,15747,3,Y,No,15,3,4,80,1,5,3,4,3,2,1,2 -29,Yes,Travel_Frequently,115,Sales,13,3,Technical Degree,1,1487,1,Female,51,3,2,Sales Executive,2,Single,5765,17485,5,Y,No,11,3,1,80,0,7,4,1,5,3,0,0 -34,Yes,Travel_Rarely,790,Sales,24,4,Medical,1,1489,1,Female,40,2,2,Sales Executive,2,Single,4599,7815,0,Y,Yes,23,4,3,80,0,16,2,4,15,9,10,10 -35,No,Travel_Rarely,660,Sales,7,1,Life Sciences,1,1492,4,Male,76,3,1,Sales Representative,3,Married,2404,16192,1,Y,No,13,3,1,80,1,1,3,3,1,0,0,0 -24,Yes,Travel_Frequently,381,Research & Development,9,3,Medical,1,1494,2,Male,89,3,1,Laboratory Technician,1,Single,3172,16998,2,Y,Yes,11,3,3,80,0,4,2,2,0,0,0,0 -24,No,Non-Travel,830,Sales,13,2,Life Sciences,1,1495,4,Female,78,3,1,Sales Representative,2,Married,2033,7103,1,Y,No,13,3,3,80,1,1,2,3,1,0,0,0 -44,No,Travel_Frequently,1193,Research & Development,2,1,Medical,1,1496,2,Male,86,3,3,Manufacturing Director,3,Single,10209,19719,5,Y,Yes,18,3,2,80,0,16,2,2,2,2,2,2 -29,No,Travel_Rarely,1246,Sales,19,3,Life Sciences,1,1497,3,Male,77,2,2,Sales Executive,3,Divorced,8620,23757,1,Y,No,14,3,3,80,2,10,3,3,10,7,0,4 -30,No,Travel_Rarely,330,Human Resources,1,3,Life Sciences,1,1499,3,Male,46,3,1,Human Resources,3,Divorced,2064,15428,0,Y,No,21,4,1,80,1,6,3,4,5,3,1,3 -55,No,Travel_Rarely,1229,Research & Development,4,4,Life Sciences,1,1501,4,Male,30,3,2,Healthcare Representative,3,Married,4035,16143,0,Y,Yes,16,3,2,80,0,4,2,3,3,2,1,2 -33,No,Travel_Rarely,1099,Research & Development,4,4,Medical,1,1502,1,Female,82,2,1,Laboratory Technician,2,Married,3838,8192,8,Y,No,11,3,4,80,0,8,5,3,5,4,0,2 -47,No,Travel_Rarely,571,Sales,14,3,Medical,1,1503,3,Female,78,3,2,Sales Executive,3,Married,4591,24200,3,Y,Yes,17,3,3,80,1,11,4,2,5,4,1,2 -28,Yes,Travel_Frequently,289,Research & Development,2,2,Medical,1,1504,3,Male,38,2,1,Laboratory Technician,1,Single,2561,5355,7,Y,No,11,3,3,80,0,8,2,2,0,0,0,0 -28,No,Travel_Rarely,1423,Research & Development,1,3,Life Sciences,1,1506,1,Male,72,2,1,Research Scientist,3,Divorced,1563,12530,1,Y,No,14,3,4,80,1,1,2,1,1,0,0,0 -28,No,Travel_Frequently,467,Sales,7,3,Life Sciences,1,1507,3,Male,55,3,2,Sales Executive,1,Single,4898,11827,0,Y,No,14,3,4,80,0,5,5,3,4,2,1,3 -49,No,Travel_Rarely,271,Research & Development,3,2,Medical,1,1509,3,Female,43,2,2,Laboratory Technician,1,Married,4789,23070,4,Y,No,25,4,1,80,1,10,3,3,3,2,1,2 -29,No,Travel_Frequently,410,Research & Development,2,1,Life Sciences,1,1513,4,Female,97,3,1,Laboratory Technician,2,Married,3180,4668,0,Y,No,13,3,3,80,3,4,3,3,3,2,0,2 -28,No,Travel_Rarely,1083,Research & Development,29,1,Life Sciences,1,1514,3,Male,96,1,2,Manufacturing Director,2,Married,6549,3173,1,Y,No,14,3,2,80,2,8,2,2,8,6,1,7 -33,No,Travel_Rarely,516,Research & Development,8,5,Life Sciences,1,1515,4,Male,69,3,2,Healthcare Representative,3,Single,6388,22049,2,Y,Yes,17,3,1,80,0,14,6,3,0,0,0,0 -32,No,Travel_Rarely,495,Research & Development,10,3,Medical,1,1516,3,Male,64,3,3,Manager,4,Single,11244,21072,2,Y,No,25,4,2,80,0,10,5,4,5,2,0,0 -54,No,Travel_Frequently,1050,Research & Development,11,4,Medical,1,1520,2,Female,87,3,4,Manager,4,Divorced,16032,24456,3,Y,No,20,4,1,80,1,26,2,3,14,9,1,12 -29,Yes,Travel_Rarely,224,Research & Development,1,4,Technical Degree,1,1522,1,Male,100,2,1,Research Scientist,1,Single,2362,7568,6,Y,No,13,3,3,80,0,11,2,1,9,7,0,7 -44,No,Travel_Rarely,136,Research & Development,28,3,Life Sciences,1,1523,4,Male,32,3,4,Research Director,1,Married,16328,22074,3,Y,No,13,3,3,80,1,24,1,4,20,6,14,17 -39,No,Travel_Rarely,1089,Research & Development,6,3,Life Sciences,1,1525,2,Female,32,3,3,Manufacturing Director,2,Single,8376,9150,4,Y,No,18,3,4,80,0,9,3,3,2,0,2,2 -46,No,Travel_Rarely,228,Sales,3,3,Life Sciences,1,1527,3,Female,51,3,4,Manager,2,Married,16606,11380,8,Y,No,12,3,4,80,1,23,2,4,13,12,5,1 -35,No,Travel_Rarely,1029,Research & Development,16,3,Life Sciences,1,1529,4,Female,91,2,3,Healthcare Representative,2,Single,8606,21195,1,Y,No,19,3,4,80,0,11,3,1,11,8,3,3 -23,No,Travel_Rarely,507,Research & Development,20,1,Life Sciences,1,1533,1,Male,97,3,2,Laboratory Technician,3,Single,2272,24812,0,Y,No,14,3,2,80,0,5,2,3,4,3,1,2 -40,Yes,Travel_Rarely,676,Research & Development,9,4,Life Sciences,1,1534,4,Male,86,3,1,Laboratory Technician,1,Single,2018,21831,3,Y,No,14,3,2,80,0,15,3,1,5,4,1,0 -34,No,Travel_Rarely,971,Sales,1,3,Technical Degree,1,1535,4,Male,64,2,3,Sales Executive,3,Married,7083,12288,1,Y,Yes,14,3,4,80,0,10,3,3,10,9,8,6 -31,Yes,Travel_Frequently,561,Research & Development,3,3,Life Sciences,1,1537,4,Female,33,3,1,Research Scientist,3,Single,4084,4156,1,Y,No,12,3,1,80,0,7,2,1,7,2,7,7 -50,No,Travel_Frequently,333,Research & Development,22,5,Medical,1,1539,3,Male,88,1,4,Research Director,4,Single,14411,24450,1,Y,Yes,13,3,4,80,0,32,2,3,32,6,13,9 -34,No,Travel_Rarely,1440,Sales,7,2,Technical Degree,1,1541,2,Male,55,3,1,Sales Representative,3,Married,2308,4944,0,Y,Yes,25,4,2,80,1,12,4,3,11,10,5,7 -42,No,Travel_Rarely,1210,Research & Development,2,3,Medical,1,1542,3,Male,68,2,1,Laboratory Technician,2,Married,4841,24052,4,Y,No,14,3,2,80,1,4,3,3,1,0,0,0 -37,No,Travel_Rarely,674,Research & Development,13,3,Medical,1,1543,1,Male,47,3,2,Research Scientist,4,Married,4285,3031,1,Y,No,17,3,1,80,0,10,2,3,10,8,3,7 -29,No,Travel_Rarely,441,Research & Development,8,1,Other,1,1544,3,Female,39,1,2,Healthcare Representative,1,Married,9715,7288,3,Y,No,13,3,3,80,1,9,3,3,7,7,0,7 -33,No,Travel_Rarely,575,Research & Development,25,3,Life Sciences,1,1545,4,Male,44,2,2,Manufacturing Director,2,Single,4320,24152,1,Y,No,13,3,4,80,0,5,2,3,5,3,0,2 -45,No,Travel_Rarely,950,Research & Development,28,3,Technical Degree,1,1546,4,Male,97,3,1,Research Scientist,4,Married,2132,4585,4,Y,No,20,4,4,80,1,8,3,3,5,4,0,3 -42,No,Travel_Frequently,288,Research & Development,2,3,Life Sciences,1,1547,4,Male,40,3,3,Healthcare Representative,4,Married,10124,18611,2,Y,Yes,14,3,3,80,1,24,3,1,20,8,13,9 -40,No,Travel_Rarely,1342,Sales,9,2,Medical,1,1548,1,Male,47,3,2,Sales Executive,1,Married,5473,19345,0,Y,No,12,3,4,80,0,9,5,4,8,4,7,1 -33,No,Travel_Rarely,589,Research & Development,28,4,Life Sciences,1,1549,2,Male,79,3,2,Laboratory Technician,3,Married,5207,22949,1,Y,Yes,12,3,2,80,1,15,3,3,15,14,5,7 -40,No,Travel_Rarely,898,Human Resources,6,2,Medical,1,1550,3,Male,38,3,4,Manager,4,Single,16437,17381,1,Y,Yes,21,4,4,80,0,21,2,3,21,7,7,7 -24,No,Travel_Rarely,350,Research & Development,21,2,Technical Degree,1,1551,3,Male,57,2,1,Laboratory Technician,1,Divorced,2296,10036,0,Y,No,14,3,2,80,3,2,3,3,1,1,0,0 -40,No,Non-Travel,1142,Research & Development,8,2,Life Sciences,1,1552,4,Male,72,3,2,Healthcare Representative,4,Divorced,4069,8841,3,Y,Yes,18,3,3,80,0,8,2,3,2,2,2,2 -45,No,Travel_Rarely,538,Research & Development,1,4,Technical Degree,1,1553,1,Male,66,3,3,Healthcare Representative,2,Divorced,7441,20933,1,Y,No,12,3,1,80,3,10,4,3,10,8,7,7 -35,No,Travel_Rarely,1402,Sales,28,4,Life Sciences,1,1554,2,Female,98,2,1,Sales Representative,3,Married,2430,26204,0,Y,No,23,4,1,80,2,6,5,3,5,3,4,2 -32,No,Travel_Rarely,824,Research & Development,5,2,Life Sciences,1,1555,4,Female,67,2,2,Research Scientist,2,Married,5878,15624,3,Y,No,12,3,1,80,1,12,2,3,7,1,2,5 -36,No,Travel_Rarely,1157,Sales,2,4,Life Sciences,1,1556,3,Male,70,3,1,Sales Representative,4,Single,2644,17001,3,Y,Yes,21,4,4,80,0,7,3,2,3,2,1,2 -48,No,Travel_Rarely,492,Sales,16,4,Life Sciences,1,1557,3,Female,96,3,2,Sales Executive,3,Divorced,6439,13693,8,Y,No,14,3,3,80,1,18,2,3,8,7,7,7 -29,No,Travel_Rarely,598,Research & Development,9,3,Life Sciences,1,1558,3,Male,91,4,1,Research Scientist,3,Married,2451,22376,6,Y,No,18,3,1,80,2,5,2,2,1,0,0,0 -33,No,Travel_Rarely,1242,Sales,8,4,Life Sciences,1,1560,1,Male,46,3,2,Sales Executive,1,Married,6392,10589,2,Y,No,13,3,4,80,1,8,6,1,2,2,2,2 -30,Yes,Travel_Rarely,740,Sales,1,3,Life Sciences,1,1562,2,Male,64,2,2,Sales Executive,1,Married,9714,5323,1,Y,No,11,3,4,80,1,10,4,3,10,8,6,7 -38,No,Travel_Frequently,888,Human Resources,10,4,Human Resources,1,1563,3,Male,71,3,2,Human Resources,3,Married,6077,14814,3,Y,No,11,3,3,80,0,10,2,3,6,3,1,2 -35,No,Travel_Rarely,992,Research & Development,1,3,Medical,1,1564,4,Male,68,2,1,Laboratory Technician,1,Single,2450,21731,1,Y,No,19,3,2,80,0,3,3,3,3,0,1,2 -30,No,Travel_Rarely,1288,Sales,29,4,Technical Degree,1,1568,3,Male,33,3,3,Sales Executive,2,Married,9250,17799,3,Y,No,12,3,2,80,1,9,3,3,4,2,1,3 -35,Yes,Travel_Rarely,104,Research & Development,2,3,Life Sciences,1,1569,1,Female,69,3,1,Laboratory Technician,1,Divorced,2074,26619,1,Y,Yes,12,3,4,80,1,1,2,3,1,0,0,0 -53,Yes,Travel_Rarely,607,Research & Development,2,5,Technical Degree,1,1572,3,Female,78,2,3,Manufacturing Director,4,Married,10169,14618,0,Y,No,16,3,2,80,1,34,4,3,33,7,1,9 -38,Yes,Travel_Rarely,903,Research & Development,2,3,Medical,1,1573,3,Male,81,3,2,Manufacturing Director,2,Married,4855,7653,4,Y,No,11,3,1,80,2,7,2,3,5,2,1,4 -32,No,Non-Travel,1200,Research & Development,1,4,Technical Degree,1,1574,4,Male,62,3,2,Research Scientist,1,Married,4087,25174,4,Y,No,14,3,2,80,1,9,3,2,6,5,1,2 -48,No,Travel_Rarely,1108,Research & Development,15,4,Other,1,1576,3,Female,65,3,1,Research Scientist,1,Married,2367,16530,8,Y,No,12,3,4,80,1,10,3,2,8,2,7,6 -34,No,Travel_Rarely,479,Research & Development,7,4,Medical,1,1577,1,Male,35,3,1,Research Scientist,4,Single,2972,22061,1,Y,No,13,3,3,80,0,1,4,1,1,0,0,0 -55,No,Travel_Rarely,685,Sales,26,5,Marketing,1,1578,3,Male,60,2,5,Manager,4,Married,19586,23037,1,Y,No,21,4,3,80,1,36,3,3,36,6,2,13 -34,No,Travel_Rarely,1351,Research & Development,1,4,Life Sciences,1,1580,2,Male,45,3,2,Research Scientist,4,Married,5484,13008,9,Y,No,17,3,2,80,1,9,3,2,2,2,2,1 -26,No,Travel_Rarely,474,Research & Development,3,3,Life Sciences,1,1581,1,Female,89,3,1,Research Scientist,4,Married,2061,11133,1,Y,No,21,4,1,80,0,1,5,3,1,0,0,0 -38,No,Travel_Rarely,1245,Sales,14,3,Life Sciences,1,1582,3,Male,80,3,2,Sales Executive,2,Married,9924,12355,0,Y,No,11,3,4,80,1,10,3,3,9,8,7,7 -38,No,Travel_Rarely,437,Sales,16,3,Life Sciences,1,1583,2,Female,90,3,2,Sales Executive,2,Single,4198,16379,2,Y,No,12,3,2,80,0,8,5,4,3,2,1,2 -36,No,Travel_Rarely,884,Sales,1,4,Life Sciences,1,1585,2,Female,73,3,2,Sales Executive,3,Single,6815,21447,6,Y,No,13,3,1,80,0,15,5,3,1,0,0,0 -29,No,Travel_Rarely,1370,Research & Development,3,1,Medical,1,1586,2,Male,87,3,1,Laboratory Technician,1,Single,4723,16213,1,Y,Yes,18,3,4,80,0,10,3,3,10,9,1,5 -35,No,Travel_Rarely,670,Research & Development,10,4,Medical,1,1587,1,Female,51,3,2,Healthcare Representative,3,Single,6142,4223,3,Y,Yes,16,3,3,80,0,10,4,3,5,2,0,4 -39,No,Travel_Rarely,1462,Sales,6,3,Medical,1,1588,4,Male,38,4,3,Sales Executive,3,Married,8237,4658,2,Y,No,11,3,1,80,1,11,3,3,7,6,7,6 -29,No,Travel_Frequently,995,Research & Development,2,1,Life Sciences,1,1590,1,Male,87,3,2,Healthcare Representative,4,Divorced,8853,24483,1,Y,No,19,3,4,80,1,6,0,4,6,4,1,3 -50,No,Travel_Rarely,264,Sales,9,3,Marketing,1,1591,3,Male,59,3,5,Manager,3,Married,19331,19519,4,Y,Yes,16,3,3,80,1,27,2,3,1,0,0,0 -23,No,Travel_Rarely,977,Research & Development,10,3,Technical Degree,1,1592,4,Male,45,4,1,Research Scientist,3,Married,2073,12826,2,Y,No,16,3,4,80,1,4,2,3,2,2,2,2 -36,No,Travel_Frequently,1302,Research & Development,6,4,Life Sciences,1,1594,1,Male,80,4,2,Laboratory Technician,1,Married,5562,19711,3,Y,Yes,13,3,4,80,1,9,3,3,3,2,0,2 -42,No,Travel_Rarely,1059,Research & Development,9,2,Other,1,1595,4,Male,93,2,5,Manager,4,Single,19613,26362,8,Y,No,22,4,4,80,0,24,2,3,1,0,0,1 -35,No,Travel_Rarely,750,Research & Development,28,3,Life Sciences,1,1596,2,Male,46,4,2,Laboratory Technician,3,Married,3407,25348,1,Y,No,17,3,4,80,2,10,3,2,10,9,6,8 -34,No,Travel_Frequently,653,Research & Development,10,4,Technical Degree,1,1597,4,Male,92,2,2,Healthcare Representative,3,Married,5063,15332,1,Y,No,14,3,2,80,1,8,3,2,8,2,7,7 -40,No,Travel_Rarely,118,Sales,14,2,Life Sciences,1,1598,4,Female,84,3,2,Sales Executive,1,Married,4639,11262,1,Y,No,15,3,3,80,1,5,2,3,5,4,1,2 -43,No,Travel_Rarely,990,Research & Development,27,3,Technical Degree,1,1599,4,Male,87,4,1,Laboratory Technician,2,Divorced,4876,5855,5,Y,No,12,3,3,80,1,8,0,3,6,4,0,2 -35,No,Travel_Rarely,1349,Research & Development,7,2,Life Sciences,1,1601,3,Male,63,2,1,Laboratory Technician,4,Married,2690,7713,1,Y,No,18,3,4,80,1,1,5,2,1,0,0,1 -46,No,Travel_Rarely,563,Sales,1,4,Life Sciences,1,1602,4,Male,56,4,4,Manager,1,Single,17567,3156,1,Y,No,15,3,2,80,0,27,5,1,26,0,0,12 -28,Yes,Travel_Rarely,329,Research & Development,24,3,Medical,1,1604,3,Male,51,3,1,Laboratory Technician,2,Married,2408,7324,1,Y,Yes,17,3,3,80,3,1,3,3,1,1,0,0 -22,No,Non-Travel,457,Research & Development,26,2,Other,1,1605,2,Female,85,2,1,Research Scientist,3,Married,2814,10293,1,Y,Yes,14,3,2,80,0,4,2,2,4,2,1,3 -50,No,Travel_Frequently,1234,Research & Development,20,5,Medical,1,1606,2,Male,41,3,4,Healthcare Representative,3,Married,11245,20689,2,Y,Yes,15,3,3,80,1,32,3,3,30,8,12,13 -32,No,Travel_Rarely,634,Research & Development,5,4,Other,1,1607,2,Female,35,4,1,Research Scientist,4,Married,3312,18783,3,Y,No,17,3,4,80,2,6,3,3,3,2,0,2 -44,No,Travel_Rarely,1313,Research & Development,7,3,Medical,1,1608,2,Female,31,3,5,Research Director,4,Divorced,19049,3549,0,Y,Yes,14,3,4,80,1,23,4,2,22,7,1,10 -30,No,Travel_Rarely,241,Research & Development,7,3,Medical,1,1609,2,Male,48,2,1,Research Scientist,2,Married,2141,5348,1,Y,No,12,3,2,80,1,6,3,2,6,4,1,1 -45,No,Travel_Rarely,1015,Research & Development,5,5,Medical,1,1611,3,Female,50,1,2,Laboratory Technician,1,Single,5769,23447,1,Y,Yes,14,3,1,80,0,10,3,3,10,7,1,4 -45,No,Non-Travel,336,Sales,26,3,Marketing,1,1612,1,Male,52,2,2,Sales Executive,1,Married,4385,24162,1,Y,No,15,3,1,80,1,10,2,3,10,7,4,5 -31,No,Travel_Frequently,715,Sales,2,4,Other,1,1613,4,Male,54,3,2,Sales Executive,1,Single,5332,21602,7,Y,No,13,3,4,80,0,10,3,3,5,2,0,3 -36,No,Travel_Rarely,559,Research & Development,12,4,Life Sciences,1,1614,3,Female,76,3,2,Manufacturing Director,3,Married,4663,12421,9,Y,Yes,12,3,2,80,2,7,2,3,3,2,1,1 -34,No,Travel_Frequently,426,Research & Development,10,4,Life Sciences,1,1615,3,Male,42,4,2,Manufacturing Director,4,Divorced,4724,17000,1,Y,No,13,3,1,80,1,9,3,3,9,7,7,2 -49,No,Travel_Rarely,722,Research & Development,25,4,Life Sciences,1,1617,3,Female,84,3,1,Laboratory Technician,1,Married,3211,22102,1,Y,No,14,3,4,80,1,10,3,2,9,6,1,4 -39,No,Travel_Rarely,1387,Research & Development,10,5,Medical,1,1618,2,Male,76,3,2,Manufacturing Director,1,Married,5377,3835,2,Y,No,13,3,4,80,3,10,3,3,7,7,7,7 -27,No,Travel_Rarely,1302,Research & Development,19,3,Other,1,1619,4,Male,67,2,1,Laboratory Technician,1,Divorced,4066,16290,1,Y,No,11,3,1,80,2,7,3,3,7,7,0,7 -35,No,Travel_Rarely,819,Research & Development,18,5,Life Sciences,1,1621,2,Male,48,4,2,Research Scientist,1,Married,5208,26312,1,Y,No,11,3,4,80,0,16,2,3,16,15,1,10 -28,No,Travel_Rarely,580,Research & Development,27,3,Medical,1,1622,2,Female,39,1,2,Manufacturing Director,1,Divorced,4877,20460,0,Y,No,21,4,2,80,1,6,5,2,5,3,0,0 -21,No,Travel_Rarely,546,Research & Development,5,1,Medical,1,1623,3,Male,97,3,1,Research Scientist,4,Single,3117,26009,1,Y,No,18,3,3,80,0,3,2,3,2,2,2,2 -18,Yes,Travel_Frequently,544,Sales,3,2,Medical,1,1624,2,Female,70,3,1,Sales Representative,4,Single,1569,18420,1,Y,Yes,12,3,3,80,0,0,2,4,0,0,0,0 -47,No,Travel_Rarely,1176,Human Resources,26,4,Life Sciences,1,1625,4,Female,98,3,5,Manager,3,Married,19658,5220,3,Y,No,11,3,3,80,1,27,2,3,5,2,1,0 -39,No,Travel_Rarely,170,Research & Development,3,2,Medical,1,1627,3,Male,76,2,2,Laboratory Technician,3,Divorced,3069,10302,0,Y,No,15,3,4,80,1,11,3,3,10,8,0,7 -40,No,Travel_Rarely,884,Research & Development,15,3,Life Sciences,1,1628,1,Female,80,2,3,Manufacturing Director,3,Married,10435,25800,1,Y,No,13,3,4,80,2,18,2,3,18,15,14,12 -35,No,Non-Travel,208,Research & Development,8,4,Life Sciences,1,1630,3,Female,52,3,2,Healthcare Representative,3,Married,4148,12250,1,Y,No,12,3,4,80,1,15,5,3,14,11,2,9 -37,No,Travel_Rarely,671,Research & Development,19,3,Life Sciences,1,1631,3,Male,85,3,2,Manufacturing Director,3,Married,5768,26493,3,Y,No,17,3,1,80,3,9,2,2,4,3,0,2 -39,No,Travel_Frequently,711,Research & Development,4,3,Medical,1,1633,1,Female,81,3,2,Manufacturing Director,3,Single,5042,3140,0,Y,No,13,3,4,80,0,10,2,1,9,2,3,8 -45,No,Travel_Rarely,1329,Research & Development,2,2,Other,1,1635,4,Female,59,2,2,Manufacturing Director,4,Divorced,5770,5388,1,Y,No,19,3,1,80,2,10,3,3,10,7,3,9 -38,No,Travel_Rarely,397,Research & Development,2,2,Medical,1,1638,4,Female,54,2,3,Manufacturing Director,3,Married,7756,14199,3,Y,Yes,19,3,4,80,1,10,6,4,5,4,0,2 -35,Yes,Travel_Rarely,737,Sales,10,3,Medical,1,1639,4,Male,55,2,3,Sales Executive,1,Married,10306,21530,9,Y,No,17,3,3,80,0,15,3,3,13,12,6,0 -37,No,Travel_Rarely,1470,Research & Development,10,3,Medical,1,1640,2,Female,71,3,1,Research Scientist,2,Married,3936,9953,1,Y,No,11,3,1,80,1,8,2,1,8,4,7,7 -40,No,Travel_Rarely,448,Research & Development,16,3,Life Sciences,1,1641,3,Female,84,3,3,Manufacturing Director,4,Single,7945,19948,6,Y,Yes,15,3,4,80,0,18,2,2,4,2,3,3 -44,No,Travel_Frequently,602,Human Resources,1,5,Human Resources,1,1642,1,Male,37,3,2,Human Resources,4,Married,5743,10503,4,Y,Yes,11,3,3,80,0,14,3,3,10,7,0,2 -48,No,Travel_Frequently,365,Research & Development,4,5,Medical,1,1644,3,Male,89,2,4,Manager,4,Married,15202,5602,2,Y,No,25,4,2,80,1,23,3,3,2,2,2,2 -35,Yes,Travel_Rarely,763,Sales,15,2,Medical,1,1645,1,Male,59,1,2,Sales Executive,4,Divorced,5440,22098,6,Y,Yes,14,3,4,80,2,7,2,2,2,2,2,2 -24,No,Travel_Frequently,567,Research & Development,2,1,Technical Degree,1,1646,1,Female,32,3,1,Research Scientist,4,Single,3760,17218,1,Y,Yes,13,3,3,80,0,6,2,3,6,3,1,3 -27,No,Travel_Rarely,486,Research & Development,8,3,Medical,1,1647,2,Female,86,4,1,Research Scientist,3,Married,3517,22490,7,Y,No,17,3,1,80,0,5,0,3,3,2,0,2 -27,No,Travel_Frequently,591,Research & Development,2,3,Medical,1,1648,4,Male,87,3,1,Research Scientist,4,Single,2580,6297,2,Y,No,13,3,3,80,0,6,0,2,4,2,1,2 -40,Yes,Travel_Rarely,1329,Research & Development,7,3,Life Sciences,1,1649,1,Male,73,3,1,Laboratory Technician,1,Single,2166,3339,3,Y,Yes,14,3,2,80,0,10,3,1,4,2,0,3 -29,No,Travel_Rarely,469,Sales,10,3,Medical,1,1650,3,Male,42,2,2,Sales Executive,3,Single,5869,23413,9,Y,No,11,3,3,80,0,8,2,3,5,2,1,4 -36,No,Travel_Rarely,711,Research & Development,5,4,Life Sciences,1,1651,2,Female,42,3,3,Healthcare Representative,1,Married,8008,22792,4,Y,No,12,3,3,80,2,9,6,3,3,2,0,2 -25,No,Travel_Frequently,772,Research & Development,2,1,Life Sciences,1,1653,4,Male,77,4,2,Manufacturing Director,3,Divorced,5206,4973,1,Y,No,17,3,3,80,2,7,6,3,7,7,0,7 -39,No,Travel_Rarely,492,Research & Development,12,3,Medical,1,1654,4,Male,66,3,2,Manufacturing Director,2,Married,5295,7693,4,Y,No,21,4,3,80,0,7,3,3,5,4,1,0 -49,No,Travel_Rarely,301,Research & Development,22,4,Other,1,1655,1,Female,72,3,4,Research Director,2,Married,16413,3498,3,Y,No,16,3,2,80,2,27,2,3,4,2,1,2 -50,No,Travel_Rarely,813,Research & Development,17,5,Life Sciences,1,1656,4,Female,50,2,3,Research Director,1,Divorced,13269,21981,5,Y,No,15,3,3,80,3,19,3,3,14,11,1,11 -20,No,Travel_Rarely,1141,Sales,2,3,Medical,1,1657,3,Female,31,3,1,Sales Representative,3,Single,2783,13251,1,Y,No,19,3,1,80,0,2,3,3,2,2,2,2 -34,No,Travel_Rarely,1130,Research & Development,3,3,Life Sciences,1,1658,4,Female,66,3,2,Research Scientist,2,Divorced,5433,19332,1,Y,No,12,3,3,80,1,11,2,3,11,8,7,9 -36,No,Travel_Rarely,311,Research & Development,7,3,Life Sciences,1,1659,1,Male,77,3,1,Laboratory Technician,2,Single,2013,10950,2,Y,No,11,3,3,80,0,15,4,3,4,3,1,3 -49,No,Travel_Rarely,465,Research & Development,6,1,Life Sciences,1,1661,3,Female,41,2,4,Healthcare Representative,3,Married,13966,11652,2,Y,Yes,19,3,2,80,1,30,3,3,15,11,2,12 -36,No,Non-Travel,894,Research & Development,1,4,Medical,1,1662,4,Female,33,2,2,Manufacturing Director,3,Married,4374,15411,0,Y,No,15,3,3,80,0,4,6,3,3,2,1,2 -36,No,Travel_Rarely,1040,Research & Development,3,2,Life Sciences,1,1664,4,Male,79,4,2,Healthcare Representative,1,Divorced,6842,26308,6,Y,No,20,4,1,80,1,13,3,3,5,4,0,4 -54,No,Travel_Rarely,584,Research & Development,22,5,Medical,1,1665,2,Female,91,3,4,Manager,3,Married,17426,18685,3,Y,No,25,4,3,80,1,36,6,3,10,8,4,7 -43,No,Travel_Rarely,1291,Research & Development,15,2,Life Sciences,1,1666,3,Male,65,2,4,Research Director,3,Married,17603,3525,1,Y,No,24,4,1,80,1,14,3,3,14,10,6,11 -35,Yes,Travel_Frequently,880,Sales,12,4,Other,1,1667,4,Male,36,3,2,Sales Executive,4,Single,4581,10414,3,Y,Yes,24,4,1,80,0,13,2,4,11,9,6,7 -38,No,Travel_Frequently,1189,Research & Development,1,3,Life Sciences,1,1668,4,Male,90,3,2,Research Scientist,4,Married,4735,9867,7,Y,No,15,3,4,80,2,19,4,4,13,11,2,9 -29,No,Travel_Rarely,991,Sales,5,3,Medical,1,1669,1,Male,43,2,2,Sales Executive,2,Divorced,4187,3356,1,Y,Yes,13,3,2,80,1,10,3,2,10,0,0,9 -33,No,Travel_Rarely,392,Sales,2,4,Medical,1,1670,4,Male,93,3,2,Sales Executive,4,Divorced,5505,3921,1,Y,No,14,3,3,80,2,6,5,3,6,2,0,4 -32,No,Travel_Rarely,977,Research & Development,2,3,Medical,1,1671,4,Male,45,3,2,Research Scientist,2,Divorced,5470,25518,0,Y,No,13,3,3,80,2,10,4,2,9,5,1,6 -31,No,Travel_Rarely,1112,Sales,5,4,Life Sciences,1,1673,1,Female,67,3,2,Sales Executive,4,Married,5476,22589,1,Y,No,11,3,1,80,2,10,2,3,10,0,0,2 -49,No,Travel_Rarely,464,Research & Development,16,3,Medical,1,1674,4,Female,74,3,1,Laboratory Technician,1,Divorced,2587,24941,4,Y,Yes,16,3,2,80,1,17,2,2,2,2,2,2 -38,No,Travel_Frequently,148,Research & Development,2,3,Medical,1,1675,4,Female,42,2,1,Laboratory Technician,2,Single,2440,23826,1,Y,No,22,4,2,80,0,4,3,3,4,3,3,3 -47,No,Travel_Rarely,1225,Sales,2,4,Life Sciences,1,1676,2,Female,47,4,4,Manager,2,Divorced,15972,21086,6,Y,No,14,3,3,80,3,29,2,3,3,2,1,2 -49,No,Travel_Rarely,809,Research & Development,1,3,Life Sciences,1,1677,3,Male,36,3,4,Manager,3,Single,15379,22384,4,Y,No,14,3,1,80,0,23,2,3,8,7,0,0 -41,No,Travel_Rarely,1206,Sales,23,2,Life Sciences,1,1678,4,Male,80,3,3,Sales Executive,3,Single,7082,11591,3,Y,Yes,16,3,4,80,0,21,2,3,2,0,0,2 -20,No,Travel_Rarely,727,Sales,9,1,Life Sciences,1,1680,4,Male,54,3,1,Sales Representative,1,Single,2728,21082,1,Y,No,11,3,1,80,0,2,3,3,2,2,0,2 -33,No,Non-Travel,530,Sales,16,3,Life Sciences,1,1681,3,Female,36,3,2,Sales Executive,4,Divorced,5368,16130,1,Y,Yes,25,4,3,80,1,7,2,3,6,5,1,2 -36,No,Travel_Rarely,1351,Research & Development,26,4,Life Sciences,1,1682,1,Male,80,3,2,Healthcare Representative,3,Married,5347,7419,6,Y,No,14,3,2,80,2,10,2,2,3,2,0,2 -44,No,Travel_Rarely,528,Human Resources,1,3,Life Sciences,1,1683,3,Female,44,3,1,Human Resources,4,Divorced,3195,4167,4,Y,Yes,18,3,1,80,3,8,2,3,2,2,2,2 -23,Yes,Travel_Rarely,1320,Research & Development,8,1,Medical,1,1684,4,Male,93,2,1,Laboratory Technician,3,Single,3989,20586,1,Y,Yes,11,3,1,80,0,5,2,3,5,4,1,2 -38,No,Travel_Rarely,1495,Research & Development,4,2,Medical,1,1687,4,Female,87,3,1,Laboratory Technician,3,Married,3306,26176,7,Y,No,19,3,4,80,1,7,5,2,0,0,0,0 -53,No,Travel_Rarely,1395,Research & Development,24,4,Medical,1,1689,2,Male,48,4,3,Healthcare Representative,4,Married,7005,3458,3,Y,No,15,3,3,80,0,11,2,3,4,3,1,2 -48,Yes,Travel_Frequently,708,Sales,7,2,Medical,1,1691,4,Female,95,3,1,Sales Representative,3,Married,2655,11740,2,Y,Yes,11,3,3,80,2,19,3,3,9,7,7,7 -32,Yes,Travel_Rarely,1259,Research & Development,2,4,Life Sciences,1,1692,4,Male,95,3,1,Laboratory Technician,2,Single,1393,24852,1,Y,No,12,3,1,80,0,1,2,3,1,0,0,0 -26,No,Non-Travel,786,Research & Development,7,3,Medical,1,1693,4,Male,76,3,1,Laboratory Technician,4,Single,2570,11925,1,Y,No,20,4,3,80,0,7,5,3,7,7,5,7 -55,No,Travel_Rarely,1441,Research & Development,22,3,Technical Degree,1,1694,1,Male,94,2,1,Research Scientist,2,Divorced,3537,23737,5,Y,No,12,3,4,80,1,8,1,3,4,2,1,2 -34,No,Travel_Rarely,1157,Research & Development,5,2,Medical,1,1696,2,Male,57,2,2,Laboratory Technician,4,Married,3986,11912,1,Y,No,14,3,3,80,1,15,3,4,15,10,4,13 -60,No,Travel_Rarely,370,Research & Development,1,4,Medical,1,1697,3,Male,92,1,3,Healthcare Representative,4,Divorced,10883,20467,3,Y,No,20,4,3,80,1,19,2,4,1,0,0,0 -33,No,Travel_Rarely,267,Research & Development,21,3,Medical,1,1698,2,Male,79,4,1,Laboratory Technician,2,Married,2028,13637,1,Y,No,18,3,4,80,3,14,6,3,14,11,2,13 -37,No,Travel_Frequently,1278,Sales,1,4,Medical,1,1700,3,Male,31,1,2,Sales Executive,4,Divorced,9525,7677,1,Y,No,14,3,3,80,2,6,2,2,6,3,1,3 -34,No,Travel_Rarely,678,Research & Development,19,3,Life Sciences,1,1701,2,Female,35,2,1,Research Scientist,4,Married,2929,20338,1,Y,No,12,3,2,80,0,10,3,3,10,9,8,7 -23,Yes,Travel_Rarely,427,Sales,7,3,Life Sciences,1,1702,3,Male,99,3,1,Sales Representative,4,Divorced,2275,25103,1,Y,Yes,21,4,2,80,1,3,2,3,3,2,0,2 -44,No,Travel_Rarely,921,Research & Development,2,3,Life Sciences,1,1703,3,Female,96,4,3,Healthcare Representative,4,Married,7879,14810,1,Y,Yes,19,3,2,80,1,9,2,3,8,7,6,7 -35,No,Travel_Frequently,146,Research & Development,2,4,Medical,1,1704,1,Male,79,2,1,Research Scientist,4,Single,4930,13970,0,Y,Yes,14,3,3,80,0,6,2,4,5,4,1,4 -43,No,Travel_Rarely,1179,Sales,2,3,Medical,1,1706,4,Male,73,3,2,Sales Executive,4,Married,7847,6069,1,Y,Yes,17,3,1,80,1,10,3,3,10,9,8,8 -24,No,Travel_Rarely,581,Research & Development,9,3,Medical,1,1707,3,Male,62,4,1,Research Scientist,3,Married,4401,17616,1,Y,No,16,3,4,80,1,5,1,3,5,3,0,4 -41,No,Travel_Rarely,918,Sales,6,3,Marketing,1,1708,4,Male,35,3,3,Sales Executive,3,Single,9241,15869,1,Y,No,12,3,2,80,0,10,3,3,10,8,8,7 -29,No,Travel_Rarely,1082,Research & Development,9,4,Medical,1,1709,4,Female,43,3,1,Laboratory Technician,3,Married,2974,25412,9,Y,No,17,3,3,80,1,9,2,3,5,3,1,2 -36,No,Travel_Rarely,530,Sales,2,4,Life Sciences,1,1710,3,Female,51,3,2,Sales Representative,4,Single,4502,7439,3,Y,No,15,3,3,80,0,17,2,2,13,7,6,7 -45,No,Non-Travel,1238,Research & Development,1,1,Life Sciences,1,1712,3,Male,74,2,3,Healthcare Representative,3,Married,10748,3395,3,Y,No,23,4,4,80,1,25,3,2,23,15,14,4 -24,Yes,Travel_Rarely,240,Human Resources,22,1,Human Resources,1,1714,4,Male,58,1,1,Human Resources,3,Married,1555,11585,1,Y,No,11,3,3,80,1,1,2,3,1,0,0,0 -47,Yes,Travel_Frequently,1093,Sales,9,3,Life Sciences,1,1716,3,Male,82,1,4,Sales Executive,3,Married,12936,24164,7,Y,No,11,3,3,80,0,25,3,1,23,5,14,10 -26,No,Travel_Rarely,390,Research & Development,17,4,Medical,1,1718,4,Male,62,1,1,Laboratory Technician,3,Married,2305,6217,1,Y,No,15,3,3,80,3,3,3,4,3,2,0,2 -45,No,Travel_Rarely,1005,Research & Development,28,2,Technical Degree,1,1719,4,Female,48,2,4,Research Director,2,Single,16704,17119,1,Y,No,11,3,3,80,0,21,2,3,21,6,8,6 -32,No,Travel_Frequently,585,Research & Development,10,3,Life Sciences,1,1720,1,Male,56,3,1,Research Scientist,3,Married,3433,17360,6,Y,No,13,3,1,80,1,10,3,2,5,2,1,3 -31,No,Travel_Rarely,741,Research & Development,2,4,Life Sciences,1,1721,2,Male,69,3,1,Laboratory Technician,3,Married,3477,18103,1,Y,No,14,3,4,80,1,6,2,4,5,2,0,3 -41,No,Non-Travel,552,Human Resources,4,3,Human Resources,1,1722,3,Male,60,1,2,Human Resources,2,Married,6430,20794,6,Y,No,19,3,2,80,1,10,4,3,3,2,1,2 -40,No,Travel_Rarely,369,Research & Development,8,2,Life Sciences,1,1724,2,Female,92,3,2,Manufacturing Director,1,Married,6516,5041,2,Y,Yes,16,3,2,80,1,18,3,3,1,0,0,0 -24,No,Travel_Rarely,506,Research & Development,29,1,Medical,1,1725,2,Male,91,3,1,Laboratory Technician,1,Divorced,3907,3622,1,Y,No,13,3,2,80,3,6,2,4,6,2,1,2 -46,No,Travel_Rarely,717,Research & Development,13,4,Life Sciences,1,1727,3,Male,34,3,2,Healthcare Representative,2,Single,5562,9697,6,Y,No,14,3,4,80,0,19,3,3,10,7,0,9 -35,No,Travel_Rarely,1370,Research & Development,27,4,Life Sciences,1,1728,4,Male,49,3,2,Manufacturing Director,3,Married,6883,5151,2,Y,No,16,3,2,80,1,17,3,3,7,7,0,7 -30,No,Travel_Rarely,793,Research & Development,16,1,Life Sciences,1,1729,2,Male,33,3,1,Research Scientist,4,Married,2862,3811,1,Y,No,12,3,2,80,1,10,2,2,10,0,0,8 -47,No,Non-Travel,543,Sales,2,4,Marketing,1,1731,3,Male,87,3,2,Sales Executive,2,Married,4978,3536,7,Y,No,11,3,4,80,1,4,3,1,1,0,0,0 -46,No,Travel_Rarely,1277,Sales,2,3,Life Sciences,1,1732,3,Male,74,3,3,Sales Executive,4,Divorced,10368,5596,4,Y,Yes,12,3,2,80,1,13,5,2,10,6,0,3 -36,Yes,Travel_Rarely,1456,Sales,13,5,Marketing,1,1733,2,Male,96,2,2,Sales Executive,1,Divorced,6134,8658,5,Y,Yes,13,3,2,80,3,16,3,3,2,2,2,2 -32,Yes,Travel_Rarely,964,Sales,1,2,Life Sciences,1,1734,1,Male,34,1,2,Sales Executive,2,Single,6735,12147,6,Y,No,15,3,2,80,0,10,2,3,0,0,0,0 -23,No,Travel_Rarely,160,Research & Development,4,1,Medical,1,1735,3,Female,51,3,1,Laboratory Technician,2,Single,3295,12862,1,Y,No,13,3,3,80,0,3,3,1,3,2,1,2 -31,No,Travel_Frequently,163,Research & Development,24,1,Technical Degree,1,1736,4,Female,30,3,2,Manufacturing Director,4,Single,5238,6670,2,Y,No,20,4,4,80,0,9,3,2,5,4,1,4 -39,No,Non-Travel,792,Research & Development,1,3,Life Sciences,1,1737,4,Male,77,3,2,Laboratory Technician,4,Married,6472,8989,1,Y,Yes,15,3,4,80,1,9,2,3,9,8,5,8 -32,No,Travel_Rarely,371,Sales,19,3,Life Sciences,1,1739,4,Male,80,1,3,Sales Executive,3,Married,9610,3840,3,Y,No,13,3,3,80,1,10,2,1,4,3,0,2 -40,No,Travel_Rarely,611,Sales,7,4,Medical,1,1740,2,Male,88,3,5,Manager,2,Single,19833,4349,1,Y,No,14,3,2,80,0,21,3,2,21,8,12,8 -45,No,Travel_Rarely,176,Human Resources,4,3,Life Sciences,1,1744,3,Female,56,1,3,Human Resources,3,Married,9756,6595,4,Y,No,21,4,3,80,2,9,2,4,5,0,0,3 -30,No,Travel_Frequently,1312,Research & Development,2,4,Technical Degree,1,1745,4,Female,78,2,1,Research Scientist,1,Single,4968,26427,0,Y,No,16,3,4,80,0,10,2,3,9,7,0,7 -24,No,Travel_Frequently,897,Human Resources,10,3,Medical,1,1746,1,Male,59,3,1,Human Resources,4,Married,2145,2097,0,Y,No,14,3,4,80,1,3,2,3,2,2,2,1 -30,Yes,Travel_Frequently,600,Human Resources,8,3,Human Resources,1,1747,3,Female,66,2,1,Human Resources,4,Divorced,2180,9732,6,Y,No,11,3,3,80,1,6,0,2,4,2,1,2 -31,No,Travel_Rarely,1003,Sales,5,3,Technical Degree,1,1749,1,Male,51,3,2,Sales Executive,3,Married,8346,20943,1,Y,No,19,3,3,80,1,6,3,3,5,2,0,2 -27,No,Travel_Rarely,1054,Research & Development,8,3,Medical,1,1751,3,Female,67,3,1,Research Scientist,4,Single,3445,6152,1,Y,No,11,3,3,80,0,6,5,2,6,2,1,4 -29,Yes,Travel_Rarely,428,Sales,9,3,Marketing,1,1752,2,Female,52,1,1,Sales Representative,2,Single,2760,14630,1,Y,No,13,3,3,80,0,2,3,3,2,2,2,2 -29,No,Travel_Frequently,461,Research & Development,1,3,Life Sciences,1,1753,4,Male,70,4,2,Healthcare Representative,3,Single,6294,23060,8,Y,Yes,12,3,4,80,0,10,5,4,3,2,0,2 -30,No,Travel_Rarely,979,Sales,15,2,Marketing,1,1754,3,Male,94,2,3,Sales Executive,1,Divorced,7140,3088,2,Y,No,11,3,1,80,1,12,2,3,7,7,1,7 -34,No,Travel_Rarely,181,Research & Development,2,4,Medical,1,1755,4,Male,97,4,1,Research Scientist,4,Married,2932,5586,0,Y,Yes,14,3,1,80,3,6,3,3,5,0,1,2 -33,No,Non-Travel,1283,Sales,2,3,Marketing,1,1756,4,Female,62,3,2,Sales Executive,2,Single,5147,10697,8,Y,No,15,3,4,80,0,13,2,2,11,7,1,7 -49,No,Travel_Rarely,1313,Sales,11,4,Marketing,1,1757,4,Female,80,3,2,Sales Executive,4,Single,4507,8191,3,Y,No,12,3,3,80,0,8,1,4,5,1,0,4 -33,Yes,Travel_Rarely,211,Sales,16,3,Life Sciences,1,1758,1,Female,74,3,3,Sales Executive,1,Single,8564,10092,2,Y,Yes,20,4,3,80,0,11,2,2,0,0,0,0 -38,No,Travel_Frequently,594,Research & Development,2,2,Medical,1,1760,3,Female,75,2,1,Laboratory Technician,2,Married,2468,15963,4,Y,No,14,3,2,80,1,9,4,2,6,1,0,5 -31,Yes,Travel_Rarely,1079,Sales,16,4,Marketing,1,1761,1,Male,70,3,3,Sales Executive,3,Married,8161,19002,2,Y,No,13,3,1,80,3,10,2,3,1,0,0,0 -29,No,Travel_Rarely,590,Research & Development,4,3,Technical Degree,1,1762,4,Female,91,2,1,Research Scientist,1,Divorced,2109,10007,1,Y,No,13,3,3,80,1,1,2,3,1,0,0,0 -30,No,Travel_Rarely,305,Research & Development,16,3,Life Sciences,1,1763,3,Male,58,4,2,Healthcare Representative,3,Married,5294,9128,3,Y,No,16,3,3,80,1,10,3,3,7,0,1,7 -32,No,Non-Travel,953,Research & Development,5,4,Technical Degree,1,1764,2,Male,65,3,1,Research Scientist,2,Single,2718,17674,2,Y,No,14,3,2,80,0,12,3,3,7,7,0,7 -38,No,Travel_Rarely,833,Research & Development,18,3,Medical,1,1766,2,Male,60,1,2,Healthcare Representative,4,Married,5811,24539,3,Y,Yes,16,3,3,80,1,15,2,3,1,0,1,0 -43,Yes,Travel_Frequently,807,Research & Development,17,3,Technical Degree,1,1767,3,Male,38,2,1,Research Scientist,3,Married,2437,15587,9,Y,Yes,16,3,4,80,1,6,4,3,1,0,0,0 -42,No,Travel_Rarely,855,Research & Development,12,3,Medical,1,1768,2,Male,57,3,1,Laboratory Technician,2,Divorced,2766,8952,8,Y,No,22,4,2,80,3,7,6,2,5,3,0,4 -55,No,Travel_Rarely,478,Research & Development,2,3,Medical,1,1770,3,Male,60,2,5,Research Director,1,Married,19038,19805,8,Y,No,12,3,2,80,3,34,2,3,1,0,0,0 -33,No,Non-Travel,775,Research & Development,4,3,Technical Degree,1,1771,4,Male,90,3,2,Research Scientist,2,Divorced,3055,6194,5,Y,No,15,3,4,80,2,11,2,2,9,8,1,7 -41,No,Travel_Rarely,548,Research & Development,9,4,Life Sciences,1,1772,3,Male,94,3,1,Laboratory Technician,1,Divorced,2289,20520,1,Y,No,20,4,2,80,2,5,2,3,5,3,0,4 -34,No,Non-Travel,1375,Sales,10,3,Life Sciences,1,1774,4,Male,87,3,2,Sales Executive,3,Divorced,4001,12313,1,Y,Yes,14,3,3,80,1,15,3,3,15,14,0,7 -53,No,Non-Travel,661,Research & Development,1,4,Medical,1,1775,1,Female,60,2,4,Manufacturing Director,3,Married,12965,22308,4,Y,Yes,20,4,4,80,3,27,2,2,3,2,0,2 -43,No,Travel_Rarely,244,Human Resources,2,3,Life Sciences,1,1778,2,Male,97,3,1,Human Resources,4,Single,3539,5033,0,Y,No,13,3,2,80,0,10,5,3,9,7,1,8 -34,No,Travel_Rarely,511,Sales,3,2,Life Sciences,1,1779,4,Female,32,1,2,Sales Executive,4,Single,6029,25353,5,Y,No,12,3,1,80,0,6,3,3,2,2,2,2 -21,Yes,Travel_Rarely,337,Sales,7,1,Marketing,1,1780,2,Male,31,3,1,Sales Representative,2,Single,2679,4567,1,Y,No,13,3,2,80,0,1,3,3,1,0,1,0 -38,No,Travel_Rarely,1153,Research & Development,6,2,Other,1,1782,4,Female,40,2,1,Laboratory Technician,3,Married,3702,16376,1,Y,No,11,3,2,80,1,5,3,3,5,4,0,4 -22,Yes,Travel_Rarely,1294,Research & Development,8,1,Medical,1,1783,3,Female,79,3,1,Laboratory Technician,1,Married,2398,15999,1,Y,Yes,17,3,3,80,0,1,6,3,1,0,0,0 -31,No,Travel_Rarely,196,Sales,29,4,Marketing,1,1784,1,Female,91,2,2,Sales Executive,4,Married,5468,13402,1,Y,No,14,3,1,80,2,13,3,3,12,7,5,7 -51,No,Travel_Rarely,942,Research & Development,3,3,Technical Degree,1,1786,1,Female,53,3,3,Manager,3,Married,13116,22984,2,Y,No,11,3,4,80,0,15,2,3,2,2,2,2 -37,No,Travel_Rarely,589,Sales,9,2,Marketing,1,1787,2,Male,46,2,2,Sales Executive,2,Married,4189,8800,1,Y,No,14,3,1,80,2,5,2,3,5,2,0,3 -46,No,Travel_Rarely,734,Research & Development,2,4,Medical,1,1789,3,Male,46,3,5,Research Director,4,Divorced,19328,14218,7,Y,Yes,17,3,3,80,1,24,3,3,2,1,2,2 -36,No,Travel_Rarely,1383,Research & Development,10,3,Life Sciences,1,1790,4,Male,90,3,3,Healthcare Representative,1,Married,8321,25949,7,Y,Yes,13,3,4,80,1,15,1,3,12,8,5,7 -44,Yes,Travel_Frequently,429,Research & Development,1,2,Medical,1,1792,3,Male,99,3,1,Research Scientist,2,Divorced,2342,11092,1,Y,Yes,12,3,3,80,3,6,2,2,5,3,2,3 -37,No,Travel_Rarely,1239,Human Resources,8,2,Other,1,1794,3,Male,89,3,2,Human Resources,2,Divorced,4071,12832,2,Y,No,13,3,3,80,0,19,4,2,10,0,4,7 -35,Yes,Travel_Rarely,303,Sales,27,3,Life Sciences,1,1797,3,Male,84,3,2,Sales Executive,4,Single,5813,13492,1,Y,Yes,18,3,4,80,0,10,2,3,10,7,7,7 -33,No,Travel_Rarely,867,Research & Development,8,4,Life Sciences,1,1798,4,Male,90,4,1,Research Scientist,1,Married,3143,6076,6,Y,No,19,3,2,80,1,14,1,3,10,8,7,6 -28,No,Travel_Rarely,1181,Research & Development,1,3,Life Sciences,1,1799,3,Male,82,3,1,Research Scientist,4,Married,2044,5531,1,Y,No,11,3,3,80,1,5,6,4,5,3,0,3 -39,No,Travel_Rarely,1253,Research & Development,10,1,Medical,1,1800,3,Male,65,3,3,Research Director,3,Single,13464,7914,7,Y,No,21,4,3,80,0,9,3,3,4,3,2,2 -46,No,Non-Travel,849,Sales,26,2,Life Sciences,1,1801,2,Male,98,2,2,Sales Executive,2,Single,7991,25166,8,Y,No,15,3,3,80,0,6,3,3,2,2,2,2 -40,No,Travel_Rarely,616,Research & Development,2,2,Life Sciences,1,1802,3,Female,99,3,1,Laboratory Technician,1,Married,3377,25605,4,Y,No,17,3,4,80,1,7,5,2,4,3,0,2 -42,No,Travel_Rarely,1128,Research & Development,13,3,Medical,1,1803,2,Male,95,4,2,Healthcare Representative,1,Married,5538,5696,5,Y,No,18,3,3,80,2,10,2,2,0,0,0,0 -35,No,Non-Travel,1180,Research & Development,2,2,Medical,1,1804,2,Male,90,3,2,Manufacturing Director,4,Divorced,5762,24442,2,Y,No,14,3,3,80,1,15,6,3,7,7,1,7 -38,No,Non-Travel,1336,Human Resources,2,3,Human Resources,1,1805,1,Male,100,3,1,Human Resources,2,Divorced,2592,7129,5,Y,No,13,3,4,80,3,13,3,3,11,10,3,8 -34,Yes,Travel_Frequently,234,Research & Development,9,4,Life Sciences,1,1807,4,Male,93,3,2,Laboratory Technician,1,Married,5346,6208,4,Y,No,17,3,3,80,1,11,3,2,7,1,0,7 -37,Yes,Travel_Rarely,370,Research & Development,10,4,Medical,1,1809,4,Male,58,3,2,Manufacturing Director,1,Single,4213,4992,1,Y,No,15,3,2,80,0,10,4,1,10,3,0,8 -39,No,Travel_Frequently,766,Sales,20,3,Life Sciences,1,1812,3,Male,83,3,2,Sales Executive,4,Divorced,4127,19188,2,Y,No,18,3,4,80,1,7,6,3,2,1,2,2 -43,No,Non-Travel,343,Research & Development,9,3,Life Sciences,1,1813,1,Male,52,3,1,Research Scientist,3,Single,2438,24978,4,Y,No,13,3,3,80,0,7,2,2,3,2,1,2 -41,No,Travel_Rarely,447,Research & Development,5,3,Life Sciences,1,1814,2,Male,85,4,2,Healthcare Representative,2,Single,6870,15530,3,Y,No,12,3,1,80,0,11,3,1,3,2,1,2 -41,No,Travel_Rarely,796,Sales,4,1,Marketing,1,1815,3,Female,81,3,3,Sales Executive,3,Divorced,10447,26458,0,Y,Yes,13,3,4,80,1,23,3,4,22,14,13,5 -30,No,Travel_Rarely,1092,Research & Development,10,3,Medical,1,1816,1,Female,64,3,3,Manufacturing Director,3,Single,9667,2739,9,Y,No,14,3,2,80,0,9,3,3,7,7,0,2 -26,Yes,Travel_Rarely,920,Human Resources,20,2,Medical,1,1818,4,Female,69,3,1,Human Resources,2,Married,2148,6889,0,Y,Yes,11,3,3,80,0,6,3,3,5,1,1,4 -46,Yes,Travel_Rarely,261,Research & Development,21,2,Medical,1,1821,4,Female,66,3,2,Healthcare Representative,2,Married,8926,10842,4,Y,No,22,4,4,80,1,13,2,4,9,7,3,7 -40,No,Travel_Rarely,1194,Research & Development,1,3,Life Sciences,1,1822,3,Female,52,3,2,Healthcare Representative,4,Divorced,6513,9060,4,Y,No,17,3,4,80,1,12,3,3,5,3,0,3 -34,No,Travel_Rarely,810,Sales,8,2,Technical Degree,1,1823,2,Male,92,4,2,Sales Executive,3,Married,6799,22128,1,Y,No,21,4,3,80,2,10,5,3,10,8,4,8 -58,No,Non-Travel,350,Sales,2,3,Medical,1,1824,2,Male,52,3,4,Manager,2,Divorced,16291,22577,4,Y,No,22,4,4,80,1,37,0,2,16,9,14,14 -35,No,Travel_Rarely,185,Research & Development,23,4,Medical,1,1826,2,Male,91,1,1,Laboratory Technician,3,Married,2705,9696,0,Y,No,16,3,2,80,1,6,2,4,5,4,0,3 -47,No,Travel_Rarely,1001,Research & Development,4,3,Life Sciences,1,1827,3,Female,92,2,3,Manufacturing Director,2,Divorced,10333,19271,8,Y,Yes,12,3,3,80,1,28,4,3,22,11,14,10 -40,No,Travel_Rarely,750,Research & Development,12,3,Life Sciences,1,1829,2,Female,47,3,2,Healthcare Representative,1,Divorced,4448,10748,2,Y,No,12,3,2,80,1,15,3,3,7,4,7,7 -54,No,Travel_Rarely,431,Research & Development,7,4,Medical,1,1830,4,Female,68,3,2,Research Scientist,4,Married,6854,15696,4,Y,No,15,3,2,80,1,14,2,2,7,1,1,7 -31,No,Travel_Frequently,1125,Sales,7,4,Marketing,1,1833,1,Female,68,3,3,Sales Executive,1,Married,9637,8277,2,Y,No,14,3,4,80,2,9,3,3,3,2,2,2 -28,No,Travel_Rarely,1217,Research & Development,1,3,Medical,1,1834,3,Female,67,3,1,Research Scientist,1,Married,3591,12719,1,Y,No,25,4,3,80,1,3,3,3,3,2,1,2 -38,No,Travel_Rarely,723,Sales,2,4,Marketing,1,1835,2,Female,77,1,2,Sales Representative,4,Married,5405,4244,2,Y,Yes,20,4,1,80,2,20,4,2,4,2,0,3 -26,No,Travel_Rarely,572,Sales,10,3,Medical,1,1836,3,Male,46,3,2,Sales Executive,4,Single,4684,9125,1,Y,No,13,3,1,80,0,5,4,3,5,3,1,2 -58,No,Travel_Frequently,1216,Research & Development,15,4,Life Sciences,1,1837,1,Male,87,3,4,Research Director,3,Married,15787,21624,2,Y,Yes,14,3,2,80,0,23,3,3,2,2,2,2 -18,No,Non-Travel,1431,Research & Development,14,3,Medical,1,1839,2,Female,33,3,1,Research Scientist,3,Single,1514,8018,1,Y,No,16,3,3,80,0,0,4,1,0,0,0,0 -31,Yes,Travel_Rarely,359,Human Resources,18,5,Human Resources,1,1842,4,Male,89,4,1,Human Resources,1,Married,2956,21495,0,Y,No,17,3,3,80,0,2,4,3,1,0,0,0 -29,Yes,Travel_Rarely,350,Human Resources,13,3,Human Resources,1,1844,1,Male,56,2,1,Human Resources,1,Divorced,2335,3157,4,Y,Yes,15,3,4,80,3,4,3,3,2,2,2,0 -45,No,Non-Travel,589,Sales,2,4,Life Sciences,1,1845,3,Female,67,3,2,Sales Executive,3,Married,5154,19665,4,Y,No,22,4,2,80,2,10,3,4,8,7,5,7 -36,No,Travel_Rarely,430,Research & Development,2,4,Other,1,1847,4,Female,73,3,2,Research Scientist,2,Married,6962,19573,4,Y,Yes,22,4,4,80,1,15,2,3,1,0,0,0 -43,No,Travel_Frequently,1422,Sales,2,4,Life Sciences,1,1849,1,Male,92,3,2,Sales Executive,4,Married,5675,19246,1,Y,No,20,4,3,80,1,7,5,3,7,7,7,7 -27,No,Travel_Frequently,1297,Research & Development,5,2,Life Sciences,1,1850,4,Female,53,3,1,Laboratory Technician,4,Single,2379,19826,0,Y,Yes,14,3,3,80,0,6,3,2,5,4,0,2 -29,No,Travel_Frequently,574,Research & Development,20,1,Medical,1,1852,4,Male,40,3,1,Laboratory Technician,4,Married,3812,7003,1,Y,No,13,3,2,80,0,11,3,4,11,8,3,10 -32,No,Travel_Frequently,1318,Sales,10,4,Marketing,1,1853,4,Male,79,3,2,Sales Executive,4,Single,4648,26075,8,Y,No,13,3,3,80,0,4,2,4,0,0,0,0 -42,No,Non-Travel,355,Research & Development,10,4,Technical Degree,1,1854,3,Male,38,3,1,Research Scientist,3,Married,2936,6161,3,Y,No,22,4,2,80,2,10,1,2,6,3,3,3 -47,No,Travel_Rarely,207,Research & Development,9,4,Life Sciences,1,1856,2,Female,64,3,1,Laboratory Technician,3,Single,2105,5411,4,Y,No,12,3,3,80,0,7,2,3,2,2,2,0 -46,No,Travel_Rarely,706,Research & Development,2,2,Life Sciences,1,1857,4,Male,82,3,3,Manufacturing Director,4,Divorced,8578,19989,3,Y,No,14,3,3,80,1,12,4,2,9,8,4,7 -28,No,Non-Travel,280,Human Resources,1,2,Life Sciences,1,1858,3,Male,43,3,1,Human Resources,4,Divorced,2706,10494,1,Y,No,15,3,2,80,1,3,2,3,3,2,2,2 -29,No,Travel_Rarely,726,Research & Development,29,1,Life Sciences,1,1859,4,Male,93,1,2,Healthcare Representative,3,Divorced,6384,21143,8,Y,No,17,3,4,80,2,11,3,3,7,0,1,6 -42,No,Travel_Rarely,1142,Research & Development,8,3,Life Sciences,1,1860,4,Male,81,3,1,Laboratory Technician,3,Single,3968,13624,4,Y,No,13,3,4,80,0,8,3,3,0,0,0,0 -32,Yes,Travel_Rarely,414,Sales,2,4,Marketing,1,1862,3,Male,82,2,2,Sales Executive,2,Single,9907,26186,7,Y,Yes,12,3,3,80,0,7,3,2,2,2,2,2 -46,No,Travel_Rarely,1319,Sales,3,3,Technical Degree,1,1863,1,Female,45,4,4,Sales Executive,1,Divorced,13225,7739,2,Y,No,12,3,4,80,1,25,5,3,19,17,2,8 -27,No,Travel_Rarely,728,Sales,23,1,Medical,1,1864,2,Female,36,2,2,Sales Representative,3,Married,3540,7018,1,Y,No,21,4,4,80,1,9,5,3,9,8,5,8 -29,No,Travel_Rarely,352,Human Resources,6,1,Medical,1,1865,4,Male,87,2,1,Human Resources,2,Married,2804,15434,1,Y,No,11,3,4,80,0,1,3,3,1,0,0,0 -43,No,Travel_Rarely,823,Research & Development,6,3,Medical,1,1866,1,Female,81,2,5,Manager,3,Married,19392,22539,7,Y,No,13,3,4,80,0,21,2,3,16,12,6,14 -48,No,Travel_Rarely,1224,Research & Development,10,3,Life Sciences,1,1867,4,Male,91,2,5,Research Director,2,Married,19665,13583,4,Y,No,12,3,4,80,0,29,3,3,22,10,12,9 -29,Yes,Travel_Frequently,459,Research & Development,24,2,Life Sciences,1,1868,4,Male,73,2,1,Research Scientist,4,Single,2439,14753,1,Y,Yes,24,4,2,80,0,1,3,2,1,0,1,0 -46,Yes,Travel_Rarely,1254,Sales,10,3,Life Sciences,1,1869,3,Female,64,3,3,Sales Executive,2,Married,7314,14011,5,Y,No,21,4,3,80,3,14,2,3,8,7,0,7 -27,No,Travel_Frequently,1131,Research & Development,15,3,Life Sciences,1,1870,4,Female,77,2,1,Research Scientist,1,Married,4774,23844,0,Y,No,19,3,4,80,1,8,2,2,7,6,7,3 -39,No,Travel_Rarely,835,Research & Development,19,4,Other,1,1871,4,Male,41,3,2,Research Scientist,4,Divorced,3902,5141,8,Y,No,14,3,2,80,3,7,2,3,2,2,2,2 -55,No,Travel_Rarely,836,Research & Development,2,4,Technical Degree,1,1873,2,Male,98,2,1,Research Scientist,4,Married,2662,7975,8,Y,No,20,4,2,80,1,19,2,4,5,2,0,4 -28,No,Travel_Rarely,1172,Sales,3,3,Medical,1,1875,2,Female,78,3,1,Sales Representative,2,Married,2856,3692,1,Y,No,19,3,4,80,1,1,3,3,1,0,0,0 -30,Yes,Travel_Rarely,945,Sales,9,3,Medical,1,1876,2,Male,89,3,1,Sales Representative,4,Single,1081,16019,1,Y,No,13,3,3,80,0,1,3,2,1,0,0,0 -22,Yes,Travel_Rarely,391,Research & Development,7,1,Life Sciences,1,1878,4,Male,75,3,1,Research Scientist,2,Single,2472,26092,1,Y,Yes,23,4,1,80,0,1,2,3,1,0,0,0 -36,No,Travel_Rarely,1266,Sales,10,4,Technical Degree,1,1880,2,Female,63,2,2,Sales Executive,3,Married,5673,6060,1,Y,Yes,13,3,1,80,1,10,4,3,10,9,1,7 -31,No,Travel_Rarely,311,Research & Development,20,3,Life Sciences,1,1881,2,Male,89,3,2,Laboratory Technician,3,Divorced,4197,18624,1,Y,No,11,3,1,80,1,10,2,3,10,8,0,2 -34,No,Travel_Rarely,1480,Sales,4,3,Life Sciences,1,1882,3,Male,64,3,3,Sales Executive,4,Married,9713,24444,2,Y,Yes,13,3,4,80,3,9,3,3,5,3,1,0 -29,No,Travel_Rarely,592,Research & Development,7,3,Life Sciences,1,1883,4,Male,59,3,1,Laboratory Technician,1,Single,2062,19384,3,Y,No,14,3,2,80,0,11,2,3,3,2,1,2 -37,No,Travel_Rarely,783,Research & Development,7,4,Medical,1,1885,4,Male,78,3,2,Research Scientist,1,Married,4284,13588,5,Y,Yes,22,4,3,80,1,16,2,3,5,3,0,4 -35,No,Travel_Rarely,219,Research & Development,16,2,Other,1,1886,4,Female,44,2,2,Manufacturing Director,2,Married,4788,25388,0,Y,Yes,11,3,4,80,0,4,2,3,3,2,0,2 -45,No,Travel_Rarely,556,Research & Development,25,2,Life Sciences,1,1888,2,Female,93,2,2,Manufacturing Director,4,Married,5906,23888,0,Y,No,13,3,4,80,2,10,2,2,9,8,3,8 -36,No,Travel_Frequently,1213,Human Resources,2,1,Human Resources,1,1890,2,Male,94,2,2,Human Resources,4,Single,3886,4223,1,Y,No,21,4,4,80,0,10,2,2,10,1,0,8 -40,No,Travel_Rarely,1137,Research & Development,1,4,Life Sciences,1,1892,1,Male,98,3,4,Manager,1,Divorced,16823,18991,2,Y,No,11,3,1,80,1,22,3,3,19,7,11,16 -26,No,Travel_Rarely,482,Research & Development,1,2,Life Sciences,1,1893,2,Female,90,2,1,Research Scientist,3,Married,2933,14908,1,Y,Yes,13,3,3,80,1,1,3,2,1,0,1,0 -27,No,Travel_Rarely,511,Sales,2,2,Medical,1,1898,1,Female,89,4,2,Sales Executive,3,Single,6500,26997,0,Y,No,14,3,2,80,0,9,5,2,8,7,0,7 -48,No,Travel_Frequently,117,Research & Development,22,3,Medical,1,1900,4,Female,58,3,4,Manager,4,Divorced,17174,2437,3,Y,No,11,3,2,80,1,24,3,3,22,17,4,7 -44,No,Travel_Rarely,170,Research & Development,1,4,Life Sciences,1,1903,2,Male,78,4,2,Healthcare Representative,1,Married,5033,9364,2,Y,No,15,3,4,80,1,10,5,3,2,0,2,2 -34,Yes,Non-Travel,967,Research & Development,16,4,Technical Degree,1,1905,4,Male,85,1,1,Research Scientist,1,Married,2307,14460,1,Y,Yes,23,4,2,80,1,5,2,3,5,2,3,0 -56,Yes,Travel_Rarely,1162,Research & Development,24,2,Life Sciences,1,1907,1,Male,97,3,1,Laboratory Technician,4,Single,2587,10261,1,Y,No,16,3,4,80,0,5,3,3,4,2,1,0 -36,No,Travel_Rarely,335,Sales,17,2,Marketing,1,1908,3,Male,33,2,2,Sales Executive,2,Married,5507,16822,2,Y,No,16,3,3,80,2,12,1,1,4,2,1,3 -41,No,Travel_Rarely,337,Sales,8,3,Marketing,1,1909,3,Female,54,3,2,Sales Executive,2,Married,4393,26841,5,Y,No,21,4,3,80,1,14,3,3,5,4,1,4 -42,No,Travel_Rarely,1396,Research & Development,6,3,Medical,1,1911,3,Male,83,3,3,Research Director,1,Married,13348,14842,9,Y,No,13,3,2,80,1,18,3,4,13,7,5,7 -31,No,Travel_Rarely,1079,Sales,10,2,Medical,1,1912,3,Female,86,3,2,Sales Executive,4,Divorced,6583,20115,2,Y,Yes,11,3,4,80,1,8,2,3,5,2,1,4 -34,No,Travel_Rarely,735,Sales,3,1,Medical,1,1915,4,Female,75,2,2,Sales Executive,4,Married,8103,16495,3,Y,Yes,12,3,3,80,0,9,3,2,4,2,0,1 -31,No,Travel_Rarely,471,Research & Development,4,3,Medical,1,1916,1,Female,62,4,1,Laboratory Technician,3,Divorced,3978,16031,8,Y,No,12,3,2,80,1,4,0,2,2,2,2,2 -26,No,Travel_Frequently,1096,Research & Development,6,3,Other,1,1918,3,Male,61,4,1,Laboratory Technician,4,Married,2544,7102,0,Y,No,18,3,1,80,1,8,3,3,7,7,7,7 -45,No,Travel_Frequently,1297,Research & Development,1,4,Medical,1,1922,2,Male,44,3,2,Healthcare Representative,3,Single,5399,14511,4,Y,No,12,3,3,80,0,12,3,3,4,2,0,3 -33,No,Travel_Rarely,217,Sales,10,4,Marketing,1,1924,2,Male,43,3,2,Sales Executive,3,Single,5487,10410,1,Y,No,14,3,2,80,0,10,2,2,10,4,0,9 -28,No,Travel_Frequently,783,Sales,1,2,Life Sciences,1,1927,3,Male,42,2,2,Sales Executive,4,Married,6834,19255,1,Y,Yes,12,3,3,80,1,7,2,3,7,7,0,7 -29,Yes,Travel_Frequently,746,Sales,24,3,Technical Degree,1,1928,3,Male,45,4,1,Sales Representative,1,Single,1091,10642,1,Y,No,17,3,4,80,0,1,3,3,1,0,0,0 -39,No,Non-Travel,1251,Sales,21,4,Life Sciences,1,1929,1,Female,32,1,2,Sales Executive,3,Married,5736,3987,6,Y,No,19,3,3,80,1,10,1,3,3,2,1,2 -27,No,Travel_Rarely,1354,Research & Development,2,4,Technical Degree,1,1931,2,Male,41,3,1,Research Scientist,2,Married,2226,6073,1,Y,No,11,3,3,80,1,6,3,2,5,3,1,2 -34,No,Travel_Frequently,735,Research & Development,22,4,Other,1,1932,3,Male,86,2,2,Research Scientist,4,Married,5747,26496,1,Y,Yes,15,3,2,80,0,16,3,3,15,10,6,11 -28,Yes,Travel_Rarely,1475,Sales,13,2,Marketing,1,1933,4,Female,84,3,2,Sales Executive,3,Single,9854,23352,3,Y,Yes,11,3,4,80,0,6,0,3,2,0,2,2 -47,No,Non-Travel,1169,Research & Development,14,4,Technical Degree,1,1934,3,Male,64,3,2,Research Scientist,2,Married,5467,2125,8,Y,No,18,3,3,80,1,16,4,4,8,7,1,7 -56,No,Travel_Rarely,1443,Sales,11,5,Marketing,1,1935,4,Female,89,2,2,Sales Executive,1,Married,5380,20328,4,Y,No,16,3,3,80,1,6,3,3,0,0,0,0 -39,No,Travel_Rarely,867,Research & Development,9,2,Medical,1,1936,1,Male,87,3,2,Manufacturing Director,1,Married,5151,12315,1,Y,No,25,4,4,80,1,10,3,3,10,0,7,9 -38,No,Travel_Frequently,1394,Research & Development,8,3,Medical,1,1937,4,Female,58,2,2,Research Scientist,2,Divorced,2133,18115,1,Y,Yes,16,3,3,80,1,20,3,3,20,11,0,7 -58,No,Travel_Rarely,605,Sales,21,3,Life Sciences,1,1938,4,Female,72,3,4,Manager,4,Married,17875,11761,4,Y,Yes,13,3,3,80,1,29,2,2,1,0,0,0 -32,Yes,Travel_Frequently,238,Research & Development,5,2,Life Sciences,1,1939,1,Female,47,4,1,Research Scientist,3,Single,2432,15318,3,Y,Yes,14,3,1,80,0,8,2,3,4,1,0,3 -38,No,Travel_Rarely,1206,Research & Development,9,2,Life Sciences,1,1940,2,Male,71,3,1,Research Scientist,4,Divorced,4771,14293,2,Y,No,19,3,4,80,2,10,0,4,5,2,0,3 -49,No,Travel_Frequently,1064,Research & Development,2,1,Life Sciences,1,1941,2,Male,42,3,5,Research Director,4,Married,19161,13738,3,Y,No,15,3,4,80,0,28,3,3,5,4,4,3 -42,No,Travel_Rarely,419,Sales,12,4,Marketing,1,1943,2,Male,77,3,2,Sales Executive,4,Divorced,5087,2900,3,Y,Yes,12,3,3,80,2,14,4,3,0,0,0,0 -27,Yes,Travel_Frequently,1337,Human Resources,22,3,Human Resources,1,1944,1,Female,58,2,1,Human Resources,2,Married,2863,19555,1,Y,No,12,3,1,80,0,1,2,3,1,0,0,0 -35,No,Travel_Rarely,682,Sales,18,4,Medical,1,1945,2,Male,71,3,2,Sales Executive,1,Married,5561,15975,0,Y,No,16,3,4,80,1,6,2,1,5,3,0,4 -28,No,Non-Travel,1103,Research & Development,16,3,Medical,1,1947,3,Male,49,3,1,Research Scientist,3,Single,2144,2122,1,Y,No,14,3,3,80,0,5,3,2,5,3,1,4 -31,No,Non-Travel,976,Research & Development,3,2,Medical,1,1948,3,Male,48,3,1,Research Scientist,1,Divorced,3065,3995,1,Y,Yes,13,3,4,80,1,4,3,4,4,2,2,3 -36,No,Non-Travel,1351,Research & Development,9,4,Life Sciences,1,1949,1,Male,66,4,1,Laboratory Technician,2,Married,2810,9238,1,Y,No,22,4,2,80,0,5,3,3,5,4,0,2 -34,No,Travel_Rarely,937,Sales,1,3,Marketing,1,1950,1,Male,32,3,3,Sales Executive,4,Single,9888,6770,1,Y,No,21,4,1,80,0,14,3,2,14,8,2,1 -34,No,Travel_Rarely,1239,Sales,13,4,Medical,1,1951,4,Male,39,3,3,Sales Executive,3,Divorced,8628,22914,1,Y,No,18,3,3,80,1,9,2,2,8,7,1,1 -26,No,Travel_Rarely,157,Research & Development,1,3,Medical,1,1952,3,Male,95,3,1,Laboratory Technician,1,Single,2867,20006,0,Y,No,13,3,4,80,0,8,6,2,7,7,7,6 -29,No,Travel_Rarely,136,Research & Development,1,3,Life Sciences,1,1954,1,Male,89,3,2,Healthcare Representative,1,Married,5373,6225,0,Y,No,12,3,1,80,1,6,5,2,5,3,0,2 -32,No,Non-Travel,1146,Research & Development,15,4,Medical,1,1955,3,Female,34,3,2,Healthcare Representative,4,Divorced,6667,16542,5,Y,No,18,3,2,80,1,9,6,3,5,1,1,2 -31,No,Travel_Frequently,1125,Research & Development,1,3,Life Sciences,1,1956,4,Male,48,1,2,Research Scientist,1,Married,5003,5771,1,Y,No,21,4,2,80,0,10,6,3,10,8,8,7 -28,Yes,Travel_Rarely,1404,Research & Development,17,3,Technical Degree,1,1960,3,Male,32,2,1,Laboratory Technician,4,Divorced,2367,18779,5,Y,No,12,3,1,80,1,6,2,2,4,1,0,3 -38,No,Travel_Rarely,1404,Sales,1,3,Life Sciences,1,1961,1,Male,59,2,1,Sales Representative,1,Single,2858,11473,4,Y,No,14,3,1,80,0,20,3,2,1,0,0,0 -35,No,Travel_Rarely,1224,Sales,7,4,Life Sciences,1,1962,3,Female,55,3,2,Sales Executive,4,Married,5204,13586,1,Y,Yes,11,3,4,80,0,10,2,3,10,8,0,9 -27,No,Travel_Rarely,954,Sales,9,3,Marketing,1,1965,4,Male,44,3,2,Sales Executive,4,Single,4105,5099,1,Y,No,14,3,1,80,0,7,5,3,7,7,0,7 -32,No,Travel_Rarely,1373,Research & Development,5,4,Life Sciences,1,1966,4,Male,56,2,2,Manufacturing Director,4,Single,9679,10138,8,Y,No,24,4,2,80,0,8,1,3,1,0,0,0 -31,Yes,Travel_Frequently,754,Sales,26,4,Marketing,1,1967,1,Male,63,3,2,Sales Executive,4,Married,5617,21075,1,Y,Yes,11,3,3,80,0,10,4,3,10,7,0,8 -53,Yes,Travel_Rarely,1168,Sales,24,4,Life Sciences,1,1968,1,Male,66,3,3,Sales Executive,1,Single,10448,5843,6,Y,Yes,13,3,2,80,0,15,2,2,2,2,2,2 -54,No,Travel_Rarely,155,Research & Development,9,2,Life Sciences,1,1969,1,Female,67,3,2,Research Scientist,3,Married,2897,22474,3,Y,No,11,3,3,80,2,9,6,2,4,3,2,3 -33,No,Travel_Frequently,1303,Research & Development,7,2,Life Sciences,1,1970,4,Male,36,3,2,Healthcare Representative,3,Divorced,5968,18079,1,Y,No,20,4,3,80,3,9,2,3,9,7,2,8 -43,No,Travel_Rarely,574,Research & Development,11,3,Life Sciences,1,1971,1,Male,30,3,3,Healthcare Representative,3,Married,7510,16873,1,Y,No,17,3,2,80,1,10,1,3,10,9,0,9 -38,No,Travel_Frequently,1444,Human Resources,1,4,Other,1,1972,4,Male,88,3,1,Human Resources,2,Married,2991,5224,0,Y,Yes,11,3,2,80,1,7,2,3,6,2,1,2 -55,No,Travel_Rarely,189,Human Resources,26,4,Human Resources,1,1973,3,Male,71,4,5,Manager,2,Married,19636,25811,4,Y,Yes,18,3,1,80,1,35,0,3,10,9,1,4 -31,No,Travel_Rarely,1276,Research & Development,2,1,Medical,1,1974,4,Female,59,1,1,Laboratory Technician,4,Divorced,1129,17536,1,Y,Yes,11,3,3,80,3,1,4,3,1,0,0,0 -39,No,Travel_Rarely,119,Sales,15,4,Marketing,1,1975,2,Male,77,3,4,Sales Executive,1,Single,13341,25098,0,Y,No,12,3,1,80,0,21,3,3,20,8,11,10 -42,No,Non-Travel,335,Research & Development,23,2,Life Sciences,1,1976,4,Male,37,2,2,Research Scientist,3,Single,4332,14811,1,Y,No,12,3,4,80,0,20,2,3,20,9,3,7 -31,No,Non-Travel,697,Research & Development,10,3,Medical,1,1979,3,Female,40,3,3,Research Director,3,Married,11031,26862,4,Y,No,20,4,3,80,1,13,2,4,11,7,4,8 -54,No,Travel_Rarely,157,Research & Development,10,3,Medical,1,1980,3,Female,77,3,2,Manufacturing Director,1,Single,4440,25198,6,Y,Yes,19,3,4,80,0,9,3,3,5,2,1,4 -24,No,Travel_Rarely,771,Research & Development,1,2,Life Sciences,1,1981,2,Male,45,2,2,Healthcare Representative,3,Single,4617,14120,1,Y,No,12,3,2,80,0,4,2,2,4,3,1,2 -23,No,Travel_Rarely,571,Research & Development,12,2,Other,1,1982,4,Male,78,3,1,Laboratory Technician,4,Single,2647,13672,1,Y,No,13,3,3,80,0,5,6,4,5,2,1,4 -40,No,Travel_Frequently,692,Research & Development,11,3,Technical Degree,1,1985,4,Female,73,3,2,Laboratory Technician,3,Married,6323,26849,1,Y,No,11,3,1,80,1,10,2,4,10,9,9,4 -40,No,Travel_Rarely,444,Sales,2,2,Marketing,1,1986,2,Female,92,3,2,Sales Executive,2,Married,5677,4258,3,Y,No,14,3,3,80,1,15,4,3,11,8,5,10 -25,No,Travel_Rarely,309,Human Resources,2,3,Human Resources,1,1987,3,Female,82,3,1,Human Resources,2,Married,2187,19655,4,Y,No,14,3,3,80,0,6,3,3,2,0,1,2 -30,No,Travel_Rarely,911,Research & Development,1,2,Medical,1,1989,4,Male,76,3,1,Laboratory Technician,2,Married,3748,4077,1,Y,No,13,3,3,80,0,12,6,2,12,8,1,7 -25,No,Travel_Rarely,977,Research & Development,2,1,Other,1,1992,4,Male,57,3,1,Laboratory Technician,3,Divorced,3977,7298,6,Y,Yes,19,3,3,80,1,7,2,2,2,2,0,2 -47,No,Travel_Rarely,1180,Research & Development,25,3,Medical,1,1993,1,Male,84,3,3,Healthcare Representative,3,Single,8633,13084,2,Y,No,23,4,2,80,0,25,3,3,17,14,12,11 -33,No,Non-Travel,1313,Research & Development,1,2,Medical,1,1994,2,Male,59,2,1,Laboratory Technician,3,Divorced,2008,20439,1,Y,No,12,3,3,80,3,1,2,2,1,1,0,0 -38,No,Travel_Rarely,1321,Sales,1,4,Life Sciences,1,1995,4,Male,86,3,2,Sales Executive,2,Married,4440,7636,0,Y,No,15,3,1,80,2,16,3,3,15,13,5,8 -31,No,Travel_Rarely,1154,Sales,2,2,Life Sciences,1,1996,1,Male,54,3,1,Sales Representative,3,Married,3067,6393,0,Y,No,19,3,3,80,1,3,1,3,2,2,1,2 -38,No,Travel_Frequently,508,Research & Development,6,4,Life Sciences,1,1997,1,Male,72,2,2,Manufacturing Director,3,Married,5321,14284,2,Y,No,11,3,4,80,1,10,1,3,8,3,7,7 -42,No,Travel_Rarely,557,Research & Development,18,4,Life Sciences,1,1998,4,Male,35,3,2,Research Scientist,1,Divorced,5410,11189,6,Y,Yes,17,3,3,80,1,9,3,2,4,3,1,2 -41,No,Travel_Rarely,642,Research & Development,1,3,Life Sciences,1,1999,4,Male,76,3,1,Research Scientist,4,Married,2782,21412,3,Y,No,22,4,1,80,1,12,3,3,5,3,1,0 -47,No,Non-Travel,1162,Research & Development,1,1,Medical,1,2000,3,Female,98,3,3,Research Director,2,Married,11957,17231,0,Y,No,18,3,1,80,2,14,3,1,13,8,5,12 -35,No,Travel_Rarely,1490,Research & Development,11,4,Medical,1,2003,4,Male,43,3,1,Laboratory Technician,3,Married,2660,20232,7,Y,Yes,11,3,3,80,1,5,3,3,2,2,2,2 -22,No,Travel_Rarely,581,Research & Development,1,2,Life Sciences,1,2007,4,Male,63,3,1,Research Scientist,3,Single,3375,17624,0,Y,No,12,3,4,80,0,4,2,4,3,2,1,2 -35,No,Travel_Rarely,1395,Research & Development,9,4,Medical,1,2008,2,Male,48,3,2,Research Scientist,3,Single,5098,18698,1,Y,No,19,3,2,80,0,10,5,3,10,7,0,8 -33,No,Travel_Rarely,501,Research & Development,15,2,Medical,1,2009,2,Female,95,3,2,Healthcare Representative,4,Married,4878,21653,0,Y,Yes,13,3,1,80,1,10,6,3,9,7,8,1 -32,No,Travel_Rarely,267,Research & Development,29,4,Life Sciences,1,2010,3,Female,49,2,1,Laboratory Technician,2,Single,2837,15919,1,Y,No,13,3,3,80,0,6,3,3,6,2,4,1 -40,No,Travel_Rarely,543,Research & Development,1,4,Life Sciences,1,2012,1,Male,83,3,1,Laboratory Technician,4,Married,2406,4060,8,Y,No,19,3,3,80,2,8,3,2,1,0,0,0 -32,No,Travel_Rarely,234,Sales,1,4,Medical,1,2013,2,Male,68,2,1,Sales Representative,2,Married,2269,18024,0,Y,No,14,3,2,80,1,3,2,3,2,2,2,2 -39,No,Travel_Rarely,116,Research & Development,24,1,Life Sciences,1,2014,1,Male,52,3,2,Research Scientist,4,Single,4108,5340,7,Y,No,13,3,1,80,0,18,2,3,7,7,1,7 -38,No,Travel_Rarely,201,Research & Development,10,3,Medical,1,2015,2,Female,99,1,3,Research Director,3,Married,13206,3376,3,Y,No,12,3,1,80,1,20,3,3,18,16,1,11 -32,No,Travel_Rarely,801,Sales,1,4,Marketing,1,2016,3,Female,48,3,3,Sales Executive,4,Married,10422,24032,1,Y,No,19,3,3,80,2,14,3,3,14,10,5,7 -37,No,Travel_Rarely,161,Research & Development,10,3,Life Sciences,1,2017,3,Female,42,4,3,Research Director,4,Married,13744,15471,1,Y,Yes,25,4,1,80,1,16,2,3,16,11,6,8 -25,No,Travel_Rarely,1382,Sales,8,2,Other,1,2018,1,Female,85,3,2,Sales Executive,3,Divorced,4907,13684,0,Y,Yes,22,4,2,80,1,6,3,2,5,3,0,4 -52,No,Non-Travel,585,Sales,29,4,Life Sciences,1,2019,1,Male,40,3,1,Sales Representative,4,Divorced,3482,19788,2,Y,No,15,3,2,80,2,16,3,2,9,8,0,0 -44,No,Travel_Rarely,1037,Research & Development,1,3,Medical,1,2020,2,Male,42,3,1,Research Scientist,4,Single,2436,13422,6,Y,Yes,12,3,3,80,0,6,2,3,4,3,1,2 -21,No,Travel_Rarely,501,Sales,5,1,Medical,1,2021,3,Male,58,3,1,Sales Representative,1,Single,2380,25479,1,Y,Yes,11,3,4,80,0,2,6,3,2,2,1,2 -39,No,Non-Travel,105,Research & Development,9,3,Life Sciences,1,2022,4,Male,87,3,5,Manager,4,Single,19431,15302,2,Y,No,13,3,3,80,0,21,3,2,6,0,1,3 -23,Yes,Travel_Frequently,638,Sales,9,3,Marketing,1,2023,4,Male,33,3,1,Sales Representative,1,Married,1790,26956,1,Y,No,19,3,1,80,1,1,3,2,1,0,1,0 -36,No,Travel_Rarely,557,Sales,3,3,Medical,1,2024,1,Female,94,2,3,Sales Executive,4,Married,7644,12695,0,Y,No,19,3,3,80,2,10,2,3,9,7,3,4 -36,No,Travel_Frequently,688,Research & Development,4,2,Life Sciences,1,2025,4,Female,97,3,2,Manufacturing Director,2,Divorced,5131,9192,7,Y,No,13,3,2,80,3,18,3,3,4,2,0,2 -56,No,Non-Travel,667,Research & Development,1,4,Life Sciences,1,2026,3,Male,57,3,2,Healthcare Representative,3,Divorced,6306,26236,1,Y,No,21,4,1,80,1,13,2,2,13,12,1,9 -29,Yes,Travel_Rarely,1092,Research & Development,1,4,Medical,1,2027,1,Male,36,3,1,Research Scientist,4,Married,4787,26124,9,Y,Yes,14,3,2,80,3,4,3,4,2,2,2,2 -42,No,Travel_Rarely,300,Research & Development,2,3,Life Sciences,1,2031,1,Male,56,3,5,Manager,3,Married,18880,17312,5,Y,No,11,3,1,80,0,24,2,2,22,6,4,14 -56,Yes,Travel_Rarely,310,Research & Development,7,2,Technical Degree,1,2032,4,Male,72,3,1,Laboratory Technician,3,Married,2339,3666,8,Y,No,11,3,4,80,1,14,4,1,10,9,9,8 -41,No,Travel_Rarely,582,Research & Development,28,4,Life Sciences,1,2034,1,Female,60,2,4,Manufacturing Director,2,Married,13570,5640,0,Y,No,23,4,3,80,1,21,3,3,20,7,0,10 -34,No,Travel_Rarely,704,Sales,28,3,Marketing,1,2035,4,Female,95,2,2,Sales Executive,3,Married,6712,8978,1,Y,No,21,4,4,80,2,8,2,3,8,7,1,7 -36,No,Non-Travel,301,Sales,15,4,Marketing,1,2036,4,Male,88,1,2,Sales Executive,4,Divorced,5406,10436,1,Y,No,24,4,1,80,1,15,4,2,15,12,11,11 -41,No,Travel_Rarely,930,Sales,3,3,Life Sciences,1,2037,3,Male,57,2,2,Sales Executive,2,Divorced,8938,12227,2,Y,No,11,3,3,80,1,14,5,3,5,4,0,4 -32,No,Travel_Rarely,529,Research & Development,2,3,Technical Degree,1,2038,4,Male,78,3,1,Research Scientist,1,Single,2439,11288,1,Y,No,14,3,4,80,0,4,4,3,4,2,1,2 -35,No,Travel_Rarely,1146,Human Resources,26,4,Life Sciences,1,2040,3,Female,31,3,3,Human Resources,4,Single,8837,16642,1,Y,Yes,16,3,3,80,0,9,2,3,9,0,1,7 -38,No,Travel_Rarely,345,Sales,10,2,Life Sciences,1,2041,1,Female,100,3,2,Sales Executive,4,Married,5343,5982,1,Y,No,11,3,3,80,1,10,1,3,10,7,1,9 -50,Yes,Travel_Frequently,878,Sales,1,4,Life Sciences,1,2044,2,Male,94,3,2,Sales Executive,3,Divorced,6728,14255,7,Y,No,12,3,4,80,2,12,3,3,6,3,0,1 -36,No,Travel_Rarely,1120,Sales,11,4,Marketing,1,2045,2,Female,100,2,2,Sales Executive,4,Married,6652,14369,4,Y,No,13,3,1,80,1,8,2,2,6,3,0,0 -45,No,Travel_Rarely,374,Sales,20,3,Life Sciences,1,2046,4,Female,50,3,2,Sales Executive,3,Single,4850,23333,8,Y,No,15,3,3,80,0,8,3,3,5,3,0,1 -40,No,Travel_Rarely,1322,Research & Development,2,4,Life Sciences,1,2048,3,Male,52,2,1,Research Scientist,3,Single,2809,2725,2,Y,No,14,3,4,80,0,8,2,3,2,2,2,2 -35,No,Travel_Frequently,1199,Research & Development,18,4,Life Sciences,1,2049,3,Male,80,3,2,Healthcare Representative,3,Married,5689,24594,1,Y,Yes,14,3,4,80,2,10,2,4,10,2,0,2 -40,No,Travel_Rarely,1194,Research & Development,2,4,Medical,1,2051,3,Female,98,3,1,Research Scientist,3,Married,2001,12549,2,Y,No,14,3,2,80,3,20,2,3,5,3,0,2 -35,No,Travel_Rarely,287,Research & Development,1,4,Life Sciences,1,2052,3,Female,62,1,1,Research Scientist,4,Married,2977,8952,1,Y,No,12,3,4,80,1,4,5,3,4,3,1,1 -29,No,Travel_Rarely,1378,Research & Development,13,2,Other,1,2053,4,Male,46,2,2,Laboratory Technician,2,Married,4025,23679,4,Y,Yes,13,3,1,80,1,10,2,3,4,3,0,3 -29,No,Travel_Rarely,468,Research & Development,28,4,Medical,1,2054,4,Female,73,2,1,Research Scientist,1,Single,3785,8489,1,Y,No,14,3,2,80,0,5,3,1,5,4,0,4 -50,Yes,Travel_Rarely,410,Sales,28,3,Marketing,1,2055,4,Male,39,2,3,Sales Executive,1,Divorced,10854,16586,4,Y,Yes,13,3,2,80,1,20,3,3,3,2,2,0 -39,No,Travel_Rarely,722,Sales,24,1,Marketing,1,2056,2,Female,60,2,4,Sales Executive,4,Married,12031,8828,0,Y,No,11,3,1,80,1,21,2,2,20,9,9,6 -31,No,Non-Travel,325,Research & Development,5,3,Medical,1,2057,2,Male,74,3,2,Manufacturing Director,1,Single,9936,3787,0,Y,No,19,3,2,80,0,10,2,3,9,4,1,7 -26,No,Travel_Rarely,1167,Sales,5,3,Other,1,2060,4,Female,30,2,1,Sales Representative,3,Single,2966,21378,0,Y,No,18,3,4,80,0,5,2,3,4,2,0,0 -36,No,Travel_Frequently,884,Research & Development,23,2,Medical,1,2061,3,Male,41,4,2,Laboratory Technician,4,Married,2571,12290,4,Y,No,17,3,3,80,1,17,3,3,5,2,0,3 -39,No,Travel_Rarely,613,Research & Development,6,1,Medical,1,2062,4,Male,42,2,3,Healthcare Representative,1,Married,9991,21457,4,Y,No,15,3,1,80,1,9,5,3,7,7,1,7 -27,No,Travel_Rarely,155,Research & Development,4,3,Life Sciences,1,2064,2,Male,87,4,2,Manufacturing Director,2,Married,6142,5174,1,Y,Yes,20,4,2,80,1,6,0,3,6,2,0,3 -49,No,Travel_Frequently,1023,Sales,2,3,Medical,1,2065,4,Male,63,2,2,Sales Executive,2,Married,5390,13243,2,Y,No,14,3,4,80,0,17,3,2,9,6,0,8 -34,No,Travel_Rarely,628,Research & Development,8,3,Medical,1,2068,2,Male,82,4,2,Laboratory Technician,3,Married,4404,10228,2,Y,No,12,3,1,80,0,6,3,4,4,3,1,2 From 8e355cc8969c6a2b7fc3c5c98f4301cc7aaff676 Mon Sep 17 00:00:00 2001 From: Abhishek Raut Date: Sun, 29 Aug 2021 07:47:36 +0530 Subject: [PATCH 3/4] Random Grid Search grid search --- 007/solution/IBM_HR.ipynb | 160 +++++++++++++++++++++++++------------- 1 file changed, 107 insertions(+), 53 deletions(-) diff --git a/007/solution/IBM_HR.ipynb b/007/solution/IBM_HR.ipynb index d77b5f34..5c7ed38d 100644 --- a/007/solution/IBM_HR.ipynb +++ b/007/solution/IBM_HR.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "be9dc5bb", + "id": "58b8d173", "metadata": {}, "source": [ "# IBM HR Analytics Employee Attrition & Performance\n", @@ -11,7 +11,7 @@ }, { "cell_type": "markdown", - "id": "3ab5962a", + "id": "6583191c", "metadata": {}, "source": [ "https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset" @@ -20,7 +20,7 @@ { "cell_type": "code", "execution_count": 115, - "id": "9c6b93b1", + "id": "eb2d8515", "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "ea28bfd0", + "id": "984c2c1d", "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "4d0e7f33", + "id": "eab63e4a", "metadata": {}, "source": [ "# EDA (Exploratory Data Analysis)" @@ -130,7 +130,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "7bd19822", + "id": "65602e41", "metadata": {}, "outputs": [ { @@ -168,7 +168,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "603c2166", + "id": "7f5efb2f", "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "5d978166", + "id": "29817817", "metadata": {}, "outputs": [ { @@ -649,7 +649,7 @@ { "cell_type": "code", "execution_count": 75, - "id": "61d8b64e", + "id": "9b90d068", "metadata": {}, "outputs": [], "source": [ @@ -658,7 +658,7 @@ }, { "cell_type": "markdown", - "id": "a41b4ff4", + "id": "8443eb80", "metadata": {}, "source": [ "# Pre-Processing (Encoding & Normalization)" @@ -667,7 +667,7 @@ { "cell_type": "code", "execution_count": 76, - "id": "f6b2fc8c", + "id": "0e737b53", "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ { "cell_type": "code", "execution_count": 77, - "id": "10888cd9", + "id": "7f6479b1", "metadata": {}, "outputs": [], "source": [ @@ -921,7 +921,7 @@ { "cell_type": "code", "execution_count": 78, - "id": "acec1805", + "id": "8f38caee", "metadata": {}, "outputs": [ { @@ -952,7 +952,7 @@ { "cell_type": "code", "execution_count": 79, - "id": "7d66745f", + "id": "c6222450", "metadata": {}, "outputs": [], "source": [ @@ -962,7 +962,7 @@ }, { "cell_type": "markdown", - "id": "f0789751", + "id": "aac6db42", "metadata": {}, "source": [ "# Feature Enginnering" @@ -971,7 +971,7 @@ { "cell_type": "code", "execution_count": 80, - "id": "514dbd2d", + "id": "63864d8c", "metadata": {}, "outputs": [ { @@ -1237,7 +1237,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "c60b78ba", + "id": "f6d7aa54", "metadata": {}, "outputs": [ { @@ -1635,7 +1635,7 @@ }, { "cell_type": "markdown", - "id": "93320780", + "id": "5487dc20", "metadata": {}, "source": [ "## Classification into Groups" @@ -1644,7 +1644,7 @@ { "cell_type": "code", "execution_count": 82, - "id": "28483d8c", + "id": "a371322c", "metadata": {}, "outputs": [ { @@ -1695,7 +1695,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "481fc5f0", + "id": "39e70f91", "metadata": {}, "outputs": [], "source": [ @@ -1711,7 +1711,7 @@ { "cell_type": "code", "execution_count": 84, - "id": "64216063", + "id": "9550a758", "metadata": {}, "outputs": [], "source": [ @@ -1728,7 +1728,7 @@ { "cell_type": "code", "execution_count": 85, - "id": "84549d94", + "id": "9070b8d4", "metadata": {}, "outputs": [], "source": [ @@ -1745,7 +1745,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "50692bb0", + "id": "630f9886", "metadata": {}, "outputs": [], "source": [ @@ -1763,7 +1763,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "ce265bd1", + "id": "2c1edb61", "metadata": {}, "outputs": [], "source": [ @@ -1781,7 +1781,7 @@ { "cell_type": "code", "execution_count": 88, - "id": "835b397e", + "id": "f177d30c", "metadata": {}, "outputs": [], "source": [ @@ -1799,7 +1799,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "a885232a", + "id": "026134f5", "metadata": {}, "outputs": [], "source": [ @@ -1817,7 +1817,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "7492ee20", + "id": "cc70d7ed", "metadata": {}, "outputs": [], "source": [ @@ -1835,7 +1835,7 @@ { "cell_type": "code", "execution_count": 91, - "id": "429c7dcd", + "id": "1a08672b", "metadata": {}, "outputs": [], "source": [ @@ -1853,7 +1853,7 @@ { "cell_type": "code", "execution_count": 92, - "id": "765001fc", + "id": "3dd6a4cd", "metadata": {}, "outputs": [], "source": [ @@ -1871,7 +1871,7 @@ { "cell_type": "code", "execution_count": 93, - "id": "745ee8cf", + "id": "f2f43308", "metadata": {}, "outputs": [ { @@ -1922,7 +1922,7 @@ { "cell_type": "code", "execution_count": 94, - "id": "17dff408", + "id": "9b959308", "metadata": {}, "outputs": [], "source": [ @@ -1933,7 +1933,7 @@ { "cell_type": "code", "execution_count": 107, - "id": "94f9caeb", + "id": "f13a1d75", "metadata": {}, "outputs": [ { @@ -1968,7 +1968,7 @@ { "cell_type": "code", "execution_count": 95, - "id": "7cdcf1e5", + "id": "12e62351", "metadata": {}, "outputs": [ { @@ -2005,7 +2005,7 @@ { "cell_type": "code", "execution_count": 96, - "id": "0c12289a", + "id": "b2dbed5a", "metadata": {}, "outputs": [ { @@ -2044,7 +2044,7 @@ { "cell_type": "code", "execution_count": 97, - "id": "d5e3e14b", + "id": "e3347235", "metadata": {}, "outputs": [ { @@ -2085,7 +2085,7 @@ { "cell_type": "code", "execution_count": 98, - "id": "38275bff", + "id": "b01b8dc4", "metadata": {}, "outputs": [ { @@ -2122,7 +2122,7 @@ { "cell_type": "code", "execution_count": 99, - "id": "10eab55d", + "id": "64cd318b", "metadata": {}, "outputs": [ { @@ -2157,7 +2157,7 @@ { "cell_type": "code", "execution_count": 100, - "id": "74280bf0", + "id": "3fb9cdd2", "metadata": {}, "outputs": [ { @@ -2189,7 +2189,7 @@ { "cell_type": "code", "execution_count": 101, - "id": "3ec57269", + "id": "11c1ad66", "metadata": {}, "outputs": [ { @@ -2220,7 +2220,7 @@ { "cell_type": "code", "execution_count": 102, - "id": "c18b69cf", + "id": "c465cf0b", "metadata": {}, "outputs": [ { @@ -2257,7 +2257,7 @@ { "cell_type": "code", "execution_count": 108, - "id": "a5812c17", + "id": "312ba9b8", "metadata": {}, "outputs": [ { @@ -2294,7 +2294,7 @@ { "cell_type": "code", "execution_count": 103, - "id": "444411e6", + "id": "aeb58e52", "metadata": {}, "outputs": [ { @@ -2329,7 +2329,7 @@ { "cell_type": "code", "execution_count": 104, - "id": "09ae2655", + "id": "a5f1e4af", "metadata": {}, "outputs": [ { @@ -2360,7 +2360,7 @@ { "cell_type": "code", "execution_count": 105, - "id": "456cb596", + "id": "dd32ed40", "metadata": {}, "outputs": [ { @@ -2397,7 +2397,7 @@ { "cell_type": "code", "execution_count": 106, - "id": "f3d62b84", + "id": "437bda25", "metadata": {}, "outputs": [ { @@ -2434,7 +2434,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ccb1fb94", + "id": "436105ac", "metadata": {}, "outputs": [], "source": [ @@ -2445,7 +2445,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ccdb3dab", + "id": "00863846", "metadata": {}, "outputs": [], "source": [ @@ -2458,7 +2458,7 @@ { "cell_type": "code", "execution_count": 116, - "id": "102deb16", + "id": "0e7b28fb", "metadata": {}, "outputs": [ { @@ -2727,10 +2727,64 @@ " depth=4, l2_leaf_reg= 4, iterations=800, learning_rate= 0.036)\n" ] }, + { + "cell_type": "code", + "execution_count": 142, + "id": "5971f8df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subsample': 0.8,\n", + " 'reg_lambda': 1,\n", + " 'reg_alpha': 2,\n", + " 'n_estimators': 300,\n", + " 'min_child_weight': 1,\n", + " 'max_depth': 6,\n", + " 'learning_rate': 0.1,\n", + " 'gamma': 1.5,\n", + " 'colsample_bytree': 0.6}" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import RandomizedSearchCV\n", + "import random\n", + "\n", + "\n", + "# model_selection.GridSearchCV(estimator, …)\n", + "# model_selection.HalvingGridSearchCV(…[, …])\n", + "# model_selection.ParameterGrid(param_grid)\n", + "# model_selection.ParameterSampler(…[, …])\n", + "# model_selection.RandomizedSearchCV(…[, …])\n", + "# model_selection.HalvingRandomSearchCV(…[, …])\n", + "\n", + "param_dist = {\n", + " 'min_child_weight': [0.1, 0.5,1],\n", + " 'gamma': [0.5, 1, 1.5, 2, 5],\n", + " 'subsample': [1.2, 0.8, 1.0],\n", + " 'colsample_bytree': [0.6, 0.8, 1.0],\n", + " 'max_depth': [4,5,6],\n", + " 'learning_rate': [0.1, 1,1.5,3],\n", + " 'n_estimators': [350, 250, 300],\n", + " 'reg_alpha': [ 2, 1,3],\n", + " 'reg_lambda': [ 2, 1,1.5]\n", + "}\n", + "clf = xgboost.XGBClassifier()\n", + "rsh = RandomizedSearchCV(estimator=clf, param_distributions=param_dist)\n", + "rsh.fit(X, y)\n", + "rsh.best_params_" + ] + }, { "cell_type": "code", "execution_count": 122, - "id": "f90a39fe", + "id": "a31efaab", "metadata": {}, "outputs": [ { @@ -2763,7 +2817,7 @@ { "cell_type": "code", "execution_count": 123, - "id": "36928172", + "id": "996aab51", "metadata": {}, "outputs": [ { @@ -2823,7 +2877,7 @@ { "cell_type": "code", "execution_count": 125, - "id": "b407a643", + "id": "435dd2be", "metadata": {}, "outputs": [ { @@ -2855,7 +2909,7 @@ }, { "cell_type": "markdown", - "id": "7a64dfc2", + "id": "4f023d9b", "metadata": {}, "source": [ "# Key Features\n", @@ -2899,7 +2953,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e3df1df6", + "id": "139a5684", "metadata": {}, "outputs": [], "source": [] From 6e3f438fa884c685bd6340dd7b70e3ae1a73ce43 Mon Sep 17 00:00:00 2001 From: Abhishek Raut Date: Sun, 29 Aug 2021 08:24:03 +0530 Subject: [PATCH 4/4] Documentation titles headlines sections readme --- 007/solution/IBM_HR.ipynb | 469 ++-- .../IBM_HR_Employee_Attrition_Profil.html | 2300 ++++++++--------- .../catboost_info/catboost_training.json | 1600 ++++++------ .../catboost_info/learn/events.out.tfevents | Bin 43870 -> 43870 bytes 007/solution/catboost_info/learn_error.tsv | 1600 ++++++------ 007/solution/catboost_info/time_left.tsv | 1576 +++++------ 007/solution/readme.md | 41 +- 7 files changed, 3815 insertions(+), 3771 deletions(-) diff --git a/007/solution/IBM_HR.ipynb b/007/solution/IBM_HR.ipynb index 5c7ed38d..5b342d4b 100644 --- a/007/solution/IBM_HR.ipynb +++ b/007/solution/IBM_HR.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "58b8d173", + "id": "18dba6f5", "metadata": {}, "source": [ "# IBM HR Analytics Employee Attrition & Performance\n", @@ -11,7 +11,7 @@ }, { "cell_type": "markdown", - "id": "6583191c", + "id": "d6371c2d", "metadata": {}, "source": [ "https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset" @@ -19,8 +19,8 @@ }, { "cell_type": "code", - "execution_count": 115, - "id": "eb2d8515", + "execution_count": 157, + "id": "f67961e4", "metadata": {}, "outputs": [], "source": [ @@ -110,8 +110,8 @@ }, { "cell_type": "code", - "execution_count": 73, - "id": "984c2c1d", + "execution_count": 158, + "id": "a82a5479", "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "markdown", - "id": "eab63e4a", + "id": "0bd17b48", "metadata": {}, "source": [ "# EDA (Exploratory Data Analysis)" @@ -129,14 +129,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "65602e41", + "execution_count": 159, + "id": "dc408e53", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82686f5e9dbe4a32af50251b25a60b6d", + "model_id": "7ea8e8d331604637ac4dcc16429cee2f", "version_major": 2, "version_minor": 0 }, @@ -167,19 +167,19 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "7f5efb2f", + "execution_count": 160, + "id": "a670b7b1", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85e9b39633f74187a4e9403976a1e967", + "model_id": "e46cdcad899a40d9865f3e3f9811034e", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Summarize dataset: 0%| | 0/43 [00:00" + "
" ] }, "metadata": {}, @@ -951,8 +951,8 @@ }, { "cell_type": "code", - "execution_count": 79, - "id": "c6222450", + "execution_count": 166, + "id": "9ec9eb4d", "metadata": {}, "outputs": [], "source": [ @@ -962,7 +962,7 @@ }, { "cell_type": "markdown", - "id": "aac6db42", + "id": "dcaafb25", "metadata": {}, "source": [ "# Feature Enginnering" @@ -970,8 +970,8 @@ }, { "cell_type": "code", - "execution_count": 80, - "id": "63864d8c", + "execution_count": 167, + "id": "5be5780a", "metadata": {}, "outputs": [ { @@ -1208,7 +1208,7 @@ "31 EmployeeCount 0.00" ] }, - "execution_count": 80, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -1236,8 +1236,8 @@ }, { "cell_type": "code", - "execution_count": 81, - "id": "f6d7aa54", + "execution_count": 168, + "id": "f1b9f9d3", "metadata": {}, "outputs": [ { @@ -1623,7 +1623,7 @@ "[1470 rows x 32 columns]" ] }, - "execution_count": 81, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -1635,7 +1635,7 @@ }, { "cell_type": "markdown", - "id": "5487dc20", + "id": "fb9399bd", "metadata": {}, "source": [ "## Classification into Groups" @@ -1643,8 +1643,8 @@ }, { "cell_type": "code", - "execution_count": 82, - "id": "a371322c", + "execution_count": 169, + "id": "35eb39d1", "metadata": {}, "outputs": [ { @@ -1694,8 +1694,8 @@ }, { "cell_type": "code", - "execution_count": 83, - "id": "39e70f91", + "execution_count": 170, + "id": "126608d3", "metadata": {}, "outputs": [], "source": [ @@ -1710,8 +1710,8 @@ }, { "cell_type": "code", - "execution_count": 84, - "id": "9550a758", + "execution_count": 171, + "id": "e4e760ae", "metadata": {}, "outputs": [], "source": [ @@ -1727,8 +1727,8 @@ }, { "cell_type": "code", - "execution_count": 85, - "id": "9070b8d4", + "execution_count": 172, + "id": "0aad681f", "metadata": {}, "outputs": [], "source": [ @@ -1744,8 +1744,8 @@ }, { "cell_type": "code", - "execution_count": 86, - "id": "630f9886", + "execution_count": 173, + "id": "0e129e26", "metadata": {}, "outputs": [], "source": [ @@ -1762,8 +1762,8 @@ }, { "cell_type": "code", - "execution_count": 87, - "id": "2c1edb61", + "execution_count": 174, + "id": "2bedd67f", "metadata": {}, "outputs": [], "source": [ @@ -1780,8 +1780,8 @@ }, { "cell_type": "code", - "execution_count": 88, - "id": "f177d30c", + "execution_count": 175, + "id": "570a968f", "metadata": {}, "outputs": [], "source": [ @@ -1798,8 +1798,8 @@ }, { "cell_type": "code", - "execution_count": 89, - "id": "026134f5", + "execution_count": 176, + "id": "0be404ca", "metadata": {}, "outputs": [], "source": [ @@ -1816,8 +1816,8 @@ }, { "cell_type": "code", - "execution_count": 90, - "id": "cc70d7ed", + "execution_count": 177, + "id": "18762dcf", "metadata": {}, "outputs": [], "source": [ @@ -1834,8 +1834,8 @@ }, { "cell_type": "code", - "execution_count": 91, - "id": "1a08672b", + "execution_count": 178, + "id": "6a2d733a", "metadata": {}, "outputs": [], "source": [ @@ -1852,8 +1852,8 @@ }, { "cell_type": "code", - "execution_count": 92, - "id": "3dd6a4cd", + "execution_count": 179, + "id": "8ec59f1d", "metadata": {}, "outputs": [], "source": [ @@ -1870,8 +1870,8 @@ }, { "cell_type": "code", - "execution_count": 93, - "id": "f2f43308", + "execution_count": 180, + "id": "6e35f71f", "metadata": {}, "outputs": [ { @@ -1921,8 +1921,8 @@ }, { "cell_type": "code", - "execution_count": 94, - "id": "9b959308", + "execution_count": 181, + "id": "f6854e96", "metadata": {}, "outputs": [], "source": [ @@ -1930,10 +1930,18 @@ "Train1[\"Output\"]=Y" ] }, + { + "cell_type": "markdown", + "id": "80e30f22", + "metadata": {}, + "source": [ + "# EDA with Features" + ] + }, { "cell_type": "code", - "execution_count": 107, - "id": "f13a1d75", + "execution_count": 182, + "id": "6c9af473", "metadata": {}, "outputs": [ { @@ -1942,15 +1950,15 @@ "" ] }, - "execution_count": 107, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -1967,8 +1975,8 @@ }, { "cell_type": "code", - "execution_count": 95, - "id": "12e62351", + "execution_count": 183, + "id": "1422c93b", "metadata": {}, "outputs": [ { @@ -1977,15 +1985,15 @@ "" ] }, - "execution_count": 95, + "execution_count": 183, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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3W8eOHVN9fb3Gjh1ramEAgODQpSOKnJwczZgxQ1lZWXI6nXr99deVm5url156yez6AAAB1qUjitOnTysrK0uSFBERoVmzZsnhcJhaGAAgOHT5YnZ9fb1vurGxUV6v17SiAADBo0unnmbNmqX09HTde++9slgsqqqqYggPAOgjuhQUU6dO1YgRI7R//36Fhobq0Ucf1e233252bQCAINCloJCkYcOGadiwYWbWAgAIQpf1PgoAQN9BUAAADJkaFBs3blRKSopSUlJUUFAgSaqqqlJqaqqSkpK0fv1637JHjx5VRkaGxo0bp2XLlqm9vd3M0gAAXWRaUFRVVemDDz5QcXGxdu7cqSNHjqisrEy5ubl67rnnVF5ersOHD6uyslKStGTJEq1YsUK7du2S1+tVUVGRWaUBALrBtKCwWq3KyclRv379FB4erltuuUU1NTWKjY3VkCFDFBYWptTUVFVUVKiurk5tbW2Kj4+XJGVkZKiiosKs0gAA3WBaUNx2222+L/6amhq9++67slgsslqtvmVsNpvq6+vV0NDQqd1qtXZ6wA8AEDhdvj32cn322WeaN2+eli5dqtDQUNXU1Pjmeb1eWSwWeTweWSyW89p7Iirq2h6t3xus1shAlxA02BfAlcvUoKiurtaiRYuUm5urlJQUHThwoNMYUQ6HQzabTdHR0Z3aGxsbZbPZetR3U1OrPJ7Ow4z4+8vK4Wjxa3/dwb4A8LWQEIvhj2vTTj2dPHlSCxYsUGFhoVJSUiRJd9xxh44fP67a2lp1dHSorKxMiYmJGjx4sCIiIlRdXS1JKikpUWJiolmlAQC6wbQjik2bNsnpdCo/P9/XNn36dOXn5ys7O1tOp1N2u13JycmSpMLCQuXl5am1tVVxcXG+0WoBAIFlWlDk5eUpLy/vgvNKS0vPaxs2bJjeeusts8oBAFwmnswGABgy/a4nINhFXtdf/SPC/dJXm9OtlrNtfukL6C0EBfq8/hHhyly6zS99vVYwQy0iKHBl4dQTAMAQQQEAMERQAAAMERQAAEMEBQDAEEEBADBEUAAADBEUAABDBAUAwBBBAQAwRFAAAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEO8uMgknna3rNZIv/TV7nLq9BmXX/oC0PcQFCYJCQtXdcEcv/Q1aunLkggKAObg1BMAwBBBAQAwRFAAAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEMEBQDAEEEBADBEUAAADBEUAABDBAUAwJCpQdHa2qqJEyfqxIkTkqSqqiqlpqYqKSlJ69ev9y139OhRZWRkaNy4cVq2bJna29vNLAsA0A2mBcXBgwf10EMPqaamRpLU1tam3NxcPffccyovL9fhw4dVWVkpSVqyZIlWrFihXbt2yev1qqioyKyyAADdZFpQFBUV6amnnpLNZpMkHTp0SLGxsRoyZIjCwsKUmpqqiooK1dXVqa2tTfHx8ZKkjIwMVVRUmFUWAKCbTHtx0Zo1azpNNzQ0yGq1+qZtNpvq6+vPa7daraqvrzerLAAGIq/rr/4R4X7pq83pVsvZNr/0hZ7x2xvuPB6PLBaLb9rr9cpisVy0vaeioq7t8TauJP567erlCvb6/CnY90Xm0m1+6ee1ghnqb/VPKKFn/BYU0dHRcjgcvmmHwyGbzXZee2Njo+90VU80NbXK4/F2agv2P9CecDhaurW8v/dFd+vzJ/bFN9gXfVNIiMXwx7Xfbo+94447dPz4cdXW1qqjo0NlZWVKTEzU4MGDFRERoerqaklSSUmJEhMT/VUWAOAS/HZEERERofz8fGVnZ8vpdMputys5OVmSVFhYqLy8PLW2tiouLk5ZWVn+KgsAcAmmB8X777/v+3dCQoJKS0vPW2bYsGF66623zC4FAHAZeDIbAGCIoAAAGCIoAACGCAoAgCGCAgBgiKAAABgiKAAAhggKAIAhggIAYIigAAAY8ttYTwBwJeHdHN8gKADgAvpHhPv13RwtCt6g4NQTAMAQQQEAMERQAAAMcY0CpvO0u/32is12l1Onz7j80hfQVxAUMF1IWLiqC+b4pa9RS1+WRFAAvYlTTwAAQwQFAMAQQQEAMERQAAAMERQAAEMEBQDAEEEBADDEcxSAH/HwIa5EBAXgRzx8iCsRp54AAIYICgCAIYICAGCIoAAAGCIoAACGCAoAgCGCAgBgiKAAABgKqqB4++23NWHCBCUlJWnbtm2BLgcAoCB6Mru+vl7r16/Xjh071K9fP02fPl133323br311kCXBgB9WtAERVVVlX70ox9pwIABkqRx48apoqJCCxcuDGxhAEzBuFffCPZ9ETRB0dDQIKvV6pu22Ww6dOjQZW8vJMRywfYbb/juZW+zu/pdF+W3vi72eY2wL77BvviGv/ZFSFi4/vSbf/dLX/8yf51CQtzdXq+v7ItL/T+xeL1er5lFddXzzz8vp9Opn//855KkoqIiHT58WKtWrQpsYQDQxwXNxezo6Gg5HA7ftMPhkM1mC2BFAAApiILixz/+sfbt26dTp07pH//4h9577z0lJiYGuiwA6POC5hrFoEGDtHjxYmVlZcntdmvq1Kn64Q9/GOiyAKDPC5prFACA4BQ0p54AAMGJoAAAGCIoAACGCAoAgCGCAgBgiKAAABgiKAAAhggKAIAhgsIEra2tmjhxok6cOBHoUgJq48aNSklJUUpKigoKCgJdTkA9++yzmjBhglJSUrR58+ZAlxMU1q1bp5ycnECXEVCPPPKIUlJSlJaWprS0NB08eDDQJV1Q0AzhcbU4ePCg8vLyVFNTE+hSAqqqqkoffPCBiouLZbFYNGfOHP3ud7/T2LFjA12a3x04cED79+9XaWmp2tvbNWHCBNntdn3/+98PdGkBs2/fPhUXF+u+++4LdCkB4/V6VVNToz179igsLLi/ijmi6GVFRUV66qmn+vzIt1arVTk5OerXr5/Cw8N1yy236G9/+1ugywqIu+66S1u2bFFYWJiamprU0dGh73znO4EuK2Cam5u1fv16zZ8/P9ClBNQXX3whSZo9e7YmTZqk3/72twGu6OKCO8auQGvWrAl0CUHhtttu8/27pqZG7777rl5//fUAVhRY4eHh2rBhg1555RUlJydr0KBBgS4pYFasWKHFixfr5MmTgS4loM6ePauEhAQtX75cbrdbWVlZuvnmm3XPPfcEurTzcEQBU3322WeaPXu2li5dqu9973uBLiegFi1apH379unkyZMqKioKdDkB8eabbyomJkYJCQmBLiXgRo4cqYKCAkVGRmrgwIGaOnWqKisrA13WBXFEAdNUV1dr0aJFys3NVUpKSqDLCZhjx47J5XJp+PDhuuaaa5SUlKQ///nPgS4rIMrLy+VwOJSWlqYzZ87oyy+/1NNPP63c3NxAl+Z3H3/8sdxuty80vV5v0F6r4IgCpjh58qQWLFigwsLCPh0SknTixAnl5eXJ5XLJ5XJp9+7dGjVqVKDLCojNmzerrKxMJSUlWrRoke6///4+GRKS1NLSooKCAjmdTrW2tqq4uDhob/YIzvjCFW/Tpk1yOp3Kz8/3tU2fPl0PPfRQAKsKDLvdrkOHDik9PV2hoaFKSkrq8+EJacyYMTp48KDS09Pl8XiUmZmpkSNHBrqsC+LFRQAAQ5x6AgAYIigAAIYICgCAIYICAGCIoAAAGCIoAANDhw7VqVOnLjo/JydHmzZtuuC8+++/X3/605/MKs3v/aDvIigAAIYICqAL3njjDU2cOFGTJk3S7Nmzdfz4cd+86upqPfjgg5owYYLWrFmj9vb2S27vzTffVEZGhtLT0zVr1iwdO3ZMLS0tuvPOO+VwOHzLTZs2TZWVlXK5XHr66ac1efJkTZo0STk5OWptbTXlswL/jKAALmH//v16+eWXtWXLFpWWlmrixIlasGCBvn5W9e9//7teffVV7dy5U59++uklB/w7cOCAdu7cqW3btmnnzp2aM2eOFi5cqMjISI0dO1alpaWSvhojqrGxUffee69efPFFhYaGaseOHSotLZXNZlNhYaHpnx2QGMIDuKQ//OEPmjBhggYOHChJysjI0Jo1a3xvMExLS/O9X2LSpEmqrKxUZmbmRbe3d+9e1dbWavr06b62s2fPqrm5WdOmTdPKlSv16KOPavv27ZoyZYpCQkK0d+9etbS0qKqqSpLkdrsVFRVl1kcGOiEogG/55JNPFBoaqqFDh/qOGCIjI89bzuv1+k4xhYaGdmq/1AigHo9HaWlpWrJkiW+6oaFB119/vUaPHq329nYdOnRIZWVleuONN3zL5Obmym63S5LOnTsnp9PZ8w8MdAGnnoBv2bNnj15++WVJ0pEjRzRw4EDZ7XaVl5f77n7avn27BgwYoNjYWEnSO++8I5fLJafTqeLiYiUmJhr28ZOf/ETvvPOOGhoaJEmvv/66Zs6c6Zs/bdo0/epXv9LQoUMVExPjW2fbtm1yuVzyeDxavny5nnnmmV7//MCFcEQBfMvDDz+sJ554QhMnTpTb7dbq1at1zz33aNasWZo5c6Y8Ho8GDhyoF154QSEhX/3Ouummm5SZmalz585p7Nixmjx5cqftfb2cJD355JOaMWOG5s6dq9mzZ8tisejaa6/Vxo0bZbFYJEnp6el65plnOgXB448/rnXr1mny5Mnq6OjQ8OHDlZOT46e9gr6O0WMBAIY49QQAMERQAAAMERQAAEMEBQDAEEEBADBEUAAADBEUAABDBAUAwND/AmsZ8wOpI6EtAAAAAElFTkSuQmCC\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2004,8 +2012,8 @@ }, { "cell_type": "code", - "execution_count": 96, - "id": "b2dbed5a", + "execution_count": 184, + "id": "de3d19ee", "metadata": {}, "outputs": [ { @@ -2014,15 +2022,15 @@ "" ] }, - "execution_count": 96, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": {}, @@ -2043,8 +2051,8 @@ }, { "cell_type": "code", - "execution_count": 97, - "id": "e3347235", + "execution_count": 185, + "id": "49207823", "metadata": {}, "outputs": [ { @@ -2053,15 +2061,15 @@ "" ] }, - "execution_count": 97, + "execution_count": 185, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2084,8 +2092,8 @@ }, { "cell_type": "code", - "execution_count": 98, - "id": "b01b8dc4", + "execution_count": 186, + "id": "d95bb138", "metadata": {}, "outputs": [ { @@ -2094,15 +2102,15 @@ "" ] }, - "execution_count": 98, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2121,8 +2129,8 @@ }, { "cell_type": "code", - "execution_count": 99, - "id": "64cd318b", + "execution_count": 187, + "id": "9a2b6ceb", "metadata": {}, "outputs": [ { @@ -2131,15 +2139,15 @@ "" ] }, - "execution_count": 99, + "execution_count": 187, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2156,8 +2164,8 @@ }, { "cell_type": "code", - "execution_count": 100, - "id": "3fb9cdd2", + "execution_count": 188, + "id": "b673cc93", "metadata": {}, "outputs": [ { @@ -2166,15 +2174,15 @@ "" ] }, - "execution_count": 100, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": {}, @@ -2188,8 +2196,8 @@ }, { "cell_type": "code", - "execution_count": 101, - "id": "11c1ad66", + "execution_count": 189, + "id": "154ccdb3", "metadata": {}, "outputs": [ { @@ -2198,15 +2206,15 @@ "" ] }, - "execution_count": 101, + "execution_count": 189, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": {}, @@ -2219,8 +2227,8 @@ }, { "cell_type": "code", - "execution_count": 102, - "id": "c465cf0b", + "execution_count": 190, + "id": "3b3e832f", "metadata": {}, "outputs": [ { @@ -2229,15 +2237,15 @@ "" ] }, - "execution_count": 102, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2256,8 +2264,8 @@ }, { "cell_type": "code", - "execution_count": 108, - "id": "312ba9b8", + "execution_count": 191, + "id": "f7323353", "metadata": {}, "outputs": [ { @@ -2266,15 +2274,15 @@ "" ] }, - "execution_count": 108, + "execution_count": 191, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2293,8 +2301,8 @@ }, { "cell_type": "code", - "execution_count": 103, - "id": "aeb58e52", + "execution_count": 192, + "id": "4c9ac00d", "metadata": {}, "outputs": [ { @@ -2303,15 +2311,15 @@ "" ] }, - "execution_count": 103, + "execution_count": 192, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2328,8 +2336,8 @@ }, { "cell_type": "code", - "execution_count": 104, - "id": "a5f1e4af", + "execution_count": 193, + "id": "4653b0b7", "metadata": {}, "outputs": [ { @@ -2338,15 +2346,15 @@ "" ] }, - "execution_count": 104, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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Wl708aCgnjv/O+HffZs3qlbm2MZly/+zfbV8wfw5zZ88yqEIRERHJC6cGi4SEI6SlphJdsxYlSwYSGRXF7l2/4uPjy/nz5wGw2WwA+Pj64ePjQ9KZ047P22w2fP38c+273YOP0Dqm3RVtrfTt2a3gDkZERESceynk6JHDvPvWGPbvi+e3346yf188lSpXJiKyOj+sWcWZ06dZvWoFoaHhuLm5EREZxb74vezfF8+O7ds4mZhIRERkrn2bzWY8PT0dP8WLe97koxMREbn9OHXEolHjpiQkHGHsmFHY7XbuvqchLe5vzV13NWDS++N58fl+lAsOplef/gDUrlOXlq1iGDtmFC6uLsQ93YXg8hWceQgiIiJyBacGC5PJRGxcZ2LjOufYHliqFENHjMy1fcfYODrGxt2sEkVEROQ6FIq7QkREROTWoGAhIiIihlGwEBEREcMoWIiIiIhhFCxERETEMAoWIiIiYhgFCxERETGMgoWIiIgYRsFCREREDKNgISIiIoZRsBARERHDKFiIiIiIYRQsRERExDAKFiIiImIYBQsRERExjIKFiIiIGEbBQkRERAyjYCEiIiKGUbAQERERwyhYiIiIiGEULERERMQwChYiIiJiGAULERERMYyChYiIiBhGwUJEREQM4+rsAr5dMJ+F331DevoFomvVpmfv/vy8ZTMTx73jaOPt48NHn07Dbrczc8ZU1qxaiYuLCzFt29M6pq0TqxcREZErOTVY7NyxnVkzZ/DyoKH4lyjB6FeHs3jht5jNboSHR/DSK4MBMJmyB1a2b/uZ5UsXM3j4SNJSU3nrjVFE1YimYkglJx6FiIiIXObUYOHq6soTsXHUiK4JQNly5UhLTcPV1ZUSAQF4eXnnaL9n9y5Cw6pRpUpVAIKCShO/d4+ChYiISCHh1DkWkdWjHJcyEo4c5sD+fTS8tzEpKcns3xdPnx5dGfjS8/yycwcAFosFDw8Px+c9vbywWCy59p2RkYHVanX82GzWgj8gERGR21y+Riye7dODxk2b8XCHx65679OPP+S3o0cZ/uqoPPeXlHSGsWNG0SamHVVDw2jW/N9ERtUgLKwaSxZ9z/h332LS5E9z/azJlHufC+bPYe7sWXmuQURERG7cdQWLPbt3AXD69CmOJhx2vL4sM/MSu3/9hTNnzuS5T4vFwuiRI6geVYNHOz4JQNlywVStGoabuzv3t2rD8mWLSU5OxsfHh6Qzpx2ftdls+Pr559pvuwcfoXVMuyvaWunbs1ue6xIREZHrd13BYuSIIYAJMPHT5s38tHlzLq3sRFaPylN/F2w23hw9ksDAQGI7dcZqPY/JVIy33hhNYGApOnV5hvXr1uLt7UNAQAARkVEsXbyQ/fvisVqtnExMJCIiMte+zWYzZrP5eg5PREREbtB1BYu69e4ETPy8ZTOlgoKoWLFSjvddXFwoU7Ysrdo8kKf+Nm36kYMH9wPQvUscAIGlSvHigEF8MvkDnu/Xk6DSZXj2hZdwNZupXacuLVvFMHbMKFxcXYh7ugvB5StczyGIiIhIAbquYPGfAYMAeOLRB6ldpx5Pd3nmhnbepGkzmjRtlut7r45+46ptJpOJjrFxdIyNu6H9ioiISMHI1+TNce9/SPHinkbXIiIiIkVcvoJFQEBJVi5fyp7du7BY0rDbc75vMsGQ4SONqE9ERESKkHwFi6mffcKyJYsB+9+0+Jt7QEVEROSWlq9gsfHH9Xh5e9HtmV6ULlMG098tJiEiIiK3lXwFi4yLGdS/uwH1G9xjdD0iIiJShOVrSe9adepy5oqFqkREREQgnyMWVUND+WLaVCZ/8D7BweVzbdPmgXa5bhcREZFbV76CxfTPpwCweuVyrp6oaQdMChYiIiK3oXwFi4ceefTvn/4lIiIit618BYtHHu1odB0iIiJyC8hXsLjW48hNJnjokasfqS4iIiK3tnwGiy/Jnlvx1wWy/rw8omAhIiJy+8lXsHiqU5dctx///XdWLF+SPQdDREREbjv5Chb/9Fj0344mEL93b74LEhERkaIrXwtk/ZPAoCD271OwEBERuR3la8Ti8KGDV22z2+0kJp5g+9YteHl733BhIiIiUvTkK1gMHvgif/8EUzstWrbOf0UiIiJSZOUrWFSLiLzqiaYmkwk/P39q1q7DvY2bGlGbiIiIFDH5ChbD/jvK6DpERETkFpCvYHHZwYMH2Ld3DxcuXMDP35+oqGiCSpc2qjYREREpYvIVLDIzM3l/wnts3LD+jy3ZDx4zmeD+Vm2Ie7qrcRWKiIhIkZGvYDF/3mw2bliHn58/DRo2wr9ECVKSk9m4YT2LF35PYKlStG7T1uhaRUREpJDLV7D4YfUqSpYM5PWx7+J9xa2lD3d4nFcGPM+KZUsVLERERG5D+VogKyUlmcjqUTlCBYC3tzeRkVGcPnXKkOJERESkaMlXsAgIKMnBA/vJyMjIsf1SRgYHDx7Az8/PkOJERESkaMnXpZBGTZoyb/Yshr7yEvc2+RclAgJIS03lh7WrOf7777Rt/2Ce+/p2wXwWfvcN6ekXiK5Vm569+3Pu7FkmvT+OQwcPULZcMD169yMkpBJ2u52ZM6ayZtVKXFxciGnbntYxuuQiIiJSWOQrWDz4UAd+S0jgp80bmTHt8yvesVOrdl0e6fB4nvrZuWM7s2bO4OVBQ/EvUYLRrw5n8cJvOXLkMO7uHox9ZwJzvprJ5EkTGTXmLbZv+5nlSxczePhI0lJTeeuNUUTViKZiSKX8HIaIiIgYLF/BwsXFhedffJm9e3bx06ZNXLhgw9PLizJly3Ff8xZ537mrK0/ExlEjuiYAZcuVIy01jT27d9GpczcCS5WiabPm/HfYINLT09mzexehYdWoUqUqAEFBpYnfu0fBQkREpJDIV7BIT09n0sRxbN60kUFDRxBVI5oLFy7QJa4j27duod9zL+Lm5nbNfiKrRxFZPQqAhCOHObB/H0/EdmLJ4u/x8PAAwMvLC4CzFgsWi8WxHcDTywuLxZJr3xkZGTnmgNhs1vwcqoiIiFyHfAWLebNnsXnTj5QrF4y/f4nsjlxcqFT5Dn7esoX5c77isSdi89xfUtIZxo4ZRZuYdlQNDcu1jelvnnn2d9sXzJ/D3Nmz8lyDiIiI3Lh8BYuNG9dToWIIr7/xNsVcXLI7MpsZNeYtBr/8H9b9sCbPwcJisTB65AiqR9Xg0Y5PAuDj48v58+cBsNls2dt8/fDx8SHpzGnHZ202G75+/rn22+7BR2gd0+6Ktlb69ux23ccqIiIieZe/dSySU6hc+Q5HqLjMZDJRMaQSaWmpeerngs3Gm6NHEhgYSGynzlit57FarUREVueHNas4c/o0q1etIDQ0HDc3NyIio9gXv5f9++LZsX0bJxMTiYiIzLVvs9mMp6en46d4cc/8HKqIiIhch3yNWAQFBfHrLzs5d/Ys3j4+ju1nz1r49ZedBJUuk6d+Nm36kYMH9wPQvUscAIGlSjH8v6OZ9P54Xny+H+WCg+nVpz8AtevUpWWrGMaOGYWLqwtxT3chuHyF/ByCiIiIFIB8BYt/39+Kz6d8zHP9elIjuhY+vr6ctVj4ZecOrFYrT3fJ2yWHJk2b0aRps1zfGzpi5FXbTCYTHWPj6Bgbl5+yRUREpIDlK1jc36oNaWmpfPfN12zauOHPzlxdaf/Qw7Ro2dqwAkVERKToyFewAHj08Sdp1eYB9u/bx/nz5/D29iY0NDzHpRERERG5veQ7WED23Rt16tYzqhYREREp4vJ1V4iIiIhIbhQsRERExDAKFiIiImIYBQsRERExjIKFiIiIGEbBQkRERAyjYCEiIiKGUbAQERERwyhYiIiIiGFuaOXNW0HIqO2G9ZUwuJZhfYmIiBRFGrEQERERwyhYiIiIiGEULERERMQwChYiIiJiGAULERERMYyChYiIiBhGwUJEREQMo2AhIiIihlGwEBEREcMoWIiIiIhhFCxERETEMAoWIiIiYhinP4QsLS2V9evWsnb1Kvr0e44KFUNYv24tE8e942jj7ePDR59Ow263M3PGVNasWomLiwsxbdvTOqatE6sXERGRKzk1WNhsNvr16k6lSpVJOHLYsT01JYXw8AheemUwACZT9sDK9m0/s3zpYgYPH0laaipvvTGKqBrRVAyp5IzyRURE5C+cGizc3NyYMGkyF9PT6d+nh2N7akoKJQIC8PLyztF+z+5dhIZVo0qVqgAEBZUmfu8eBQsREZFCwqnBwsXFBT8/f06fOplje0pKMvv3xdOnR1d8fH158qmnqRFdE4vFgoeHh6Odp5cXFosl174zMjLIyMhwvLbZrAVzECIiIuLg9DkWuWnW/N9ERtUgLKwaSxZ9z/h332LS5E9zbWsy5d7HgvlzmDt7VgFWKSIiIn9VKINF2XLBVK0ahpu7O/e3asPyZYtJTk7Gx8eHpDOnHe1sNhu+fv659tHuwUdoHdPuirZW+vbsVtCli4iI3NYKZbB4643RBAaWolOXZ1i/bi3e3j4EBAQQERnF0sUL2b8vHqvVysnERCIiInPtw2w2Yzabb3LlIiIit7dCGSy69+zDJ5M/4Pl+PQkqXYZnX3gJV7OZ2nXq0rJVDGPHjMLF1YW4p7sQXL6Cs8sVERGRPxSKYFEqqDQzZ3/teB1SqTKvjn7jqnYmk4mOsXF0jI27idWJ/Clk1HZD+kkYXMuQfkREChutvCkiIiKGUbAQERERwyhYiIiIiGEULERERMQwChYiIiJiGAULERERMYyChYiIiBhGwUJEREQMUygWyJLbi1GLTIEWmhIRKWw0YiEiIiKGUbAQERERwyhYiIiIiGEULERERMQwChYiIiJiGN0VInIb0J04InKzaMRCREREDKNgISIiIoZRsBARERHDKFiIiIiIYRQsRERExDAKFiIiImIYBQsRERExjIKFiIiIGEbBQkRERAyjYCEiIiKGcfqS3mlpqaxft5a1q1fRp99zVKgYwpnTp5n0/jgOHTxA2XLB9Ojdj5CQStjtdmbOmMqaVStxcXEhpm17Wse0dfYhiIiIyB+cOmJhs9no16s7G9evJ+HIYcf26dOm4O7uwdh3JlCxYgiTJ00EYPu2n1m+dDEDBg2lW4/eTJ86haMJR5xUvYiIiPyVU4OFm5sbEyZNpt9zL+TYvmf3Lho1bkJgqVI0bdacQwcPkJ6ezp7duwgNq0aVKlWpU7ceQUGlid+7x0nVi4iIyF859VKIi4sLfn7+nD51Msf2sxYLHh4eAHh5eTm2Wa7YDuDp5YXFYsm174yMDDIyMhyvbTar0eWLiIjIXzh9jkVemUzXt33B/DnMnT2r4AoSERGRqxTKYOHj48v58+eB7HkYAD6+fvj4+JB05rSjnc1mw9fPP9c+2j34CK1j2l3R1krfnt0Krugi7Niwxob1Vf7VtYb1JSIiRU+hDBYRkdX5Yc0qIiKqs3rVCkJDw3FzcyMiMoqlixeyf188VquVk4mJRERE5tqH2WzGbDbf5MpFRERub4UyWMTGdWbS++N58fl+lAsOplef/gDUrlOXlq1iGDtmFC6uLsQ93YXg8hWcXK2IiIhcViiCRamg0syc/bXjdWCpUgwdMfKqdiaTiY6xcXSMjbuJ1YmIiEheaeVNERERMYyChYiIiBhGwUJEREQMo2AhIiIihlGwEBEREcMoWIiIiIhhFCxERETEMAoWIiIiYhgFCxERETGMgoWIiIgYRsFCREREDKNgISIiIoZRsBARERHDKFiIiIiIYRQsRERExDAKFiIiImIYBQsRERExjIKFiIiIGEbBQkRERAyjYCEiIiKGUbAQERERwyhYiIiIiGEULERERMQwChYiIiJiGFdnFyAiIoVLyKjthvSTMLiWIf1I0VKog8Xrr41g547tjtdtHmhHy1YxTHp/HIcOHqBsuWB69O5HSEglp9UoIiIifyrUwSIlJYXO3brTsFFjAMxmN/438T3c3T0Y+84E5nw1k8mTJjJqzFtOrlREJCej/uoH/eUvRUuhnmORmpJCUFBpvLy88fLyxs3NjT27d9GocRMCS5WiabPmHDp4gPT0dGeXKiIiIhTiYHEpI4Nz584yY9rn9O7ehXffeoOzZy2ctVjw8PAAwMvLC4CzFstVn8/IyMBqtTp+bDbrTa1fRETkdlR4L4WYTDzd9RnKlCmHp6cn7094jzlfffl3Ta+yYP4c5s6eVcBFioiIyJUKbbDIysqiXr36BJQsCUC9O+/iwP79+Pj4cv78eQBsNhsAPr5+V32+3YOP0DqmneO1zWalb89uN6FyERGR21ehDRYnE08w4D/P0v+5F7mjalV27thOtYhI/Pz8+GHNKiIiqrN61QpCQ8Nxc3O76vNmsxmz2eyEykXESLr1UaRoKbTBokLFEDp16cb0qVOw2azUiK5Jh8ee4ILNxqT3x/Pi8/0oFxxMrz79nV2qiIiI/KHQBguAlq1iaNkqJsc2b29vho4Y6aSKRESksNItvoVDob0rRERERIoeBQsRERExTKG+FCIiInI7uJUmKWvEQkRERAyjYCEiIiKGUbAQERERwyhYiIiIiGEULERERMQwChYiIiJiGAULERERMYyChYiIiBhGwUJEREQMo2AhIiIihtGS3gY6NqyxIf2Uf3WtIf2IiIjcbBqxEBEREcMoWIiIiIhhFCxERETEMJpjISLXRXOJJK+M+l0B/b4UJQoWUqQV1S85/R/uzVeUz3lR/T2X25MuhYiIiIhhFCxERETEMLoUIiIicosoDJf8NGIhIiIihlGwEBEREcMoWIiIiIhhiuQcix/Xr+PLL6Zx/vw56t11N12f6YnZbHZ2WSIicovQLb75V+RGLM6etfDB/8bz4COPMmLkGHZs38qqFcucXZaIiIhQBIPFwQP7sduhSdNmlK9Qgdq167Jn9y5nlyUiIiIUwUshFosFdw93TCYTAJ5eXpw8efKqdhkZGWRkZDheW63nAbDZrDnauWReMKy2C5nG9GO1Wq/dCONqN6puyFvtOuc656Bzfj10znXO86qgzvnl70673X7Nz5nseWlViKxds4ppn3/KR59OA2D61CkcOXyIIcNH5mg356uZzJ09yxklioiI3JImfvAxJUsG/mObIjdi4ePjwwWbjaysLIoVK4bNasXXz/+qdu0efITWMe0cr7Oysjh/7hzePj6O0Q4j2WxW+vbsxsQPPqZ4cU/D+y8oRbVuKLq1F9W6oejWXlTrhqJbe1GtG4pu7QVZt91u58IFGyVKBFyzbZELFlWqhlGsWDFWLFtCZFQNtm/fyoMPP3pVO7PZfNWdIt7e3gVeX/Hinnh6Fp1fxMuKat1QdGsvqnVD0a29qNYNRbf2olo3FN3aC6puLy+vPLUrcsHC19eXnr37M3PGVGbNnM6dd91Nk6bNnF2WiIiIUASDBUCDho1o0LCRs8sQERGRvyhyt5sWVmazmYc7PFbkFuoqqnVD0a29qNYNRbf2olo3FN3ai2rdUHRrLyx1F7m7QkRERKTw0oiFiIiIGEbBQkRERAyjYCEiIiKGKZJ3hRQ2Rflpq2lpqaxft5a1q1fRp99zVKgY4uyS8uTbBfNZ+N03pKdfILpWbXr27o+Hh4ezy/pHmZmZzJo5nZXLlwJQu049nunZBzc3NydXdn3ee/tNNm3cwMzZXzu7lDx7/bUR7Nyx3fG6zQPtiI3r7LyC8igrM5Ovv57LmlUrCA6uwIBXhji7pGuaNHEca9esyrGtdOkyvDfxAydVdH1WrljG3Nlfcv7ceapH1aB3v2fx8ir4NZBu1IL5c1n43TcUK1aMNm3bEfNAe6fVomBxgy4/bbVztx5UrRrGqJHDWLViGS1atnZ2addks9no16s7lSpVJuHIYWeXk2c7d2xn1swZvDxoKP4lSjD61eEsXvgt7R/q4OzS/tG6H9awcvkyXho4BG8fH14bMZSVy5fSsnWMs0vLsy0/beanzRudXcZ1S0lJoXO37jRslP0obLO5aIS5zz/7hM0bf6RLtx5ERFZ3djl50uWZnsR17up4/c7YMdxRpaoTK8q7s2ctfPzhJDp36050zVqMee2/fPfNAh7r+KSzS/tHu379hdmzvuDlQcMwm828/toIQkPDCa8W4ZR6dCnkBhXlp626ubkxYdJk+j33grNLuS6urq48ERtHjeiaVKhQkbLlypGWmubssq6pSdNmfPzZdMKrReDv74+rqysuLi7OLivPrFYrUz75kPtbtXF2KdctNSWFoKDSeHl54+XlXSRGiVJTU1mxbAldu/fkzvp34+3j4+yS8sTd3d1xnpPOnCF+7x7ub1k0fmfsdihWrBglS5YkIKAk7u7uuLgU/q/JA/v3UbZcMDWia1ItIpKIyCi2bd3itHoK/xkr5HJ72qrFYnFyVXnj4uKCXy7PWSnsIqtH0TqmLQAJRw5zYP8+Gt7b2MlV5d2A/zxL9y5xlClbluYtWjq7nDybOX0qNaJrEVUj2tmlXJdLGRmcO3eWGdM+p3f3Lrz71hucPVv4/zd68MA+srKy2LhhPX16dmXMqFdJSjrj7LKuy3ffLqDeXfUJLFXK2aXkia+vL48/GcvYMaPoEteRS5cu0bb9w84u65qCgkpz6mQiJ04cJzU1lVMnE7GkOe+PLQWLAlAAzziTXCQlnWHsmFG0iWlH1dAwZ5eTZy8PGsorQ4aTcOQIa1avdHY5ebJ3z262/LSJ2KeednYp189k4umuz/BUpy688NJAjh5NYM5XXzq7qms6f/48YCKsWjVefHkwaWmpzJj2ubPLyrPkpCQ2rPuBVq0fcHYpeZaclMS82bPo3K07I14bQ2ZmJgu//8bZZV3TXXc3oHpUNP95tg+DB/6H9Ivp+Jco4bR6NMfiBuX1aatiLIvFwuiRI6geVYNHC/n1z8sSEo6QlppKdM1alCwZSGRUFLt3/UrTf93n7NKuaf7c2Zw9e5Zn+/YgMzMLgK6dnuCTz79wcmXXlpWVRb169QkoWRKAenfexYH9+51c1bX5+vrh5uZGi/uz52s1aNiItatXXeNThcei778lpFIlp13nz4/4+D1kZWU5zvmd9e9mx7attH/wESdX9s9cXFwY8MoQzp07h5ubG8/26eHUifgasbhBVz5t9fffj7F9+1Yiq0c5u6xb2gWbjTdHjyQwMJDYTp2xWs9jtVqdXdY1HT1ymHffGsP+ffH89ttR9u+Lp1Llys4uK0969enPu+PfZ8zYd+nUpRsAY8a+6+Sq8uZk4gn69OzKj+vXcfJkIjt3bKdiSOG/+yksLByz2cySRd9z+tRJtmzeROXKdzi7rDyxWq2sWL6UVm2KzmgFQLng8ly8eJEfN6zj1MmT/LJzB8HlKzi7rDy5lJFBWmoKH3/4Pzw9Palfv4HTatGIxQ3S01Zvvk2bfuTgwey/OLt3iQMgsFQpJvzvI2eWdU2NGjclIeEIY8eMwm63c/c9DR1/GRV2Vw6r+vkdBaBUUGlnlXNdKlQMoVOXbkyfOgWbzUqN6Jp0eOwJZ5d1TZ5eXjz/4gCmfPIRX34xjcjqUcR2Kvy3yAKsWLYEdw937m7Q0NmlXJeQkEo83fUZpk+dgvX8eapHRfPo40VjRHTjxg1M+fhDompEM3DwMIo5cWK4nhUiIiIihtGlEBERETGMgoWIiIgYRsFCREREDKNgISIiIoZRsBARERHDKFiIiIiIYRQsRK7Tq8MH07FDe6Z+9slV761ZtYKOHdozaeK4m1bPqZMnmTxpIn16duWpjo/Qr9czfPLRB059VkBBmzH1M57p8hSbNm4wrM85s7+kY4f2fDE957LZH3/4Pzp2aM+qFctybB826GU6dmhvyEMHO3Zoz9Oxj99wP391+tRJOnZoz0sv9De8b5G/o2Ahkk9LFn3PoYMHnFpDQsIRBr/yIqtWLicgoCQN722Cr58fy5cuZvjQV4rEiqT5UbtuPVq0aEXlylUM6zMsrBrAVf+d7t71a47/BMjMzCQh4TAuLq5UKSKPBBe5WbTypkg+ZWVl8dGH/2PU62Odtsrdh++P59zZs3R4rCMPPfKYY/vkD95n1YplzJszi9i4orFa4/WIrB5l+NL5oWHhmEzFOHzoIHa7HZPJREpKMidOHAdg9+4/g8Wx345y8eJFqlQNxc3dPd/7zMzMxMWJKySKFAQFC5F88vX148jhQyxa+B1tHmh31funT52kf58elK9QkbHvjHdsH/jicyQkHGH8+x9SKqg0/Xo/Q0pyCr37Pcu82bM4eTKRwFJBPPnU0/j4+DDt8085mpBAiRIlaPNAO1q0zF4G/PDhQxw+fAg/P3/atnsox77/fX8r1qxawc4d2x3bLthszPpyBps3/ojFkkZQUGn+3bIVLVvFONrkt5Y1q1bwwf8m0LJ1DMlJZ9i9axeYoFGjJjz5VCdczWYALl68yIJ5c1i/bi3JyUmUCAjg3ib/ov2Dj+Dqmv1/Rx07tKdM2XI89HAH5s+dzZkzZwguX55OnbtRLSISgEkTx7F2zSqee2EA9RvcA0BaWiozp09l689bSE+/wB1VQnkiNo7QsHDH8a1ft5ZvF8wn8cRxvL19qFPvTh597Am8fXwoXrw4FStWJCHhCImJJyhbthy7f/0FgHLlgjl+/HdOJp6gdJmyjlGNsPBq131+z5w+zWNPxLJs8SLKlC3L0BGvXfW7s3jhd3w+5WNKly7Dq6PfxNfXlwsXLvDVlzP4ccM6zp87R/kKFenwWEdq16nn+NzePbuZ9vmn/Hb0KKXLlOFf9zW/qm+RgqZLISL59GjHJzGb3Zg9ayanT5+6ob4yMy/x6UcfUDEk+2mQiSeOM+G9t3j9tf/i5+dPvbvqk5ycxJRPJrMvfi8ABw9kPy8lpFIlxxf3ZZUr38GMWfN48+1xf/SfyeiRI1i88Dv8/Pyo36AhlrMWPv/046vmFOSnlssWL/yO06dPU7N2HUyYWLzoO2Z+Mc3x/vvj32Xe3K8o7ulJo3ubYMLE3K++ZO5fHmN+6mQi0z6fQlh4NapUrcqRw4eY8N7bXMrIyPX82Ww2hg8eyNo1q6hSNZQ76zfg8KEDjHp1OMnJyQBs+nEDE8e9Q3JSEnff0wg/f3+WLVnE22Nfd/QTGp7zcsjuXb9iMpl4qMNjjtcAB/8SLK7n/AJ8M38ekdWjqFW77lXvbflpE1M/+xRvHx9eHjwMX19fsjIzef21ESz6/lvKli1Hg3sacfrUKcaOGc3hw4eA7Ed+jxn1Xw4dPEC1iAhCKlXmq5mF/+mzcuvRiIVIPpUtW5YHH+7AV1/OYMrHkxnwypAb6m/AK0MdX1TDhwxkX/xe/n1/K7p06wHArJkz+HrebH79dSdh4dWwWLInZ/r5+V+z7/Xr1rJ/fzzRNWvx8qBhFCtWjJOJJ3jphf4s/O4bWrZ+gICAgHzXcll0zdoMHDwMk8nEiRPHeen5/ixfupiHH3kMj+LFKe7pyX3NW/B01+64urqSnJREn55d2fjjeh57ItbRj8lkYuTrb1K6dBkAXny+H78f+43ExETKV7j6aZOLvv+GkycTebjDYzzyaEcANm3cwHtvv8nC7xYQG9eZn7dsBqDrMz2p3+CeP8LAcCwWCxaLBV9fX8LCq7F86WIOHTxAw0aN2b17FxVDKnHnnfVxdXVl965f+dd9//5zxOKPeRnXe34HDh6W47xddvDgASaOewdXV1deHDCIsmXLOfrfF7+Xxk3+Ra++zwLZ4WfwwBf5eu5snn/xZb7/bgHp6em0bBXjeALtLzt3MHrk8Gv+fogYScFC5AY80O5BNqz/gW1bt7Dxx/U31FeZP75EAMqUKcu++L1Ur17jim3ZX7JpqakA2LOynx+Yl+cI7ty+DYBm97WgWLHsgcrSZcpSs1Zttvy0mYP79xFQ/+5813JlW5PJBEDZsuWIiIzk1192cuLEcapUDaVn736cTDzB8qWLSUo6w8X0iwCkpqbk6MfV1ewIFZf7/f3Yb3+EqauDxc7t2wH4/dgxpnw8GQCr9TwAvx1NAOCOqlX5Ye1qvvv2azBB9ajoqy5DhIdHAHDo4EGSk5NJPHGcNg+0w83dnaqhYezZvYtLGRn8djSBwFKlCChZ8obP72WZmZd4Z+zrpKen07N3P8KrRTje2/HHJa20tDTH8dmx5zi+QwcPAtC46b+uOG9/nkORm0XBQuQGuLq60q17L/47bBCff/oxbds/dO0P3ag/coSPrw+QPbfgWtL+uPW0xBV/NQP4+2e/vnDBdkO1/B0fXz/gz+Aw56uZzJszG7s9K2c31+jnj6zytzu83H9u4S4lJfu9lq1isGfZWbzwO957+01MpmJERFbniac6Oe7sCCpdGv8SJUg4cohdv+wAIKpGNADVo2owd/Ysftq8iUuXLjlGK8CY83vp0iWSk5IA2LN7F03+dd+f/f9xfDu2b/3bY3eMYPmXuKqNyM2kYCFyg8KrRdCseQtWLFvCN1/Pc2w3mbL/cs3LiEJ+VKp0BwAJR45wKSMjxzyLI4cPMXLEEEqWDOTNd8bj7e0NXD3CkJKSPf/Az9+/QGpM/aN/f/8SJCWdYd6cryhevDjPvjCAatUicHN3p2OH9je8n+KengC8OuqNHJM1/6pVmwdo1eYBEk+cYOvPPzFr5nRG/Xco4//3keMchYWFs3nTRlYsW4qLiyvVqmVPGK0eFc3c2bOyRzwgx4iCUee3bbuH2LD+B9asXkm9u+pT78762cdXPPv4+j33H+5peG+un/X3L8Hx349x1pKW47KLyM2myZsiBuj45FP4+5fIMaTv6+eHyWQiOSmJi+npAFjPnyf1L18++RUaFk65csFYLGksW7o4x3tLFy/EarUS/seXYlR0TQBWrVxOVlb2aMGpkyfZuWM77u7uOf76vhFXTmI9cfx39sXvxd3dnbLlgklNTcVutxMcXIHomrVwc3fnzOnThuz38q2n33+7wHF8drudJYu+d6zl0a/3M3SJ60hqaiplypaldUxbKt9RBZvNxulTJx19hf1xOSQ+fg9VQ0PxKF4cgNDQMNzc3BzzK0KvmCNhxPl1d/egY2wcPXr3xWQy8dGH/3MschYZlX18ixd+R8YVE1jXrFrhGOWoWjUUgLWrVzne//333/Nw9kSMpRELEQN4eXkT17kr4999y7HNzc2NiMjq7N71K0MHv0yFiiHs/vWXPF26yAuTyUTPPv15/bURTP3sE7Zt3UJgYCkOHz7EkcOHCAwsxcOPZq/m2LjJv1i+dDHbtm5h6KABlCsXzM4d28nIuEhsXGfHl+eN2rZ1CyOGvEJQmTJs3/ozmZmZtI5pi6enJxUqVCQgoCT798fzxuiRuLu788vO7QBcupThWDsiPx5o9xAbN6xn08YNDBrwAndUqcqhQwdJOHKYzKxMWrdpS5OmzZg7exZDX3mJGtE1SU5OJn7vHgJLlaJ8hYqOvq6cVHn5MgiAq9lMWHgEv/6yAw8PD0IqhjjeM/L8RtWoSfMWLVm2ZBEfffg//jPgFf7V7N+sWrGc/fviGfBCf6pFRHLi+HHi4/fQsnUMnTp3o1WbB1iyeCELv/+GY8d+w8fHhy0/bcrX+RS5ERqxEDFIg3sa5VhTAKBn735E16zNqZOJ/LJjOw3uaWTowk6hYeGMHP0m9zS8l6MJCaxds5pz587S4v7WvDZmLP5/DMGbzWYGD3uV+/59P0lnzrBp4wb8S5SgZ5/+ua7BkV93N2hIQMmSbPt5C3bstGwdw6OPPwlkB60Bg4YSWb0Ge/fsIuHIYR5/4inKBZcnMzOTxMQT+d6vv78/r45+g3sbNyU5OZkf1q4mMzOTzl2706r1AwA83OFxOnXphruHB+t+WEPCkcM0aHgvQ4aPxHzFZaTKle/AbHYDcgYLgOo1siewVqkammNRNKPP7xOxnShdugxbftrEmtUrcXd3Z9h/X6PF/a1JT0/nh7VrSE1NocNjHXkytlP2OShRgkFDR3BHlars2b2LhIQjdO/VFxcX/f0oN5fJXlAXgEXktnF5gawW97emc7fuzi5HRJxIIxYiIiJiGAULERERMYwuhYiIiIhhNGIhIiIihlGwEBEREcMoWIiIiIhhFCxERETEMAoWIiIiYhgFCxERETGMgoWIiIgYRsFCREREDKNgISIiIob5PyDmowd4kX2FAAAAAElFTkSuQmCC\n", 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" + "
" ] }, "metadata": {}, @@ -2359,8 +2367,8 @@ }, { "cell_type": "code", - "execution_count": 105, - "id": "dd32ed40", + "execution_count": 194, + "id": "679a7714", "metadata": {}, "outputs": [ { @@ -2369,15 +2377,15 @@ "" ] }, - "execution_count": 105, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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pomqkevTsrXSrVV5eXpIkX19fSVK61Srrde2S5OPrK6vVmmPfSxbN14J5c/J/EAAAwMncUyEWi5597nmVLVtePj4+mjThPc2fO/tWm95Re9v2HRQb19b52m63qV+vHndbMQAAuA1Tg0V2drbq1WuokqVKSZLq1W+g/fv2yd8/QBcuXJAk2e12SZJ/QKD8/f11+tRJ5/52u10BgUE59u3u7i53d/f8HQAAALiBqWssThz/VX17PafvN3yrEyeOa8f2JFUMD1dUdDWtX5uoUydPak3iKkVERMrDw0NR0THam7JH+/amaHvSNp04flxRUdFmDgEAAFzH1BmLsIrheqZ7D82YNlV2u03Va9RUx789qYt2uyZP+kADX+6v8qGh6t13gCSpdp26atU6TuPHJcjVzVXxz3ZXaIUwM4cAAACuY/rlpq1ax6lV67gb2vz8/DR81OibtrVYLOrSNV5dusYXVHkAAOAOmH65KQAAKD4IFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAyTp2DxYt+eWjBvTo7v/fejf+vVEUPvqigAAFA0ud3Jxsm7d0mSTp78TUdSDzlfX5OVdVm7f9qpU6dOGVchAAAoMu4oWIweNUySRZJFWzZv1pbNm3PYyqHoajGGFAcAAIqWOwoWdevVl2TRD1s3KzgkRBUrVrrhfVdXV5UtV06tH3vcwBIBAEBRcUfB4h+DhkiSnuzUXrXr1NOz3Z/Pl6IAAEDRdEfB4pr3J/1b3t4+RtcCAACKuDwFi5IlS2n1yhVK3r1LVus5ORw3vm+xSMNGjjaiPgAAUITkKVhM++RjfbN8mSTHLbaw5L0iAABQZOUpWGz8foN8/XzV4/neKlO2rCyWuw8S7739pjZt/E6z5i3WqZMnNXnS+zp4YL/KlQ9Vzz79FR5eSQ6HQ7NmTtPaxNVydXVVXJt2io1rc9efDQAAjJGnYJF5KVMNH3xIDR/6kyFFbN2yWVs2b3S+njF9qjw9vTT+nQmaP3eWpkyeqIRxbylp2w9auWKZho4crXNnz+qtNxIUU72GKoZXMqQOAABwd/J0581aderq1KmThhRgs9k09eN/69HWjznbknfvUuOmzVQ6OFjNW7TUwQP7lZGRoeTduxRR9QFVrlxFderWU0hIGaXsSTakDgAAcPfyNGNRJSJCn02fpikfTlJoaIUct3ns8ba56mvWjGmqXqOWYqrX0NKvvpAkpVut8vLykiT5+vo626zXtUuSj6+vrFZrjv1mZmYqMzPT+dput+WqHgAAkHd5ChYzPp0qSVqzeqVuXqjpkGTJVbDYk7xbW7ds0vh3PtDevXtuu+2tlnHcqn3Jovm3fJ4JAADIH3kKFn/t0OnW3+h3YNGCeUpPT9eL/XoqKytbkvTcM0/K3z9AFy5ckCTZ7XZJkn9AoPz9/XX6ulMwdrtdAYFBOfbdtn0Hxca1vW5bm/r16nHXNQMAgFvLU7Do0KmLIR/eu+8AZWZekiT99NNOTZk8UePGv6uZ0z/V+rWJioqqpjWJqxQRESkPDw9FRcdoxbKvtW9vimw2m04cP66oqOgc+3Z3d5e7u7shdQIAgNzJU7D4o1MMFov01w5/+8N+gkqUcP4eGHhEkhQcUkZd47tp8qQPNPDl/iofGqrefQdIkmrXqatWreM0flyCXN1cFf9sd4VWCMvLEAAAQD7IY7CYrStrK/73Blm/nx7JTbC4Xp269TVr3mJJUungYA0fdfOdOy0Wi7p0jVeXrvF3VjAAACgQeQoWTz/TPcf2Y7/8olUrl19ZgwEAAO45eQoWt3ss+s9HUpWy5/ZXeAAAgOIpTzfIup3SISHa9weXjgIAgOIpTzMWhw4euKnN4XDo+PFflfTjVvn6+d11YQAAoOjJU7AYOnigbv0EU4ceaRWb94oAAECRladg8UBU9E1PNLVYLAoMDFLN2nXUpGlzI2oDAABFTJ6CxYhXE4yuAwAAFAN5ChbXHDiwX3v3JOvixYsKDApSTEwNhZQpY1RtAACgiMlTsMjKytKkCe9p43cbrrZcefCYxSI92voxxT/7nHEVAgCAIiNPwWLRwnna+N23CgwM0kONGiuoRAmdSUvTxu82aNnXX6l0cLBiH2tjdK0AAKCQy1OwWL8mUaVKldbY8e/K77pLS5/o2FmvDHpZq75ZQbAAAOAelKcbZJ05k6boajE3hApJ8vPzU3R0jE7+9pshxQEAgKIlT8GiZMlSOrB/nzIzM29ov5yZqQMH9iswMNCQ4gAAQNGSp1MhjZs118J5czT8lX+qSbM/q0TJkjp39qzWr1ujY7/8ojbt2htdJwAAKALyFCza/7Wjfk5N1ZbNGzVz+qfXveNQrdp11aFjZ4PKAwAARUmegoWrq6teHvh/2pO8S1s2bdLFi3b5+PqqbLnyerjlI0bXCAAAiog8BYuMjAxNnvi+Nm/aqCHDRymmeg1dvHhR3eO7KOnHrer/0kB5eHgYXSsAACjk8hQsFs6bo82bvlf58qEKCipxpSNXV1W67379sHWrFs2fq7892dXQQlG8hSckmV3CTVKH1jK7BAAocvJ0VcjGjRsUVjFcb779viqEhUmS3NzdlTDuLVWqVEnfrl9raJEAAKBoyNt9LNLO6L777peLq+sN7RaLRRXDK+ncubNG1AYAAIqYPAWLkJAQ/bRzh86np9/Qnp5u1U87dyikTFlDigMAAEVLntZY/OXR1vp06kd6qX8vVa9RS/4BAUq3WrVzx3bZbDY9272H0XUCAIAiIE/B4tHWj+ncubP68vPF2rTxu987c3NTu78+oUdaxRpWIAAAKDryFCwkqVPnp9T6sce1b+9eXbhwXn5+foqIiJSfv7+R9QEAgCIkz8FCkvz9A1Snbj2jagEAAEVcnhZvAgAA5IRgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMHd1uendysrK0pxZM7R65QpJUu069fR8r76ynjunyZPe18ED+1WufKh69umv8PBKcjgcmjVzmtYmrparq6vi2rRTbFwbM4cAAACuY+qMxbfr12r1ym/0z8HD9GrCG/pp5w6tXrlCM6ZPlaenl8a/M0EVK4ZryuSJkqSkbT9o5YplGjRkuHr07KMZ06bqSOphM4cAAACuY2qwaNa8hT76ZIYiH4hSUFCQ3Nzc5OrqquTdu9S4aTOVDg5W8xYtdfDAfmVkZCh59y5FVH1AlStXUZ269RQSUkYpe5Jz7DszM1M2m835Y7fbCnh0AADce0w9FXLNoH+8qF+O/qzoajFq+UgrTf14iry8vCRJvr6+kqR0q1VWq9XZLkk+vr6yWq059rlk0XwtmDcn/4sHAABOhSJY/N+Q4fr12C/64N23tXbN6hy3sVhy3vdW7W3bd1BsXFvna7vdpn69eOoqAAD5ydRgkZp6WOfOnlWNmrVUqlRpRcfEaPeun+TvH6ALFy5Ikux2uyTJPyBQ/v7+On3qpHN/u92ugMCgHPt2d3eXu7t7vo8BAAD8ztQ1FkcOH9K7b43Tvr0p+vnnI9q3N0WV7rtPUdHVtH5tok6dPKk1iasUEREpDw8PRUXHaG/KHu3bm6LtSdt04vhxRUVFmzkEAABwHVNnLBo3ba7U1MMaPy5BDodDD/6pkR55NFYNGjykyZM+0MCX+6t8aKh69x0gSapdp65atY7T+HEJcnVzVfyz3RVaIczMIQAAgOuYGiwsFou6xndT1/huN7SXDg7W8FGjc9y+S9d4dekaX1AlAgCAO8CdNwEAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADONmdgFfLFmkr7/8XBkZF1WjVm316jNA59PTNXnS+zp4YL/KlQ9Vzz79FR5eSQ6HQ7NmTtPaxNVydXVVXJt2io1rY/YQAADAVabOWOzYnqQ5s2aqT/+X9GrCG0pJTtayr7/QjOlT5enppfHvTFDFiuGaMnmiJClp2w9auWKZBg0Zrh49+2jGtKk6knrYzCEAAIDrmBos3Nzc9GTXeFWvUVNhYRVVrnx5nTt7Tsm7d6lx02YqHRys5i1a6uCB/crIyFDy7l2KqPqAKleuojp16ykkpIxS9iSbOQQAAHAdU4NFdLUY56mM1MOHtH/fXjVq0lTpVqu8vLwkSb6+vpKkdKtV1uvaJcnH11dWqzXHvjMzM2Wz2Zw/drstn0cDAABMX2MhSadPn9L4cQl6LK6tqkRUzXEbiyXnfW/VvmTRfC2YN8egCgEAQG6YHiysVqteHz1K1WKqq1OXpyRJ/v4BunDhgiTJbrdfaQsIlL+/v06fOunc1263KyAwKMd+27bvoNi4ttdta1O/Xj3yaRQAAEAyOVhctNv15uujVbp0aXV9pptstguyWFwUFV1N69cmKiqqmtYkrlJERKQ8PDwUFR2jFcu+1r69KbLZbDpx/LiioqJz7Nvd3V3u7u4FPCIAAO5tpgaLTZu+14ED+yRJL3SPlySVDg7WyFdf1+RJH2jgy/1VPjRUvfsOkCTVrlNXrVrHafy4BLm6uSr+2e4KrRBmWv0AAOBGpgaLZs1bqFnzFjm+N3zU6JvaLBaLunSNV5eu8fldGgAAyAPuvAkAAAxDsAAAAIYx/aoQ5M7REU3NLuEmFV5bZ3YJAIBChhkLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwBAsAAGAYggUAADAMwQIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMG5mFwDAHOEJSWaXcJPUobXMLgHAXWLGAgAAGIZgAQAADEOwAAAAhjF9jcW5c2e14dt1WrcmUX37v6SwiuE6dfKkJk96XwcP7Fe58qHq2ae/wsMryeFwaNbMaVqbuFqurq6Ka9NOsXFtzB4CAAC4ytQZC7vdrv69X9DGDRuUeviQs33G9Kny9PTS+HcmqGLFcE2ZPFGSlLTtB61csUyDhgxXj559NGPaVB1JPWxS9QAA4H+ZGiw8PDw0YfIU9X/p7ze0J+/epcZNm6l0cLCat2ipgwf2KyMjQ8m7dymi6gOqXLmK6tStp5CQMkrZk5xj35mZmbLZbM4fu91WEEMCAOCeZuqpEFdXVwUGBunkbyduaE+3WuXl5SVJ8vX1dbZZr2uXJB9fX1mt1hz7XrJovhbMm5NPlQMAgJyYvsYityyWO2tv276DYuPaOl/b7Tb169UjHyoDAADXFMpg4e8foAsXLki6sg5DkvwDAuXv76/Tp046t7Pb7QoIDMqxD3d3d7m7u+d7rQAA4HeF8nLTqOhqWr82UadOntSaxFWKiIiUh4eHoqJjtDdlj/btTdH2pG06cfy4oqKizS4XAABcVShnLLrGd9PkSR9o4Mv9VT40VL37DpAk1a5TV61ax2n8uAS5urkq/tnuCq0QZnK1AADgmkIRLIJDymjWvMXO16WDgzV81OibtrNYLOrSNV5dusYXYHUAACC3CuWpEAAAUDQRLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBh3MwuACisjo5oanYJN6nw2jqzSwCA22LGAgAAGIYZCwAoIOEJSWaXcJPUobXMLgHFDDMWAADAMAQLAABgGIIFAAAwDMECAAAYhmABAAAMQ7AAAACGIVgAAADDECwAAIBhCBYAAMAwRfLOm99v+FazP5uuCxfOq16DB/Xc873k7u5udlkA7hLPZwGKviIXLNLTrfrwXx+oW4+eqlKlqhJGj1Diqm/0SKtYs0sDABQy3Ea94BW5YHFg/z45HFKz5i1ksVhUu3ZdJe/eRbAAgDxglghGK3LBwmq1ytPLUxaLRZLk4+urEydO3LRdZmamMjMzna9ttguSJLvd9oef4Zp10aBqjXMxy+wKbmaz/fGxzC2Oee5wzAsex7zgFfdjbuT4Csq1706Hw/GH21ocudmqEFm3NlHTP/2v/vPf6ZKkGdOm6vChgxo2cvQN282fO0sL5s0xo0QAAIqliR9+pFKlSt92myI3Y+Hv76+Ldruys7Pl4uIiu82mgMCgm7Zr276DYuPaOl9nZ2frwvnz8vP3d852FBV2u039evXQxA8/kre3j9nl3BM45gWPY17wOOYFr6gec4fDoYsX7SpRouQfblvkgkXlKlXl4uKiVd8sV3RMdSUl/aj2T3S6aTt3d/ebrhTx8/MrqDLzhbe3j3x8is4fxOKAY17wOOYFj2Ne8IriMff19c3VdkUuWAQEBKhXnwGaNXOa5syaofoNHlSz5i3MLgsAAKgIBgtJeqhRYz3UqLHZZQAAgP/BnTeLAHd3dz3R8W/cBKwAccwLHse84HHMC969cMyL3FUhAACg8GLGAgAAGIZgAQAADEOwAAAAhimSV4XcS3iSqznOnTurDd+u07o1ierb/yWFVQw3u6Ri7Ysli/T1l58rI+OiatSqrV59BsjLy8vssoqtrKwszZk1Q6tXrpAk1a5TT8/36isPDw+TK7s3vPf2m9q08TvNmrfY7FLyBTMWhdi1J7m279BJo0aP0/akH5W46huzyyr27Ha7+vd+QRs3bFDq4UNml1Ps7diepDmzZqpP/5f0asIbSklO1rKvvzC7rGLt2/VrtXrlN/rn4GF6NeEN/bRzhzNkIH9t3bJZWzZvNLuMfEWwKMSuf5JrhbAw55Nckb88PDw0YfIU9X/p72aXck9wc3PTk13jVb1GTYWFVVS58uV17uw5s8sq1po1b6GPPpmhyAeiFBQUJDc3N7m6uppdVrFns9k09eN/69HWj5ldSr4iWBRiOT3J1Wq1mlxV8efq6qrAHJ4/g/wRXS1GsXFtJEmphw9p/769atSk8D3Kuzga9I8X9UL3eJUtV04tH2lldjnF3qwZ01S9Ri3FVK9hdin5imBRxBSx56cBuXb69CmNH5egx+LaqkpEVbPLuSf835DhemXYSKUePqy1a1abXU6xtid5t7Zu2aSuTz9rdin5jmBRiF3/JFdJt3ySK1DUWa1WvT56lKrFVFenLk+ZXU6xl5p6WDu2J6lUqdKKqV5T0TEx2r3rJ7PLKtYWLZin9PR0vdivpz54921J0nPPPGlyVfmDq0IKsdw+yRUoyi7a7Xrz9dEqXbq0uj7TTTbbBVksLkXuyY9FyZHDh/Tfj/6tIcNflZe3t/btTdFjj7c1u6xirXffAcrMvCRJ+umnnZoyeaLGjX/X5KryB8GiEONJrrgXbNr0vQ4c2CdJeqF7vCSpdHCwJvzrP2aWVaw1btpcqamHNX5cghwOhx78UyM98mis2WUVa0ElSjh/Dww8IkkKDiljVjn5imeFAAAAw7DGAgAAGIZgAQAADEOwAAAAhiFYAAAAwxAsAACAYQgWAADAMNzHAiggY8eM0o7tSYqNa6Onn+l+w3tLv/pC0z75WGEVwzVu/LtycTEv85/87YQG9O2Z53tJpKYe1uIF87QnebfOn09XqVKl1fChRmr/1w7y8vbOh4oLxvy5s7Rg3hzna4vFRaVKldJ991fWE506Kzy80h3199rIoUrevUtjxr2lypWrGFwtYB5mLIAC8rcuXWWxWLTqm+Wynvv96Z2XMzP15eeLJUmdOj9laqi4Wzu2J2nEkEHatPE7lQ+toEaNm8rNzU2fL16gca+/pqysLLNLzJXBA19Sl47tdPK3Eze9V7lyhB55NFYtH3lUZcuV15bNGzVs8EDtTdljQqVA4cOMBVBA7q9cRfUbPKjNm77XV19+ri5PPS1JSkxcpbS006oSUVX16jfIt8/PysrK10djX8rI0L//NUGXLl1Sn/4vqUnT5pKk7OxsvfH6aO3Yvk0rVyy7q0dGZ2dlyeV/xuBwOORwOAoskNWqU0cdOnVxvl64YK7mzf5Mny9eqIH/N6RAagAKM4IFUIA6dXlKW7ds0jfLv9bjbdvLy8tLny9eIOnKjMY1O3ds1/y5s3T40EF5eXmrfoOGeiq+m7yvnkq4dOmSliycrw3frlNa2mmVKFlSTZr9We3ad5Cb25W/1l06tlOJEiX1p8ZNtH7tGtWqXUe9+72oM2fSNHvmdO3YnqSLF+2qEFZR7Z/oqDp169+y7mvT9v8Y9Iq+XLJYBw8eUFBQkNq0f0It//KoJGl70jalpZ1Wpfvud4YKSXJxcVHLvzyqn3Zu147t2/Ro68ecpxWeevpZxbVpJ0k6fOigXhn0d0VFV9OIVxO0e9dOjR41XDVr1ZGbm5t+2rlD3Z57XpL04b8mqFGTZjqTdlr79qZo8NARiq5WXefOndWsGdP04w9blZFxUfdXjtCTXeMVUTVSkjR54vtatzZRvfoO0PcbvlXy7p/k5+evPz/cUh06dXF+5jUD+vZ01nMr9es31LzZn+nYL0edbRftds2ZPVObN34vq/WcQkLK6C+tWqtV67jb/vk4/uuvmjXzU+36aaeysrL0QFQ1Pf1Md5UPDb3tfkBhUnTnXIEiKDS0gpo0bS673a6lX32h9evW6NTJk4qpXkMx1WtIkn78YYvGjnlVx479ogcfaqTyoRW0auUKTZ74vrOfSR+8q4UL5srbx0eNmzSTRRYtmDtbC+bOvuHzzpxJ0/p1a1S7bj09EF1NDodDY8e8qnVrExVaoYLq1W+on4+k6q03xmrnju1/WP+E995WUIkSqlO3nk6fPqX//udDHUk9LEnav//K8z5yWi9Qv+GDmjlnof45eNgdH7PtST/qxInjatS4icqULeds37B+rS5duqQmTZsrMKiE7Ha7Rg4drHVrE1W5SoTqN3xIhw7uV8JrI5WWlnZDn//5cJJcXV3UoOFDOn8+XQvmzdGPP2xViZKl9MijsfL3D5AkNWn2Z9Vv8OBt67NarZIkPz9/SVdmhl4fPUrLvv5SgYGBavhQI1nTrfr0vx/psxmf3rKf06dOavjQQfph6xbFVK+pGjVra+eOJCW8NkIZGRl3fNwAszBjARSwJzp11oZv12n50i/l6+cn6cbZimlTP5aLi4teS3hD5cqVlySNHzdGWzZv1M9HUhVaIUzePj56uOUjeva5F+Tm5qa006fVt9dz2vj9Bv3tyd/7cnV1VcLYt1Q6OFiSdOLEcf18JFUVwipq2MjRkq58QS9etEB7U/aoeo2at639uRd6q2mzP0uS/vvRv/XN8qXas2e3KoZXUrr1yrqRwMAgYw7UVRXCKmrsm+84Z2JOHP9VklSjZm0NHjpCFotFkrRw/hydOHFcT3T8m/NUxaaN3+m9t9/U118uUdf4bs4+2z/RSU90/JskqeKSSvpsxqdK3v2T6tStp249XlDKnt1KT7eqY6fOt3xQVHZ2tn45+rNmTv9EktTgoYckSRu+Xad9+1JUo2Yt/d+QEXJxcdGJ47/qn38foK+//FytYh9XyZIlb+pv/tzZOp+erl59+qvZnx+WJH35xWLNnPaJ1qxeeVenkICCRLAAClhwcIha/qWVli39UhcuXFC9+g1UJaKqpCtT4SdOHJefn7+WffWlc5+zZ89Kko4cSVVYxXD16tNfJ47/qpUrlun06VO6lHHp6nZnbvgsNzd3Z6iQpBIlSqpEiZL69dgxzZ87S3XrN9SfGjdVoybNclV7aIUw5+9lr84eXPsfe/bV5xk6ZOxzDYODQ5yh4nply5ZzhgpJ2pGUJEn65ehRTf1oiiTJZrsgSfr5SOoN+1bIYRzpV8fxRxbMm3PD1SGS1LhJM8XGPn61jm2SpBYPP+Jc91GmbDnVrFVbW7ds1oF9e1Wy4c2zIDu2X6k/ZU+yDh44IElKSzuVY/1AYUawAEzQ7okOWrb0SnBo276Ds/1aMDh/Pl0rln99035nz1x5f/7cWVo4f54cjuwb3v+jZxV7eHho+Ktj9Nn0T7Vk0QItmDdHAYGBavmXVmr/RMccv8Bv6dp3+tUP9fe/cirg3NUQVNCuHbuN32+46b0zZ87c1OZ0dRy5fdBz5coRqlwlQpmZl5S4eqV8ff303PO9nItKz1294qfE/8xKBAVdeX3xov229SeuXnln9QOFDMECMMH1pwsCAwOdv3v7+EiSypcP1dvvT8px39OnT2nh/Lny9vbWi38fpAceiJKHp6e6dGyXq88uV668/jHoFWVkZChlz259sWSRFs6fI4cjW506P5XnMVWqdJ8k6cDVtRbX27plsyZPfE9VI6P0f0OGy2K58j/53H6Z58a1Y/dawhvOxZr54fqrQqxWq37YullffrlEHTp2liT5XT299b8B68yZK+s8AoOCcuzX29tHFy6c17+m/FclStx8qgQoKli8CRQiYRXCFBAYqGPHftHWLZud7WfPntXqVd84f3c4HAoNDVONmrXk4empUydP5qr/Vd8s17NdO+vTqR/J09NTNWrWVtv2T0iSUg8fvqva69RrID9/f6WmHtbWLZuc7dlZWVqx7CvZbDZFRVeT9PuX6/Ffjzm3+/XYMd2N6GoxkqSvvlii7OwrMzkOh0PLl1757Dtxbfbh8h/cd6PLU0/LxcVFX3+xxHlKKObqOpXE1Suddfx24oR2bE+Sp6enqlZ9IMe+qsVcqf/aPU0k6fLly/r6y891+fLlO6ofMBMzFkAh4uLqqq7x3fSvCe/p3bfGqU7devL29tG2H39QZuYl1apdV2FhFVWyZCnt25eiN14fLU9PT+3ckSRJunw5Uw6H44a1B9er16Ch5s75TMuXfq0Tx48rMDBQSUk/SpJq1613V7V7e3vrhV599f47b+ndt95Qnbr15Ovnr30pe3Ts2C+qdN/9anV1AWL16jXk6uqqxNWrZLValZ2dpe1X10jk1eNt/6qN323Qpo3facigv+v+ylV08OABpR4+pKzsLMU+1ibXfZUtV16HDh7QhPfeVu3addWx85M5bhdaIUzNmrdQ4uqVWrxwnuKffU5Nm/1ZK1cs07Yft2r4kEEqXz5UO7YnKTPzkrrGd7vl3Uc7dX5KO3ds19dffq79+/YqNLSCkpN36/ivxxQcEvKHV6cAhQUzFkAh06Rpc/1z8DBViaiqHduTtHXLZlWuEqGRr72ukiVLysPDQ4OGDFd0terak7xLqYcPqfOTT6t8aAVlZWXp+NWrJnISGBikUaPHqkHDB7U3ZY++27Be/v4Beu6F3s77UdyN+g0e1MjXElSrdh3t2ZOsb9etVbbDoXbtO2jkqwny8PSUdGUxY9/+L6tcuXLanvSjfvvtNz179R4VeRUUFKTXXn9DTZo2V1ralctss7Ky1O25F9T66sLK3OrQsbPuu7+yfjn6822PpyR16NRFHh4eWrlimU6dPCl3d3cNHfGaHv7Lozp96pQ2bfxOQSVKqFffAXrs8ba37Ce0QpheS3hT9Rs8qKM/H9G369fJx8dH/V/6B6ECRYrFYeRJTgAAcE9jxgIAABiGYAEAAAxDsAAAAIYhWAAAAMMQLAAAgGEIFgAAwDAECwAAYBiCBQAAMAzBAgAAGIZgAQAADEOwAAAAhiFYAAAAw/w/yh6I3B7Xg3cAAAAASUVORK5CYII=\n", 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" + "
" ] }, "metadata": {}, @@ -2396,8 +2404,8 @@ }, { "cell_type": "code", - "execution_count": 106, - "id": "437bda25", + "execution_count": 195, + "id": "8284bfc6", "metadata": {}, "outputs": [ { @@ -2406,15 +2414,15 @@ "" ] }, - "execution_count": 106, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": {}, @@ -2433,8 +2441,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "436105ac", + "execution_count": 196, + "id": "e984bfd7", "metadata": {}, "outputs": [], "source": [ @@ -2444,10 +2452,31 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "00863846", + "execution_count": 197, + "id": "88767296", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "g = sns.PairGrid(Train2, hue=\"Output\")\n", "g.map_diag(sns.histplot)\n", @@ -2457,8 +2486,8 @@ }, { "cell_type": "code", - "execution_count": 116, - "id": "0e7b28fb", + "execution_count": 198, + "id": "2025369e", "metadata": {}, "outputs": [ { @@ -2468,45 +2497,45 @@ "svm._classes.SVC ShuffleSplit\n", " precision recall f1-score support\n", "\n", - " 0 0.91 1.00 0.95 1233\n", - " 1 1.00 0.46 0.63 237\n", + " 0 0.89 1.00 0.94 1233\n", + " 1 1.00 0.36 0.53 237\n", "\n", - " accuracy 0.91 1470\n", - " macro avg 0.95 0.73 0.79 1470\n", - "weighted avg 0.92 0.91 0.90 1470\n", + " accuracy 0.90 1470\n", + " macro avg 0.95 0.68 0.74 1470\n", + "weighted avg 0.91 0.90 0.88 1470\n", " \n", "\n", "linear_model._stochastic_gradient.SGDClassifier ShuffleSplit\n", " precision recall f1-score support\n", "\n", - " 0 0.91 0.91 0.91 1233\n", - " 1 0.53 0.54 0.53 237\n", + " 0 0.91 0.95 0.93 1233\n", + " 1 0.64 0.49 0.56 237\n", "\n", - " accuracy 0.85 1470\n", - " macro avg 0.72 0.72 0.72 1470\n", - "weighted avg 0.85 0.85 0.85 1470\n", + " accuracy 0.87 1470\n", + " macro avg 0.77 0.72 0.74 1470\n", + "weighted avg 0.86 0.87 0.87 1470\n", " \n", "\n", "linear_model._perceptron.Perceptron ShuffleSplit\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 1233\n", - " 1 1.00 1.00 1.00 237\n", + " 1 1.00 0.99 0.99 237\n", "\n", " accuracy 1.00 1470\n", - " macro avg 1.00 1.00 1.00 1470\n", + " macro avg 1.00 0.99 1.00 1470\n", "weighted avg 1.00 1.00 1.00 1470\n", " \n", "\n", "naive_bayes.MultinomialNB ShuffleSplit\n", " precision recall f1-score support\n", "\n", - " 0 1.00 0.95 0.97 1233\n", - " 1 0.80 1.00 0.89 237\n", + " 0 1.00 0.94 0.96 1233\n", + " 1 0.74 0.98 0.85 237\n", "\n", - " accuracy 0.96 1470\n", - " macro avg 0.90 0.97 0.93 1470\n", - "weighted avg 0.97 0.96 0.96 1470\n", + " accuracy 0.94 1470\n", + " macro avg 0.87 0.96 0.91 1470\n", + "weighted avg 0.96 0.94 0.95 1470\n", " \n", "\n", "linear_model._passive_aggressive.PassiveAggressiveClassifier ShuffleSplit\n", @@ -2520,18 +2549,7 @@ "weighted avg 1.00 1.00 1.00 1470\n", " \n", "\n", - "neighbors._classification.KNeighborsClassifier ShuffleSplit\n", - " precision recall f1-score support\n", - "\n", - " 0 0.91 0.99 0.95 1233\n", - " 1 0.92 0.50 0.65 237\n", - "\n", - " accuracy 0.91 1470\n", - " macro avg 0.92 0.75 0.80 1470\n", - "weighted avg 0.91 0.91 0.90 1470\n", - " \n", - "\n", - "ensemble._forest.RandomForestClassifier ShuffleSplit\n", + "naive_bayes.GaussianNB ShuffleSplit\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 1233\n", @@ -2542,26 +2560,37 @@ "weighted avg 1.00 1.00 1.00 1470\n", " \n", "\n", - "naive_bayes.GaussianNB ShuffleSplit\n", + "gaussian_process._gpc.GaussianProcessClassifier ShuffleSplit\n", " precision recall f1-score support\n", "\n", - " 0 1.00 1.00 1.00 1233\n", - " 1 1.00 1.00 1.00 237\n", + " 0 0.91 0.95 0.93 1233\n", + " 1 0.68 0.54 0.60 237\n", "\n", - " accuracy 1.00 1470\n", - " macro avg 1.00 1.00 1.00 1470\n", - "weighted avg 1.00 1.00 1.00 1470\n", + " accuracy 0.89 1470\n", + " macro avg 0.80 0.75 0.77 1470\n", + "weighted avg 0.88 0.89 0.88 1470\n", " \n", "\n", - "gaussian_process._gpc.GaussianProcessClassifier ShuffleSplit\n", + "neighbors._classification.KNeighborsClassifier ShuffleSplit\n", " precision recall f1-score support\n", "\n", - " 0 0.92 0.96 0.94 1233\n", - " 1 0.73 0.58 0.65 237\n", + " 0 0.90 0.99 0.94 1233\n", + " 1 0.90 0.43 0.59 237\n", "\n", " accuracy 0.90 1470\n", - " macro avg 0.83 0.77 0.79 1470\n", - "weighted avg 0.89 0.90 0.89 1470\n", + " macro avg 0.90 0.71 0.77 1470\n", + "weighted avg 0.90 0.90 0.89 1470\n", + " \n", + "\n", + "ensemble._forest.RandomForestClassifier ShuffleSplit\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 1233\n", + " 1 1.00 1.00 1.00 237\n", + "\n", + " accuracy 1.00 1470\n", + " macro avg 1.00 1.00 1.00 1470\n", + "weighted avg 1.00 1.00 1.00 1470\n", " \n", "\n", "ensemble._weight_boosting.AdaBoostClassifier ShuffleSplit\n", @@ -2646,10 +2675,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 116, + "execution_count": 198, "metadata": {}, "output_type": "execute_result" } @@ -2658,7 +2687,7 @@ "from sklearn.model_selection import KFold,GroupKFold,ShuffleSplit,RepeatedStratifiedKFold,StratifiedKFold,GroupShuffleSplit,StratifiedShuffleSplit,TimeSeriesSplit\n", "from catboost import CatBoostClassifier\n", "from datetime import datetime\n", - "n_split=8\n", + "n_split=10\n", "acc=[]\n", "\n", "X=Train\n", @@ -2729,25 +2758,25 @@ }, { "cell_type": "code", - "execution_count": 142, - "id": "5971f8df", + "execution_count": 199, + "id": "a89a524d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'subsample': 0.8,\n", - " 'reg_lambda': 1,\n", + " 'reg_lambda': 1.5,\n", " 'reg_alpha': 2,\n", " 'n_estimators': 300,\n", " 'min_child_weight': 1,\n", - " 'max_depth': 6,\n", - " 'learning_rate': 0.1,\n", - " 'gamma': 1.5,\n", + " 'max_depth': 5,\n", + " 'learning_rate': 1,\n", + " 'gamma': 2,\n", " 'colsample_bytree': 0.6}" ] }, - "execution_count": 142, + "execution_count": 199, "metadata": {}, "output_type": "execute_result" } @@ -2783,8 +2812,8 @@ }, { "cell_type": "code", - "execution_count": 122, - "id": "a31efaab", + "execution_count": 200, + "id": "6e0e7e42", "metadata": {}, "outputs": [ { @@ -2811,38 +2840,38 @@ "train_tensor = torch.utils.data.TensorDataset(torch.Tensor(Train),torch.Tensor(Y)) \n", "train_loader = torch.utils.data.DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)\n", "\n", - "train_x, test_x, train_y, test_y = train_test_split(Train, Y,test_size=0.33, random_state=1)\n" + "train_x, test_x, train_y, test_y = train_test_split(Train, Y,test_size=0.2, random_state=1)\n" ] }, { "cell_type": "code", - "execution_count": 123, - "id": "996aab51", + "execution_count": 201, + "id": "5484955d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_2\"\n", + "Model: \"sequential_4\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "conv1d_10 (Conv1D) (None, 32, 64) 192 \n", + "conv1d_20 (Conv1D) (None, 32, 64) 192 \n", "_________________________________________________________________\n", - "conv1d_11 (Conv1D) (None, 31, 32) 4128 \n", + "conv1d_21 (Conv1D) (None, 31, 32) 4128 \n", "_________________________________________________________________\n", - "conv1d_12 (Conv1D) (None, 30, 16) 1040 \n", + "conv1d_22 (Conv1D) (None, 30, 16) 1040 \n", "_________________________________________________________________\n", - "conv1d_13 (Conv1D) (None, 29, 8) 264 \n", + "conv1d_23 (Conv1D) (None, 29, 8) 264 \n", "_________________________________________________________________\n", - "conv1d_14 (Conv1D) (None, 28, 4) 68 \n", + "conv1d_24 (Conv1D) (None, 28, 4) 68 \n", "_________________________________________________________________\n", - "flatten_2 (Flatten) (None, 112) 0 \n", + "flatten_4 (Flatten) (None, 112) 0 \n", "_________________________________________________________________\n", - "dense_4 (Dense) (None, 2) 226 \n", + "dense_8 (Dense) (None, 2) 226 \n", "_________________________________________________________________\n", - "dense_5 (Dense) (None, 1) 3 \n", + "dense_9 (Dense) (None, 1) 3 \n", "=================================================================\n", "Total params: 5,921\n", "Trainable params: 5,921\n", @@ -2876,18 +2905,18 @@ }, { "cell_type": "code", - "execution_count": 125, - "id": "435dd2be", + "execution_count": 202, + "id": "d32c9c97", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "16/16 [==============================] - 0s 3ms/step - loss: 0.1484\n", - "0.14840067726839717\n", - "MSE: 0.1484\n", - "SCORE= 99.61477191426371 %\n", + "10/10 [==============================] - 0s 4ms/step - loss: 0.1900\n", + "0.174648752017897\n", + "MSE: 0.1746\n", + "SCORE= 99.58209001882484 %\n", "\n" ] } @@ -2909,7 +2938,7 @@ }, { "cell_type": "markdown", - "id": "4f023d9b", + "id": "2a5e7bb9", "metadata": {}, "source": [ "# Key Features\n", @@ -2949,14 +2978,6 @@ " 29 'StandardHours'\n", " 30 'Over18'" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "139a5684", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/007/solution/IBM_HR_Employee_Attrition_Profil.html b/007/solution/IBM_HR_Employee_Attrition_Profil.html index 3783750f..ea462869 100644 --- a/007/solution/IBM_HR_Employee_Attrition_Profil.html +++ b/007/solution/IBM_HR_Employee_Attrition_Profil.html @@ -1,4 +1,4 @@ -IBM_HR_Employee_Attrition_Report

Overview

Dataset statistics

Number of variables35
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Total size in memory1.1 MiB
Average record size in memory796.8 B

Variable types

Numeric26
Categorical9

Warnings

EmployeeCount has constant value "1" Constant
Over18 has constant value "Y" Constant
StandardHours has constant value "80" Constant
EmployeeNumber has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
StockOptionLevel has 631 (42.9%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2021-08-28 23:32:49.776096
Analysis finished2021-08-28 23:32:50.550593
Duration0.77 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:52.084272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
IBM_HR_Employee_Attrition_Report

Overview

Dataset statistics

Number of variables34
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Total size in memory1.0 MiB
Average record size in memory738.8 B

Variable types

Numeric26
Categorical8

Warnings

EmployeeCount has constant value "1" Constant
StandardHours has constant value "80" Constant
EmployeeNumber has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
StockOptionLevel has 631 (42.9%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2021-08-29 02:40:42.830974
Analysis finished2021-08-29 02:40:43.993418
Duration1.16 second
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92380952
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:44.201418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2021-08-29T05:02:52.364340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.135373489
Coefficient of variation (CV)0.2474114564
Kurtosis-0.4041451372
Mean36.92380952
Median Absolute Deviation (MAD)6
Skewness0.4132863019
Sum54278
Variance83.45504879
MonotonicityNot monotonic
2021-08-29T08:10:44.446519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.1 KiB
No
1233 
Yes
237 

Characters and Unicode

Total characters3177
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No1233
83.9%
Yes237
 
16.1%
ValueCountFrequency (%)
no1233
83.9%
yes237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1707
53.7%
Uppercase Letter1470
46.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1233
72.2%
e237
 
13.9%
s237
 
13.9%
Uppercase Letter
ValueCountFrequency (%)
N1233
83.9%
Y237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3177
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely1043
71.0%
Travel_Frequently277
 
18.8%
Non-Travel150
 
10.2%
ValueCountFrequency (%)
travel_rarely1043
71.0%
travel_frequently277
 
18.8%
non-travel150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15358
77.7%
Uppercase Letter2940
 
14.9%
Connector Punctuation1320
 
6.7%
Dash Punctuation150
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3067
20.0%
r2790
18.2%
l2790
18.2%
a2513
16.4%
v1470
9.6%
y1320
8.6%
n427
 
2.8%
q277
 
1.8%
u277
 
1.8%
t277
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T1470
50.0%
R1043
35.5%
F277
 
9.4%
N150
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_1320
100.0%
Dash Punctuation
ValueCountFrequency (%)
-150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18298
92.6%
Common1470
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3067
16.8%
r2790
15.2%
l2790
15.2%
a2513
13.7%
T1470
8.0%
v1470
8.0%
y1320
7.2%
R1043
 
5.7%
n427
 
2.3%
F277
 
1.5%
Other values (5)1131
 
6.2%
Common
ValueCountFrequency (%)
_1320
89.8%
-150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII19768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:52.869185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.1 KiB
No
1233 
Yes
237 

Characters and Unicode

Total characters3177
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No1233
83.9%
Yes237
 
16.1%
ValueCountFrequency (%)
no1233
83.9%
yes237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1707
53.7%
Uppercase Letter1470
46.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1233
72.2%
e237
 
13.9%
s237
 
13.9%
Uppercase Letter
ValueCountFrequency (%)
N1233
83.9%
Y237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3177
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1233
38.8%
o1233
38.8%
Y237
 
7.5%
e237
 
7.5%
s237
 
7.5%

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size101.3 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely1043
71.0%
Travel_Frequently277
 
18.8%
Non-Travel150
 
10.2%
ValueCountFrequency (%)
travel_rarely1043
71.0%
travel_frequently277
 
18.8%
non-travel150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15358
77.7%
Uppercase Letter2940
 
14.9%
Connector Punctuation1320
 
6.7%
Dash Punctuation150
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3067
20.0%
r2790
18.2%
l2790
18.2%
a2513
16.4%
v1470
9.6%
y1320
8.6%
n427
 
2.8%
q277
 
1.8%
u277
 
1.8%
t277
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T1470
50.0%
R1043
35.5%
F277
 
9.4%
N150
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_1320
100.0%
Dash Punctuation
ValueCountFrequency (%)
-150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18298
92.6%
Common1470
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3067
16.8%
r2790
15.2%
l2790
15.2%
a2513
13.7%
T1470
8.0%
v1470
8.0%
y1320
7.2%
R1043
 
5.7%
n427
 
2.3%
F277
 
1.5%
Other values (5)1131
 
6.2%
Common
ValueCountFrequency (%)
_1320
89.8%
-150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII19768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3067
15.5%
r2790
14.1%
l2790
14.1%
a2513
12.7%
T1470
7.4%
v1470
7.4%
_1320
6.7%
y1320
6.7%
R1043
 
5.3%
n427
 
2.2%
Other values (7)1558
7.9%

DailyRate
Real number (ℝ≥0)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.4857143
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:45.232543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5090999
Coefficient of variation (CV)0.5028240288
Kurtosis-1.203822808
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003518568352
Sum1179654
Variance162819.5937
MonotonicityNot monotonic
2021-08-29T05:02:53.125745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5090999
Coefficient of variation (CV)0.5028240288
Kurtosis-1.203822808
Mean802.4857143
Median Absolute Deviation (MAD)344
Skewness-0.003518568352
Sum1179654
Variance162819.5937
MonotonicityNot monotonic
2021-08-29T08:10:45.541061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916
 
0.4%
4085
 
0.3%
5305
 
0.3%
13295
 
0.3%
10825
 
0.3%
3295
 
0.3%
8294
 
0.3%
14694
 
0.3%
2674
 
0.3%
2174
 
0.3%
Other values (876)1423
96.8%
ValueCountFrequency (%)
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1061
 
0.1%
1071
 
0.1%
1091
 
0.1%
1113
0.2%
1151
 
0.1%
1162
0.1%
ValueCountFrequency (%)
14991
 
0.1%
14981
 
0.1%
14962
0.1%
14953
0.2%
14921
 
0.1%
14904
0.3%
14881
 
0.1%
14853
0.2%
14821
 
0.1%
14802
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size105.7 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18877
77.6%
Uppercase Letter2494
 
10.3%
Space Separator1985
 
8.2%
Other Punctuation961
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5377
28.5%
s1533
 
8.1%
a1470
 
7.8%
l1407
 
7.5%
r1024
 
5.4%
c1024
 
5.4%
o1024
 
5.4%
m1024
 
5.4%
n1024
 
5.4%
h961
 
5.1%
Other values (4)3009
15.9%
Uppercase Letter
ValueCountFrequency (%)
R1024
41.1%
D961
38.5%
S446
17.9%
H63
 
2.5%
Space Separator
ValueCountFrequency (%)
1985
100.0%
Other Punctuation
ValueCountFrequency (%)
&961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21371
87.9%
Common2946
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5377
25.2%
s1533
 
7.2%
a1470
 
6.9%
l1407
 
6.6%
R1024
 
4.8%
r1024
 
4.8%
c1024
 
4.8%
o1024
 
4.8%
m1024
 
4.8%
n1024
 
4.8%
Other values (8)5440
25.5%
Common
ValueCountFrequency (%)
1985
67.4%
&961
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII24317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:53.418707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6916
 
0.4%
4085
 
0.3%
5305
 
0.3%
13295
 
0.3%
10825
 
0.3%
3295
 
0.3%
8294
 
0.3%
14694
 
0.3%
2674
 
0.3%
2174
 
0.3%
Other values (876)1423
96.8%
ValueCountFrequency (%)
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1061
 
0.1%
1071
 
0.1%
1091
 
0.1%
1113
0.2%
1151
 
0.1%
1162
0.1%
ValueCountFrequency (%)
14991
 
0.1%
14981
 
0.1%
14962
0.1%
14953
0.2%
14921
 
0.1%
14904
0.3%
14881
 
0.1%
14853
0.2%
14821
 
0.1%
14802
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size105.7 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development961
65.4%
Sales446
30.3%
Human Resources63
 
4.3%
ValueCountFrequency (%)
research961
27.8%
961
27.8%
development961
27.8%
sales446
12.9%
human63
 
1.8%
resources63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18877
77.6%
Uppercase Letter2494
 
10.3%
Space Separator1985
 
8.2%
Other Punctuation961
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5377
28.5%
s1533
 
8.1%
a1470
 
7.8%
l1407
 
7.5%
r1024
 
5.4%
c1024
 
5.4%
o1024
 
5.4%
m1024
 
5.4%
n1024
 
5.4%
h961
 
5.1%
Other values (4)3009
15.9%
Uppercase Letter
ValueCountFrequency (%)
R1024
41.1%
D961
38.5%
S446
17.9%
H63
 
2.5%
Space Separator
ValueCountFrequency (%)
1985
100.0%
Other Punctuation
ValueCountFrequency (%)
&961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21371
87.9%
Common2946
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5377
25.2%
s1533
 
7.2%
a1470
 
6.9%
l1407
 
6.6%
R1024
 
4.8%
r1024
 
4.8%
c1024
 
4.8%
o1024
 
4.8%
m1024
 
4.8%
n1024
 
4.8%
Other values (8)5440
25.5%
Common
ValueCountFrequency (%)
1985
67.4%
&961
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII24317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5377
22.1%
1985
 
8.2%
s1533
 
6.3%
a1470
 
6.0%
l1407
 
5.8%
R1024
 
4.2%
r1024
 
4.2%
c1024
 
4.2%
o1024
 
4.2%
m1024
 
4.2%
Other values (10)7425
30.5%

DistanceFromHome
Real number (ℝ≥0)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517007
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:46.121162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2021-08-29T05:02:53.604715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.106864436
Coefficient of variation (CV)0.8818982254
Kurtosis-0.2248334049
Mean9.192517007
Median Absolute Deviation (MAD)5
Skewness0.9581179957
Sum13513
Variance65.72125098
MonotonicityNot monotonic
2021-08-29T08:10:46.307613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Education
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.91292517
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:53.742818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

Education
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.91292517
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:46.566451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.024164945
Coefficient of variation (CV)0.3515932902
Kurtosis-0.5591149664
Mean2.91292517
Median Absolute Deviation (MAD)1
Skewness-0.289681082
Sum4282
Variance1.048913834
MonotonicityNot monotonic
2021-08-29T05:02:53.867962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.024164945
Coefficient of variation (CV)0.3515932902
Kurtosis-0.5591149664
Mean2.91292517
Median Absolute Deviation (MAD)1
Skewness-0.289681082
Sum4282
Variance1.048913834
MonotonicityNot monotonic
2021-08-29T08:10:46.770969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%
ValueCountFrequency (%)
1170
 
11.6%
2282
19.2%
3572
38.9%
4398
27.1%
548
 
3.3%
ValueCountFrequency (%)
548
 
3.3%
4398
27.1%
3572
38.9%
2282
19.2%
1170
 
11.6%

EducationField
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.1 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Characters and Unicode

Total characters15484
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences606
41.2%
Medical464
31.6%
Marketing159
 
10.8%
Technical Degree132
 
9.0%
Other82
 
5.6%
Human Resources27
 
1.8%
ValueCountFrequency (%)
life606
27.1%
sciences606
27.1%
medical464
20.8%
marketing159
 
7.1%
technical132
 
5.9%
degree132
 
5.9%
other82
 
3.7%
human27
 
1.2%
resources27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12484
80.6%
Uppercase Letter2235
 
14.4%
Space Separator765
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3105
24.9%
i1967
15.8%
c1967
15.8%
n924
 
7.4%
a782
 
6.3%
s660
 
5.3%
f606
 
4.9%
l596
 
4.8%
d464
 
3.7%
r400
 
3.2%
Other values (7)1013
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M623
27.9%
L606
27.1%
S606
27.1%
T132
 
5.9%
D132
 
5.9%
O82
 
3.7%
H27
 
1.2%
R27
 
1.2%
Space Separator
ValueCountFrequency (%)
765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14719
95.1%
Common765
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3105
21.1%
i1967
13.4%
c1967
13.4%
n924
 
6.3%
a782
 
5.3%
s660
 
4.5%
M623
 
4.2%
L606
 
4.1%
f606
 
4.1%
S606
 
4.1%
Other values (15)2873
19.5%
Common
ValueCountFrequency (%)
765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

EmployeeCount
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:54.208514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%
ValueCountFrequency (%)
1170
 
11.6%
2282
19.2%
3572
38.9%
4398
27.1%
548
 
3.3%
ValueCountFrequency (%)
548
 
3.3%
4398
27.1%
3572
38.9%
2282
19.2%
1170
 
11.6%

EducationField
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.1 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Characters and Unicode

Total characters15484
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences606
41.2%
Medical464
31.6%
Marketing159
 
10.8%
Technical Degree132
 
9.0%
Other82
 
5.6%
Human Resources27
 
1.8%
ValueCountFrequency (%)
life606
27.1%
sciences606
27.1%
medical464
20.8%
marketing159
 
7.1%
technical132
 
5.9%
degree132
 
5.9%
other82
 
3.7%
human27
 
1.2%
resources27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12484
80.6%
Uppercase Letter2235
 
14.4%
Space Separator765
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3105
24.9%
i1967
15.8%
c1967
15.8%
n924
 
7.4%
a782
 
6.3%
s660
 
5.3%
f606
 
4.9%
l596
 
4.8%
d464
 
3.7%
r400
 
3.2%
Other values (7)1013
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M623
27.9%
L606
27.1%
S606
27.1%
T132
 
5.9%
D132
 
5.9%
O82
 
3.7%
H27
 
1.2%
R27
 
1.2%
Space Separator
ValueCountFrequency (%)
765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14719
95.1%
Common765
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3105
21.1%
i1967
13.4%
c1967
13.4%
n924
 
6.3%
a782
 
5.3%
s660
 
4.5%
M623
 
4.2%
L606
 
4.1%
f606
 
4.1%
S606
 
4.1%
Other values (15)2873
19.5%
Common
ValueCountFrequency (%)
765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3105
20.1%
i1967
12.7%
c1967
12.7%
n924
 
6.0%
a782
 
5.1%
765
 
4.9%
s660
 
4.3%
M623
 
4.0%
L606
 
3.9%
f606
 
3.9%
Other values (16)3479
22.5%

EmployeeCount
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:47.172364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1470
Variance0
MonotonicityIncreasing
2021-08-29T05:02:54.308045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum1470
Variance0
MonotonicityIncreasing
2021-08-29T08:10:47.497000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%

EmployeeNumber
Real number (ℝ≥0)

UNIQUE

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.865306
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:54.459066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%
ValueCountFrequency (%)
11470
100.0%

EmployeeNumber
Real number (ℝ≥0)

UNIQUE

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.865306
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:47.753078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.0243348
Coefficient of variation (CV)0.5874180063
Kurtosis-1.223178906
Mean1024.865306
Median Absolute Deviation (MAD)533.5
Skewness0.01657401958
Sum1506552
Variance362433.2997
MonotonicityStrictly increasing
2021-08-29T05:02:54.685068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.0243348
Coefficient of variation (CV)0.5874180063
Kurtosis-1.223178906
Mean1024.865306
Median Absolute Deviation (MAD)533.5
Skewness0.01657401958
Sum1506552
Variance362433.2997
MonotonicityStrictly increasing
2021-08-29T08:10:47.982239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
13911
 
0.1%
13891
 
0.1%
13871
 
0.1%
13831
 
0.1%
13821
 
0.1%
13801
 
0.1%
13791
 
0.1%
13771
 
0.1%
13751
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

EnvironmentSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.721768707
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:55.732091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
13911
 
0.1%
13891
 
0.1%
13871
 
0.1%
13831
 
0.1%
13821
 
0.1%
13801
 
0.1%
13791
 
0.1%
13771
 
0.1%
13751
 
0.1%
Other values (1460)1460
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
41
0.1%
51
0.1%
71
0.1%
81
0.1%
101
0.1%
111
0.1%
121
0.1%
131
0.1%
ValueCountFrequency (%)
20681
0.1%
20651
0.1%
20641
0.1%
20621
0.1%
20611
0.1%
20601
0.1%
20571
0.1%
20561
0.1%
20551
0.1%
20541
0.1%

EnvironmentSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.721768707
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:48.245258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.093082215
Coefficient of variation (CV)0.4016073121
Kurtosis-1.202520522
Mean2.721768707
Median Absolute Deviation (MAD)1
Skewness-0.3216544477
Sum4001
Variance1.194828728
MonotonicityNot monotonic
2021-08-29T05:02:55.861089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.093082215
Coefficient of variation (CV)0.4016073121
Kurtosis-1.202520522
Mean2.721768707
Median Absolute Deviation (MAD)1
Skewness-0.3216544477
Sum4001
Variance1.194828728
MonotonicityNot monotonic
2021-08-29T08:10:48.383258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
ValueCountFrequency (%)
1284
19.3%
2287
19.5%
3453
30.8%
4446
30.3%
ValueCountFrequency (%)
4446
30.3%
3453
30.8%
2287
19.5%
1284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
Male
882 
Female
588 

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5586
79.2%
Uppercase Letter1470
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2058
36.8%
a1470
26.3%
l1470
26.3%
m588
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.205973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
ValueCountFrequency (%)
1284
19.3%
2287
19.5%
3453
30.8%
4446
30.3%
ValueCountFrequency (%)
4446
30.3%
3453
30.8%
2287
19.5%
1284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size88.8 KiB
Male
882 
Female
588 

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male882
60.0%
Female588
40.0%
ValueCountFrequency (%)
male882
60.0%
female588
40.0%

Most occurring characters

ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5586
79.2%
Uppercase Letter1470
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2058
36.8%
a1470
26.3%
l1470
26.3%
m588
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
M882
60.0%
F588
40.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2058
29.2%
a1470
20.8%
l1470
20.8%
M882
12.5%
F588
 
8.3%
m588
 
8.3%

HourlyRate
Real number (ℝ≥0)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.89115646
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:48.732179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
MonotonicityNot monotonic
2021-08-29T05:02:56.421027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.32942759
Coefficient of variation (CV)0.3085304415
Kurtosis-1.196398456
Mean65.89115646
Median Absolute Deviation (MAD)18
Skewness-0.0323109529
Sum96860
Variance413.2856263
MonotonicityNot monotonic
2021-08-29T08:10:49.090399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6629
 
2.0%
9828
 
1.9%
4228
 
1.9%
4828
 
1.9%
8428
 
1.9%
5727
 
1.8%
7927
 
1.8%
9627
 
1.8%
5426
 
1.8%
5226
 
1.8%
Other values (61)1196
81.4%
ValueCountFrequency (%)
3019
1.3%
3115
1.0%
3224
1.6%
3319
1.3%
3412
0.8%
3518
1.2%
3618
1.2%
3718
1.2%
3813
0.9%
3917
1.2%
ValueCountFrequency (%)
10019
1.3%
9920
1.4%
9828
1.9%
9721
1.4%
9627
1.8%
9523
1.6%
9422
1.5%
9316
1.1%
9225
1.7%
9118
1.2%

JobInvolvement
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.729931973
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.578028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6629
 
2.0%
9828
 
1.9%
4228
 
1.9%
4828
 
1.9%
8428
 
1.9%
5727
 
1.8%
7927
 
1.8%
9627
 
1.8%
5426
 
1.8%
5226
 
1.8%
Other values (61)1196
81.4%
ValueCountFrequency (%)
3019
1.3%
3115
1.0%
3224
1.6%
3319
1.3%
3412
0.8%
3518
1.2%
3618
1.2%
3718
1.2%
3813
0.9%
3917
1.2%
ValueCountFrequency (%)
10019
1.3%
9920
1.4%
9828
1.9%
9721
1.4%
9627
1.8%
9523
1.6%
9422
1.5%
9316
1.1%
9225
1.7%
9118
1.2%

JobInvolvement
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.729931973
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:49.358543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.711561143
Coefficient of variation (CV)0.2606516023
Kurtosis0.2709987665
Mean2.729931973
Median Absolute Deviation (MAD)0
Skewness-0.498419364
Sum4013
Variance0.5063192602
MonotonicityNot monotonic
2021-08-29T05:02:56.706030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.711561143
Coefficient of variation (CV)0.2606516023
Kurtosis0.2709987665
Mean2.729931973
Median Absolute Deviation (MAD)0
Skewness-0.498419364
Sum4013
Variance0.5063192602
MonotonicityNot monotonic
2021-08-29T08:10:49.521486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%
ValueCountFrequency (%)
183
 
5.6%
2375
25.5%
3868
59.0%
4144
 
9.8%
ValueCountFrequency (%)
4144
 
9.8%
3868
59.0%
2375
25.5%
183
 
5.6%

JobLevel
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.063945578
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:56.829028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%
ValueCountFrequency (%)
183
 
5.6%
2375
25.5%
3868
59.0%
4144
 
9.8%
ValueCountFrequency (%)
4144
 
9.8%
3868
59.0%
2375
25.5%
183
 
5.6%

JobLevel
Real number (ℝ≥0)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.063945578
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:49.689501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106939899
Coefficient of variation (CV)0.5363222319
Kurtosis0.3991520554
Mean2.063945578
Median Absolute Deviation (MAD)1
Skewness1.025401283
Sum3034
Variance1.22531594
MonotonicityNot monotonic
2021-08-29T05:02:57.028030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.106939899
Coefficient of variation (CV)0.5363222319
Kurtosis0.3991520554
Mean2.063945578
Median Absolute Deviation (MAD)1
Skewness1.025401283
Sum3034
Variance1.22531594
MonotonicityNot monotonic
2021-08-29T08:10:49.849517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
569
 
4.7%
4106
 
7.2%
3218
14.8%
2534
36.3%
1543
36.9%

JobRole
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size107.9 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22358
84.2%
Uppercase Letter2838
 
10.7%
Space Separator1368
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3905
17.5%
a2580
11.5%
t2098
9.4%
c2061
9.2%
i2012
9.0%
r1984
8.9%
n1468
 
6.6%
s1391
 
6.2%
o795
 
3.6%
h762
 
3.4%
Other values (10)3302
14.8%
Uppercase Letter
ValueCountFrequency (%)
S701
24.7%
R638
22.5%
E326
11.5%
L259
 
9.1%
T259
 
9.1%
M247
 
8.7%
D225
 
7.9%
H183
 
6.4%
Space Separator
ValueCountFrequency (%)
1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25196
94.9%
Common1368
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3905
15.5%
a2580
10.2%
t2098
 
8.3%
c2061
 
8.2%
i2012
 
8.0%
r1984
 
7.9%
n1468
 
5.8%
s1391
 
5.5%
o795
 
3.2%
h762
 
3.0%
Other values (18)6140
24.4%
Common
ValueCountFrequency (%)
1368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

JobSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.728571429
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:57.376150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
ValueCountFrequency (%)
569
 
4.7%
4106
 
7.2%
3218
14.8%
2534
36.3%
1543
36.9%

JobRole
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size107.9 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive326
22.2%
Research Scientist292
19.9%
Laboratory Technician259
17.6%
Manufacturing Director145
9.9%
Healthcare Representative131
8.9%
Manager102
 
6.9%
Sales Representative83
 
5.6%
Research Director80
 
5.4%
Human Resources52
 
3.5%
ValueCountFrequency (%)
sales409
14.4%
research372
13.1%
executive326
11.5%
scientist292
10.3%
laboratory259
9.1%
technician259
9.1%
director225
7.9%
representative214
7.5%
manufacturing145
 
5.1%
healthcare131
 
4.6%
Other values (3)206
7.3%

Most occurring characters

ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22358
84.2%
Uppercase Letter2838
 
10.7%
Space Separator1368
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3905
17.5%
a2580
11.5%
t2098
9.4%
c2061
9.2%
i2012
9.0%
r1984
8.9%
n1468
 
6.6%
s1391
 
6.2%
o795
 
3.6%
h762
 
3.4%
Other values (10)3302
14.8%
Uppercase Letter
ValueCountFrequency (%)
S701
24.7%
R638
22.5%
E326
11.5%
L259
 
9.1%
T259
 
9.1%
M247
 
8.7%
D225
 
7.9%
H183
 
6.4%
Space Separator
ValueCountFrequency (%)
1368
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25196
94.9%
Common1368
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3905
15.5%
a2580
10.2%
t2098
 
8.3%
c2061
 
8.2%
i2012
 
8.0%
r1984
 
7.9%
n1468
 
5.8%
s1391
 
5.5%
o795
 
3.2%
h762
 
3.0%
Other values (18)6140
24.4%
Common
ValueCountFrequency (%)
1368
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3905
14.7%
a2580
 
9.7%
t2098
 
7.9%
c2061
 
7.8%
i2012
 
7.6%
r1984
 
7.5%
n1468
 
5.5%
s1391
 
5.2%
1368
 
5.1%
o795
 
3.0%
Other values (19)6902
26.0%

JobSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.728571429
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:50.238626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.102846123
Coefficient of variation (CV)0.404184443
Kurtosis-1.222192568
Mean2.728571429
Median Absolute Deviation (MAD)1
Skewness-0.3296719587
Sum4011
Variance1.216269571
MonotonicityNot monotonic
2021-08-29T05:02:57.519179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.102846123
Coefficient of variation (CV)0.404184443
Kurtosis-1.222192568
Mean2.728571429
Median Absolute Deviation (MAD)1
Skewness-0.3296719587
Sum4011
Variance1.216269571
MonotonicityNot monotonic
2021-08-29T08:10:50.410607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
ValueCountFrequency (%)
1289
19.7%
2280
19.0%
3442
30.1%
4459
31.2%
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
2280
19.0%
1289
19.7%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size91.9 KiB
Married
673 
Single
470 
Divorced
327 

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8677
85.5%
Uppercase Letter1470
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1673
19.3%
i1470
16.9%
e1470
16.9%
d1000
11.5%
a673
7.8%
n470
 
5.4%
g470
 
5.4%
l470
 
5.4%
v327
 
3.8%
o327
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M673
45.8%
S470
32.0%
D327
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin10147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

MonthlyIncome
Real number (ℝ≥0)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:57.860181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
ValueCountFrequency (%)
1289
19.7%
2280
19.0%
3442
30.1%
4459
31.2%
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
2280
19.0%
1289
19.7%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size91.9 KiB
Married
673 
Single
470 
Divorced
327 

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married673
45.8%
Single470
32.0%
Divorced327
22.2%
ValueCountFrequency (%)
married673
45.8%
single470
32.0%
divorced327
22.2%

Most occurring characters

ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8677
85.5%
Uppercase Letter1470
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r1673
19.3%
i1470
16.9%
e1470
16.9%
d1000
11.5%
a673
7.8%
n470
 
5.4%
g470
 
5.4%
l470
 
5.4%
v327
 
3.8%
o327
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M673
45.8%
S470
32.0%
D327
22.2%

Most occurring scripts

ValueCountFrequency (%)
Latin10147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII10147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r1673
16.5%
i1470
14.5%
e1470
14.5%
d1000
9.9%
M673
6.6%
a673
6.6%
S470
 
4.6%
n470
 
4.6%
g470
 
4.6%
l470
 
4.6%
Other values (4)1308
12.9%

MonthlyIncome
Real number (ℝ≥0)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.931293
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:50.821279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2021-08-29T05:02:58.055215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.956783
Coefficient of variation (CV)0.7239745541
Kurtosis1.005232691
Mean6502.931293
Median Absolute Deviation (MAD)2199
Skewness1.369816681
Sum9559309
Variance22164857.07
MonotonicityNot monotonic
2021-08-29T08:10:51.039049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:58.361324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

MonthlyRate
Real number (ℝ≥0)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.1034
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:51.264504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
MonotonicityNot monotonic
2021-08-29T05:02:58.586336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786044
Coefficient of variation (CV)0.4972915967
Kurtosis-1.2149561
Mean14313.1034
Median Absolute Deviation (MAD)6206.5
Skewness0.01857780789
Sum21040262
Variance50662878.17
MonotonicityNot monotonic
2021-08-29T08:10:51.541522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42233
 
0.2%
91503
 
0.2%
95582
 
0.1%
128582
 
0.1%
220742
 
0.1%
253262
 
0.1%
90962
 
0.1%
130082
 
0.1%
123552
 
0.1%
77442
 
0.1%
Other values (1417)1448
98.5%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.1%
21371
0.1%
22271
0.1%
22431
0.1%
22531
0.1%
ValueCountFrequency (%)
269991
0.1%
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269331
0.1%
269141
0.1%
268971
0.1%
268941
0.1%
268621
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:58.760371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42233
 
0.2%
91503
 
0.2%
95582
 
0.1%
128582
 
0.1%
220742
 
0.1%
253262
 
0.1%
90962
 
0.1%
130082
 
0.1%
123552
 
0.1%
77442
 
0.1%
Other values (1417)1448
98.5%
ValueCountFrequency (%)
20941
0.1%
20971
0.1%
21041
0.1%
21121
0.1%
21221
0.1%
21252
0.1%
21371
0.1%
22271
0.1%
22431
0.1%
22531
0.1%
ValueCountFrequency (%)
269991
0.1%
269971
0.1%
269681
0.1%
269591
0.1%
269561
0.1%
269331
0.1%
269141
0.1%
268971
0.1%
268941
0.1%
268621
0.1%

NumCompaniesWorked
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.693197279
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:51.753525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2021-08-29T05:02:58.870367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009006
Coefficient of variation (CV)0.9275254455
Kurtosis0.01021381669
Mean2.693197279
Median Absolute Deviation (MAD)1
Skewness1.026471112
Sum3959
Variance6.240048994
MonotonicityNot monotonic
2021-08-29T08:10:51.910075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

Over18
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size83.4 KiB
Y
1470 

Characters and Unicode

Total characters1470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1470
100.0%
ValueCountFrequency (%)
y1470
100.0%

Most occurring characters

ValueCountFrequency (%)
Y1470
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1470
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1470
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y1470
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y1470
100.0%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
No
1054 
Yes
416 

Characters and Unicode

Total characters3356
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No1054
71.7%
Yes416
 
28.3%
ValueCountFrequency (%)
no1054
71.7%
yes416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1886
56.2%
Uppercase Letter1470
43.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1054
55.9%
e416
 
22.1%
s416
 
22.1%
Uppercase Letter
ValueCountFrequency (%)
N1054
71.7%
Y416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

PercentSalaryHike
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.468263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

OverTime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.2 KiB
No
1054 
Yes
416 

Characters and Unicode

Total characters3356
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No1054
71.7%
Yes416
 
28.3%
ValueCountFrequency (%)
no1054
71.7%
yes416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1886
56.2%
Uppercase Letter1470
43.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1054
55.9%
e416
 
22.1%
s416
 
22.1%
Uppercase Letter
ValueCountFrequency (%)
N1054
71.7%
Y416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N1054
31.4%
o1054
31.4%
Y416
 
12.4%
e416
 
12.4%
s416
 
12.4%

PercentSalaryHike
Real number (ℝ≥0)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.20952381
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:52.248130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
MonotonicityNot monotonic
2021-08-29T05:02:59.615269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.659937717
Coefficient of variation (CV)0.2406346025
Kurtosis-0.3005982221
Mean15.20952381
Median Absolute Deviation (MAD)2
Skewness0.8211279756
Sum22358
Variance13.39514409
MonotonicityNot monotonic
2021-08-29T08:10:52.418100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Real number (ℝ≥0)

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.153741497
Minimum3
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.746264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

PerformanceRating
Real number (ℝ≥0)

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.153741497
Minimum3
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:52.599839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile4
Maximum4
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3608235246
Coefficient of variation (CV)0.1144112556
Kurtosis1.69593867
Mean3.153741497
Median Absolute Deviation (MAD)0
Skewness1.921882702
Sum4636
Variance0.1301936159
MonotonicityNot monotonic
2021-08-29T05:02:59.863298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile4
Maximum4
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3608235246
Coefficient of variation (CV)0.1144112556
Kurtosis1.69593867
Mean3.153741497
Median Absolute Deviation (MAD)0
Skewness1.921882702
Sum4636
Variance0.1301936159
MonotonicityNot monotonic
2021-08-29T08:10:52.762441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
4226
 
15.4%
31244
84.6%

RelationshipSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.712244898
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:02:59.968264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
ValueCountFrequency (%)
4226
 
15.4%
31244
84.6%

RelationshipSatisfaction
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.712244898
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:52.912623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.081208886
Coefficient of variation (CV)0.3986398453
Kurtosis-1.184813982
Mean2.712244898
Median Absolute Deviation (MAD)1
Skewness-0.3028275652
Sum3987
Variance1.169012656
MonotonicityNot monotonic
2021-08-29T05:03:00.103265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.081208886
Coefficient of variation (CV)0.3986398453
Kurtosis-1.184813982
Mean2.712244898
Median Absolute Deviation (MAD)1
Skewness-0.3028275652
Sum3987
Variance1.169012656
MonotonicityNot monotonic
2021-08-29T08:10:53.074621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
ValueCountFrequency (%)
1276
18.8%
2303
20.6%
3459
31.2%
4432
29.4%
ValueCountFrequency (%)
4432
29.4%
3459
31.2%
2303
20.6%
1276
18.8%

StandardHours
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80
Minimum80
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.231297image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
ValueCountFrequency (%)
1276
18.8%
2303
20.6%
3459
31.2%
4432
29.4%
ValueCountFrequency (%)
4432
29.4%
3459
31.2%
2303
20.6%
1276
18.8%

StandardHours
Real number (ℝ≥0)

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80
Minimum80
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:53.246593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile80
Q180
median80
Q380
95-th percentile80
Maximum80
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean80
Median Absolute Deviation (MAD)0
Skewness0
Sum117600
Variance0
MonotonicityIncreasing
2021-08-29T05:03:00.352326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile80
Q180
median80
Q380
95-th percentile80
Maximum80
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean80
Median Absolute Deviation (MAD)0
Skewness0
Sum117600
Variance0
MonotonicityIncreasing
2021-08-29T08:10:53.435590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%

StockOptionLevel
Real number (ℝ≥0)

ZEROS

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.793877551
Minimum0
Maximum3
Zeros631
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.481292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%
ValueCountFrequency (%)
801470
100.0%

StockOptionLevel
Real number (ℝ≥0)

ZEROS

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.793877551
Minimum0
Maximum3
Zeros631
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:53.585593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8520766679
Coefficient of variation (CV)1.073309942
Kurtosis0.3646343338
Mean0.793877551
Median Absolute Deviation (MAD)1
Skewness0.9689803168
Sum1167
Variance0.726034648
MonotonicityNot monotonic
2021-08-29T05:03:00.600291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8520766679
Coefficient of variation (CV)1.073309942
Kurtosis0.3646343338
Mean0.793877551
Median Absolute Deviation (MAD)1
Skewness0.9689803168
Sum1167
Variance0.726034648
MonotonicityNot monotonic
2021-08-29T08:10:53.738689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
385
 
5.8%
2158
 
10.7%
1596
40.5%
0631
42.9%

TotalWorkingYears
Real number (ℝ≥0)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:00.789573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%
ValueCountFrequency (%)
385
 
5.8%
2158
 
10.7%
1596
40.5%
0631
42.9%

TotalWorkingYears
Real number (ℝ≥0)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.27959184
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:53.946271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2021-08-29T05:03:00.976547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.780781676
Coefficient of variation (CV)0.6898105701
Kurtosis0.9182695366
Mean11.27959184
Median Absolute Deviation (MAD)4
Skewness1.117171853
Sum16581
Variance60.54056348
MonotonicityNot monotonic
2021-08-29T08:10:54.160631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.123560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

TrainingTimesLastYear
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.799319728
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:54.339492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
MonotonicityNot monotonic
2021-08-29T05:03:01.222559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.289270621
Coefficient of variation (CV)0.4605656896
Kurtosis0.494992986
Mean2.799319728
Median Absolute Deviation (MAD)1
Skewness0.5531241711
Sum4115
Variance1.662218734
MonotonicityNot monotonic
2021-08-29T08:10:54.484818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

WorkLifeBalance
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.76122449
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.340562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

WorkLifeBalance
Real number (ℝ≥0)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.76122449
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:54.645540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7064758297
Coefficient of variation (CV)0.2558559915
Kurtosis0.4194604953
Mean2.76122449
Median Absolute Deviation (MAD)0
Skewness-0.5524802991
Sum4059
Variance0.499108098
MonotonicityNot monotonic
2021-08-29T05:03:01.459699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7064758297
Coefficient of variation (CV)0.2558559915
Kurtosis0.4194604953
Mean2.76122449
Median Absolute Deviation (MAD)0
Skewness-0.5524802991
Sum4059
Variance0.499108098
MonotonicityNot monotonic
2021-08-29T08:10:54.817055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%
ValueCountFrequency (%)
180
 
5.4%
2344
 
23.4%
3893
60.7%
4153
 
10.4%
ValueCountFrequency (%)
4153
 
10.4%
3893
60.7%
2344
 
23.4%
180
 
5.4%

YearsAtCompany
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.597768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%
ValueCountFrequency (%)
180
 
5.4%
2344
 
23.4%
3893
60.7%
4153
 
10.4%
ValueCountFrequency (%)
4153
 
10.4%
3893
60.7%
2344
 
23.4%
180
 
5.4%

YearsAtCompany
Real number (ℝ≥0)

ZEROS

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.008163265
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:55.035058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityNot monotonic
2021-08-29T05:03:01.773307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.126525152
Coefficient of variation (CV)0.8741984056
Kurtosis3.935508756
Mean7.008163265
Median Absolute Deviation (MAD)3
Skewness1.764529454
Sum10302
Variance37.53431044
MonotonicityNot monotonic
2021-08-29T08:10:55.291054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:01.917293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

YearsInCurrentRole
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.229251701
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:55.492924image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
MonotonicityNot monotonic
2021-08-29T05:03:02.054959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137035
Coefficient of variation (CV)0.856685128
Kurtosis0.4774207735
Mean4.229251701
Median Absolute Deviation (MAD)3
Skewness0.9173631563
Sum6217
Variance13.12712197
MonotonicityNot monotonic
2021-08-29T08:10:55.659925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:02.184954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%

YearsSinceLastPromotion
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.187755102
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:55.861405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2021-08-29T05:03:02.377955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.222430279
Coefficient of variation (CV)1.472939213
Kurtosis3.612673115
Mean2.187755102
Median Absolute Deviation (MAD)1
Skewness1.984289983
Sum3216
Variance10.3840569
MonotonicityNot monotonic
2021-08-29T08:10:56.044938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T05:03:02.513957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

YearsWithCurrManager
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.123129252
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2021-08-29T08:10:56.223067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2021-08-29T05:03:02.721961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.568136121
Coefficient of variation (CV)0.8653951654
Kurtosis0.1710580839
Mean4.123129252
Median Absolute Deviation (MAD)3
Skewness0.833450992
Sum6061
Variance12.73159537
MonotonicityNot monotonic
2021-08-29T08:10:56.392066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%