Credit_Card_Customer_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T9b0ZciKX-92"
   },
   "source": [
    "# **Import Library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eHyRgiudeZfQ",
    "outputId": "8c985523-61c2-4168-ff11-7d6475ded875"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ETq7JG4VYTbU"
   },
   "source": [
    "# **Load Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition_Flag  Customer_Age Gender  Dependent_count Education_Level  \\\n",
       "0  Existing Customer            45      M                3     High School   \n",
       "1  Existing Customer            49      F                5        Graduate   \n",
       "2  Existing Customer            51      M                3        Graduate   \n",
       "3  Existing Customer            40      F                4     High School   \n",
       "4  Existing Customer            40      M                3      Uneducated   \n",
       "\n",
       "  Marital_Status Income_Category Card_Category  Months_on_book  \\\n",
       "0        Married     $60K - $80K          Blue              39   \n",
       "1         Single  Less than $40K          Blue              44   \n",
       "2        Married    $80K - $120K          Blue              36   \n",
       "3        Unknown  Less than $40K          Blue              34   \n",
       "4        Married     $60K - $80K          Blue              21   \n",
       "\n",
       "   Total_Relationship_Count  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "0                         5                       1                      3   \n",
       "1                         6                       1                      2   \n",
       "2                         4                       1                      0   \n",
       "3                         3                       4                      1   \n",
       "4                         5                       1                      0   \n",
       "\n",
       "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0       12691.0                  777          11914.0                 1.335   \n",
       "1        8256.0                  864           7392.0                 1.541   \n",
       "2        3418.0                    0           3418.0                 2.594   \n",
       "3        3313.0                 2517            796.0                 1.405   \n",
       "4        4716.0                    0           4716.0                 2.175   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0             1144              42                1.625                  0.061  \n",
       "1             1291              33                3.714                  0.105  \n",
       "2             1887              20                2.333                  0.000  \n",
       "3             1171              20                2.333                  0.760  \n",
       "4              816              28                2.500                  0.000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankchurn = pd.read_csv('https://raw.githubusercontent.com/nicoardimas/Credit_Card_Customer_Churn/main/BankChurners.csv')\n",
    "bankchurn = bankchurn.drop(['CLIENTNUM'], axis = 1)\n",
    "bankchurn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lxTY-UPoY4bW"
   },
   "source": [
    "# **Exploratory Data Analysist**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6HfReuxDsoHB"
   },
   "source": [
    "## **Descriptive Statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "_dCexfTpspOH",
    "outputId": "b860b1d7-a54d-4172-9abf-62b96c1f3ac0"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>46.325960</td>\n",
       "      <td>2.346203</td>\n",
       "      <td>35.928409</td>\n",
       "      <td>3.812580</td>\n",
       "      <td>2.341167</td>\n",
       "      <td>2.455317</td>\n",
       "      <td>8631.953698</td>\n",
       "      <td>1162.814061</td>\n",
       "      <td>7469.139637</td>\n",
       "      <td>0.759941</td>\n",
       "      <td>4404.086304</td>\n",
       "      <td>64.858695</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>0.274894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.016814</td>\n",
       "      <td>1.298908</td>\n",
       "      <td>7.986416</td>\n",
       "      <td>1.554408</td>\n",
       "      <td>1.010622</td>\n",
       "      <td>1.106225</td>\n",
       "      <td>9088.776650</td>\n",
       "      <td>814.987335</td>\n",
       "      <td>9090.685324</td>\n",
       "      <td>0.219207</td>\n",
       "      <td>3397.129254</td>\n",
       "      <td>23.472570</td>\n",
       "      <td>0.238086</td>\n",
       "      <td>0.275691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1438.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2555.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1324.500000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2155.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4549.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>0.736000</td>\n",
       "      <td>3899.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.702000</td>\n",
       "      <td>0.176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11067.500000</td>\n",
       "      <td>1784.000000</td>\n",
       "      <td>9859.000000</td>\n",
       "      <td>0.859000</td>\n",
       "      <td>4741.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>0.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>3.397000</td>\n",
       "      <td>18484.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>3.714000</td>\n",
       "      <td>0.999000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age  Dependent_count  Months_on_book  \\\n",
       "count  10127.000000     10127.000000    10127.000000   \n",
       "mean      46.325960         2.346203       35.928409   \n",
       "std        8.016814         1.298908        7.986416   \n",
       "min       26.000000         0.000000       13.000000   \n",
       "25%       41.000000         1.000000       31.000000   \n",
       "50%       46.000000         2.000000       36.000000   \n",
       "75%       52.000000         3.000000       40.000000   \n",
       "max       73.000000         5.000000       56.000000   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "count              10127.000000            10127.000000   \n",
       "mean                   3.812580                2.341167   \n",
       "std                    1.554408                1.010622   \n",
       "min                    1.000000                0.000000   \n",
       "25%                    3.000000                2.000000   \n",
       "50%                    4.000000                2.000000   \n",
       "75%                    5.000000                3.000000   \n",
       "max                    6.000000                6.000000   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "count           10127.000000  10127.000000         10127.000000   \n",
       "mean                2.455317   8631.953698          1162.814061   \n",
       "std                 1.106225   9088.776650           814.987335   \n",
       "min                 0.000000   1438.300000             0.000000   \n",
       "25%                 2.000000   2555.000000           359.000000   \n",
       "50%                 2.000000   4549.000000          1276.000000   \n",
       "75%                 3.000000  11067.500000          1784.000000   \n",
       "max                 6.000000  34516.000000          2517.000000   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "count     10127.000000          10127.000000     10127.000000    10127.000000   \n",
       "mean       7469.139637              0.759941      4404.086304       64.858695   \n",
       "std        9090.685324              0.219207      3397.129254       23.472570   \n",
       "min           3.000000              0.000000       510.000000       10.000000   \n",
       "25%        1324.500000              0.631000      2155.500000       45.000000   \n",
       "50%        3474.000000              0.736000      3899.000000       67.000000   \n",
       "75%        9859.000000              0.859000      4741.000000       81.000000   \n",
       "max       34516.000000              3.397000     18484.000000      139.000000   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "count         10127.000000           10127.000000  \n",
       "mean              0.712222               0.274894  \n",
       "std               0.238086               0.275691  \n",
       "min               0.000000               0.000000  \n",
       "25%               0.582000               0.023000  \n",
       "50%               0.702000               0.176000  \n",
       "75%               0.818000               0.503000  \n",
       "max               3.714000               0.999000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankchurn.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "IKFViGDTsoak",
    "outputId": "7226c3ca-f136-4f4a-a1a5-373fd3cdce77"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10127</td>\n",
       "      <td>10127</td>\n",
       "      <td>10127</td>\n",
       "      <td>10127</td>\n",
       "      <td>10127</td>\n",
       "      <td>10127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>F</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>8500</td>\n",
       "      <td>5358</td>\n",
       "      <td>3128</td>\n",
       "      <td>4687</td>\n",
       "      <td>3561</td>\n",
       "      <td>9436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Attrition_Flag Gender Education_Level Marital_Status  \\\n",
       "count               10127  10127           10127          10127   \n",
       "unique                  2      2               7              4   \n",
       "top     Existing Customer      F        Graduate        Married   \n",
       "freq                 8500   5358            3128           4687   \n",
       "\n",
       "       Income_Category Card_Category  \n",
       "count            10127         10127  \n",
       "unique               6             4  \n",
       "top     Less than $40K          Blue  \n",
       "freq              3561          9436  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankchurn.describe(include = 'O')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uwG2yY5nY8T2"
   },
   "source": [
    "## **Proportion of Target Class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "Xts2VbhNY7s0",
    "outputId": "120f0b2e-27be-483a-b317-78a4403820cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of customers: 10127\n",
      "Existing Customer    8500\n",
      "Attrited Customer    1627\n",
      "Name: Attrition_Flag, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "target = bankchurn[\"Attrition_Flag\"].value_counts()\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(target, labels = target.index, autopct = '%1.1f%%', shadow = True, explode = [0.1, 0])\n",
    "ax1.axis('equal')\n",
    "plt.title(\"Amount of churned customers\", fontsize = 14)\n",
    "plt.show()\n",
    "print('Total number of customers:', bankchurn['Attrition_Flag'].count())\n",
    "print(bankchurn['Attrition_Flag'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c83-rlPjZXX2"
   },
   "source": [
    "## **Numerical Variables by Target**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "4SJXzRo_ZcQ6",
    "outputId": "ba778800-fcdd-4406-98c5-d9ce336cc36c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Customer Age\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Customer_Age[bankchurn.Attrition_Flag == 'Attrited Customer'], bins = [26, 35, 45, 55, 75], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Customer_Age[bankchurn.Attrition_Flag == 'Existing Customer'], bins = [26, 35, 45, 55, 75], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Customer Age\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "Z45GtjMXauFz",
    "outputId": "244647c2-2a24-4900-bfdb-205b1d7c241e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dependent_count\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Dependent_count[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Dependent_count[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Dependent Count\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "OSxL9EY2pjJw",
    "outputId": "f3647386-c712-4566-db1e-b4e14b094cb0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Months_on_book\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Months_on_book[bankchurn.Attrition_Flag == 'Attrited Customer'], bins = 12, color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Months_on_book[bankchurn.Attrition_Flag == 'Existing Customer'], bins = 12, color = 'g', label = 'Existing Customer')\n",
    "plt.title('Month on Book\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "pfxDSUX5pnzj",
    "outputId": "0bfdd68d-0e2f-4485-9199-1853a3356097"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Relationship_Count\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Relationship_Count[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Relationship_Count[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Relationship Count\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "6n6mTJpfpucf",
    "outputId": "18264389-40f5-408e-c59b-917a09fc60cc"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Months_Inactive_12_mon\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Months_Inactive_12_mon[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Months_Inactive_12_mon[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Months Inactive 12 mon\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "z9Lo1eq-p0sH",
    "outputId": "9eac87ab-6b2f-42d0-b7b3-35f0c4ab38da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contacts_Count_12_mon\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Contacts_Count_12_mon[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Contacts_Count_12_mon[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Contacts Count 12 mon\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "-Y_jiULNp64M",
    "outputId": "1f4aaa72-753d-4e7a-974b-0290b55f73a5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Credit_Limit\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Credit_Limit[bankchurn.Attrition_Flag == 'Attrited Customer'], bins = [0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Credit_Limit[bankchurn.Attrition_Flag == 'Existing Customer'], bins = [0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Credit Limit\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "TCgd_30Op_1X",
    "outputId": "35a28153-6462-4e57-c973-e125bccfc851"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Revolving_Bal\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Revolving_Bal[bankchurn.Attrition_Flag == 'Attrited Customer'], bins = [0, 500, 1000, 1500, 2000, 2500, 3000], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Revolving_Bal[bankchurn.Attrition_Flag == 'Existing Customer'], bins = [0, 500, 1000, 1500, 2000, 2500, 3000], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Revolving Balance\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "Ia2JZnGSqEcX",
    "outputId": "70c6423a-7d1d-4cd6-abc4-0b0d63f1b825"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avg_Open_To_Buy\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Avg_Open_To_Buy[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Avg_Open_To_Buy[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Average Open to Buy\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "jbkdayrQqI4W",
    "outputId": "38f19bf8-6a49-492c-d0fb-d17683c9b3b0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Amt_Chng_Q4_Q1\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Amt_Chng_Q4_Q1[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Amt_Chng_Q4_Q1[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Amt Chng Q4 Q1\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "6mlEuNOWqL0z",
    "outputId": "77e7f17e-fb6d-42a7-f43e-6d7f153f9b67"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Trans_Amt\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Trans_Amt[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Trans_Amt[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Trans Amt\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "ZH8SkH_TqO1-",
    "outputId": "5f084603-891c-4543-9de9-0a3fa2467a8f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Trans_Ct\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Trans_Ct[bankchurn.Attrition_Flag == 'Attrited Customer'], bins = 10, color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Trans_Ct[bankchurn.Attrition_Flag == 'Existing Customer'], bins = 10, color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Trans Ct\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "9_y-5rvAqSSe",
    "outputId": "9fe2035f-48e9-41b1-b89d-97187e674923"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total_Ct_Chng_Q4_Q1\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Total_Ct_Chng_Q4_Q1[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Total_Ct_Chng_Q4_Q1[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Total Ct Chng Q4 Q1\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "_ncH7SxdqVBb",
    "outputId": "19a1c3fd-f427-4359-bce3-499288a3004c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VZdSoUTz66KNHTRsXF1fvdn1P1sZvjPGb2bNnc+2117J582Y2bdrEli1byMjI4MsvvyQ5OZmcnJyKupU/V8ezbkpKChkZGbz++uuAc7J2+fLlAJx++ukV5xVeffVVr/M6++yzKSws5J///GdF2TfffMMXX3xBx44dWbVqFYWFhRw8eJBPP/0UcI4yDh48yHnnncekSZMqNjqecTVp0oSmTZsyf/58AP71r39V7P37YsiQIcyePZtdu5ze6vft28fmzV57Va432+M3pjEL8hVhM2bM4N577z2i7NJLL+W1117jkUceYeLEiWRmZnLfffdxwQUXcNlll/H222/z9NNPVzvfkSNHcuONN/LUU08xe/ZsXn31VW6++WYefvhhiouLGTlyJCeddBKTJ0/mqquuYvLkyVU2jYgIc+bMYfz48UycOJG4uLiKyznbt2/Pr3/9a3r37k2XLl3o06cPADk5OVx44YUUFBSgqjz55JNe43rppZcYN24chw4dolOnTrz44os+f3fdu3fn4YcfZujQoZSVlREdHc2UKVPo2LGjz/PwlRwLXeVkZWVpo34Qi13Oafxk9erVdOvWLdRhmBDw9rcXkaWqmlW5rjX1GGNMmLHEb4wxYcYSvzHGhBlL/MYYE2Ys8RtjTJixxG+MMWEmkI9efAEYDuxS1Z6Vxt0J/A1IU1V75q4xATJtqX+7ZR7br+ZLiyMjI+nVq1fF55EjRx51bb+n8847j9dee43U1FSv4ydNmsTYsWNJSEjwqX5t/Pjjj4wfP54ff/yR6OhoevXqxdNPP02rVq1qNZ/p06czdOhQ2rZtW++YgiGQN3BNB54BXvYsFJH2wDnAzwFctjEmROLj46vsTsGbDz74oNrxkyZN4uqrr65I/DXV91VBQQHnn38+TzzxBBdccAHg9Ny5e/fuOiX+nj17Bi3xqyqqSkRE3RptAtbUo6rzgH1eRj0J3A00/DvHjDF+cfDgQU444QTWrl0LwJVXXlnRZUJ6ejp79uwhLy+P888/n5NOOomePXsya9YsnnrqKX755RcGDx7M4MGDj6i/adMmunXrxo033kiPHj0YOnQo+fn5gNMFQ+/evRkwYAB33XUXPXv2PCqm1157jQEDBlQkfYDBgwfTs2fPii6hyw0fPpzPP/+c0tJSRo8eTc+ePenVqxdPPvkks2fPZsmSJfzmN78hMzOT/Px8Pv30U/r06UOvXr0YM2ZMRSdy6enp/PGPf2TAgAFkZWXx7bffcu6559K5c2emTp1asby//e1v9O/fn969e/PAAw8AVKzvLbfcQt++fdmyZUud/x5BbeMXkRHANlVdHszlGmOCJz8/n8zMzIrXrFmzaNKkCc888wyjR49m5syZ7N+/nxtvvPGI6T788EPatm3L8uXLWblyJcOGDeO3v/0tbdu25bPPPuOzzz47alnr1q3j1ltv5YcffiA1NbWiZ87rrruOqVOnsnDhwio7N6tL98zLli1j27ZtrFy5khUrVnDddddx2WWXkZWVxauvvsqyZcsQEUaPHs2sWbNYsWIFJSUlPPvssxXzaN++PQsXLmTgwIGMHj2a2bNn8/XXX3P//fcD8NFHH7Fu3ToWL17MsmXLWLp0KfPmzQNg7dq1XHvttXz33Xf16sohaIlfRBKAPwH3+1h/rIgsEZEltXl6vDEmtMqbespfV1xxBQDnnHMOvXr14tZbb+W55547arpevXrxySefcM899zB//nyaNGlS47IyMjLIzMwEoF+/fmzatIkDBw6Qk5NT8WSvq666ym/r1qlTJ3766Sduv/12PvzwQ1JSUo6qs3btWjIyMiqe5jVq1KiKxA1Hds98yimnkJycTFpaGnFxcRw4cICPPvqIjz76iD59+tC3b1/WrFnDunXrAOjYsSOnnnpqvdcjmHv8nYEMYLmIbALaAd+KSGtvlVV1mqpmqWpWWlpaEMM0xgRCWVkZq1evJj4+nn37jm4F7tq1K0uXLqVXr17cd999Pj10xLN748jIyIrujX3Ro0cPli5d6nVcVd0zN23alOXLlzNo0CCmTJnCDTfccNS0NS3fl+6Z77vvvooN5/r167n++usB37qt9kXQEr+qrlDVlqqarqrpwFagr6ruCFYMxpjQefLJJ+nWrRszZsxgzJgxFBcXHzH+l19+ISEhgauvvpo777yTb7/9Fqhd983gJOfk5GS+/vprgIpumiu76qqrWLBgAe+//35F2YcffsiKFStIT09n2bJllJWVsWXLFhYvXgzAnj17KCsr49JLL+Uvf/mL1xhPPPFENm3axPr164Had8987rnn8sILL1Q8JH7btm0VXTX7SyAv55wBDAJaiMhW4AFVfT5QyzPGHM2Xyy/9rbyNv9ywYcMYM2YMzz33HIsXLyY5OZkzzzyThx9+mAcffLCi3ooVK7jrrruIiIggOjq6ol187Nix/OpXv6JNmzZe2/m9ef7557nxxhtJTExk0KBBXpuN4uPjee+99xg/fjzjx48nOjqa3r17M3nyZHr27ElGRga9evWiZ8+eFU8Q27ZtG9ddd13F0UD5Q1NGjx7NuHHjiI+PZ+HChbz44otcfvnllJSU0L9//yOe71uToUOHsnr1agYMGAA4z9t95ZVX/PogFuuWuSGwbpmNn1i3zI7c3FySkpIAmDhxItu3b2fy5MkhjiqwatMtsz2IxRjT6Lz//vs8+uijlJSU0LFjx4oHtxuHJX5jTKNzxRVXVFxNZI5mffUY08gcC823xr9q+ze3xG9MIxIXF8fevXst+YcRVWXv3r3ExcX5PI019RjTiLRr146tW7diNz2Gl7i4ONq1a+dzfUv8xjQi0dHRZGRkhDoM08BZU48xxoQZS/zGGBNmLPEbY0yYscRvjDFhxhK/McaEGUv8xhgTZizxG2NMmLHEb4wxYcYSvzHGhBlL/MYYE2Ys8RtjTJixxG+MMWEmYIlfRF4QkV0istKj7G8iskZEvheROSKSGqjlG2OM8S6Qe/zTgWGVyj4Geqpqb+BH4L4ALt8YY4wXAUv8qjoP2Fep7CNVLXE/fg343oG0McYYvwhlG/8Y4D8hXL4xxoSlkCR+EfkTUAK8Wk2dsSKyRESW2NOEjDHGf4Ke+EVkFDAc+I1W82BQVZ2mqlmqmpWWlha8AI0xppEL6qMXRWQYcA9wlqoeCuayjTHGOAJ5OecMYCFwgohsFZHrgWeAZOBjEVkmIlMDtXxjjDHeBWyPX1Wv9FL8fKCWZ4wxxjd2564xxoQZS/zGGBNmLPEbY0yYscRvjDFhxhK/McaEGUv8xhgTZizxG2NMmLHEb4wxYcYSvzHGhBlL/MYYE2Ys8RtjTJixxG+MMWHGEr8xxoQZS/zGGBNmLPEbY0yYscRvjDFhxhK/McaEGUv8xhgTZgL5zN0XRGSXiKz0KGsmIh+LyDr3vWmglm+MMca7QO7xTweGVSq7F/hUVbsAn7qfjTHGBFHAEr+qzgP2VSq+EHjJHX4JuChQyzfGGONdsNv4W6nqdgD3vWWQl2+MMWGvwZ7cFZGxIrJERJbs3r071OEYY0yjEezEv1NE2gC477uqqqiq01Q1S1Wz0tLSghagMcY0dsFO/O8Ao9zhUcDbQV6+McaEvUBezjkDWAicICJbReR6YCJwjoisA85xPxtjjAmiqEDNWFWvrGLUkEAt0xhjTM0a7MldY4wxgWGJ3xhjwowlfmOMCTOW+I0xJsz4lPhF5A0ROV9EbENhjDHHOF8T+bPAVcA6EZkoIicGMCZjjDEB5FPiV9VPVPU3QF9gE/CxiCwQketEJDqQARpjjPEvn5tuRKQ5MBq4AfgOmIyzIfg4IJEZY4wJCJ9u4BKRN4ETgX8BF5T3sAnMEpElgQrOGGOM//l65+5zqvqBZ4GIxKpqoapmBSAuY4wxAeJrU8/DXsoW+jMQY4wxwVHtHr+ItAaOA+JFpA8g7qgUICHAsRljjAmAmpp6zsU5odsOeMKjPAf4Y4BiMsYYE0DVJn5VfQl4SUQuVdU3ghSTMcaYAKqpqedqVX0FSBeR31cer6pPeJnMGGNMA1ZTU0+i+54U6ECMMcYER01NPf9w3x8MTjjGGGMCzddO2v5XRFJEJFpEPhWRPSJydaCDM8YY43++Xsc/VFWzgeHAVqArcFddFyoid4jIDyKyUkRmiEhcXedljDGmdnxN/OUdsZ0HzFDVfXVdoIgcB/wWyFLVnkAkMLKu8zPGGFM7vnbZ8K6IrAHygVtEJA0oqOdy40WkGOdGsF/qMS9jjDG14Gu3zPcCA3D20ouBPODCuixQVbcBjwM/A9uBg6r6UV3mZYwxpvZ83eMH6IZzPb/nNC/XdoEi0hRno5EBHABe97hfwLPeWGAsQIcOHWq7GGOMMVXw9aqef+HspZ8B9Hdfde2V83+Ajaq62z16eBM4rXIlVZ2mqlmqmpWWllbHRRljjKnM1z3+LKC7qqoflvkzcKqIJOCcMxgCWJ/+xhgTJL5e1bMSaO2PBarqImA28C2wwo1hmj/mbYwxpma+7vG3AFaJyGKgsLxQVUfUZaGq+gDwQF2mNcYYUz++Jv4JgQzCGGNM8PiU+FX1CxHpCHRR1U/c9vnIwIZmjDEmEHy9qudGnHb5f7hFxwFvBSgmY4wxAeTryd1bgdOBbABVXQe0DFRQxhhjAsfXxF+oqkXlH9ybuPxxaacxxpgg8zXxfyEif8TpX+cc4HXg3cCFZYwxJlB8Tfz3Artxrru/CfgA+H+BCsoYY0zg+HpVT5mIvAW8paq7AxuSMcaYQKp2j18cE0RkD7AGWCsiu0Xk/uCEZ4wxxt9qauoZj3M1T39Vba6qzYBTgNNF5I5AB2eMMcb/akr81wJXqurG8gJV/Qm42h1njDHmGFNT4o9W1T2VC912/mgv9Y0xxjRwNSX+ojqOM8YY00DVdFXPSSKS7aVcgLgAxGOMMSbAqk38qmodsRljTCPj6w1cxhhjGglL/MYYE2Ys8RtjTJgJSeIXkVQRmS0ia0RktYgMCEUcxhgTjnx99KK/TQY+VNXLRCQGSAhRHMYYE3aCnvhFJAU4ExgN4Pbzb/cEGGNMkISiqacTThfPL4rIdyLynIgkhiAOY4wJS6FI/FFAX+BZVe0D5OH0938EERkrIktEZMnu3dYTtDHG+EsoEv9WYKuqLnI/z8bZEBxBVaepapaqZqWlpQU1QGOMacyCnvhVdQewRUROcIuGAKuCHYcxxoSrUF3VczvwqntFz0/AdSGKwxhjwk5IEr+qLgOyQrFsY4wJd3bnrjHGhBlL/MYYE2Ys8RtjTJixxG+MMWHGEr8xxoQZS/zGGBNmLPEbY0yYscRvjDFhJlR37hoP0w7Nq77CUudtbL+xgQ/GGNPo2R6/McaEGUv8xhgTZizxG2NMmLHEb4wxYcYSvzHGhBlL/MYYE2Ys8RtjTJixxG+MMWHGEr8xxoSZkCV+EYkUke9E5L1QxdAgqJK0L5fE/XlElJSGOhpjTBgIZZcNvwNWAykhjCF0VOHFF+H//o+rVq0CID8pllWnd2XZ2d0pjbHeNIwxgRGSPX4RaQecDzwXiuWHXEEBXHcdXH89JCTw1cX9mPfrU9jVoQX9/ruCEU9/TMKBQ6GO0hjTSIWqqWcScDdQFqLlh46qk/RfegkefBAWLeKHM09kzYDj+e+Ng/jwhrNosjubEU9/RFxOQaijNcY0QkFP/CIyHNilqktrqDdWRJaIyJLdu3cHKbog+MtfYOZMmDgR7r8fIo78E/zcox3v3zyEhJwCzn3+CyKLSkIUqDGmsQrFHv/pwAgR2QTMBM4WkVcqV1LVaaqapapZaWlpwY4xMBYvdvbyr74a7r67ymq7O7Zg7tWn0WrzHk5+f1nw4jPGhIWgJ35VvU9V26lqOjASmKuqVwc7jqArKoIxY6BtW5gyBUSqrb6pdwdWntGVXvPW0mbJ2iAFaYwJB3Ydf7BMmgQ//ADPPgspvl3ItHh4Hw62SOasv7xMZEFRYOMzxoSNkCZ+Vf1cVYeHMoag2LcP/vpXOP98GO776pbERjH/1yeTsm0PvV/5OIABGmPCie3xB8Ojj0J2tvNeS790ac1PQ/qSOf1D2LIlAMEZY8KNJf5A27kTnnkGrr0WevWq0yy+Hn8ZUlbmnBg2xph6ssQfaE8+6ZzY/dOf6jyL3DbNWX3pmTB9Oqxf77/YjDFhyRJ/IO3fD3//O1x+OXTpUq9ZLRs1DGJibK/fGFNvlvgDaepUyMmBe++t96zyWzSB226DV18Ft28fY4ypC0v8gVJa6ly6efbZkJnpn3nefTckJsKECf6ZnzEmLFniD5T33nOuwrn1Vv/Ns0ULGD8eXn8dli3z33yNMWHFEn+gTJkCxx0HI0b4d75/+AM0aQIPPeTf+RpjwoYl/kD48Uf4+GO46SaI8nO/+qmpcPvtMGcOrFjh33kbY8KCJf5AePZZiI6GG28MzPzHj4ekJHjkkcDM3xjTqFni97e8POfJWpdeCq1bB2YZzZs75w7+/W9YsyYwyzDGNFqW+P1txgw4eBBuuSWwy/n97yEuzukDyBhjasESvz+pOid1e/WCM84I7LJatoRx4+C112DDhsAuyxjTqFji96evv3Yus7z11hr72/eLu+5yTh7XofM3Y0z48vMlJ2FuyhSnr/3f/KZOk5dqGWtKdrCldD+5Wki8RNMhshnHF+cTHx1/9ARt2jgnkKdOhT//GTp2rOcKGGPCgSV+f9m1y7mx6qabnCtuakFV+ap4A+8WfM8BzQcgmkiKKXWGP/6Kfm36cVbHszihxQlHTnz33fCPfzjP8H32Wb+sijGmcbPE7y/PP+/0wlnLk7rZhdk8degzVpVs5/jINK6M7c8JUa2Jl2iKtISNpXtY0qKYRVsX0ePvPRjTZwwPnPUAx6Uc58ygfXu47jp44QWnTyDb6zfG1EBUNdQx1CgrK0uXLFkS6jCqVloKnTrB8cfDp5/6PNmeQ3s451/n8P2O5VwRl8VZMV0Qb+cGBp5JdmE2/1n3H77Y/AVREVFcdOJFDEofRIREkLhjH1dc8mc2DO3PFxNGM7bfWD+unDHmWCUiS1U1q3J50E/uikh7EflMRFaLyA8i8rtgx+B3770HP/9cq3558ovzuWDGBazZs4ZbEwYxKLar96TvSolN4YqeV/DQ4Ic4vtnxzPphFo8veJwduTvIa92MH64YTNf3v6bp+m3+WCNjTCMWiqt6SoA/qGo34FTgVhHpHoI4/Ofvf69Vvzyqyph3xrBo6yJeu+Q1eka39XlRLRJacPvJtzM6czTbc7fzl3l/4T/r/8OSa4dSlBjHyVPm1HUtjDFhIuiJX1W3q+q37nAOsBo4Lthx+M2PP8JHH9WqX57nvn2OmStn8sjZj3Bxt4trvUgRYUC7ATw46EF6t+rNW2ve4sEVT/Hv60+l4/wVMH9+redpjAkfIb2OX0TSgT7AolDGUS+17Jdn7Z61/PbD33JOp3O454x76rXolNgUbup3Ezf1u4mDhQe5rtkX3D08loL77nJuJjPGGC9ClvhFJAl4Axivqtlexo8VkSUismT37t3BD9AXteyXp0zLuOHdG4iLiuOli14iQvzz9fdt05cJZ03g5ONO4W9ZhfTJXMTCF63bZmOMdyFJ/CISjZP0X1XVN73VUdVpqpqlqllpaWnBDdBX06c7/fLcdptP1actncaXP3/Jk+c+SZvkNn4NJTEmkdGZo/lt1m0cSozh9J8ncPvbN7E/f79fl2OMOfaF4qoeAZ4HVqvqE8Fevt+UlsITT8CAAXD66TVW33toL3/89I8MTh/MqJNGBSysHq17sfLCD7nlG5jy3TS6PN2FqUumUlJWErBlGmOOLaHY4z8duAY4W0SWua/zQhBH/cyZAz/9BHfe6VP1CZ9P4GDhQSYPm1ztZZv+kHzaYJ5Jv4Vv/wE9Y9tz8/s30/cffZm7cW5Al2uMOTaE4qqeL1VVVLW3qma6rw+CHUe9qMLf/gadO8OFF9ZYfeWulTy75FnG9RtHr1a9ghAg8NhjZMal89kzOcwe8Qo5RTkMeXkI5/zrHBZsWRCcGIwxDZL1zlkXX30Fixc7feJHRlZbVVUZ/+F4UmJTeGhwcE64Tls6jWlrX+Pdey+BDT/R84Ep3DngTi7rdhmLti7i9BdOp/uU7ny84WOOhTu3jTH+ZYm/Lh5/3HkK1ujRNVZ9Z+07fLrxUx4c9CDNE5oHPjYP27NO4Lsxv+KEdxfS/T9LOKfzOTxy9iNc0u0StuVsY+grQzlp6kn8c+k/yS486sIqY0wjZYm/tlavhnfecTpjS0iotmphSSF/+OgPdE/rzriscUEK8EhLbxzOtqwTGPjXV2i1fAOxUbGc2/lc/nr2X3nxwhcBGPveWNr8XxuunXMtczfOpUzLQhKrMSY4LPHX1oQJTsK//fYaq05eNJkN+zcw6dxJREdGBz42LzQqkk8mjiWvVTOG3vksTTbvBCA6MprRmaNZPm45C69fyDW9r+HttW8z5OUhZEzO4Hf/+R1zN86luLQ4JHEbYwLHeuesje+/h5NOgj/9CR5+uNqqO3J30OXpLpydcTZvj3y72rrTJl1d/XIHnlnbSI/SZPNORtzwN0qjI3l32p3ktDv63oii0iKW7VjG4m2LWb1nNSVlJSREJ3DxiRdzbudzOTvj7MPdQRtjGryqeue0xF8bI0bAvHmwcSM0bVpt1TFvj+GV719h1a2rOL7Z8dXWDUbiB2i6fhsX3PR/lEZH8eHk29l7Qvsq6xaWFLJq9yqW71zOun3r2HNoDwAnND+BszPOZkjGEAalDwr6eQtjjO8s8dfX3LkwZIjzfNt776226qKtizj1+VO5+7S7eeycx2qcdY2J3xc+bhyart/Gr373NDE5h/jigdFsHNK3xmnKtIxt2dtYs3cNa/asYd3edRSWFgLQOqk1nZt2pnPTztw38D66Nu/qt64ojDH1Y4m/PkpLoW9fyM52Tu7GxVVZtbi0mKx/ZrH30F5W3bqKlNiUGmcfzMQPkLBrP0PvmkrLHzax6tIzWXT7JRQneXmmbxVKy0rZdGATa/eu5af9P/HT/p/IK84DoFl8Mwa0G8Bp7U/jtPan0a9NP5Jjk2u9OsaY+qsq8dujF33x97877fuzZlWb9AEmfT2J73d+z5u/ftOnpB8Kh1o25Z3n7qL/lLfo/eondJj/Pd/cejHrh52MRta8tx4ZEUnnZp3p3Kwz4NyrsDNvJ+1T2rNgywIWbF3A++veB0AQujbvSr+2/ejXxnn1adOnwX43xoQD2+Ovyc8/Q/fuMHAgfPABVNPdwqYDm+g+pTtDOw/lrZFv+byIYO/xe0pbuZEzJr5G2pqfOdCxFT9cPoh1559KUXL1l6rWJK8oj40HNrL5wGY2H9zMzwd/Zn/B4Q7jujbv6mwEWvehR8sedE/rTocmHcKymai0rJRDxYfIK84jryiP3KLciuH8knyiI6KJi4ojPjqe+Kh44qLiSIxJpHl8c+KjfT9SM+HHmnrqoqwMzjsPvvwSfvih2geZqyrnv3Y+83+ez6pbVtG+SdUnTisLZeIHoKyMTp9+S+9XPqblD5sojovhp//px88De7P1lG61agaqTnZhNpsPbKZlYkuWbl/K0u1L2Zq9tWJ8QnQCJ7Y4ke5p3enarCsZTTPISM0go2kGrZNaN4iNgqqSW5TLwcKDHCw4WPF+oOAA2YXZFQk7r/jIBF7de0FJQZ3jiY+Kp3lCc1oktKB5fHNnOL4FbZPb0ja5LW2S21QMN49vHvB+okzDYom/Lh5/HO66y3nYyrjqb8D659J/Mva9sUw6dxK/O7V2jxH2S+L3k+Zb9tF9wY90Wr6F2PwiyiKEnekt2Jmexu4Ozdl12XnktWpa7ZFPbeQV5bE9dzvbc7Y77+6w59EBQGxkLB1TO9I2uS2tk1rTKrFVxXtaYhrJMckkxSQd8Sq/d6L8f7ykrORwQvZIztmF2Ucl8SW/LCG/JJ/84nwOlRwivzif/JJ8CkoKfLrBLSoiipjIGGIjY533qNiKz7FRsYfLI2OJiXLLKw3HRsUSHRFNmZZRVFpEcVkxxaXFFJcVU1hSWBF/+QamfL1yinI4VHzIa0xNYpvQJK4JTWKbkBqXWrHBuCnrJjJSM2gS18QPf1XTUFjir60vv4TBg51O2F5/vdpEt3r3avpN68fpHU7nv1f/t9Z7pg0p8ZeT0jJabdpDh1XbaLt+J8237Sey1El4xXExHOzQioMdW3EgvTXZx7Ugr2UqeS2bktcylZKE6s+D+KKotIh9+fvYc2hPxWtv/l6yC7PJLsgmuyi7XnvKVYmQCFJiU4iUyIqmlZreE6ITKppg4qLiiImMITKi+j6cAq2otIjswmwOFByo2JgdKDxAdkE2Bwqdsn35+yquzirXNK4pGU0zSE9NJyM1g67Nu9KtRTdObHEiaYkN9LkYpkqW+Gtj/Xo49VRo1gwWLar2mv0DBQc45blT2J+/n+XjltfpASsNMfFXFlFSSvNt+2kR15zUzTtosnknqZt3kvzLXqTS/1BhUjx5LZtyqEUT8pslU9A0mfxmyeQ3TSa/eYrz3iyZ/GYplMbF1Dmm8uSWU5hDUWkRBaUFFJYUUlhSSEFpAWVlHnvm4iR1z73p8j3s+KjDiTw2MjZsmkNUlbziPGejemgve/Ldd3cju/fQXorLDt+5nRidSJ82fSo2BD1b9iSzdSYtE1uGcC1MdeyqHl/9/DMMG+YMf/BBtUm/qLSIkbNH8tP+n5h77Vy/P1WrISmLimR3xxbsrnQuIbKwmMRd+0ncdcB537mfxN0HSNy5n/h92bRcuYf4fdnEHCr0Ot/i+NiKjULFBqJ8I9EspWIDUdA0mYImiWjU4T3pmMgYWiS0oEVCi4Cue2MlIhXNYump6UeNL9My9ufvZ3vudnbk7mBH7g4A5qyZU3FDHzj3cpzU6iQyW2dWvHdp3oWoCEsvDZX9ZTytXw9Dh8K+ffDRR3B81XfclpSVcPWbV/PfDf/l+RHPM7DjwCAGGkLz5x3xsRTIdl8kAUkJ0DkBaHtEvciiEuJzC4jPLSQup8Addl85BcTlFpC44QAtljtlEWVHH4mqQEFCLPlJcRS0a1XlBqK8vDgxzm/nIsJRhETQPME5YdyzZc8jxuUW5bItextbsrewNXsrq3av4pOfPqFUSwGIjojmpNYnkdkq03lvnUnvVr3tMt4GwhJ/uY8/hiuucBLFJ59A1lFHRxXyivK48o0reffHd3li6BOM6TMmiIEem0pjoshtlkRus6SaK5cpsflFxFVsGAorNhJxuQUk5BQQV1pG87Vbid+fQ2zO0ScyAUpioihomkxhSiKFyQkUJcdTmJJAUVIChcnxFKYkUpQUT1FyglseT3F8LMUJsZTEx1ISF2MbDg8RxSVEFRQRlV9Ik/wiOubHE1WQTnR+G6JKT0I5xPaiPfz0yw9siM1j5/6NzNm8nOeiDzcXdSiMp0dhCj1KmtK9rDk9NI3use1ISm4OycmQmAgRETB2bAjXtPGzxJ+TA3/8IzzzDPTsCW+/DZ06VVl9zZ41jJw9khW7VvDMr57h1pNvDWKwYSJCKEyMpTAxloOtar7KJKKklLjcwkpHEIUkNGlB3P4cYrMPEZN7iORte2ix5hAxufnE5NV8YlgFSuLcDUFcDMUJcRTHx1ASH0tpTDRlUZGVXhEVwwBSpqCKqLrDIFpW5bDz7tZFq/x8dJxeNk5HFR1ZUHmaiNJSIguLiSosIqqgyB0urhiOzi8korR23XWXRQhlAtuTYHkrWN4aVrXIZ1VaPnPTdlLokX06HoDu66HbHjg+L5bOs/7K8Ynt6dCqK1EdM6BDh8Ovdu0gpu7nhkyIEr+IDAMmA5HAc6o6MehBZGfDCy/AX/8Ku3fDb3/rDCcmeq1+qPgQjy94nIlfTiQhOoF3r3yX87oce48KbozKoiI5lJrAoVRvN515vxJFSsuIKSgm9lARMflFxOYXEZNfTFRRCdFFxUQXljjDaa2JPlRIVEGh855fRPShAuL25xJRUkpETg4RpUpEWRkRpWVElCkRpWUogAgqbpJ1348cBvAsK//sTsvhaSumqZTAnVqVVCo6qo63SSKEkuhISqOjyI+OpCQpitKmsZRER1ESHUlJTCQlMVGUxERRHBNFSaz7Hh3ljCv/HHP4vfJd4GnAoLIy/qeolIiiYvYWHWRH6QG2lxzkl8iDrGuXx9xOBRRGFgKbgc1ElX5J+i7ovBY674Pj90Hn/dApsgXtm6XTpG0naN/eeR13nLNRaNcOWreGKNuvrUrQvxkRiQSmAOcAW4FvROQdVV0V8IVnZ8MXX8Cbb8Ls2ZCb61yy+dhj0L//UdVVldV7VvPq96/yj6X/YG/+Xi7rfhmTh02mbXJbLwswxwqNjKg4qjDBoxERFMdFQFw0SSRwPG3wPJNWpspBzWd3WQ67y3Kd95hsfmyazdedCzgo5RcJ7AH2kFL0Le0PKu2/UTp8Au2zof1B6JAjtI9J47jUDsS37eBsFFq1grQ0aNnyyPcmTcKuSS8Um8STgfWq+hOAiMwELgT8n/i//BI++8y563blSlizxulwrUkTuPxy5ylaWVmUaRk7c7azJXsLWw5uYcP+DSzatoiFWxayPXc7ERLB8K7Dufu0uzm9w+l+D9MY44gQoakk0DQiga60OmKcqpKnRewuy2FPWS779RD7Yg6xPyGPNWl5LNA8sqWovDawC9hFUtFS0g5By01Kyx+gZZ7zan4IkosgpSSS5LgmpCQ2JSW+KckJqSQlphKdnEpMcioxKc2ISklFUlMhJQWSkpw+u+LiIDb28HD559hY5zxFAxaKxH8csMXj81bglIAs6fXX4amnICPDab+/+GJnD//0050/juuXnF9o/+SRXSx0atqJwRmDOaP9GVzc7WJaJ7UOSIjGGN+ICEkSS1JELBl4v4S3SEvYX3bI2SiU5ZFdVkB2TAE5iQXkNMtne2k+OVpIthRRKuVtXqXAPvflRYHzit4OMaUQXeq8R+jhMyfiDotnGU4nhRVlIghw3uYYnv4yBSIjnQ1E+av8qKPy+wsvwFln1fVr8yroN3CJyOXAuap6g/v5GuBkVb29Ur2xQPmp/ROAtT4uogXOceCx5FiL+ViLFyzmYDnWYj7W4oXaxdxRVY860RWKPf6tgOfudTvgl8qVVHUaMK22MxeRJd7uVGvIjrWYj7V4wWIOlmMt5mMtXvBPzKFoiPoG6CIiGSISA4wE3glBHMYYE5aCvsevqiUichvwX5zLOV9Q1R+CHYcxxoSrkFzoqqofAB8EaPa1bh5qAI61mI+1eMFiDpZjLeZjLV7wQ8zHRO+cxhhj/KdhX2xqjDHG7475xC8izUTkYxFZ574f1Y+yiLQXkc9EZLWI/CAitXtEln/iHCYia0VkvYjc62W8iMhT7vjvRaRvsGP0ElNNMf/GjfV7EVkgIieFIs5KMVUbs0e9/iJSKiKXBTM+L3HUGK+IDBKRZe7/7hfBjtFLPDX9XzQRkXdFZLkb83WhiNMjnhdEZJeIrKxifEP87dUUc/1+e6p6TL+A/wXudYfvBR7zUqcN0NcdTgZ+BLoHMcZIYAPQCYgBlldePnAe8B+cez1OBRaF+Hv1JebTgKbu8K+OhZg96s3FOc90WUOOF0jFuau9g/u5ZUP/joE/lv8Ocbro2QfEhDDmM4G+wMoqxjeo356PMdfrt3fM7/HjdPfwkjv8EnBR5Qqqul1Vv3WHc4DVOHcQB0tFNxWqWgSUd1Ph6ULgZXV8DaSKSCif7FJjzKq6QFXLH477Nc49GaHky/cMcDvwBs49/aHkS7xXAW+q6s8AqnosxKxAsjiPMkvCSfwlwQ3TIxjVeVR5Wy7Q8H57NcZc399eY0j8rVR1OzgJHqj2OXAikg70ARYFPrQK3rqpqLzh8aVOMNU2nutx9ppCqcaYReQ44GJgahDjqoov33FXoKmIfC4iS0Xk2qBF550vMT8DdMO5MXMF8DtVH55QHzoN7bdXW7X+7R0T/ZaKyCeAt85y/lTL+STh7OmNV9Vsf8Tm66K9lFW+nMqXOsHkczwiMhjnn++MgEZUM19ingTco6qlDeDZur7EGwX0A4YA8cBCEflaVX8MdHBV8CXmc4FlwNlAZ+BjEZkf5N9cbTS0357P6vrbOyYSv6r+T1XjRGSniLRR1e3u4ZnXQ2ERicZJ+q+q6psBCrUqvnRT4VNXFkHkUzwi0ht4DviVqu4NUmxV8SXmLGCmm/RbAOeJSImqvhWUCI/k6//FHlXNA/JEZB5wEs55qlDwJebrgInqNECvF5GNwInA4uCEWGsN7bfnk/r89hpDU887wCh3eBTwduUKblvj88BqVX0iiLGV86WbineAa90rDE4FDpY3YYVIjTGLSAfgTeCaEO6BeqoxZlXNUNV0VU0HZgO3hCjpg2//F28DA0UkSkQScHqyXR3kOD35EvPPOEcoiEgrnE4WfwpqlLXT0H57Nar3by/UZ6/9cPa7OfApsM59b+aWtwU+cIfPwDl0+x7nEHQZcF6Q4zwPZy9tA/Ant2wcMM4dFpwH1GzAaRfNagDfbU0xPwfs9/hOlzT0mCvVnU4Ir+rxNV7gLpwre1biNFM26O/Y/e195P4frwSuDnG8M4DtQDHO3v31x8Bvr6aY6/Xbszt3jTEmzDSGph5jjDG1YInfGGPCjCV+Y4wJM5b4jTEmzFjiN8aYMGOJ3xhjwowlfhMSInKxiKiInOjHeU6v3M2yiOS67+kicpVHeZaIPOUOjxaRZ9zhcXXpD8ftOvk0j891mk8N8z8oIt+JyBoRedyHaS4Ske4enx8SkSrvgjfhwxK/CZUrgS9x7vwMhnScni4BUNUlqvrbypVUdaqqvlyH+Q/C6Sq3vvOpznxV7YPTyeBwETm9hvoXARWJX1XvV9VP/ByTOQZZ4jdB53aWdzrO3YgjReRXIvJvj/GDRORdd/h6EfnR7Z3yn+V75nUwEafrg2Uicoe7jPe8xDZBRO4UkbZu3fJXqYh0FJELRGSRu+f9iYi0cnt8HQfc4dYdWD4fd56ZIvK1+9CMOeI+LMhdp8dEZLG7jgN9WRFVzce5W/M4dz43isg34jz45A0RSXCPPkYAf3Nj6ux5RCQiQ9x1WCHOQz9i6/i9mmOQJX4TChcBH6rTx8g+YC9wqogkuuOvAGaJSFvgzzgPxzgHp6OvuroXZ485U1WfrKmyqv7i1s0E/gm8oaqbcY5STnX3vGcCd6vqJpxunp90p5lfaXYv4/QI2hunS4AHPMZFqerJwPhK5VVyNxxdgHlu0Zuq2l9VT8Lpx+d6VV2A0wfNXW5MGzymj8PpruIKVe2F01njzb4s2zQOlvhNKFyJkzRx3y8HPgQuEJEo4HyczslOBr5Q1X2qWgy8XsN8vfU/Uq8+SdzmlBuAMW5RO+C/IrICpw+dHjVM3wRIVdXyRya+hPN0pXLlPcUuxWmOqs5AEfke2AG8p6o73PKeIjLfjek3NcWE02naRj3cuVflmEwjd0x0y2waDxFpjtNPe08RUZxH+SlOV7634hwBfKOqOSK17jB/L1DxzGURaQbsqUesbXB6dR2hqrlu8dPAE6r6jogMAibUdf6uQve9lJp/j/NVdbiIdAW+FJE5qroMZ+/9IlVdLiKjcc43VCfkDyIwoWV7/CbYLsN5zF1HdbpHbg9sxHk0X1/gRmCWW3cxcJaINHWPBC6tYd6fA1e43QUDjAY+c4dzcJ637BNxnt/wb5wmGs9ub5sA29zhUR7lXuevqgeB/R7t99cA9XpguhvPo8A9blEysN2N+Tc1xQSsAdJF5Hh/xWSOLZb4TbBdCcypVPYGztU97+E8OPo9AFXdBvwV5zGZn+B0TXywqhmr6nvAfGCpiCzDOYFcnhy/B0rcE6B3+BDnaUB/4EGPE7xtcfbwXxeR+Rx5NPEucHH5yd1K8xqFc5L1eyATeMiH5ddkKnCmiGTgnAdZBHyMk9TLzQTuck/idi4vVNUCnCOs193moTIaxqMoTZBYt8ymQRORJFXNdff45wAvqGrlDYcxphZsj980dBPcvfeVOE1Cb4U0GmMaAdvjN8ccEfkTzpVAnl5X1UdCEY8/ici5wGOVijeq6sWhiMc0Tpb4jTEmzFhTjzHGhBlL/MYYE2Ys8RtjTJixxG+MMWHGEr8xxoSZ/w8jUgz9XOMXRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avg_Utilization_Ratio\n",
    "plt.figure()\n",
    "sns.distplot(bankchurn.Avg_Utilization_Ratio[bankchurn.Attrition_Flag == 'Attrited Customer'], color = 'r', label = 'Attrited Customer')\n",
    "sns.distplot(bankchurn.Avg_Utilization_Ratio[bankchurn.Attrition_Flag == 'Existing Customer'], color = 'g', label = 'Existing Customer')\n",
    "plt.title('Avg Utilization Ratio\\n', fontsize = 16, fontweight = 'semibold')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nfnTfc8hqcuw"
   },
   "source": [
    "## **Categorical Variables**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1nXqKOjlwHJ4"
   },
   "source": [
    "### **Gender**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "VAM2LTThwHRn",
    "outputId": "da912e0b-93aa-469a-aa56-9756c46e2d73"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of customers: 10127\n",
      "F    5358\n",
      "M    4769\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "gender = bankchurn[\"Gender\"].value_counts()\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(gender, labels = gender.index, autopct = '%1.1f%%', shadow = True, explode = [0.1, 0])\n",
    "ax1.axis('equal')\n",
    "plt.title(\"Amount of Customer's Gender\", fontsize = 14)\n",
    "plt.show()\n",
    "print('Total number of customers:', bankchurn['Gender'].count())\n",
    "print(bankchurn['Gender'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qm5QOcUSrzwS"
   },
   "source": [
    "### **Education Level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "c_nAh4RmryhS",
    "outputId": "ff649b36-2a65-405a-adfa-e4c03ffe1827"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate</td>\n",
       "      <td>3128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High School</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>1519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Uneducated</td>\n",
       "      <td>1487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>College</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Post-Graduate</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Education_Level  Count\n",
       "0        Graduate   3128\n",
       "1     High School   2013\n",
       "2         Unknown   1519\n",
       "3      Uneducated   1487\n",
       "4         College   1013\n",
       "5   Post-Graduate    516\n",
       "6       Doctorate    451"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_education = bankchurn['Education_Level'].value_counts()\n",
    "proportion_education = pd.DataFrame(proportion_education)\n",
    "proportion_education = proportion_education.rename(columns={'Education_Level':'Count'})\n",
    "proportion_education = proportion_education.rename_axis('Education_Level').reset_index()\n",
    "proportion_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "id": "lrfxRIJEr4-h",
    "outputId": "a87139ff-1c89-40eb-ceae-ec65d8407f4d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Education_Level\n",
    "# Show the proportion of education in bar chart\n",
    "plt.figure(figsize = (15, 7))\n",
    "\n",
    "percentage = []\n",
    "for i in proportion_education['Count']:\n",
    "  pct = (i / proportion_education['Count'].sum()) * 100\n",
    "  percentage.append(round(pct, 2))\n",
    "proportion_education['Percentage'] = percentage\n",
    "\n",
    "## Show the plot\n",
    "plots = sns.barplot(x = \"Education_Level\",\n",
    "                    y = \"Percentage\",\n",
    "                    data = proportion_education)\n",
    "\n",
    "## Show the annotation\n",
    "for p in plots.patches:\n",
    "  plots.annotate('{} %'.format(p.get_height().astype('float')),\n",
    "                 (p.get_x() + p.get_width() / 2, p.get_height()),\n",
    "                 ha='center', va='center',\n",
    "                 size=15, xytext=(0, 8),\n",
    "                 textcoords='offset points')\n",
    "\n",
    "# Setting the label for x-axis\n",
    "plt.xlabel(\"Education Level\", size=14)\n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Percentage\", size=14)\n",
    "# Setting the title for the graph\n",
    "plt.title(\"Percentage of Education Level\", size = 16, weight = 'semibold')\n",
    "# Fianlly showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A3lVPSc4r7Xl"
   },
   "source": [
    "### **Marital Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "dzKRrrHZsBOr",
    "outputId": "21b36336-d258-4911-dcd2-385e5446da75"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Married</td>\n",
       "      <td>4687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Single</td>\n",
       "      <td>3943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Marital_Status  Count\n",
       "0        Married   4687\n",
       "1         Single   3943\n",
       "2        Unknown    749\n",
       "3       Divorced    748"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_marital_status = bankchurn['Marital_Status'].value_counts()\n",
    "proportion_marital_status = pd.DataFrame(proportion_marital_status)\n",
    "proportion_marital_status = proportion_marital_status.rename(columns={'Marital_Status':'Count'})\n",
    "proportion_marital_status = proportion_marital_status.rename_axis('Marital_Status').reset_index()\n",
    "proportion_marital_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "id": "aHbhR2unsB4B",
    "outputId": "bffe00b2-ac31-4a01-b24d-40ada7c8eb53"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the proportion of education in bar chart\n",
    "plt.figure(figsize = (15, 7))\n",
    "\n",
    "percentage = []\n",
    "for i in proportion_marital_status['Count']:\n",
    "  pct = (i / proportion_marital_status['Count'].sum()) * 100\n",
    "  percentage.append(round(pct, 2))\n",
    "proportion_marital_status['Percentage'] = percentage\n",
    "\n",
    "## Show the plot\n",
    "plots = sns.barplot(x = \"Marital_Status\",\n",
    "                    y = \"Percentage\",\n",
    "                    data = proportion_marital_status)\n",
    "\n",
    "## Show the annotation\n",
    "for p in plots.patches:\n",
    "  plots.annotate('{} %'.format(p.get_height().astype('float')),\n",
    "                 (p.get_x() + p.get_width() / 2, p.get_height()),\n",
    "                 ha='center', va='center',\n",
    "                 size=15, xytext=(0, 8),\n",
    "                 textcoords='offset points')\n",
    "\n",
    "# Setting the label for x-axis\n",
    "plt.xlabel(\"Marital Status\", size=14)\n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Percentage\", size=14)\n",
    "# Setting the title for the graph\n",
    "plt.title(\"Percentage of Marital Status\", size = 16, weight = 'semibold')\n",
    "# Fianlly showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zvChGSg1sEBi"
   },
   "source": [
    "### **Income Category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "ZLVc6UoksIn_",
    "outputId": "db2726f6-f022-4042-e9a3-553ce5b80dca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>3561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>1535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>1112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>$120K +</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Income_Category  Count\n",
       "0  Less than $40K   3561\n",
       "1     $40K - $60K   1790\n",
       "2    $80K - $120K   1535\n",
       "3     $60K - $80K   1402\n",
       "4         Unknown   1112\n",
       "5         $120K +    727"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_income_category = bankchurn['Income_Category'].value_counts()\n",
    "proportion_income_category = pd.DataFrame(proportion_income_category)\n",
    "proportion_income_category = proportion_income_category.rename(columns={'Income_Category':'Count'})\n",
    "proportion_income_category = proportion_income_category.rename_axis('Income_Category').reset_index()\n",
    "proportion_income_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "wLm5Y1nZsJR4",
    "outputId": "af7b6946-1faa-464f-8c14-72b6ca57f02e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the proportion of education in bar chart\n",
    "plt.figure(figsize = (15, 7))\n",
    "\n",
    "percentage = []\n",
    "for i in proportion_income_category['Count']:\n",
    "  pct = (i / proportion_income_category['Count'].sum()) * 100\n",
    "  percentage.append(round(pct, 2))\n",
    "proportion_income_category['Percentage'] = percentage\n",
    "\n",
    "## Show the plot\n",
    "plots = sns.barplot(x = \"Income_Category\",\n",
    "                    y = \"Percentage\",\n",
    "                    data = proportion_income_category)\n",
    "\n",
    "## Show the annotation\n",
    "for p in plots.patches:\n",
    "  plots.annotate('{} %'.format(p.get_height().astype('float')),\n",
    "                 (p.get_x() + p.get_width() / 2, p.get_height()),\n",
    "                 ha='center', va='center',\n",
    "                 size=15, xytext=(0, 8),\n",
    "                 textcoords='offset points')\n",
    "\n",
    "# Setting the label for x-axis\n",
    "plt.xlabel(\"Income Category\", size=14)\n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Percentage\", size=14)\n",
    "# Setting the title for the graph\n",
    "plt.title(\"Percentage of Income Category\", size = 16, weight = 'semibold')\n",
    "# Fianlly showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O3LIQU3NsLWp"
   },
   "source": [
    "### **Card Category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "6jL2rozSsP72",
    "outputId": "a95ccc88-b738-4784-d651-cef751f12e83"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Blue</td>\n",
       "      <td>9436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Silver</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gold</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Platinum</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Card_Category  Count\n",
       "0          Blue   9436\n",
       "1        Silver    555\n",
       "2          Gold    116\n",
       "3      Platinum     20"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_card_category = bankchurn['Card_Category'].value_counts()\n",
    "proportion_card_category = pd.DataFrame(proportion_card_category)\n",
    "proportion_card_category = proportion_card_category.rename(columns={'Card_Category':'Count'})\n",
    "proportion_card_category = proportion_card_category.rename_axis('Card_Category').reset_index()\n",
    "proportion_card_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "id": "4qEw0jRgsQe4",
    "outputId": "e8f6c63e-0d97-4261-9a86-6201fdd611cf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMWIERx99NIcccggffvhh1fI777yTKVOmMGjQoPKeFEmSJKnAVsjr5PXp0yd54pX62W233Zg7dy6PPvpoVdtpp53GqFGjeO+991h55ZVrXG/WrFmsvfbaXHLJJZx22mm1bn/RokWstFL2WUHHjh0ZNGjQF0bzRo4cyYknnsiHH35I+/btAZgxYwYdO3bkmmuu4fjjj69x26effjpz585lxIgRAGy66aYMGzaMPfbYg3nz5rHZZpsxfPhw9thjj/o+HZIkSVIhRMSElFKfmpY5kldwzz33HP3791+ibdddd2XGjBk8+eSTta632mqrseqqqzJ//vw6t18Z8Ory+eef07p1a9q1a1fV1q5dO1q3bk1dHzLMnz+ftm3bVj2uqKioqufKK69k4403NuBJkiRJ1RjyCu6zzz77wmjdKqusAsDLL7+8RHtKiQULFvDee+9x5pln0qpVKw4++OBG1/D973+fiooKfvKTnzBt2jSmTZvGqaeeSocOHdh///1rXa93797ccccdvPnmmzz00EO8+OKLbLXVVrz33nsMHTrU7+JJkiRJNVghr5On+uvevTvPPPPMEm3//Oc/Afjoo4+WaL/ssss4++yzAejUqRP33HMPXbp0aXQN66+/PuPGjWPPPfdk+PDhAKy33nrcf//9dOpU4/UbAfjBD37ALbfcwkYbbUREcNFFF9GtWzcOP/xwDj30UDbddNNG1yZJkiQVjSN5BXfcccdx11138dvf/pYZM2Zw//33c8UVVwDQqlWrJfoOHDiQZ555hr/97W/07t2bPffck4kTJza6hqlTp7LffvvRu3dv7r33Xu6991569+7NHnvswVtvvVXrem3atOG+++7jzTffZNq0aZxzzjlMmDCBu+++myFDhjBlyhR222031lprLXbddVfefffdRtcqSZIkregMeQV3xBFHcNxxx3H88cez1lprse+++3LeeecBsO666y7R90tf+hJ9+vRhr732YvTo0ay99tpceumlja7h8ssvZ8GCBdx+++3svvvu7L777vzlL3+hVatWDBs2bKnrd+3alY4dOwJw8sknM2TIEDp06MBJJ51Ejx49mDJlCptssgknnXRSo2uVJEmSVnSGvIJr1aoV11xzDdOnT+eFF17g/fffZ/vttweo+lmT1q1bs/nmmy/1Egr18corr/DVr36VNm3aVLWtvPLKfPWrX+X111+v93ZuueUWPvnkE4499lgAxo0bxzHHHENFRQXHHXcc48aNa3StkiRJ0orOkPc/okOHDmy++ea0a9eOX//613zjG9+gZ8+etfb/7LPPePbZZ+nWrVuj992lSxdefPHFJc7UOW/ePF588UW6du1ar23MnTuXs846iyuvvHKJaaZz5swBYPbs2XWeqVOSJEn6X+GJVwruqaee4vHHH2errbZi5syZ3HLLLdx///08/vjjVX1uueUW7r33XnbffXfWX399pk6dyq9//WumTp26xDXybr75Zo444ghef/31qhOyTJ48uerELvPnz2fixIncfvvtrLbaagwYMACAo446iuuvv57vfe97nHDCCaSUGDFiBFOnTuWYY46p13EMHTqUrbfeeonLQfTt25fBgwdz+umnM3ToUHbaaafGPl2SJEnSCs+QV3Bt2rTh1ltvZciQIay00kp861vf4oknnmDzzTev6tOjRw9+//vfc9pppzFjxgzWW289tttuO8aPH89Xv/rVqn6LFi1i4cKFS4yYjRs3jsMPP7zq8Z///Gf+/Oc/06VLFyZNmgRkl0K47777uOCCCzj00EMB2HzzzRkzZgxbbrnlUo9hypQpXHXVVV84S+jw4cM57LDD2Hfffdlmm22qztwpSZIk/S+LFXGKW58+fdL48eNbugxJkiRJahERMSGl1KemZX4nT5IkSZIKxOmaud5n3NzSJUg1mnD5j1q6BEmSJK1AHMmTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkF0qwhLyJOjYiXIuLFiLglIlaNiLUiYkxE/Df/2aE5a5IkSZKkImm2kBcRGwAnAX1SSl8DWgEHAT8FHkopbQw8lD+WJEmSJDVAc0/XbA20jYjWQAXwLvBd4KZ8+U3APs1ckyRJkiQVRrOFvJTSO8Aw4C1gKvBJSukBYN2U0tS8z1RgnZrWj4hjImJ8RIyfPn16c5UtSZIkSSuU5pyu2YFs1K4bsD6wWkT8sL7rp5SuSyn1SSn16dSp07IqU5IkSZJWaM05XbM/8GZKaXpK6XPgDuAbwPsRsR5A/nNaM9YkSZIkSYXSnCHvLWD7iKiIiAD6AS8DfwMOy/scBtzVjDVJkiRJUqG0bq4dpZSejojbgWeBBcC/gOuAdsBtEXEkWRDcv7lqkiRJkqSiabaQB5BSOh84v1rzPLJRPUmSJElSIzX3JRQkSZIkScuQIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAJp1pAXEe0j4vaIeCUiXo6Ir0fEWhExJiL+m//s0Jw1SZIkSVKRNPdI3q+A+1JKPYEtgZeBnwIPpZQ2Bh7KH0uSJEmSGqDZQl5ErAHsCNwAkFKan1L6GPgucFPe7SZgn+aqSZIkSZKKpjlH8jYCpgO/i4h/RcT1EbEasG5KaSpA/nOdmlaOiGMiYnxEjJ8+fXrzVS1JkiRJK5DmDHmtga2B36SUegGzKWNqZkrpupRSn5RSn06dOi2rGiVJkiRphdacIW8KMCWl9HT++Hay0Pd+RKwHkP+c1ow1SZIkSVKhNFvISym9B7wdET3ypn7AROBvwGF522HAXc1VkyRJkiQVTetm3t+PgT9ExMrAG8DhZEHztog4EngL2L+Za5IkSZKkwmjWkJdSeg7oU8Oifs1ZhyRJkiQVVXNfJ0+SJEmStAwZ8iRJkiSpQBoc8iJi3YgwJEqSJEnScqSskBYRbSJiaER8CrwDdM3bL4uIE5ZBfZIkSZKkMpQ7Enc+sBfwQ2BeSfs/gYFNVJMkSZIkqYHKPbvmwcARKaVHImJRSfuLwCZNV5YkSZIkqSHKHclbH5hcQ3trmv+ae5IkSZKkasoNeS8BO9bQfgAwofHlSJIkSZIao9zRtwuA30fEl4FWwP4R0RP4AbBHUxcnSZIkSSpPWSN5KaXRZKN2uwKLyE7EsjGwV0rpwaYvT5IkSZJUjrK/R5dSuh+4fxnUIkmSJElqJC9mLkmSJEkFUu7F0BdFxMJabrMj4vmIOGlZFStJkiRJqlu50zUHAUOAO4Gn87btgH2Ay4AvA5dGREopXd1ENUqSJEmS6qnckLcbcHZK6YaSthsj4p/A3iml70bEq8CPAUOeJEmSJDWzcr+T1w94pIb2R4D++f0xQLfGFCVJkiRJaphyQ96HZFMzq9sH+CC/3w74pOElSZIkSZIaqiEXQ/9tROwM/BNIwLZk1807Ou+zCzWP9kmSJEmSlrGyQl5K6caIeJnsO3d7AwG8DHwrpfRU3mdYk1cpSZIkSaqXhlwM/UngyWVQiyRJkiSpkcoOeZUi4kvAyqVtKaW3Gl2RJEmSJKnBygp5EbEmMBw4gGoBL9eqKYqSJEmSJDVMuWfXHAZsSXY2zc+AHwBnAFOAA5u0MkmSJElS2cqdrjkAODil9FhELAQmpJRujYipwLHA7U1eoSRJkiSp3sodyWsPTM7vfwKsnd9/EvhGE9UkSZIkSWqgckPe68BG+f2XgYMiIoB9gY+asjBJkiRJUvnKDXmjgC3y+5eSTdGcD1wOXNZ0ZUmSJEmSGqLci6FfWXJ/bET0BPoA/00p/bupi5MkSZIklaeskbyI+FFErFL5OKX0VkrpDuDViPhRk1cnSZIkSSpLudM1fwesWUP76vkySZIkSVILKjfkBZBqaO9MdrZNSZIkSVILqtd38iLi32ThLgGPRMSCksWtgC7APU1fniRJkiSpHPU98UrlRc6/BtwNzCpZNh+YBPyl6cqSJEmSJDVEvUJeSukCgIiYBNyaUvpsWRYlSZIkSWqYci+hcFPl/YhoT7Xv9KWUvCC6JEmSJLWgskJeRHQBRgLfBtqULiL7vl6rpitNkiRJklSuskIe2WUS2gNHAO9S85k2JUmSJEktpNyQty2wfUrpxWVRjCRJkiSpccq9Tt6bwCrLohBJkiRJUuOVG/JOBi6JiO7LohhJkiRJUuOUO13zLrKRvFcjYh5QelF0UkprNFVhkiRJkqTylRvyBi2TKiRJkiRJTaLB18mTJEmSJC1/yv1OHhGxbkScHhG/iYiOeds3I6Jb05cnSZIkSSpHWSEvInoDrwKHAEcCld/B2wX4RdOWJkmSJEkqV7kjecOAX6WUegHzStrvB77ZZFVJkiRJkhqk3JDXG6jpe3lTgXUbX44kSZIkqTHKDXlzgQ41tPcEpjW+HEmSJElSY5Qb8u4Czo+IVfLHKSK6ApcBf2nKwiRJkiRJ5Ss35J0OrAVMByqAx4HXgI+Bc5u0MkmSJElS2cq9Tt5MYIeI2BnYmiwkPptSenBZFCdJkiRJKk9ZIa9SSmksMLaJa5EkSZIkNVK518m7MSJ+UkP7aRFxfdOVJUmSJElqiHK/k/cdah7BG5svkyRJkiS1oHJDXntgVg3ts8lOyCJJkiRJakHlhrz/UPOI3R5kZ9mUJEmSJLWgck+8cgUwMiLWYfG0zX7AKcCJTViXJEmSJKkByr2Ewk0RsSrZNfHOzpvfAU5LKf2uqYuTJEmSJJWn3iEvIloDxwB/TSldGxGdgEgpTVtm1UmSJEmSylLv7+SllBYAlwNt8sfTDXiSJEmStHwp98QrTwG9l0UhkiRJkqTGK/fEK78FhkVEZ2AC2aUTqqSUnm2qwiRJkiRJ5Ss35P0x//nLGpYloFXjypEkSZIkNUa5Ia/bMqlCkiRJktQkyr2EwuRlVYgkSZIkqfHKPfEKETEgIv4eERMj4st521ER0a/py5MkSZIklaOskBcRhwC3Af8lm7rZJl/UCjizaUuTJEmSJJWr3JG8M4GjU0qnAgtK2p8CtmqqoiRJkiRJDVNuyNsYeLKG9lnAGo0vR5IkSZLUGOWGvHeBTWpo3xF4vT4biIhWEfGviPh7/nitiBgTEf/Nf3YosyZJkiRJUq7ckHcdMDwivpk//nJEHAYMBX5Tz22cDLxc8vinwEMppY2Bh/LHkiRJkqQGKCvkpZSGAn8BxgCrAeOAkcDIlNKIpa0fERsCewDXlzR/F7gpv38TsE85NUmSJEmSFqvXdfIiogK4nCyAtQFGA1fkiyemlGbVc39XkZ28ZfWStnVTSlMBUkpTI2KdWmo4BjgGoHPnzvXcnSRJkiT9b6nvSN4FwEDgbuAWYGfgjJTSP+sb8CJiT2BaSmlCQwpNKV2XUuqTUurTqVOnhmxCkiRJkgqvXiN5wL7AkSmlPwFExB+AJyKiVUppYT238U1g74j4DrAqsEZE/B54PyLWy0fx1gOmlXkMkiRJkqRcfUfyvgw8VvkgpfRPsuvkrV/fHaWUzk4pbZhS6gocBIxNKf0Q+BtwWN7tMOCu+m5TkiRJkrSk+oa8VsD8am0LqP9IYF0uBXaJiP8Cu+SPJUmSJEkNUN+QFsDvI2JeSduqwG8jYk5lQ0pp7/psLKX0MPBwfv9DoF8965AkSZIk1aG+Ie+mGtp+35SFSJIkSZIar14hL6V0+LIuRJIkSZLUeGVdDF2SJEmStHwz5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlAmi3kRcSXI2JcRLwcES9FxMl5+1oRMSYi/pv/7NBcNUmSJElS0TTnSN4C4CcppU2B7YETI2Iz4KfAQymljYGH8seSJEmSpAZotpCXUpqaUno2v/8p8DKwAfBd4Ka8203APs1VkyRJkiQVTYt8Jy8iugK9gKeBdVNKUyELgsA6taxzTESMj4jx06dPb7ZaJUmSJGlF0uwhLyLaAX8BTkkpzazveiml61JKfVJKfTp16rTsCpQkSZKkFVizhryIaEMW8P6QUrojb34/ItbLl68HTGvOmiRJkiSpSJrz7JoB3AC8nFL6ZcmivwGH5fcPA+5qrpokSZIkqWhaN+O+vgkcCvw7Ip7L234GXArcFhFHAm8B+zdjTZIkSZJUKM0W8lJKjwNRy+J+zVWHJEmSJBVZi5xdU5IkSZK0bBjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SJEmSCsSQJ0mSJEkFYsiTJEmSpAIx5EmSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJJVp1KhRRMQXbiNHjqz3NhYtWkTv3r2JCP7+978vsWz+/PlceOGFdO/enbZt29K9e3fOP/985s2bV+c2X3nlFbbbbjvWXHNNDjroIGbNmrXE8kcffZQNNtjgC+2SJKlYWrd0AZK0oho7dixt27aterzRRhvVe93rr7+ed955p8ZlP/3pTxk5ciQ///nP6dWrF88++yznnnsuH3/8Mb/61a9q3ebAgQPp3r07F154IWeccQYXX3wxF198MZCFylNOOYVLLrmEdu3a1btOSZK04jHkSVIDbbPNNg0KTDNmzOCcc87h0ksv5aijjvrC8j/+8Y8cf/zxnHbaaQB8+9vf5p133uEPf/hDrSFv1qxZPP3004wePZpOnTrx8ccfM2zYsKqQd8MNN9CmTRsOPfTQsuuVJEkrFkOeJDWzwYMH881vfpN+/frVuPzzzz9nzTXXXKKtffv2pJRq3eb8+fMBqkYWKyoqqtpmzpzJ4MGD+etf/0pENMUhSJKk5ZjfyZOkBvrKV75C69at6dGjB9dee2291nnhhRf43e9+x7Bhw2rtc9RRR3HttdfyxBNPMGvWLB577DF+85vfMGjQoFrXWWuttejWrRtXX301H330Eddddx19+vQB4KKLLqJ///5sv/325R2gJElaITmSJ0llWm+99bjooovYdtttWbhwIbfccgvHHXccc+bM4dRTT61z3R//+MeceOKJdO/enUmTJtXY59JLL2Xu3LnssMMOVW0nnHAC5513Xp3bHjFiBPvvvz8/+9nP2HjjjRkxYgSvvfYaN9xwAy+88ELZxylJklZMhjxJKtNuu+3GbrvtVvV4wIABzJs3j5///OecfPLJrLRSzZMk/vSnP/Hqq68yevToOrd/+eWX8/vf/56rr76aLbbYgueff57Bgwez9tprc+GFF9a63oABA5g2bRpTpkzhK1/5Cq1atWLvvffm1FNPZcMNN2TEiBFcdtllQHZylxNOOKEBRy9JkpZ3hjxJagL77bcft912G5MmTarxLJuff/45Z5xxBmeddRaLFi3i448/ZubMmQDMnj2bTz/9lNVXX50PPviAc889lxEjRnD00UcDsOOOO7LyyiszaNAgBg0axDrrrFNrHRUVFWyyySYAPPjggzz//PPceuutVUHxH//4BwBf//rX2WGHHdhiiy2a+qmQJEktzO/kSVITqu3EJrNnz2bKlCmcdtppdOjQgQ4dOrDlllsCcNBBB9GrVy8A3njjDT7//HO22mqrJdbv1asXCxYsYPLkyfWqY8GCBZxyyikMHTqUtm3b8vDDD7PzzjvTs2dPevbsSb9+/XjkkUcafqCSJGm55UieJDWBv/zlL3Ts2JEuXbrUuLxdu3aMGzduibb33nuPgw8+mIsvvpidd94ZoGr9Z599lm222aaq74QJEwDo2rVrveoZOXIkHTp04MADD6xqmzNnTtX92bNn13m2TkmStOIy5ElSmb7//e+z7bbbssUWW7Bw4UJuvfVWbr31VoYPH77E9/G6d+9O3759ueGGG2jdujU77bTTEtupPPHK5ptvznbbbQfAuuuuyz777MNZZ53FZ599xhZbbMFzzz3HkCFD2H///enUqdNS65sxYwYXXHAB999/f1XbjjvuyJlnnsmNN94IZBdyv/TSSxv5TEiSpOWRIU+SytSjRw9uvPFG3n77bVJKbLbZZtx8881fuND4ggULWLhwYdnbv+mmm7jwwgsZPnw47777LhtssAHHHnssgwcPrtf6559/PnvvvTdbb711VVuvXr0YOnQo55xzDgDDhg2rmi4qSZKKJVbE6Tp9+vRJ48ePb9Jt9j7j5ibdntRUJlz+o5YuQZIkScuZiJiQUupT0zJPvCJJkiRJBeJ0TUlN4q0LN2/pEqRadT7v3y1dgiRJzcaRPEmSJEkqEEOeJEmSJBWIIU+SJDWr1157jWOPPZYtt9ySVq1afeHyIrWZP38+Z5xxBt/61rdo27YtEVFjv4EDBxIRX7i98sordW7/lVdeYbvttmPNNdfkoIMOYtasWUssf/TRR9lggw2+0C5JyxtDniRJalYvvfQS99xzD5tssgmbbLJJvdebM2cO119/PRUVFXzjG9+os2/Pnj158sknl7h17dq1znUGDhxI9+7due2225g4cSIXX3xx1bJFixZxyimncMkll9CuXbt61yxJLcETr0iSpGa111578d3vfheA/fbbjw8++KBe67Vv356PPvqIiOCaa65h7NixtfZdbbXV2H777etd06xZs3j66acZPXo0nTp14uOPP2bYsGFVQe+GG26gTZs2X7gepiQtjxzJkyRJzWqllRr+9qO2KZqNNX/+fADatm0LQEVFRVXbzJkzGTx4ML/61a+W2f6lpjBx4kT69etHRUUF66+/Pueddx4LFy6sc51nnnmGww8/nO7du1NRUUGPHj244IIL+Oyzz5a6v/POO49OnTqx0UYbMXr06C8s79evH1deeWWDj0cNZ8iTJEmFM3HiRNZYYw1WWWUVdthhBx555JE6+6+11lp069aNq6++mo8++ojrrruOPn2yawxfdNFF9O/fv6yRQam5zZgxg/79+xMR3HXXXZx33nlcccUVnH/++XWud+utt/L6669z1llncc8993DiiSfyy1/+kkMOOaTO9e677z6GDx/OiBEjOProoznkkEP48MMPq5bfeeedTJkyhUGDBjXJ8ak8TteUJEmF0qtXL7bbbjs222wzpk+fzhVXXMEuu+zC448/zrbbblvreiNGjGD//ffnZz/7GRtvvDEjRozgtdde44YbbuCFF15oxiOQyjdy5Ejmzp3LHXfcwRprrMEuu+zCzJkzGTJkCGeeeSZrrLFGjeudddZZdOrUqerxTjvtxKqrrsqxxx7L5MmT6dKlS43rPfjggxxyyCEccMABANx888089dRT7LHHHsybN4/TTz+d4cOH06ZNm6Y/WC2VI3mSJKlQTj75ZI4//nj69u3Lfvvtx9ixY9lggw2WOJFKTQYMGMC0adN49dVXefnll+ncuTOnnXYap556KhtuuCEjRoygc+fOdO7cmV//+tfNdDRS/dx7773stttuS4S5gw46iLlz59Y5kl0a8Cr16tULgGnTptW63vz586umN8OSU5yvvPJKNt54Y/bYY4+yj0NNw5AnSZIKrW3btnznO9/h2WefXWrfiooKNtlkE1q1asWDDz7I888/z+mnn87zzz/P4MGDeeCBB3jggQc455xzHN3TcuWVV16hZ8+eS7R17tyZioqKpV4+pLp//OMfrLTSSvTo0aPWPr179+aOO+7gzTff5KGHHuLFF19kq6224r333mPo0KF+F6+FOV1TkiT9TyjnpCkLFizglFNOYejQobRt25aHH36YnXfeuepNdL9+/XjkkUfYYostllW5UllmzJhB+/btv9DeoUMHZsyYUe/tvPfee/ziF7/g0EMPrXWKJ8APfvADbrnlFjbaaCMigosuuohu3bpx+OGHc+ihh7Lppps25DDURAx5kiSp0ObOncu9995L7969673OyJEj6dChAwceeGBV25w5c6ruz549m5RSk9YpNVZNH2SklOr9Acf8+fM54IADaNeu3VJH4tq0acN9993HpEmTaNeuHR07dmTChAncfffdvPrqq0yZMoUjjzySZ555hj59+jBq1CjWX3/9Bh2XymfIkyRJzWrOnDncc889ALzzzjvMnDmT22+/HYDvfOc7VFRUANC9e3f69u3LDTfcULXuvffey+zZs3nuuecAqtbbZptt6NKlC5988gl77rknP/zhD+nevTsffPABV155Je+88w633XZbveqbMWMGF1xwAffff39V24477siZZ57JjTfeCMDYsWO59NJLG/dESE2oQ4cOfPzxx19o/+STT2oc4asupcSPfvQjXnrpJZ544gk6dOhQr/127dq16v7JJ5/MkCFD6NChA0ceeSQ9evTgzjvv5Mwzz+Skk06q+nvVsmfIkyRJzWratGnsv//+S7RVPn7zzTer3jQuWLDgC9f4Ov7445k8efIX1vvd737HwIEDWWWVVejUqRM///nPmTZtGquuuipf//rXeeSRR6ouibA0559/PnvvvTdbb711VVuvXr0YOnQo55xzDgDDhg1jyy23LO/ApWWoZ8+eX/ju3dtvv83s2bO/8F29mpx66qncddddjBkzpl79q7vlllv45JNPOPbYYwEYN24cjz32GBUVFRx33HH07du37G2q4Qx5kiSpWXXt2rVeUx0nTZpUr7ZSq666KnfccUcDK8sMHz68xvaTTz6Zk08+uVHblpaVAQMGcPnll/Ppp5+y+uqrA9k18Nq2bbvUgHXJJZdw9dVXc9ttt7HDDjuUve+5c+dy1llnceONN9KqVauq9sopzk5vbn6eXVOSJElawR133HGsssoq7Lvvvjz44INcd911DBkyhNNOO22JE6h0796dI488surxH//4R372s5/xox/9iA022ICnnnqq6jZ9+vR67Xvo0KFsvfXW9O/fv6qtb9++DB48mDFjxnDuueey0047NdmxaukcyZMkaTnwzau/2dIlSLV64sdPtHQJWooOHTrw0EMPMWjQIPbaay/at2/PqaeeypAhQ5boV30a9AMPPADAqFGjGDVq1BJ9K6dB12XKlClcddVVPPPMM0u0Dx8+nMMOO4x9992XbbbZptYRci0bhjxJkiSpADbbbDPGjh1bZ5/qU55rCnfl2HDDDWu8REPnzp0ZN25cg7erxlkupmtGxO4R8WpEvBYRP23peiRJkiRpRdXiIS8iWgEjgAHAZsDBEbFZy1YlSZIkSSum5WG65rbAaymlNwAi4k/Ad4GJLVqVJEmSViiP7Ohp+rV86vvoI826v2jp05lGxH7A7imlo/LHhwLbpZQGVet3DHBM/rAH8GqzFqpydQQ+aOkipBWYf0NS4/g3JDWOf0PLvy4ppU41LVgeRvKihrYvJM+U0nXAdcu+HDWFiBifUqrfVWclfYF/Q1Lj+DckNY5/Qyu2Fv9OHjAF+HLJ4w2Bd1uoFkmSJElaoS0PIe8ZYOOI6BYRKwMHAX9r4ZokSZIkaYXU4tM1U0oLImIQcD/QCrgxpfRSC5elxnNqrdQ4/g1JjePfkNQ4/g2twFr8xCuSJEmSpKazPEzXlCRJkiQ1EUOeJEmSJBWIIU/1FhELI+K5iHg+Ip6NiG/k7V0j4sWWrk9aXkXEORHxUkS8kP8NbRcR10fEZvnySRHRsaXrlFYEEbFuRPwxIt6IiAkR8WREfK+O/jtFxN9rWebfngqh5D3aixHx54ioyNtnLWW99hFxQsnj9SPi9mVdr5Y9Q57KMTeltFVKaUvgbOCSli5IWt5FxNeBPYGtU0pbAP2Bt1NKR6WUJjbhfiIi/DddhRYRAfwVeDSltFFKqTfZWbk3bNHCpJZX+R7ta8B84Lh6rtceqAp5KaV3U0r7LYP61Mx8Q6CGWgOYUb0xIgZGxDUlj/8eETvl93fNP3F9Nv+UqV2zVSu1nPWAD1JK8wBSSh+klN6NiIcjYomLzEbEZdU+UR0SET/J758REc/ko4EX5G1dI+LliPg18CxLXnNUKqKdgfkppZGVDSmlySmlqyNi1Yj4XUT8OyL+FRHfrr5yRKwdEQ/ky68FojmLl5rJY0D30oaIaBcRD+Xvwf4dEd/NF10KfCUfBby8dHZW/p7ujoi4LyL+GxFDS7Y3q+T+fhExKr8/KiJ+ExHj8tH2vhFxY/5/1ahlfNwqYchTOdrm/wi8AlwPXFTfFfPpMOcC/VNKWwPjgdOWTZnScuUB4MsR8Z+I+HVE9K2j75+AA0seHwD8OSJ2BTYGtgW2AnpHxI55nx7AzSmlXimlyU1fvrRc+SrZBxo1OREgpbQ5cDBwU0SsWq3P+cDjKaVeZNfk7bysCpVaQkS0BgYA/6626DPge/l7sG8DV+Qj4z8FXs9HAc+oYZNbkf2/tDlwYETU58PEDmQfyJwKjAauJPvb3Twitir7oNQgLX6dPK1Q5qaUtoKqKWg3R8TX6rnu9sBmwBPZvymsDDy5LIqUlicppVkR0Rv4Ftl/rLdGxE9r6fuviFgnItYHOgEzUkpvRcRJwK7Av/Ku7chC31vA5JTSU8v8QKTlUESMAHYgm542BbgaIKX0SkRMBjaptsqOwL55n7sj4gszUqQVVNuIeC6//xhwQ7XlAVycf0C4CNgAWLce230opfQJQERMBLoAby9lndEppRQR/wbeTyn9O1//JaAr8Fwd66qJGPLUICmlJ/PRuU7VFi1gyRHiyk9RAxiTUjq4OeqTlicppYXAw8DD+X96h9XR/XZgP+BLZCN7kP39XJJSura0Y0R0BWY3db3Scuwl4PuVD1JKJ+b/F40H3qnnNrxAsIqo6oP4WhxC9p6td0rp84iYxOL3aHWZV3J/IYuzQ+nfUfXtVK6zqNr6izB7NBuna6pBIqIn0Ar4sNqiScBWEbFSPqS/bd7+FPDNiOier18REdU/YZUKJyJ6RMTGJU1bAXVNq/wT2Ykk9iMLfAD3A0dUfo81IjaIiHWWQbnS8m4ssGpEHF/SVpH/fJTsjSz5/y+dgVerrV/aZwDZtDLpf8GawLQ84H2bbEQO4FNg9QZs7/2I2DQ/4VetZ7dVyzFNqxylUwECOCyltDCfflnpCeBNsrngL5J/dyKlND0iBgK3RMQqed9zgf80Q91SS2oHXB0R7clGul8DjmFxgFtCSumliFgdeCelNDVveyAiNgWezP/eZgE/JPtUVfqfkU8B2we4MiLOBKaTjWafBdwFjMxHyxcAA1NK86r9H3UB2f9DzwKPkE15lv4X/AEYHRHjyaZLvgKQUvowIp7IT7ZyLzCintv7KfB3sqmbL5L9X6flSKTkrAVJkiRJKgqna0qSJElSgRjyJEmSJKlADHmSJEmSVCCGPEmSJEkqEEOeJEmSJBWIIU+SVHgR8WJEDGnpOiRJag6GPElSs4uIdSPiVxHxekTMi4h3IuLeiPhOC9a0ckScERH/iojZEfFRRDwVEceWXN9zadvoGhEpIvos63olSaqNF0OXJDWriOgKPAF8CpwNPE/2oWM/YCTQuYHbXYns+q9lXyQ+IlYG7gd6AecBjwEfA9sApwGvAg83pK6WFBErp5Tmt3QdkqTm5UieJKm5/RoIoE9K6baU0qsppZdTStcAW1Z2iojTIuKFfFTtnYi4PiLalywfGBGzIuI7EfEiMB/YNCLWiYi7ImJuREyOiCPqUdMpQF+gf0ppeErpXymlN1NKtwHfAJ7N97l7RDwWETPykb77I2LTku28mf98Jh/Re7ik3sMjYmJEfBYR/4mIU/NgWrl8k4h4JF/+an5csyJiYEmfzSPiwfzYPoqIURGxZsnyURHx94g4KyKmAFMi4rz8+VlCRDwREcPr8dxIklYwhjxJUrOJiLWA3YFrUkqzqi9PKc0oebiILHx9FfgBsC1wdbVVVgXOBY4FNgMmA6OA7kB/YB/gR0DXpZR2CPBgSml8DTUtSinNzB+uBlyV17IT8AkwOh8JJG8nP8b1gH3z4z4auJhslHBT4CfAWcAJ+fKVgDuBBcD2wEDgfKBqmmhEVAD3AbPy/XyPLIDeWK3kvsAWeQ398uU9I2Lbkm31yNe9YSnPiyRpBeR0TUlSc+pONor38tI6ppSuKnk4KSLOBO6KiMNSSovy9lbAj1NKEyAbDQMGADuklJ7I2w4D3ljK7jamHtMxU0p/KX0cEYcDM8lC1+PA9HzRhyml90q6DgbOTCndnj9+MyIuJQt51wC7AD2AXVNK7+TbPpVsWmulQ4B2wKEppU/zPscA4yKie0rptbzfZ8ARKaV5JXXeBxwB/DNvOgKYkFJ6fmnHLEla8TiSJ0lqTlHvjhE7R8SYiJgSEZ8CdwArA18q6bYAeK7k8aZkI4CVYYaU0mTg3aaoKyK+EhF/zE8YMxN4n+z/0lq/RxgRnYAvA9fm0y9nRcQs4FLgK3m3nsC7lQEv90x+LKXH9kJlwMv9I++zWUnbi6UBL/db4KCIaBsRrYBDcRRPkgrLkTxJUnP6L5DIAsudtXWKiC7A3WTh5DzgQ2Br4BayoFdpXrUTrdQ7RFbzn7ympRkNvEM2PfQdspA5sVpN1VV+oHocWSirSZA9L3Wpq09p++walt8NzAG+TzbFtD3ZcylJKiBH8iRJzSal9BHZWSwHRUS76stLTqzShyw4nZpSejKl9B9g/Xrs4mWy/9u2Kdlm53qs+0egf02XPoiIlSJijYhYmywIXpxSejCl9DKwOkt+YFp5JstWlQ0ppffJAuFXUkqvVb+V1L1BRJTW2Ycl/5+eCGwZEauXtH0j71Pn9NeU0gKy7yoekd/uSCl9XNc6kqQVlyFPktTcTiAblRofEftHRI+I6BkRxwMv5H3+S/Z/1CkR0S0iDiY7CUudUkqvkp2c5NqI+HpEbEUWbuYuZdWryC6bMCYiToqIrfL97kv2XbutgRnAB8DREdE9IvqSXfJhQcl2puX72i2yawFWnvlyCHBmfkbNHhHxtYj4UUScnS8fQ3aZhpsiYsuI2B74Zb7tylG6P5CN0t2cn2VzR+BassBWGRbrcj3ZSVn2xKmaklRohjxJUrNKKb1JFprGAJeRBbuxwN5k0yBJKb0AnEx2jbqJwFHA6fXcxUCySxmMJZte+Udg0lJqmgfsSvY9uSOBJ8kum3AmcBPwj/xkLweSnbnyRWAE2QlV5pVsZwFwUl7vu8Bdefv1ZCNoh5JdF/Ax4Ji8TvJtf4/sbJr/zPf5C7KA91neZw6wG7BG3ueuvM76XCKClNIbwCPAW6yA1/yTJNVfpLS0rwBIkqTmFhFbkp1Upk/l2UObYJsTgT+klH7RFNuTJC2fDHmSJC0HIuJ7ZNMx/0t2Xb9fkk1r7ZUa+Z91RKwDHAxcAnROKX3QuGolScszz64pSdLyYXWy6atfJvv+38NkJ55pik9j3yf7PuGxBjxJKj5H8iRJkiSpQDzxiiRJkiQViCFPkiRJkgrEkCdJkiRJBWLIkyRJkqQCMeRJkiRJUoH8P1eZWgOPz5JbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the proportion of education in bar chart\n",
    "plt.figure(figsize = (15, 7))\n",
    "\n",
    "percentage = []\n",
    "for i in proportion_card_category['Count']:\n",
    "  pct = (i / proportion_card_category['Count'].sum()) * 100\n",
    "  percentage.append(round(pct, 2))\n",
    "proportion_card_category['Percentage'] = percentage\n",
    "\n",
    "## Show the plot\n",
    "plots = sns.barplot(x = \"Card_Category\",\n",
    "                    y = \"Percentage\",\n",
    "                    data = proportion_card_category)\n",
    "\n",
    "## Show the annotation\n",
    "for p in plots.patches:\n",
    "  plots.annotate('{} %'.format(p.get_height().astype('float')),\n",
    "                 (p.get_x() + p.get_width() / 2, p.get_height()),\n",
    "                 ha='center', va='center',\n",
    "                 size=15, xytext=(0, 8),\n",
    "                 textcoords='offset points')\n",
    "\n",
    "# Setting the label for x-axis\n",
    "plt.xlabel(\"Card Category\", size=14)\n",
    "# Setting the label for y-axis\n",
    "plt.ylabel(\"Percentage\", size=14)\n",
    "# Setting the title for the graph\n",
    "plt.title(\"Percentage of Card Category\", size = 16, weight = 'semibold')\n",
    "# Fianlly showing the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Checking Outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfo1 = bankchurn.select_dtypes(include = ['int64'])\n",
    "plt.figure(figsize = (20, 7))\n",
    "sns.boxplot(x = 'variable', y = 'value', color = 'green', orient = 'v', data = pd.melt(dfo1))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfo2 = bankchurn.select_dtypes(include = ['float64']) # memilih kolom numerik\n",
    "plt.figure(figsize = (20, 7))\n",
    "sns.boxplot(x = 'variable', y = 'value', color = 'green', orient = 'v', data = pd.melt(dfo2))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OTM0xswiuzbM"
   },
   "source": [
    "## **Checking Correlation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 728
    },
    "id": "rEv_stCpu4aW",
    "outputId": "132d38ed-3bc8-40d9-ec09-a474ac5248bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfk = bankchurn.select_dtypes(include = ['int64', 'float64']) # memilih kolom numerik\n",
    "k = len(dfk.columns)\n",
    "cm = dfk.corr()\n",
    "plt.figure(figsize = (14, 10))\n",
    "sns.heatmap(cm, annot = True, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XTmL5My1sS3a"
   },
   "source": [
    "## **Normality Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "hI_5oAQZsWtx",
    "outputId": "19d34065-c5f9-460d-e071-f3492b9482c9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7061</th>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4100.0</td>\n",
       "      <td>1956</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>0.617</td>\n",
       "      <td>3584</td>\n",
       "      <td>61</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9075</th>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34516.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34516.0</td>\n",
       "      <td>0.814</td>\n",
       "      <td>8736</td>\n",
       "      <td>97</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8494.0</td>\n",
       "      <td>2023</td>\n",
       "      <td>6471.0</td>\n",
       "      <td>0.871</td>\n",
       "      <td>4486</td>\n",
       "      <td>77</td>\n",
       "      <td>0.974</td>\n",
       "      <td>0.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>981</td>\n",
       "      <td>457.3</td>\n",
       "      <td>0.663</td>\n",
       "      <td>1114</td>\n",
       "      <td>33</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6871</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>0.645</td>\n",
       "      <td>2866</td>\n",
       "      <td>47</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Age  Dependent_count  Months_on_book  Total_Relationship_Count  \\\n",
       "7061            48                5              36                         4   \n",
       "9075            51                3              37                         1   \n",
       "7608            40                2              27                         6   \n",
       "1384            46                4              36                         3   \n",
       "6871            43                3              32                         6   \n",
       "\n",
       "      Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "7061                       1                      2        4100.0   \n",
       "9075                       2                      1       34516.0   \n",
       "7608                       3                      1        8494.0   \n",
       "1384                       3                      4        1438.3   \n",
       "6871                       3                      3        1897.0   \n",
       "\n",
       "      Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "7061                 1956           2144.0                 0.617   \n",
       "9075                    0          34516.0                 0.814   \n",
       "7608                 2023           6471.0                 0.871   \n",
       "1384                  981            457.3                 0.663   \n",
       "6871                    0           1897.0                 0.645   \n",
       "\n",
       "      Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "7061             3584              61                0.743   \n",
       "9075             8736              97                0.702   \n",
       "7608             4486              77                0.974   \n",
       "1384             1114              33                0.650   \n",
       "6871             2866              47                0.469   \n",
       "\n",
       "      Avg_Utilization_Ratio  \n",
       "7061                  0.477  \n",
       "9075                  0.000  \n",
       "7608                  0.238  \n",
       "1384                  0.682  \n",
       "6871                  0.000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_uji = bankchurn[['Customer_Age', 'Dependent_count', 'Months_on_book', 'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
    "                        'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1',\n",
    "                        'Total_Trans_Amt', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio']].sample(n = 50, random_state = 21)\n",
    "numeric_uji.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 483
    },
    "id": "9hArvyZqsWz-",
    "outputId": "8be0b559-19d4-4aed-b3d9-222287f9fb87"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nilai statistik</th>\n",
       "      <th>p-value</th>\n",
       "      <th>sig lvl</th>\n",
       "      <th>hasil uji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Customer_Age</th>\n",
       "      <td>0.960317</td>\n",
       "      <td>9.165532e-02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependent_count</th>\n",
       "      <td>0.921909</td>\n",
       "      <td>2.765903e-03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Months_on_book</th>\n",
       "      <td>0.957837</td>\n",
       "      <td>7.215334e-02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <td>0.922994</td>\n",
       "      <td>3.030233e-03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <td>0.796414</td>\n",
       "      <td>7.256186e-07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <td>0.924277</td>\n",
       "      <td>3.377850e-03</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_Limit</th>\n",
       "      <td>0.740214</td>\n",
       "      <td>4.706309e-08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <td>0.892568</td>\n",
       "      <td>2.755597e-04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <td>0.743039</td>\n",
       "      <td>5.350601e-08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <td>0.898228</td>\n",
       "      <td>4.201453e-04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <td>0.759301</td>\n",
       "      <td>1.139740e-07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <td>0.961039</td>\n",
       "      <td>9.826855e-02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <td>0.957512</td>\n",
       "      <td>6.993185e-02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "      <td>0.862328</td>\n",
       "      <td>3.395101e-05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>tidak normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          nilai statistik       p-value  sig lvl     hasil uji\n",
       "Customer_Age                     0.960317  9.165532e-02     0.05        normal\n",
       "Dependent_count                  0.921909  2.765903e-03     0.05  tidak normal\n",
       "Months_on_book                   0.957837  7.215334e-02     0.05        normal\n",
       "Total_Relationship_Count         0.922994  3.030233e-03     0.05  tidak normal\n",
       "Months_Inactive_12_mon           0.796414  7.256186e-07     0.05  tidak normal\n",
       "Contacts_Count_12_mon            0.924277  3.377850e-03     0.05  tidak normal\n",
       "Credit_Limit                     0.740214  4.706309e-08     0.05  tidak normal\n",
       "Total_Revolving_Bal              0.892568  2.755597e-04     0.05  tidak normal\n",
       "Avg_Open_To_Buy                  0.743039  5.350601e-08     0.05  tidak normal\n",
       "Total_Amt_Chng_Q4_Q1             0.898228  4.201453e-04     0.05  tidak normal\n",
       "Total_Trans_Amt                  0.759301  1.139740e-07     0.05  tidak normal\n",
       "Total_Trans_Ct                   0.961039  9.826855e-02     0.05        normal\n",
       "Total_Ct_Chng_Q4_Q1              0.957512  6.993185e-02     0.05        normal\n",
       "Avg_Utilization_Ratio            0.862328  3.395101e-05     0.05  tidak normal"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilai_statistik = list()\n",
    "p_value = list()\n",
    "hasil = list()\n",
    "for i in numeric_uji:\n",
    "    ns, pv = stats.shapiro(numeric_uji[i])\n",
    "    hsl = 'normal' if pv > .05 else 'tidak normal'\n",
    "    nilai_statistik.append(ns)\n",
    "    p_value.append(pv)\n",
    "    hasil.append(hsl)\n",
    "\n",
    "uji = pd.DataFrame(nilai_statistik, index = numeric_uji.columns, columns = ['nilai statistik'])\n",
    "uji['p-value'] = p_value\n",
    "uji['sig lvl'] = 0.05\n",
    "uji['hasil uji'] = hasil\n",
    "uji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xRvNXFq6seky"
   },
   "source": [
    "# **Data Preprocessing**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Remove Credit Limit**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10127 entries, 0 to 10126\n",
      "Data columns (total 19 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Attrition_Flag            10127 non-null  object \n",
      " 1   Customer_Age              10127 non-null  int64  \n",
      " 2   Gender                    10127 non-null  object \n",
      " 3   Dependent_count           10127 non-null  int64  \n",
      " 4   Education_Level           10127 non-null  object \n",
      " 5   Marital_Status            10127 non-null  object \n",
      " 6   Income_Category           10127 non-null  object \n",
      " 7   Card_Category             10127 non-null  object \n",
      " 8   Months_on_book            10127 non-null  int64  \n",
      " 9   Total_Relationship_Count  10127 non-null  int64  \n",
      " 10  Months_Inactive_12_mon    10127 non-null  int64  \n",
      " 11  Contacts_Count_12_mon     10127 non-null  int64  \n",
      " 12  Total_Revolving_Bal       10127 non-null  int64  \n",
      " 13  Avg_Open_To_Buy           10127 non-null  float64\n",
      " 14  Total_Amt_Chng_Q4_Q1      10127 non-null  float64\n",
      " 15  Total_Trans_Amt           10127 non-null  int64  \n",
      " 16  Total_Trans_Ct            10127 non-null  int64  \n",
      " 17  Total_Ct_Chng_Q4_Q1       10127 non-null  float64\n",
      " 18  Avg_Utilization_Ratio     10127 non-null  float64\n",
      "dtypes: float64(4), int64(9), object(6)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "bankchurn = bankchurn.drop('Credit_Limit', axis = 1)\n",
    "bankchurn.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ZAz9UPOs2pP"
   },
   "source": [
    "## **Feature Scalling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "uaKVDKuisg3Q"
   },
   "outputs": [],
   "source": [
    "stand_var = ['Customer_Age', 'Months_on_book', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1']\n",
    "norm_var = ['Dependent_count', 'Total_Relationship_Count', 'Months_Inactive_12_mon', 'Contacts_Count_12_mon',\n",
    "            'Total_Revolving_Bal', 'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt', 'Avg_Utilization_Ratio']\n",
    "\n",
    "for stand in bankchurn[stand_var]:\n",
    "    bankchurn[stand] = StandardScaler().fit_transform(bankchurn[stand].values.reshape(len(bankchurn), 1))\n",
    "for norm in bankchurn[norm_var]:\n",
    "    bankchurn[norm] = MinMaxScaler().fit_transform(bankchurn[norm].values.reshape(len(bankchurn), 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WSaMIz-s_ZM"
   },
   "source": [
    "## **Categorical Encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "QCc2O09TtGV6",
    "outputId": "f62f24c2-3dce-4a24-b404-0e32d52f60bc"
   },
   "outputs": [],
   "source": [
    "# Ordinal Encoding\n",
    "LE = LabelEncoder()\n",
    "for cat in list(['Attrition_Flag', 'Education_Level', 'Income_Category', 'Card_Category']):\n",
    "    bankchurn[cat] = LE.fit_transform(bankchurn[cat])\n",
    "\n",
    "# Nominal Encoding\n",
    "nominal_cats = ['Gender', 'Marital_Status']\n",
    "for cat in nominal_cats:\n",
    "    onehot = pd.get_dummies(bankchurn[cat], prefix = cat)\n",
    "    bankchurn = bankchurn.join(onehot)\n",
    "\n",
    "bankchurn = bankchurn.drop(['Gender', 'Marital_Status'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yszmhTCwtKC3"
   },
   "source": [
    "## **Train Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "LWgKmnC8tNPG"
   },
   "outputs": [],
   "source": [
    "X = bankchurn.drop(['Attrition_Flag'], axis = 1).astype(float).values\n",
    "y = bankchurn['Attrition_Flag'].astype(float).values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "98eqbQRDtRDz"
   },
   "source": [
    "## **Imbalanced Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cKTtyO4EtcmP",
    "outputId": "ae7f88a4-2e2f-461e-9ab0-19dd63859779"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before SMOTE\n",
      "1.0    6801\n",
      "0.0    1300\n",
      "dtype: int64\n",
      "After SMOTE\n",
      "1.0    6801\n",
      "0.0    6801\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "smote = SMOTE(random_state = 0)\n",
    "X_resampled, y_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print('Before SMOTE')\n",
    "print(pd.DataFrame(y_train).value_counts())\n",
    "print('After SMOTE')\n",
    "print(pd.DataFrame(y_resampled).value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xEE88AVftlsq"
   },
   "source": [
    "# **Modeling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8568608094768015\n",
      "Precision: 0.7496765957113524\n",
      "Recall/Sensitivity: 0.8442742897873007\n",
      "Cross Entropy Loss: 2.207914637922275\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "log_model = LogisticRegression().fit(X_resampled, y_resampled)\n",
    "\n",
    "# Predict\n",
    "log_y_test_pred = log_model.predict(X_test)\n",
    "\n",
    "# Performance\n",
    "print('Accuracy:', accuracy_score(y_test, log_y_test_pred))\n",
    "print('Precision:', precision_score(y_test, log_y_test_pred, average = 'macro'))\n",
    "print('Recall/Sensitivity:', recall_score(y_test, log_y_test_pred, average = 'macro'))\n",
    "print('Cross Entropy Loss:', tf.keras.metrics.binary_crossentropy(y_test, log_y_test_pred).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Form confusion matrix as a DataFrame\n",
    "confusion_matrix_df = pd.DataFrame((confusion_matrix(y_test, log_y_test_pred)),\n",
    "                                   ('Existing Customer', 'Atrited Customer'),\n",
    "                                   ('Existing Customer', 'Atrited Customer'))\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure()\n",
    "heatmap = sns.heatmap(confusion_matrix_df, annot = True, annot_kws = {'size' : 14}, fmt = 'd', cmap = 'YlGnBu')\n",
    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation = 0, ha = 'right', fontsize = 14)\n",
    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation = 0, ha = 'center', fontsize = 14)\n",
    "\n",
    "plt.title('Confusion Matrix for Logistic Regression\\n', fontsize = 18, color = 'darkblue')\n",
    "plt.ylabel('True label', fontsize = 14)\n",
    "plt.xlabel('Predicted label',fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9550839091806516\n",
      "Precision: 0.9158161806746712\n",
      "Recall/Sensitivity: 0.9188900468525287\n",
      "Cross Entropy Loss: 0.6928283863858992\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "rf_model = RandomForestClassifier(random_state = 42).fit(X_resampled, y_resampled)\n",
    "\n",
    "# Predict\n",
    "rf_y_test_pred = rf_model.predict(X_test)\n",
    "\n",
    "# Performance\n",
    "print('Accuracy:', accuracy_score(y_test, rf_y_test_pred))\n",
    "print('Precision:', precision_score(y_test, rf_y_test_pred, average = 'macro'))\n",
    "print('Recall/Sensitivity:', recall_score(y_test, rf_y_test_pred, average = 'macro'))\n",
    "print('Cross Entropy Loss:', tf.keras.metrics.binary_crossentropy(y_test, rf_y_test_pred).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Form confusion matrix as a DataFrame\n",
    "confusion_matrix_df = pd.DataFrame((confusion_matrix(y_test, rf_y_test_pred)),\n",
    "                                   ('Existing Customer', 'Atrited Customer'),\n",
    "                                   ('Existing Customer', 'Atrited Customer'))\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure()\n",
    "heatmap = sns.heatmap(confusion_matrix_df, annot = True, annot_kws = {'size' : 14}, fmt = 'd', cmap = 'YlGnBu')\n",
    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation = 0, ha = 'right', fontsize = 14)\n",
    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation = 0, ha = 'center', fontsize = 14)\n",
    "\n",
    "plt.title('Confusion Matrix for Random Forest Classifier\\n', fontsize = 18, color = 'darkblue')\n",
    "plt.ylabel('True label', fontsize = 14)\n",
    "plt.xlabel('Predicted label', fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Gradient Boosting Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9585389930898321\n",
      "Precision: 0.9185337117257948\n",
      "Recall/Sensitivity: 0.9308281719953994\n",
      "Cross Entropy Loss: 0.6395338951248458\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "gb_model = GradientBoostingClassifier().fit(X_resampled, y_resampled)\n",
    "\n",
    "# Predict\n",
    "gb_y_test_pred = gb_model.predict(X_test)\n",
    "\n",
    "# Performance\n",
    "print('Accuracy:', accuracy_score(y_test, gb_y_test_pred))\n",
    "print('Precision:', precision_score(y_test, gb_y_test_pred, average = 'macro'))\n",
    "print('Recall/Sensitivity:', recall_score(y_test, gb_y_test_pred, average = 'macro'))\n",
    "print('Cross Entropy Loss:', tf.keras.metrics.binary_crossentropy(y_test, gb_y_test_pred).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Form confusion matrix as a DataFrame\n",
    "confusion_matrix_df = pd.DataFrame((confusion_matrix(y_test, gb_y_test_pred)),\n",
    "                                   ('Existing Customer', 'Atrited Customer'),\n",
    "                                   ('Existing Customer', 'Atrited Customer'))\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure()\n",
    "heatmap = sns.heatmap(confusion_matrix_df, annot = True, annot_kws = {'size' : 14}, fmt = 'd', cmap = 'YlGnBu')\n",
    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation = 0, ha = 'right', fontsize = 14)\n",
    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation = 0, ha = 'center', fontsize = 14)\n",
    "\n",
    "plt.title('Confusion Matrix for Gradient Boosting Classifier\\n', fontsize = 18, color = 'darkblue')\n",
    "plt.ylabel('True label', fontsize = 14)\n",
    "plt.xlabel('Predicted label', fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wnVz4GKIxI2g"
   },
   "source": [
    "# **Model Selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Logistic Regression model, as much as 270 data with the status of Existing Customers have been correctly predicted as Existing Customers, and as much as 1466 data with the status of Atrited Customers have been correctly predicted as Atrited Customers, but this model also makes prediction errors of 290 data. Based on these results, the performance of the model obtained is an Accuracy score of 85.69%, a Precision score of 74.97%, and a Recall score of 84.43% with a Cross Entropy Loss of 2.2079.\n",
    "\n",
    "The Random Forest Classifier model, as much as 283 data with the status of Existing Customers have been correctly predicted as Existing Customers, and as much as 1652 data with the status of Atrited Customers have been correctly predicted as Atrited Customers, but this model also makes prediction errors of 91 data. Based on these results, the performance of the model obtained is an Accuracy score of 95.51%, a Precision score of 91.58%, and a Recall score of 91.89% with a Cross Entropy Loss of 0.6928.\n",
    "\n",
    "The Gradient Boosting Classifier model, as much as 291 data with the status of Existing Customers have been correctly predicted as Existing Customers, and as much as 1651 data with the status of Atrited Customers have been correctly predicted as Atrited Customers, but this model also makes prediction errors of 84 data. Based on these results, the performance of the model obtained is an Accuracy score of 95.85%, a Precision score of 91.85%, and a Recall score of 93.08% with a Cross Entropy Loss of 0.6395.\n",
    "\n",
    "Based on the performance of the three models, it can be concluded that the **Gradient Boosting** model is the best performing model for this dataset case."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Credit Card Customer Churn.ipynb",
   "provenance": [],
   "toc_visible": true
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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