Capstone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Featured HR dataset from Kaggle **\n",
    "\n",
    "This dataset is simulated\n",
    "\n",
    "Why are our best and most experienced employees leaving prematurely? Have fun with this database and try to predict which valuable employees will leave next. Fields in the dataset include:\n",
    "\n",
    "    Satisfaction Level\n",
    "    Last evaluation\n",
    "    Number of projects\n",
    "    Average monthly hours\n",
    "    Time spent at the company\n",
    "    Whether they have had a work accident\n",
    "    Whether they have had a promotion in the last 5 years\n",
    "    Departments (column sales)\n",
    "    Salary\n",
    "    Whether the employee has left\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "** Problem Statement **\n",
    "            \n",
    "            Giving the Circumstances surrounding the job (Satisfaction level, no of hours)  \n",
    "            Which Person would likely to leave the Job and out of all the Circumstances \n",
    "            Surrounding the job which Circumstance motivate the people the most in leaving \n",
    "            Their Job. Thus the Problem is classic case of Binary Classification where the \n",
    "            Given set of observations our goal is to predict will the person likely to quit his job \n",
    "\n",
    "            The input Observations in the Dataset are Satisfaction Level, Last Evaluation\n",
    "            Number Projects, Average_Monthly_Hours, Time_Spent_Company,Work  \n",
    "            Accident , promotion Last 5 years, sales and Salary\n",
    "            The Output (Labels ) would be Left wether the person is likely to quit his job or  \n",
    "            not\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Project Overview and Metrices**\n",
    "    \n",
    "            A Featured Dataset from Kaggle Human Resource  Analytics Dataset explores  \n",
    "            The motivation of recognizing the Persons Which will tend to leave the job giving                             \n",
    "            The circumstances surrounding the Job and what are the top characteristic feature \n",
    "            Which motivate the People the most in leaving his/her Job.\n",
    "            The Dataset will be important for the Companies in constantly recognizing that will\n",
    "            It suffer the Brain Drain due to changing Circumstances and taking Steps accordingly\n",
    "            Here is the link of Dataset https://www.kaggle.com/ludobenistant/hr-analytics\n",
    "            \n",
    "            To calculate the Final results the precision would be my option which will tell how     \n",
    "            Accurately i classified my Test as well as Holdout Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('HR_comma_sep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()   #Reading the Dataset Head or top 5 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      "satisfaction_level       14999 non-null float64\n",
      "last_evaluation          14999 non-null float64\n",
      "number_project           14999 non-null int64\n",
      "average_montly_hours     14999 non-null int64\n",
      "time_spend_company       14999 non-null int64\n",
      "Work_accident            14999 non-null int64\n",
      "left                     14999 non-null int64\n",
      "promotion_last_5years    14999 non-null int64\n",
      "sales                    14999 non-null object\n",
      "salary                   14999 non-null object\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No null Values in the Dataset and therefore no preprocessing in terms of filling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81793e8d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rx8eAZcDHI2JyrWMjsD0iFgLPAbsy81REbKcKhnnA5sw8FhE7gJ0RsR84AdzU5folqe91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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f816531ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='left',data=data)\n",
    "#Here O defines the People are not Survived\n",
    "#Here 1 defined the people who survived"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Labels are heavily Unbalanced fortunately ensemble classifiers are pretty robust to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f81792c208>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9c2YeSNnx711N6wHrwaj5nA38U2YeQDnY7A08Djg1Mw8F3gO8pLnFnLDR+4ad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\ngTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f817b2e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(x='sales',y='satisfaction_level',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f817b0a128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Cb6lytAQ4MjN/NNExDXwH+DB/uo/dAlwLPJTSQrsz8DggM/OFEXF/Sv53AtYD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\nSZIkqSaLZ0mSJKkmi2dJkiSppv8PHc3HT/sPRssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f817fad080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(x='sales',y='last_evaluation',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f817b009e8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8181920b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.boxplot(x='sales',y='average_montly_hours',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So far the Continuous Dataset Features are pretty robust and no change is likely to be needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "count        14999.000000     14999.000000    14999.000000   \n",
       "mean             0.612834         0.716102        3.803054   \n",
       "std              0.248631         0.171169        1.232592   \n",
       "min              0.090000         0.360000        2.000000   \n",
       "25%              0.440000         0.560000        3.000000   \n",
       "50%              0.640000         0.720000        4.000000   \n",
       "75%              0.820000         0.870000        5.000000   \n",
       "max              1.000000         1.000000        7.000000   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident          left  \\\n",
       "count          14999.000000        14999.000000   14999.000000  14999.000000   \n",
       "mean             201.050337            3.498233       0.144610      0.238083   \n",
       "std               49.943099            1.460136       0.351719      0.425924   \n",
       "min               96.000000            2.000000       0.000000      0.000000   \n",
       "25%              156.000000            3.000000       0.000000      0.000000   \n",
       "50%              200.000000            3.000000       0.000000      0.000000   \n",
       "75%              245.000000            4.000000       0.000000      0.000000   \n",
       "max              310.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  \n",
       "count           14999.000000  \n",
       "mean                0.021268  \n",
       "std                 0.144281  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apart from the Average_mothly hours which is expected none continuos feature seems to be too far from each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'sales_IT', 'sales_RandD', 'sales_accounting',\n",
       "       'sales_hr', 'sales_management', 'sales_marketing', 'sales_product_mng',\n",
       "       'sales_sales', 'sales_support', 'sales_technical', 'salary_high',\n",
       "       'salary_low', 'salary_medium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Above the Sales and Salary Categorical feature has been succesfully converted into one hot encding while the other two features need not reauire encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data1=pd.get_dummies(data,drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here the drop true mathed is Marked True to avoid perfect Relationship and avoid chance of overhitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=data1.drop('left',axis=1)\n",
    "y=data1['left']\n",
    "\n",
    "# Converting the dataset into Training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
       "       'promotion_last_5years', 'sales_RandD', 'sales_accounting', 'sales_hr',\n",
       "       'sales_management', 'sales_marketing', 'sales_product_mng',\n",
       "       'sales_sales', 'sales_support', 'sales_technical', 'salary_low',\n",
       "       'salary_medium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "\n",
    "# Splitting the dataset into Train and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_holdout, X_test, y_holdout, y_test = train_test_split(X_test, y_test, test_size=0.33, random_state=42)\n",
    "\n",
    "# Splitting the test dataset into actual test and holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(random_state=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=14, splitter='best')"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_holdout)  #Predicting againt Holdout dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.98      2542\n",
      "          1       0.92      0.96      0.94       774\n",
      "\n",
      "avg / total       0.97      0.97      0.97      3316\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_holdout,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree our benchmark model have got a massive Accuracy in holdout dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV #Importing Grid Search\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier #Importing Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Classifier=RandomForestClassifier(random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=21,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier.fit(X_train,y_train) #Fitting against Train Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parameters={\n",
    "    \"n_estimators\": [10,15,5,20],\n",
    "    \"min_samples_split\": [2,3,4],\n",
    "    \"min_samples_leaf\": [1,2,3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import fbeta_score\n",
    "scorer = make_scorer(precision_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rid_obj = GridSearchCV(Classifier, parameters,scoring=scorer) #Making A GRID SEARCH OBJECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_fit = rid_obj.fit(X_test, y_test) #Training And Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['bootstrap', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'min_impurity_split', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start'])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Classifier.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_clf = grid_fit.best_estimator_ #Get the Best Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Predict=best_clf.predict(X_holdout)  #predicting against Holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.99      0.98      2542\n",
      "          1       0.97      0.89      0.93       774\n",
      "\n",
      "avg / total       0.97      0.97      0.97      3316\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_holdout,Predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We find that Decison Tree was very robust in this Dataset and got the Same Score as Decision Tree which signifies prescencs of non dominating Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=best_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.34765793,  0.11709217,  0.18474546,  0.12733978,  0.18379782,\n",
       "        0.00901906,  0.0022888 ,  0.00071283,  0.0008868 ,  0.0017607 ,\n",
       "        0.00106316,  0.00098549,  0.00076705,  0.00234416,  0.0040063 ,\n",
       "        0.00465434,  0.00756838,  0.00330977])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Most_Ratio=pd.Series(X,index=['satisfaction_level', 'last_evaluation', 'number_project',\n",
    "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
    "       'promotion_last_5years', 'sales_RandD', 'sales_accounting', 'sales_hr',\n",
    "       'sales_management', 'sales_marketing', 'sales_product_mng',\n",
    "       'sales_sales', 'sales_support', 'sales_technical', 'salary_low',\n",
    "       'salary_medium'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level       0.347658\n",
      "last_evaluation          0.117092\n",
      "number_project           0.184745\n",
      "average_montly_hours     0.127340\n",
      "time_spend_company       0.183798\n",
      "Work_accident            0.009019\n",
      "promotion_last_5years    0.002289\n",
      "sales_RandD              0.000713\n",
      "sales_accounting         0.000887\n",
      "sales_hr                 0.001761\n",
      "sales_management         0.001063\n",
      "sales_marketing          0.000985\n",
      "sales_product_mng        0.000767\n",
      "sales_sales              0.002344\n",
      "sales_support            0.004006\n",
      "sales_technical          0.004654\n",
      "salary_low               0.007568\n",
      "salary_medium            0.003310\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Most_Ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We find that Out of all Satisfaction Level of individual matters a lost in determing if a person will lose the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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