Project_Churn_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJECT CHURN PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing common libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28382, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame = pd.read_csv(\"churn_prediction.csv\")\n",
    "cp_frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>vintage</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>dependents</th>\n",
       "      <th>occupation</th>\n",
       "      <th>city</th>\n",
       "      <th>customer_nw_category</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>days_since_last_transaction</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_month_end_balance</th>\n",
       "      <th>average_monthly_balance_prevQ</th>\n",
       "      <th>average_monthly_balance_prevQ2</th>\n",
       "      <th>current_month_credit</th>\n",
       "      <th>previous_month_credit</th>\n",
       "      <th>current_month_debit</th>\n",
       "      <th>previous_month_debit</th>\n",
       "      <th>current_month_balance</th>\n",
       "      <th>previous_month_balance</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3135</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>187.0</td>\n",
       "      <td>2</td>\n",
       "      <td>755</td>\n",
       "      <td>224.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1449.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3214</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8704.66</td>\n",
       "      <td>7799.26</td>\n",
       "      <td>12419.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5486.27</td>\n",
       "      <td>100.56</td>\n",
       "      <td>6496.78</td>\n",
       "      <td>8787.61</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2356</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>salaried</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5815.29</td>\n",
       "      <td>4910.17</td>\n",
       "      <td>2815.94</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>6046.73</td>\n",
       "      <td>259.23</td>\n",
       "      <td>5006.28</td>\n",
       "      <td>5070.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>478</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>582</td>\n",
       "      <td>147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2291.91</td>\n",
       "      <td>2084.54</td>\n",
       "      <td>1006.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2143.33</td>\n",
       "      <td>2291.91</td>\n",
       "      <td>1669.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2531</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3</td>\n",
       "      <td>388</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1401.72</td>\n",
       "      <td>1643.31</td>\n",
       "      <td>1871.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>714.61</td>\n",
       "      <td>588.62</td>\n",
       "      <td>1538.06</td>\n",
       "      <td>1157.15</td>\n",
       "      <td>1677.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  vintage  age gender  dependents     occupation    city  \\\n",
       "0            1     3135   66   Male         0.0  self_employed   187.0   \n",
       "1            2      310   35   Male         0.0  self_employed     NaN   \n",
       "2            4     2356   31   Male         0.0       salaried   146.0   \n",
       "3            5      478   90    NaN         NaN  self_employed  1020.0   \n",
       "4            6     2531   42   Male         2.0  self_employed  1494.0   \n",
       "\n",
       "   customer_nw_category  branch_code  days_since_last_transaction  ...  \\\n",
       "0                     2          755                        224.0  ...   \n",
       "1                     2         3214                         60.0  ...   \n",
       "2                     2           41                          NaN  ...   \n",
       "3                     2          582                        147.0  ...   \n",
       "4                     3          388                         58.0  ...   \n",
       "\n",
       "   previous_month_end_balance  average_monthly_balance_prevQ  \\\n",
       "0                     1458.71                        1458.71   \n",
       "1                     8704.66                        7799.26   \n",
       "2                     5815.29                        4910.17   \n",
       "3                     2291.91                        2084.54   \n",
       "4                     1401.72                        1643.31   \n",
       "\n",
       "   average_monthly_balance_prevQ2  current_month_credit  \\\n",
       "0                         1449.07                  0.20   \n",
       "1                        12419.41                  0.56   \n",
       "2                         2815.94                  0.61   \n",
       "3                         1006.54                  0.47   \n",
       "4                         1871.12                  0.33   \n",
       "\n",
       "   previous_month_credit  current_month_debit  previous_month_debit  \\\n",
       "0                   0.20                 0.20                  0.20   \n",
       "1                   0.56              5486.27                100.56   \n",
       "2                   0.61              6046.73                259.23   \n",
       "3                   0.47                 0.47               2143.33   \n",
       "4                 714.61               588.62               1538.06   \n",
       "\n",
       "   current_month_balance  previous_month_balance  churn  \n",
       "0                1458.71                 1458.71      0  \n",
       "1                6496.78                 8787.61      0  \n",
       "2                5006.28                 5070.14      0  \n",
       "3                2291.91                 1669.79      1  \n",
       "4                1157.15                 1677.16      1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>vintage</th>\n",
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       "      <th>...</th>\n",
       "      <th>previous_month_end_balance</th>\n",
       "      <th>average_monthly_balance_prevQ</th>\n",
       "      <th>average_monthly_balance_prevQ2</th>\n",
       "      <th>current_month_credit</th>\n",
       "      <th>previous_month_credit</th>\n",
       "      <th>current_month_debit</th>\n",
       "      <th>previous_month_debit</th>\n",
       "      <th>current_month_balance</th>\n",
       "      <th>previous_month_balance</th>\n",
       "      <th>churn</th>\n",
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       "  </thead>\n",
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       "      <td>30297</td>\n",
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       "      <td>1020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1207</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1076.43</td>\n",
       "      <td>2282.19</td>\n",
       "      <td>2787.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28378</th>\n",
       "      <td>30298</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>2</td>\n",
       "      <td>223</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4069.21</td>\n",
       "      <td>3668.83</td>\n",
       "      <td>3865.55</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.29</td>\n",
       "      <td>901.00</td>\n",
       "      <td>1014.07</td>\n",
       "      <td>3738.54</td>\n",
       "      <td>3690.32</td>\n",
       "      <td>0</td>\n",
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       "      <th>28379</th>\n",
       "      <td>30299</td>\n",
       "      <td>297</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>salaried</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>2</td>\n",
       "      <td>588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61017.55</td>\n",
       "      <td>53444.81</td>\n",
       "      <td>21925.81</td>\n",
       "      <td>4666.84</td>\n",
       "      <td>3883.06</td>\n",
       "      <td>168.23</td>\n",
       "      <td>71.80</td>\n",
       "      <td>61078.50</td>\n",
       "      <td>57564.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28380</th>\n",
       "      <td>30300</td>\n",
       "      <td>2585</td>\n",
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       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>3</td>\n",
       "      <td>274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1625.55</td>\n",
       "      <td>1683.20</td>\n",
       "      <td>1857.42</td>\n",
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       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
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       "      <td>1625.55</td>\n",
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       "    <tr>\n",
       "      <th>28381</th>\n",
       "      <td>30301</td>\n",
       "      <td>2349</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>student</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>2</td>\n",
       "      <td>474</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2821.34</td>\n",
       "      <td>3213.44</td>\n",
       "      <td>4447.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>7.44</td>\n",
       "      <td>714.40</td>\n",
       "      <td>1094.09</td>\n",
       "      <td>2402.62</td>\n",
       "      <td>3260.58</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "       customer_id  vintage  age  gender  dependents     occupation    city  \\\n",
       "28377        30297     1845   10  Female         0.0        student  1020.0   \n",
       "28378        30298     4919   34  Female         0.0  self_employed  1046.0   \n",
       "28379        30299      297   47    Male         0.0       salaried  1096.0   \n",
       "28380        30300     2585   50    Male         3.0  self_employed  1219.0   \n",
       "28381        30301     2349   18    Male         0.0        student  1232.0   \n",
       "\n",
       "       customer_nw_category  branch_code  days_since_last_transaction  ...  \\\n",
       "28377                     2         1207                         70.0  ...   \n",
       "28378                     2          223                         14.0  ...   \n",
       "28379                     2          588                          0.0  ...   \n",
       "28380                     3          274                          NaN  ...   \n",
       "28381                     2          474                         59.0  ...   \n",
       "\n",
       "       previous_month_end_balance  average_monthly_balance_prevQ  \\\n",
       "28377                     1076.43                        2282.19   \n",
       "28378                     4069.21                        3668.83   \n",
       "28379                    61017.55                       53444.81   \n",
       "28380                     1625.55                        1683.20   \n",
       "28381                     2821.34                        3213.44   \n",
       "\n",
       "       average_monthly_balance_prevQ2  current_month_credit  \\\n",
       "28377                         2787.70                  0.30   \n",
       "28378                         3865.55                  1.71   \n",
       "28379                        21925.81               4666.84   \n",
       "28380                         1857.42                  0.20   \n",
       "28381                         4447.45                  0.11   \n",
       "\n",
       "       previous_month_credit  current_month_debit  previous_month_debit  \\\n",
       "28377                   0.30                 0.30                  0.30   \n",
       "28378                   2.29               901.00               1014.07   \n",
       "28379                3883.06               168.23                 71.80   \n",
       "28380                   0.20                 0.20                  0.20   \n",
       "28381                   7.44               714.40               1094.09   \n",
       "\n",
       "       current_month_balance  previous_month_balance  churn  \n",
       "28377                1076.43                 1076.43      0  \n",
       "28378                3738.54                 3690.32      0  \n",
       "28379               61078.50                57564.24      1  \n",
       "28380                1625.55                 1625.55      0  \n",
       "28381                2402.62                 3260.58      1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                         int64\n",
       "vintage                             int64\n",
       "age                                 int64\n",
       "gender                             object\n",
       "dependents                        float64\n",
       "occupation                         object\n",
       "city                              float64\n",
       "customer_nw_category                int64\n",
       "branch_code                         int64\n",
       "days_since_last_transaction       float64\n",
       "current_balance                   float64\n",
       "previous_month_end_balance        float64\n",
       "average_monthly_balance_prevQ     float64\n",
       "average_monthly_balance_prevQ2    float64\n",
       "current_month_credit              float64\n",
       "previous_month_credit             float64\n",
       "current_month_debit               float64\n",
       "previous_month_debit              float64\n",
       "current_month_balance             float64\n",
       "previous_month_balance            float64\n",
       "churn                               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>vintage</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>dependents</th>\n",
       "      <th>occupation</th>\n",
       "      <th>city</th>\n",
       "      <th>customer_nw_category</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>days_since_last_transaction</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_month_end_balance</th>\n",
       "      <th>average_monthly_balance_prevQ</th>\n",
       "      <th>average_monthly_balance_prevQ2</th>\n",
       "      <th>current_month_credit</th>\n",
       "      <th>previous_month_credit</th>\n",
       "      <th>current_month_debit</th>\n",
       "      <th>previous_month_debit</th>\n",
       "      <th>current_month_balance</th>\n",
       "      <th>previous_month_balance</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28382.000000</td>\n",
       "      <td>28382.000000</td>\n",
       "      <td>28382.000000</td>\n",
       "      <td>27857</td>\n",
       "      <td>25919.000000</td>\n",
       "      <td>28302</td>\n",
       "      <td>27579.000000</td>\n",
       "      <td>28382.000000</td>\n",
       "      <td>28382.000000</td>\n",
       "      <td>25159.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>2.838200e+04</td>\n",
       "      <td>28382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15143.508667</td>\n",
       "      <td>2364.336446</td>\n",
       "      <td>48.208336</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.347236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>796.109576</td>\n",
       "      <td>2.225530</td>\n",
       "      <td>925.975019</td>\n",
       "      <td>69.997814</td>\n",
       "      <td>...</td>\n",
       "      <td>7.495771e+03</td>\n",
       "      <td>7.496780e+03</td>\n",
       "      <td>7.124209e+03</td>\n",
       "      <td>3.433252e+03</td>\n",
       "      <td>3.261694e+03</td>\n",
       "      <td>3.658745e+03</td>\n",
       "      <td>3.339761e+03</td>\n",
       "      <td>7.451133e+03</td>\n",
       "      <td>7.495177e+03</td>\n",
       "      <td>0.185329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8746.454456</td>\n",
       "      <td>1610.124506</td>\n",
       "      <td>17.807163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.997661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>432.872102</td>\n",
       "      <td>0.660443</td>\n",
       "      <td>937.799129</td>\n",
       "      <td>86.341098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.252935e+04</td>\n",
       "      <td>4.172622e+04</td>\n",
       "      <td>4.457581e+04</td>\n",
       "      <td>7.707145e+04</td>\n",
       "      <td>2.968889e+04</td>\n",
       "      <td>5.198542e+04</td>\n",
       "      <td>2.430111e+04</td>\n",
       "      <td>4.203394e+04</td>\n",
       "      <td>4.243198e+04</td>\n",
       "      <td>0.388571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.149570e+03</td>\n",
       "      <td>1.428690e+03</td>\n",
       "      <td>-1.650610e+04</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-3.374180e+03</td>\n",
       "      <td>-5.171920e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7557.250000</td>\n",
       "      <td>1121.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.906000e+03</td>\n",
       "      <td>2.180945e+03</td>\n",
       "      <td>1.832507e+03</td>\n",
       "      <td>3.100000e-01</td>\n",
       "      <td>3.300000e-01</td>\n",
       "      <td>4.100000e-01</td>\n",
       "      <td>4.100000e-01</td>\n",
       "      <td>1.996765e+03</td>\n",
       "      <td>2.074408e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15150.500000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>834.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.379915e+03</td>\n",
       "      <td>3.542865e+03</td>\n",
       "      <td>3.359600e+03</td>\n",
       "      <td>6.100000e-01</td>\n",
       "      <td>6.300000e-01</td>\n",
       "      <td>9.193000e+01</td>\n",
       "      <td>1.099600e+02</td>\n",
       "      <td>3.447995e+03</td>\n",
       "      <td>3.465235e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22706.750000</td>\n",
       "      <td>3176.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.656535e+03</td>\n",
       "      <td>6.666887e+03</td>\n",
       "      <td>6.517960e+03</td>\n",
       "      <td>7.072725e+02</td>\n",
       "      <td>7.492350e+02</td>\n",
       "      <td>1.360435e+03</td>\n",
       "      <td>1.357553e+03</td>\n",
       "      <td>6.667958e+03</td>\n",
       "      <td>6.654693e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30301.000000</td>\n",
       "      <td>12899.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1649.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4782.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.740439e+06</td>\n",
       "      <td>5.700290e+06</td>\n",
       "      <td>5.010170e+06</td>\n",
       "      <td>1.226985e+07</td>\n",
       "      <td>2.361808e+06</td>\n",
       "      <td>7.637857e+06</td>\n",
       "      <td>1.414168e+06</td>\n",
       "      <td>5.778185e+06</td>\n",
       "      <td>5.720144e+06</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         customer_id       vintage           age gender    dependents  \\\n",
       "count   28382.000000  28382.000000  28382.000000  27857  25919.000000   \n",
       "unique           NaN           NaN           NaN      2           NaN   \n",
       "top              NaN           NaN           NaN   Male           NaN   \n",
       "freq             NaN           NaN           NaN  16548           NaN   \n",
       "mean    15143.508667   2364.336446     48.208336    NaN      0.347236   \n",
       "std      8746.454456   1610.124506     17.807163    NaN      0.997661   \n",
       "min         1.000000    180.000000      1.000000    NaN      0.000000   \n",
       "25%      7557.250000   1121.000000     36.000000    NaN      0.000000   \n",
       "50%     15150.500000   2018.000000     46.000000    NaN      0.000000   \n",
       "75%     22706.750000   3176.000000     60.000000    NaN      0.000000   \n",
       "max     30301.000000  12899.000000     90.000000    NaN     52.000000   \n",
       "\n",
       "           occupation          city  customer_nw_category   branch_code  \\\n",
       "count           28302  27579.000000          28382.000000  28382.000000   \n",
       "unique              5           NaN                   NaN           NaN   \n",
       "top     self_employed           NaN                   NaN           NaN   \n",
       "freq            17476           NaN                   NaN           NaN   \n",
       "mean              NaN    796.109576              2.225530    925.975019   \n",
       "std               NaN    432.872102              0.660443    937.799129   \n",
       "min               NaN      0.000000              1.000000      1.000000   \n",
       "25%               NaN    409.000000              2.000000    176.000000   \n",
       "50%               NaN    834.000000              2.000000    572.000000   \n",
       "75%               NaN   1096.000000              3.000000   1440.000000   \n",
       "max               NaN   1649.000000              3.000000   4782.000000   \n",
       "\n",
       "        days_since_last_transaction  ...  previous_month_end_balance  \\\n",
       "count                  25159.000000  ...                2.838200e+04   \n",
       "unique                          NaN  ...                         NaN   \n",
       "top                             NaN  ...                         NaN   \n",
       "freq                            NaN  ...                         NaN   \n",
       "mean                      69.997814  ...                7.495771e+03   \n",
       "std                       86.341098  ...                4.252935e+04   \n",
       "min                        0.000000  ...               -3.149570e+03   \n",
       "25%                       11.000000  ...                1.906000e+03   \n",
       "50%                       30.000000  ...                3.379915e+03   \n",
       "75%                       95.000000  ...                6.656535e+03   \n",
       "max                      365.000000  ...                5.740439e+06   \n",
       "\n",
       "        average_monthly_balance_prevQ  average_monthly_balance_prevQ2  \\\n",
       "count                    2.838200e+04                    2.838200e+04   \n",
       "unique                            NaN                             NaN   \n",
       "top                               NaN                             NaN   \n",
       "freq                              NaN                             NaN   \n",
       "mean                     7.496780e+03                    7.124209e+03   \n",
       "std                      4.172622e+04                    4.457581e+04   \n",
       "min                      1.428690e+03                   -1.650610e+04   \n",
       "25%                      2.180945e+03                    1.832507e+03   \n",
       "50%                      3.542865e+03                    3.359600e+03   \n",
       "75%                      6.666887e+03                    6.517960e+03   \n",
       "max                      5.700290e+06                    5.010170e+06   \n",
       "\n",
       "        current_month_credit  previous_month_credit  current_month_debit  \\\n",
       "count           2.838200e+04           2.838200e+04         2.838200e+04   \n",
       "unique                   NaN                    NaN                  NaN   \n",
       "top                      NaN                    NaN                  NaN   \n",
       "freq                     NaN                    NaN                  NaN   \n",
       "mean            3.433252e+03           3.261694e+03         3.658745e+03   \n",
       "std             7.707145e+04           2.968889e+04         5.198542e+04   \n",
       "min             1.000000e-02           1.000000e-02         1.000000e-02   \n",
       "25%             3.100000e-01           3.300000e-01         4.100000e-01   \n",
       "50%             6.100000e-01           6.300000e-01         9.193000e+01   \n",
       "75%             7.072725e+02           7.492350e+02         1.360435e+03   \n",
       "max             1.226985e+07           2.361808e+06         7.637857e+06   \n",
       "\n",
       "        previous_month_debit  current_month_balance  previous_month_balance  \\\n",
       "count           2.838200e+04           2.838200e+04            2.838200e+04   \n",
       "unique                   NaN                    NaN                     NaN   \n",
       "top                      NaN                    NaN                     NaN   \n",
       "freq                     NaN                    NaN                     NaN   \n",
       "mean            3.339761e+03           7.451133e+03            7.495177e+03   \n",
       "std             2.430111e+04           4.203394e+04            4.243198e+04   \n",
       "min             1.000000e-02          -3.374180e+03           -5.171920e+03   \n",
       "25%             4.100000e-01           1.996765e+03            2.074408e+03   \n",
       "50%             1.099600e+02           3.447995e+03            3.465235e+03   \n",
       "75%             1.357553e+03           6.667958e+03            6.654693e+03   \n",
       "max             1.414168e+06           5.778185e+06            5.720144e+06   \n",
       "\n",
       "               churn  \n",
       "count   28382.000000  \n",
       "unique           NaN  \n",
       "top              NaN  \n",
       "freq             NaN  \n",
       "mean        0.185329  \n",
       "std         0.388571  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting with Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "sn.set(style = 'darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Appending all the categorical variables in a separate list and doing the same for continuous variables \n",
    "categ = []\n",
    "contin = []\n",
    "for i in cp_frame.columns:\n",
    "    if cp_frame[i].dtypes == 'object':\n",
    "        categ.append(i)\n",
    "    else:\n",
    "        if i != 'churn':\n",
    "            contin.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['gender', 'occupation'],\n",
       " ['customer_id',\n",
       "  'vintage',\n",
       "  'age',\n",
       "  'dependents',\n",
       "  'city',\n",
       "  'customer_nw_category',\n",
       "  'branch_code',\n",
       "  'days_since_last_transaction',\n",
       "  'current_balance',\n",
       "  'previous_month_end_balance',\n",
       "  'average_monthly_balance_prevQ',\n",
       "  'average_monthly_balance_prevQ2',\n",
       "  'current_month_credit',\n",
       "  'previous_month_credit',\n",
       "  'current_month_debit',\n",
       "  'previous_month_debit',\n",
       "  'current_month_balance',\n",
       "  'previous_month_balance'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ,contin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning different colors to a total of 18 for each of the continuous variable\n",
    "colour = ['coral','red','green','blue','black','brown','crimson','purple','lime','blueviolet','darkorange',\n",
    "          'orangered','violet','sienna','deepskyblue','olive','cyan','maroon']\n",
    "\n",
    "#Defining index1 for the iterative value of axes[index1][]\n",
    "index1 = []\n",
    "for i in range(0,9):\n",
    "    index1.append(i)\n",
    "    index1.append(i)\n",
    "\n",
    "#Defining index1 for the iterative value of axes[][index2]\n",
    "c = 0\n",
    "index2 = []\n",
    "for j in range(1,19):\n",
    "    index2.append(c)\n",
    "    if c == 0:\n",
    "        c = 1\n",
    "    else:\n",
    "        c = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1872x5400 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the Histogram of all the continuous variables \n",
    "f, axes = plt.subplots(9, 2, figsize = (26, 75))\n",
    "for i in range(len(contin)):\n",
    "    sn.distplot(cp_frame[contin[i]], color = colour[i], ax = axes[index1[i], index2[i]], kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#After observation we see that the listed variables are non-symmetric\n",
    "# 'vintage'\n",
    "# 'branch_code'\n",
    "# 'dependents'\n",
    "# 'days_since_last_transaction'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Vintage')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BGiQPboPQhG8yuPoV/ocOHWL79u20trYyY8YMCgoKqKqqYvPmzfj9fubNm8e6desAuHDhAhs3bqSlpYWMjAyKi4ux2fRvjwyNniZXgz9PsMa4wW0QmvBNBlfEwz4ffvghRUVFlJaWsm/fPt5//32OHDnChg0bKC0tpbKykvfee48jR44AkJ+fT2FhIQcPHsQwDMrKyqLWCRER6ZuIw/9Xv/oV8+fPJyUlBbvdzrZt2xg1ahT33Xcf48ePx2azkZubi9vt5vLly7S1tZGeng6Ay+XC7Q6fDmG4G9nqvfnf8tpaEhrrQ79sgcBQN01EpE8iHnepra3Fbrfz7LPPUl9fz+zZs5kwYQIOhyO0jNPppKGhgStXrnSqOxwOGhoa+tfyIRCawyd5JMHmtlBdc/iIyN0m4vDv6OjgzJkz7Nq1i4SEBFatWsXIkSOxWCyhZQzDwGKxEAwGu633xZgxw2DM03cNkkcCkPzn3wGw24i79fVwrUe4LujS38Fo61B+RgnxADgcvTzpe8v3YkDalBBPYm/b0k+97nMMMWOfoR/hP3bsWKZPn87o0aMB+NrXvobb7WbEiBGhZTweD06nk5SUFDweT6je2NiI0+ns0/auXvUSDBqRNjcqEnx+gs1tJCePpPmWI//4QDv+W14P13qk64qDTv0dyj4Mxmdk9flJBDye8Gm6u/PJ92Kg2mT1+fH1si394XAk97rPsSKW+2y1Wm570BzxmP+cOXM4fvw4169fp6Ojg2PHjpGdnc3Fixepra2lo6ODiooKMjMzSU1NJT4+nurqagDKy8vJzMyMdNMiItJPER/5T5kyhRUrVrBs2TICgQAzZsxg6dKlfPazn2X16tX4/X5mzZpFdnY2ACUlJRQUFOD1epk4cSJ5eXlR64SIiPRNvy60X7JkCUuWLOlUmz59Ovv27QtbNi0tjd27d/dncyIiEiWa3kFExIQU/iIiJqTwFxExIYW/iIgJaWY1kWFOs33KQFD4iwxzmu1TBoKGfURETEjhLyJiQgp/ERET0ph/N0a2erG2hE/2ZAsEuDEE7RERiTaFfzdC8/Z3oXn7RSRWaNhHRMSEFP4iIiZk6mEfje2LiFmZOvw1ti8iZqVhHxERE1L4i4iYkMJfRMSEFP4iIiak8BcRMSFTX+0j0he6NFhiSVTC/8UXX6SpqYktW7Zw4cIFNm7cSEtLCxkZGRQXF2Oz2airqyM/P5+rV69y//33U1JSQmJiYjQ2LzIodGmwxJJ+D/u88847vPXWW6HX+fn5FBYWcvDgQQzDoKysDIDi4mKWLVuG2+1m0qRJlJaW9nfTIiISoX6F/0cffcS2bdt49tlnAbh8+TJtbW2kp6cD4HK5cLvdBAIBTp8+TVZWVqe6iIgMjX4N+xQWFrJu3Trq628+X/TKlSs4HI7Q+w6Hg4aGBpqamkhKSsJms3Wq98WYMf14XF1TE1y/Hl63BCF5ZHjdbiPuDvXkW9/vxfLDoh7huqBLfwejrUP5GSXEA+BwJHeu+65F/H0ZkHpCPIld29hPYX02ATP2GfoR/m+++Sbjxo1j+vTp7NmzB4BgMIjFYgktYxgGFosl9Putur6+k6tXvQSDRkRtTWj0dDtWGz97Jv7mtvB6oP229eTkkTTf8v6dlh8u9UjXFQed+juUfRiMz8jq85MIeDydT+4m+PwEh1FbrT4/Pk/4CehIORzJYX2OdbHcZ6vVctuD5ojDv7KyEo/Hw8KFC/n444/x+XxYLBY8Hk9omcbGRpxOJ6NHj6a5uZmOjg5GjBiBx+PB6XRGumkREemniMP/1VdfDf15z549nDp1is2bN7NgwQKqq6t5+OGHKS8vJzMzE7vdTkZGBpWVleTm5rJ3714yMzOj0gERs7IRJKGxPqw+Ii6OjhvhF5/2VA8mJtM2qh/DqnJXivp1/iUlJRQUFOD1epk4cSJ5eXkAFBUVsX79enbs2MG4cePYunVrtDctYioWn4/g4eNhdfvsmQT6ULfOzwaFv+lEJfxdLhculwuAtLQ0du/eHbZMamoqu3btisbmRESknzS9g4iICSn8RURMSOEvImJCCn8RERNS+IuImJDCX0TEhBT+IiImpPAXETEhhb+IiAkp/EVETEjhLyJiQgp/ERETUviLiJiQwl9ExISiPp+/yN3ORhBqa0nw+TvXAwHCH4UicndS+It0YfH54NjRsOf1WmbPHKIWiUSfhn1ERExI4S8iYkIKfxERE1L4i4iYUL/Cf/v27eTk5JCTk8NLL70EQFVVFbm5ucydO5dt27aFlr1w4QIul4usrCw2btxIe3t7/1ouIiIRizj8q6qqOH78OG+99RZ79+7lf/7nf6ioqGDDhg2UlpZSWVnJe++9x5EjRwDIz8+nsLCQgwcPYhgGZWVlUeuEiIj0TcTh73A4WL9+PXFxcdjtdh544AFqamq47777GD9+PDabjdzcXNxuN5cvX6atrY309HQAXC4Xbrc7ap0QkcjZCJLQWH/z3obG+k6/RrZ6h7p5MkAivs5/woQJoT/X1NRw4MABnnjiCRwOR6judDppaGjgypUrneoOh4OGhoZINy0iUWTx+QgePg7JI8PubbDOz4ZRSUPUMhlI/b7J64MPPuAf//Ef+e53v8uIESOoqakJvWcYBhaLhWAwiMViCav3xZgx/fgC+q5B8sjwut1GXIT15Fvf78d6BrUe4bqgS38Ho61D/BmBifrc0/caICGeREdy+M/EEEeM968n/Qr/6upq1qxZw4YNG8jJyeHUqVN4PJ7Q+x6PB6fTSUpKSqd6Y2MjTqezT9u6etVLMGhE1M4Enz/siAYgPtCOP4J6cvJImm95P9L1DHY90nXFQaf+DmUfBuszMlOfe/peA1h9fnye5rCfiRUORzKeGO2f1Wq57UFzxGP+9fX1fPvb36akpIScnBwApkyZwsWLF6mtraWjo4OKigoyMzNJTU0lPj6e6upqAMrLy8nMzIx00yIySD45H6BzAbEn4iP/nTt34vf72bJlS6j2+OOPs2XLFlavXo3f72fWrFlkZ2cDUFJSQkFBAV6vl4kTJ5KXl9f/1ovIgAqdD+hC5wLufhGHf0FBAQUFBd2+t2/fvrBaWloau3fvjnRzIiISRbrDV0TEhBT+IiImpPAXETEhhb+IiAkp/EVETEjhLyJiQgp/ERETUviLiJiQwl9ExIQU/iIiJtTvKZ1FxHxCD4DpIpiYTJvm/LkrKPxFpM804dvdT8M+IiImpPAXETEhhb+IiAkp/EVETEjhLyJiQgp/ERETUviLiJiQwl9ExIR0k5eIRI3u/L17DGr479+/nx07dtDe3s43vvENli9fPpibF5EBpjt/7x6DFv4NDQ1s27aNPXv2EBcXx+OPP84jjzzC5z73ucFqgoiI/NmghX9VVRVf/vKX+fSnPw1AVlYWbrebf/qnf+rVz1utloi3bbHbsNyTHP5GXFxk9cR4LBZ7/9cz2PUI10VSUuf+DmUfBukzMlWfe/peR3EbFrutX3+HB9JwbVd/3alfFsMwjMFoyM9+9jN8Ph/r1q0D4M033+T8+fN873vfG4zNi4jILQbtap9gMIjF8v//EhmG0em1iIgMnkEL/5SUFDweT+i1x+PB6XQO1uZFROQWgxb+X/nKV3jnnXe4du0ara2t/PKXvyQzM3OwNi8iIrcYtBO+n/nMZ1i3bh15eXkEAgGWLFnCQw89NFibFxGRWwzaCV8RERk+NL2DiIgJKfxFRExI4S8iYkIKfxERE1L438H27dvJyckhJyeHl156Cbg5VUVubi5z585l27ZtQ9zCgfHiiy+yfv16AC5cuIDL5SIrK4uNGzfS3t4+xK2LrkOHDuFyuZg3bx7f//73gdjfx+Xl5aHv9YsvvgjE7n72er0sWLCAS5cuAT3v21jtf48M6dGJEyeMv//7vzf8fr9x48YNIy8vz9i/f78xa9Ys409/+pMRCASMp59+2jh8+PBQNzWqqqqqjEceecR4/vnnDcMwjJycHOM3v/mNYRiG8cILLxivvfbaUDYvqv70pz8ZM2fONOrr640bN24YS5cuNQ4fPhzT+9jn8xlTp041rl69agQCAWPJkiXGiRMnYnI/nz171liwYIExceJE48MPPzRaW1t73Lex2P/b0ZH/bTgcDtavX09cXBx2u50HHniAmpoa7rvvPsaPH4/NZiM3Nxe32z3UTY2ajz76iG3btvHss88CcPnyZdra2khPTwfA5XLFVH9/9atfMX/+fFJSUrDb7Wzbto1Ro0bF9D7u6OggGAzS2tpKe3s77e3t2Gy2mNzPZWVlFBUVhWYTOH/+fLf7Nta/593Rw1xuY8KECaE/19TUcODAAZ544gkcDkeo7nQ6aWhoGIrmDYjCwkLWrVtHff3NB3JcuXKlU38dDkdM9be2tha73c6zzz5LfX09s2fPZsKECTG9j5OSknjuueeYN28eo0aNYurUqdjt9pjcz5s2ber0uuv3+ZN9G+vf8+7oyL8XPvjgA55++mm++93vMn78+JidoO7NN99k3LhxTJ8+PVSL9Qn5Ojo6eOedd/jBD37AG2+8wfnz5/nwww9jus+/+93v+PnPf87bb7/NsWPHsFqtnDhxIqb7/Imevs+x/j3vjo7876C6upo1a9awYcMGcnJyOHXqVMxOUFdZWYnH42HhwoV8/PHH+Hw+LBZLp/42NjbGTH8Bxo4dy/Tp0xk9ejQAX/va13C73YwYMSK0TCztY4Djx48zffp0xowZA9wc4ti5c2dM7+dP9DTBZNd6rPb/Vjryv436+nq+/e1vU1JSQk5ODgBTpkzh4sWL1NbW0tHRQUVFRcxMUPfqq69SUVFBeXk5a9as4atf/SqbN28mPj6e6upq4OZVIrHSX4A5c+Zw/Phxrl+/TkdHB8eOHSM7Oztm9zFAWloaVVVV+Hw+DMPg0KFDTJs2Lab38yd6+vubmppqiv7fSkf+t7Fz5078fj9btmwJ1R5//HG2bNnC6tWr8fv9zJo1i+zs7CFs5cArKSmhoKAAr9fLxIkTycvLG+omRc2UKVNYsWIFy5YtIxAIMGPGDJYuXcpnP/vZmN3HM2fO5P3338flcmG325k8eTIrV67k0Ucfjdn9/In4+Pge//7G8ve8O5rYTUTEhDTsIyJiQgp/ERETUviLiJiQwl9ExIQU/iIR0HUScrdT+EtM+sY3vsGcOXN6DOnf//73PPjgg+zYsYMHH3yQa9eu9Xrdv/71rykqKopWU0WGhMJfYtLixYupq6vjN7/5Tbfv79+/n8TERL7+9a/zxhtvcM899/R63f/+7/8e8/O+SOxT+EtMmjt3LgkJCVRWVoa9ZxgGv/jFL5g3bx6pqamkp6djs+l+RzEXhb/EpISEBObOnYvb7SYYDHZ678yZM9TV1bF48WL27NnTadjnq1/9Kv/6r/9KUVER06ZN40tf+hLPP/88Xq8XgCeffJJTp05x+PBhHnzwwdADQo4dO8YTTzzBF7/4RSZPnszChQv55S9/2Wm7J0+eZMmSJTz00EPk5ORw7NgxvvCFL7Bnz57QMrW1tXzrW9/ii1/8IhkZGeTn5/dpSEqktxT+ErMWLVqEx+PhzJkzner79+/nr/7qr8jIyOj25372s59x/fp1tm7dytq1a/nFL37Bjh07ACgqKuILX/gCX/rSl3jjjTdwOp2cP3+elStXMmHCBEpLS0PPBPjOd74TCu7f//73/MM//ANjx47lJz/5CYsXL2bt2rV0dHSEttvY2MiyZcuoq6vjpZdeori4mLNnz/LMM89w48aNAfqUxKz0f12JWV/+8pe59957qaysZNq0aQDcuHGDgwcP3nbelpSUFLZu3YrFYmHmzJmcOnWKo0ePkp+fz+c+9zmSkpJISEgIPfjjgw8+4NFHH+10Evjee+9l8eLFnDt3jjlz5vDKK6+QkpLC9u3bsdlszJo1C6vVGnqEItw8l+D3+/m3f/u30CyjDz30EFlZWVRWVrJo0aKB+JjEpHTkLzHLYrHw9a9/nYMHD4aOsI8ePcrHH3982yCdPHlyp7ncU1JS8Pl8PS7/2GOP8eMf/xifz8dvf/tb9u/fz2uvvQYQOmI/deoUs2fP7nRuoetkce+++y7p6encc889oSdsjQ+O+tAAAALHSURBVBs3jgceeIB33nmn7x+AyG3oyF9i2qJFi/jpT3/KyZMnmTFjBhUVFUybNo3U1NQef2bUqFGdXlssltte1+/z+SgsLOTAgQMA3H///aSlpQH/fz9AU1NT6Gj+E2PHju30+qOPPuLcuXNMnDgxbBu3PmVKJBoU/hLT7r//fqZMmUJlZSVTpkzh7bffpri4OKrb+N73vseJEyd45ZVXmDp1KnFxcfzhD39g//79oWWcTmfYiduur5OSksjMzGTNmjVh20hMTIxqm0U07CMxb9GiRRw6dIjDhw9jtVqZO3duv9ZntXb+a3P27Fn+9m//lhkzZhAXFwfcvPoH/v/If+rUqRw5cqTTlUf//d//3Wk9Dz/8MP/7v//Lgw8+yOTJk5k8eTKf//zn2b59e+ghIyLRovCXmJeTk4PX6+UnP/kJ2dnZJCQk9Gt999xzD3/84x959913aWtrY/LkyRw6dIi33nqLkydP8vLLL7N161YA2traAFi5ciV1dXWsXr2ao0eP8h//8R+hZT75x+Sb3/wmzc3NrFixgl//+tccOXKElStXcvLkSSZNmtSvNot0pfCXmPepT32KOXPmUFNTw+LFi/u9vqeeeoobN26wYsUK3n//fdavX89XvvIVfvCDH7B69WpOnjzJ9u3b+eu//uvQHcYPPPAAP/3pT7l06RLf+ta3KCsr44UXXgAI/WN077338vrrrzNq1Cjy8/NZt24dwWCQV199lb/5m7/pd7tFbqUneYkMgqqqKhITE5kyZUqodvz4cZ555hnKy8tDJ4hFBotO+IoMgrNnz7Jz506ef/557r//fi5fvsyPf/xjpk6dquCXIaEjf5FB0N7ezvbt26moqKChoYFPfepTPProo3znO98hKSlpqJsnJqTwFxExIZ3wFRExIYW/iIgJKfxFRExI4S8iYkIKfxERE1L4i4iY0P8Bn0MiQmarOgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#treating 'vintage'\n",
    "#Removing right skewness\n",
    "sn.distplot(np.sqrt(cp_frame['vintage']), kde = False, color = 'red')\n",
    "cp_frame['vintage'] = np.sqrt(cp_frame['vintage']) #Changing it with the original value of dataframe\n",
    "plt.xlabel('Vintage',fontsize = 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Branch Code')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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khAkTJlBQUBCwlRDx+Wav9/RP3Vsf+V7j9fZ1dSJBw+/wb2xspKmpibq6OhoaGmhoaCAsLAy3283EiRMBcDgclJSU4PV62bVrF8nJyS3Gi0jvOn3cvq0f/WVkLn4f84+KimLJkiVMnz6dyMhIfvCDH2C1WrHZbL5pbDYblZWVnDhxgqioKMLCwlqMl+DX1snDFif8THI35IAxkM5tdCDc48Z7sPWJadDJ6dP8Dv///d//5fXXX+ett94iOjqa3/zmN7z//vtYLBbfNIZhYLFYfP82d+bw2QwbFuVvqQDYbNHdmr+3BFud3oM11H3YMizqgNPvXuTUyxniR81edw2W6LZ/aUQOjvBrme0tN/qbYas11Pf6TP62BXK5zV+frc+OtlFH2zZQ63nmdH2xbTvcBgdrqHvrI9pKGetl3/d72/aUvvjc+x3+O3bsYPLkyQwbNgw4dSjn2Wefxel0+qapqqrCbrcTExNDdXU1jY2NhIaG4nQ6sdvtXerv2DEXTU2GX7XabNE4ndV+zdubgrHO8FpPqys7oqMHUf3NOKPWQ70fNbe13NP8XWZby21ea4i30ff6TP62BWq5zevsVJ+1bmr/dqDNttCGnl3PM2vtznK7s207+n8SXuvBAm3Oe7Y+u/P/zx899bkPCbF0uNPs9zH/cePGsXPnTmprazEMg+3bt3P55ZcTERFBaWkpcOpqoKSkJKxWKwkJCRQXFwOwadMmkpKS/O1aROo8LU5sN//RiW3pDL/3/K+66iq++OILHA4HVquViy++mAULFnD99deTnZ2Ny+UiLi6OjIwMAHJzc8nKyuKJJ55g1KhRPPzwwwFbCRER6Zpu3eS1YMECFixY0GLcuHHj2LBhQ6tpY2NjWbduXXe6ExGRANGzfURETEjhLyJiQgp/ERET0oPdpF8521fw6QYekc5R+Ev/cpav4BtId6mK9CSFvwSdgfRsfZFgpfCX4GOS58+I9CWd8BURMSGFv4iICSn8RURMSOEvImJCCn8RERNS+IuImJAu9ZQe09HduLpeX7rrbPeDNPVyPf2Nwl96Tgd34+p6fek23Q/SLTrsIyJiQgp/ERET6lb4b9++HYfDwfTp01m5ciUAO3fuZNasWUybNo38/HzftGVlZTgcDpKTk1m+fDkNDQ3dq1xERPzmd/gfPHiQ3NxcCgoK2Lx5M1988QXvvPMOy5Yto6CggOLiYj777DPeeecdAJYuXUpOTg7btm3DMAwKCwsDthIiItI1fof/X/7yF2bMmMHIkSOxWq3k5+cTGRnJmDFjGD16NGFhYcyaNYuSkhIOHz6M2+1m4sSJADgcDkpKSgK2EiIi0jV+X+1z4MABrFYrCxcu5MiRI1xzzTVceOGF2Gw23zR2u53KykqOHj3aYrzNZqOysrJ7lYuIiN/8Dv/GxkZ2797NunXrGDx4MIsWLWLQoEFYLBbfNIZhYLFYaGpqanN8VwwbFuVvqQDYbNHdmr+3BFudXncNluhBrcZHfzMucnAEQ9qpub15AazWUN8yAtXWXvvp4d7s05+25q97q8/Otp3ZfuZ0wbhtPW3U2Zk+O/o/3VP64nPvd/gPHz6cyZMnExMTA8B1111HSUkJoaGhvmmcTid2u52RI0fidDp946uqqrDb7V3q79gxF01Nhl+12mzROJ3Vfs3bm4KxzvBaD65qd4tx0dGDqP5mnFHrob6dmtua97QQb6NvGYFqa6u9ea291ac/bc3r7K0+u9LWvP3MWnujT3/agDbbz9ZnR/+ne0JPfe5DQiwd7jT7fcx/6tSp7Nixg5MnT9LY2Mh7771HSkoK+/fv58CBAzQ2NrJlyxaSkpKIjY0lIiKC0tJSAIqKikhKSvK3axER6Sa/9/zj4+O59dZbSU9Px+v18sMf/pC5c+fy3e9+lzvvvBOPx8OUKVNISUkBIC8vj+zsbFwuF3FxcWRkZARsJUREpGu69XiH2bNnM3v27BbjJk+ezObNm1tNO27cODZs2NCd7kREJEB0h6+IiAkp/EVETEhP9TSJjh6vzDmDqY9o/9I3ERl4FP5m0cHjlaOuSwSFv4ip6LCPiIgJKfxFRExI4S8iYkI65j+A6DtzRaSzFP4Dib4zV0Q6SYd9RERMSOEvImJCCn8RERNS+IuImJBO+Eq3hGIQfvx42226wkj6oY7+Tw+kR6Eo/KV76jy43t/TZpOuMJJ+qYP/0wPpUSgK/36kw4ezoT1tEek8hX9/0sF1/KA9bRHpvICc8F2zZg1ZWVkAlJWV4XA4SE5OZvny5TQ0NABQUVHBvHnzSElJYdGiRdTU1ASiaxER8UO3w/+DDz7gjTfe8A0vXbqUnJwctm3bhmEYFBYWArBixQrS09MpKSlhwoQJFBQUdLdrERHxU7fC/6uvviI/P5+FCxcCcPjwYdxuNxMnTgTA4XBQUlKC1+tl165dJCcntxgvIiJ9o1vhn5OTQ2ZmJueeey4AR48exWaz+dptNhuVlZWcOHGCqKgowsLCWowXEZG+4fcJ39dee41Ro0YxefJkNm7cCEBTUxMWi8U3jWEYWCwW37/NnTl8NsOGRflbKgA2W3S35u8tHdXpdddgiW7/MjOrNZTodto7aoscHMGQdvptr8/Ty/K3z55oa6+9J2sN5HKbv+6tPjvbdmb7mdMF47b1tFFnd/vs6LPSHX2RT36Hf3FxMU6nk7S0NL7++mtqa2uxWCw4nU7fNFVVVdjtdmJiYqiurqaxsZHQ0FCcTid2u71L/R075qKpyfCrVpstGqez2q95e9PZ6gyv9eCqdrfbHuJtpLqd9o7ajFoP9e3021af0dGDfMvyt8+eaGurvadrDdRym9fZW312pa15+5m19kaf/rQBbbZ3p8+OPiv+6ql8CgmxdLjT7Pdhn+eff54tW7ZQVFTE4sWLufbaa1m9ejURERGUlpYCUFRURFJSElarlYSEBIqLiwHYtGkTSUlJ/nYtIiLdFPDr/PPy8sjOzsblchEXF0dGRgYAubm5ZGVl8cQTTzBq1CgefvjhQHcdVDq8IWsA3SIuIv1TQMLf4XDgcDgAGDduHBs2bGg1TWxsLOvWrQtEd/1DBzdkDaRbxEWkf9JTPUVETEiPdxA9mVPEhBT+oidzipiQDvuIiJiQwl9ExIQU/iIiJqRj/iIiAdDf7u1R+IuIBEI/u7dH4d8H2ru00uuuIdwSGnR7CCIy8Cj8+0I7l1ZaogdBYnzQ7SGIyCkD6Z4Yhb+ISGcNoHtiTB/+/e0kjYhIIJg+/P09SdPhLw3635+AImIuCn9/dfBLA/rfn4AiYi6mCP/Gr6r7zUmagXRCSUSClynCv6m6pt299KDbQx9AJ5REJHiZIvz9pb1wERmouhX+jz32GFu3bgVgypQp3H333ezcuZPVq1fj8XiYPn06mZmZAJSVlbF8+XJqampISEhgxYoVhIUF+e8e7YWLyADl94Pddu7cyY4dO3jjjTfYtGkTn3/+OVu2bGHZsmUUFBRQXFzMZ599xjvvvAPA0qVLycnJYdu2bRiGQWFhYcBWQkREusbv8LfZbGRlZREeHo7VauV73/se5eXljBkzhtGjRxMWFsasWbMoKSnh8OHDuN1uJk6cCJz6zt+SkpKArYSISDA7fQi5rR/vwX+eunS8l/l93OXCCy/0vS4vL2fr1q38/Oc/x2az+cbb7XYqKys5evRoi/E2m43Kykp/uxYR6V86OITcV4916fZB971793L77bdz9913ExoaSnl5ua/NMAwsFgtNTU1YLJZW47ti2LAov2v0HqwhOrrtDWu1hga8raeWG0x9nh7X2+vpzzboyVoDudzmr4PpvW6r/czpgnHbetqos6f79Hc9Bw+OYIgtut32ntCt8C8tLWXx4sUsW7aM1NRUPvroI5xOp6/d6XRit9sZOXJki/FVVVXY7fYu9XXsmIumJsOvOocC1dVt/1kV4m0MeJu/80ZHD8Lby3360xYdPcg3rrf67ExbW+09XWugltu8zt7qsyttzdvPrLU3+vSnDdr+3Afbto2OHkRtrYd6Z3W7NfkjJMTS4U6z38f8jxw5wh133EFeXh6pqakAxMfHs3//fg4cOEBjYyNbtmwhKSmJ2NhYIiIiKC0tBaCoqIikpCR/uxYRkW7ye8//2WefxePx8OCDD/rGzZkzhwcffJA777wTj8fDlClTSElJASAvL4/s7GxcLhdxcXFkZGR0v3oREfGL3+GfnZ1NdnZ2m22bN29uNW7cuHFs2LDB3+5ERCSA9AXuIiImpPAXETEhhb+IiAkp/EVETEjhLyJiQgp/ERETUviLiJiQwl9ExIQU/iIiJqTwFxExIYW/iIgJKfxFRExI4S8iYkIKfxERE+r21ziKiEj3nP6C9zadM5j6Hvh+X4W/iEhf6+AL3qOuS+yRL3fXYR8RERNS+IuImFCvhv8f//hHZsyYwbRp01i/fn1vdi0iIs302jH/yspK8vPz2bhxI+Hh4cyZM4fExEQuuOCC3ipBRES+0Wvhv3PnTq644gqGDh0KQHJyMiUlJfzqV7/q1PwhIRb/Ow8NJTR6cJtNlvCwgLf5O6/lnIhe79OfNss5EYR+80djb/XZmba22nu61kAtt3mdvdVnV9qat59Za2/06U+b5ZzIVnX2dJ9+tZ3tcx8W6lf+nW0ei2EYRpeX6oennnqK2tpaMjMzAXjttdf49NNPuf/++3ujexERaabXjvk3NTVhsfzrN5FhGC2GRUSk9/Ra+I8cORKn0+kbdjqd2O323upeRESa6bXwv/LKK/nggw84fvw4dXV1/PnPfyYpKam3uhcRkWZ67YTviBEjyMzMJCMjA6/Xy+zZs7nkkkt6q3sREWmm1074iohI8NAdviIiJqTwFxExIYW/iIgJKfxFRExoQId/sD9IzuVyMXPmTA4dOgScegTGrFmzmDZtGvn5+X1c3b889thjpKamkpqaykMPPQQEZ62PPvooM2bMIDU1leeffx4IzjpPW7NmDVlZWQCUlZXhcDhITk5m+fLlNDQ09HF1p9x8882kpqaSlpZGWloae/bsCcrP1fbt23E4HEyfPp2VK1cCwfnev/baa75tmZaWxmWXXcZ9993XN7UaA9Q///lPY+rUqcaJEyeMmpoaY9asWcbevXv7uiyfTz75xJg5c6YRFxdnHDx40KirqzOmTJli/OMf/zC8Xq8xf/584+233+7rMo3333/f+NnPfmZ4PB6jvr7eyMjIMP74xz8GXa0ffvihMWfOHMPr9Rp1dXXG1KlTjbKysqCr87SdO3caiYmJxj333GMYhmGkpqYaH3/8sWEYhnHvvfca69ev78vyDMMwjKamJuOqq64yvF6vb1wwfq7+8Y9/GFdddZVx5MgRo76+3pg7d67x9ttvB+17f9r//d//Gddff71RUVHRJ7UO2D3/5g+SGzx4sO9BcsGisLCQ3Nxc313On376KWPGjGH06NGEhYUxa9asoKjXZrORlZVFeHg4VquV733ve5SXlwddrZdffjn/+Z//SVhYGMeOHaOxsZGTJ08GXZ0AX331Ffn5+SxcuBCAw4cP43a7mThxIgAOhyMo6vzyyy8BmD9/Pj/+8Y956aWXgvJz9Ze//IUZM2YwcuRIrFYr+fn5REZGBuV739zvfvc7MjMzOXjwYJ/UOmDD/+jRo9hsNt+w3W6nsrKyDytqadWqVSQkJPiGg7XeCy+80BdK5eXlbN26FYvFEpS1Wq1W/vCHP5CamsrkyZODdpvm5OSQmZnJueeeC7R+7202W1DUefLkSSZPnszjjz/OCy+8wCuvvEJFRUXQbdMDBw7Q2NjIwoULSUtL4+WXXw7a9/60nTt34na7mT59ep/VOmDDv789SC7Y6927dy/z58/n7rvvZvTo0UFb6+LFi/nggw84cuQI5eXlQVfna6+9xqhRo5g8ebJvXLC+95MmTeKhhx4iOjqamJgYZs+ezR/+8Iegq7WxsZEPPviABx54gFdffZVPP/2UgwcPBl2dzb3yyiv88pe/BPru/R+wX+A+cuRIdu/e7RsO9gfJBfOD70pLS1m8eDHLli0jNTWVjz76KOhq3bdvH/X19YwfP57IyEimTZtGSUkJoaGhvmmCoc7i4mKcTidpaWl8/fXX1NbWYrFYWsspOpIAAAn3SURBVGzPqqqqPq8TYPfu3Xi9Xt8vKsMwiI2NDbr3fvjw4UyePJmYmBgArrvuuqB870+rr69n165dPPjgg0DfffYH7J5/f3uQXHx8PPv37/f9Cbtly5agqPfIkSPccccd5OXlkZqaCgRnrYcOHSI7O5v6+nrq6+t58803mTNnTtDV+fzzz7NlyxaKiopYvHgx1157LatXryYiIoLS0lIAioqK+rxOgOrqah566CE8Hg8ul4s33niDtWvXBt3naurUqezYsYOTJ0/S2NjIe++9R0pKStC996f97W9/4/zzz2fw4FNf3tJXn6cBu+ff3x4kFxERwYMPPsidd96Jx+NhypQppKSk9HVZPPvss3g8Ht9eCsCcOXOCrtYpU6bw6aefcsMNNxAaGsq0adNITU0lJiYmqOpsT15eHtnZ2bhcLuLi4sjIyOjrkpg6dSp79uzhhhtuoKmpifT0dC677LKg+1zFx8dz6623kp6ejtfr5Yc//CFz587lu9/9blC+9wcPHmTkyJG+4b767OvBbiIiJjRgD/uIiEj7FP4iIiak8BcRMSGFv4iICSn8RURMSOEvQePaa6/loosu8v18//vf5+qrryYnJ4eTJ0/2dXnAqfsJLrroIr+fvVJWVsbdd9/NNddcwyWXXEJycjJr1qzh+PHjAanvoosu4tlnnw3IsmRgG7DX+Uv/lJyczPz584FTd0IeOHCARx99lMOHD/f7UCsqKmL58uVMmjSJX//619jtdvbt28fTTz/N9u3bWb9+PcOHD+/rMsUkFP4SVIYPH+57kBycelpnWFgYWVlZHD58mNjY2D6szn9ffvklv/3tb0lJSWHt2rW+Z7dcccUVXH311aSlpfHwww/zwAMP9HGlYhY67CNBLzo6usXwxo0bSUxM5JlnniExMZHk5GTq6upwuVysXLmSqVOnMmHCBK644gruueeeFoeMLrroIjZu3EhmZiaTJk0iMTGRVatWtfjyFLfbzZo1a0hKSmLSpEnMmTOnxXOi4NRjmG+77Tbi4+O56qqreOKJJzpch/Xr19PU1ERWVlarh3Z9+9vf5je/+Q3jx4/3jaupqWHNmjVce+21XHLJJcyePZsdO3a0mG///v3ccsstTJo0ieuvv5733nuvVb/Hjh3j7rvv5vLLL2fSpEksXLiQgwcPdlirmIP2/CWoGIbhC+LGxkYOHDjAk08+SVJSUou9/urqajZu3EheXh41NTVERkZy++23s3fvXv793/8dm83Gnj17ePTRRznvvPN835gF8MADD5CWlkZBQQG7du3i8ccf5zvf+Q7p6ekAZGZm8tFHH7FkyRIuuOAC1q9fz2233camTZt8DwvLz8/n9ttvZ/78+RQXF/PII49w0UUXce2117a5Xjt27CAuLq7dwzrz5s3zvW5qauLWW2/lyy+/JDMzk1GjRvH666+zYMECnnrqKa6++mpcLhc333wzw4cPZ+3atZw4caLFOsKpX2IZGRm43W6ys7OJjIzkqaee4uc//zmbN29myJAhfrxDMmD0+NfFiHTS1KlTjbFjx7b6ufzyy419+/b5pnv99deNsWPHGlu3bvWNc7vdxi9/+UvjnXfeabHMhQsXGjfeeKNveOzYscatt97aYpobbrjBuP322w3DMIyysjJj7NixxhtvvOFr93g8xowZM4yNGzcaBw8eNMaOHWv89re/9bV7vV7jBz/4gfHAAw+0u27x8fHGXXfd1ant8Oabbxpjx4413n333Rbjf/rTn/rW5cUXXzS+//3vGxUVFb72bdu2GWPHjjWeeeYZwzAM47/+67+M8ePHG3//+99901RXVxsJCQnGf/zHf3SqFhm4tOcvQWX69OnccsstADQ0NFBRUcFTTz1Feno6hYWFfPvb3/ZNe8EFF/heR0RE8NxzzwGnrsgpLy9n79697Nu3j4iIiBZ9xMfHtxgeMWIEtbW1APz1r38FaLEHHx4ezp/+9CffsgEuvfRSX3tYWBgjRozo8IqkkJAQmpqaOrUNdu3axTnnnMPVV1/dYvyMGTN44IEHcLlc/PWvf2Xs2LGMGjXK1/6jH/2oxWOMP/zwQ8aMGcOYMWN8f00NGjSIyy67jP/5n//hV7/6VafqkYFJ4S9BJSYmhosvvtg3PGnSJBISEpg6dSovvPACOTk5LaZt7s0332T16tUcPHiQ8847jwkTJjBo0KBWoRsZGdliOCQkBOOb5xt+/fXXWK1W37dstWfQoEHtLqMtsbGxHDlypN32r776ioiICCIjIzl58mSbh4dOj6upqeHkyZOcd955LdpDQ0NbbJOvvvqKL7/8kri4uFbLOv/889utRcxB4S9Bb8SIEQwZMoQDBw60O015eTlLlizhxhtv5KWXXvI9MnfJkiXs27ev031FR0fj9Xqprq5ucaL5448/5txzz231V0RnXXnllbz00kscP3681S8tgEcffZTNmzfz7rvvMmTIEKqqqlpNc/oLP4YOHcrQoUNbrZdhGHz99dct1mXcuHGsXLmy1bLCw8P9Wg8ZOHS1jwS9Q4cOcfz4ccaMGdPuNF988QVer5cFCxb4gr+2tpbS0tIO98jPNGnSJADeeust37j6+nruuusuioqK/FwDSE9Px2KxsGbNmlb17Nu3j02bNvGjH/2Ic845h8suu4yamppWV+9s3bqVuLg4IiIiSExMZO/evZSXl/vaP/jgA+rr633Dl156KYcOHSI2NpaLL76Yiy++mAkTJvDCCy/w9ttv+70uMjBoz1+CSlVVFZ988olv+OjRozz++ONERET4rsZpy/jx4wkNDWXt2rXMnTuXEydO8Nxzz1FVVdWlvdy4uDimTp3KypUrcblcjBkzhldeeYW6ujp+9rOfdekXSXNjxozh3nvv5f7776eyspKf/OQnxMTE8Pnnn/PMM88wYsQIli1bBsA111xDfHw8S5cu9V3ts3HjRvbs2cOTTz4JwA033MBzzz3HwoULyczMxO12k5+fj9Vq9fU5e/Zs1q1bx/z581mwYAFDhw7l1Vdf5c9//jM//vGP/VoPGTgU/hJUtm3bxrZt2wCwWCyce+65XHrppaxcubLFCd4zfec732HNmjU89thjLFiwAJvNRlJSEjfddBP33XcflZWVjBgxolM15Ofn8/vf/57HH3+c2tpa395ybGys74SvP+bNm8f555/Piy++yOrVqzl58iTf+ta3mD17Nrfddpvv0svQ0FCeeeYZ8vLyyM/Pp66ujvHjx/P000/7vt4vIiKCF198kfvvv5+srCzOPfdcMjMzWbt2ra+/qKgo1q9fz0MPPcTvfvc76uvrufDCCykoKGDKlCl+r4cMDPomLxERE9IxfxERE1L4i4iYkMJfRMSEFP4iIiak8BcRMSGFv4iICSn8RURMSOEvImJCCn8RERP6fyi5vasMTF0ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#treating 'branch_code'\n",
    "#Removing right skewness\n",
    "temps = np.log(cp_frame['branch_code'])\n",
    "sn.distplot(np.square(temps), kde = False, color = 'crimson')\n",
    "cp_frame['branch_code'] = np.square(temps) #Changing it with the original value of dataframe\n",
    "plt.xlabel('Branch Code', fontsize = 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Dependents')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEOCAYAAABmVAtTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAeRElEQVR4nO3de1TUdeL/8dcgiBrTEjojrutap00zNUQ5plkQtQJyyZZs85JYuqtZXrLNInRF21RiSVwv2F66bDeT2oRdF9HOdrQt3FK29LjHXEslFRcHULko98/vj369vxKKgOg4+nyc08HPez7zmddntHnxeX9mPmOzLMsSAACSvNwdAABw+aAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAw9vdAS7U8eOVamho20ctunb1U0lJRTsnan/kbF/kbH+ekpWckpeXTdddd805b/f4UmhosNpcCt/d3xOQs32Rs/15SlZyNo/pIwCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAACGx39O4UIcPy4VF3dsMn7NNZY6d651QyIAcK+ruhTKyqScnKYfEImOtqlzZzcEAgA3Y/oIAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAACMFpXCqlWrFBMTo5iYGKWmpkqS8vLyFBcXp4iICKWnp5t19+zZo/j4eEVGRmrevHmqq6uTJBUWFmrChAmKiorS9OnTVVlZKUkqKyvT1KlTNWrUKE2YMEEul6u99xEA0ELnLYW8vDx9/PHHWr9+vbKysvSf//xHGzZsUFJSkjIyMpSTk6Pdu3dr69atkqS5c+dqwYIF2rRpkyzLUmZmpiRp0aJFGj9+vHJzczVgwABlZGRIkpYvX66QkBBt3LhRDzzwgBYvXnwRdxcA0JzzloLD4VBiYqI6duwoHx8f3XjjjTp48KB69+6tXr16ydvbW3FxccrNzdWRI0dUVVWlQYMGSZLi4+OVm5ur2tpabd++XZGRkY3GJWnLli2Ki4uTJMXGxuqjjz5SbS3XHQIAdzhvKdx0003mRf7gwYPauHGjbDabHA6HWcfpdKqoqEjHjh1rNO5wOFRUVKTjx4/Lz89P3t7ejcYlNbqPt7e3/Pz8VFpa2n57CABosRZfEG/fvn2aNm2ann76aXXo0EEHDx40t1mWJZvNpoaGBtlstibj3/080/eXz7yPl1fLz3937erX4nW/r6BAsts7NRnv0kVyOHzbvN2LweGwuztCi5CzfXlKTslzspKzeS0qhfz8fM2aNUtJSUmKiYnRZ5991uiEsMvlktPpVGBgYKPx4uJiOZ1OBQQEqLy8XPX19erQoYNZX/r2KKO4uFiBgYGqq6tTZWWl/P39W7wDJSUVamhoeqXTlrGrvLyqyeipUza5XDVt3Gb7czjscrnK3R3jvMjZvjwlp+Q5WckpeXnZmv1l+ry/kh89elSPP/640tLSFBMTI0kKCgrSgQMHVFBQoPr6em3YsEGhoaHq2bOnfH19lZ+fL0nKzs5WaGiofHx8FBISopycHElSVlaWQkNDJUlhYWHKysqSJOXk5CgkJEQ+Pj4XttcAgDY575HCyy+/rOrqaqWkpJixsWPHKiUlRTNnzlR1dbXCwsIUFRUlSUpLS9P8+fNVUVGh/v37KyEhQZKUnJysxMRErVmzRj169NCyZcskSbNnz1ZiYqJiYmJkt9uVlpZ2MfYTANACNsuy2jr3clm4kOmjU6fseu+9ptNH0dE2devG9FFrkbN9eUpOyXOykrMdpo8AAFcPSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIDR4lKoqKhQbGysDh8+LEl69tlnFRERodGjR2v06NH64IMPJEl5eXmKi4tTRESE0tPTzf337Nmj+Ph4RUZGat68eaqrq5MkFRYWasKECYqKitL06dNVWVnZnvsHAGiFFpXCzp07NW7cOB08eNCM7d69W2+++aays7OVnZ2tkSNHqqqqSklJScrIyFBOTo52796trVu3SpLmzp2rBQsWaNOmTbIsS5mZmZKkRYsWafz48crNzdWAAQOUkZHR/nsJAGiRFpVCZmamkpOT5XQ6JUmnT59WYWGhkpKSFBcXpxUrVqihoUG7du1S79691atXL3l7eysuLk65ubk6cuSIqqqqNGjQIElSfHy8cnNzVVtbq+3btysyMrLROADAPbxbstLixYsbLRcXF2vYsGFKTk6W3W7XtGnT9N5776lLly5yOBxmPafTqaKiIh07dqzRuMPhUFFRkY4fPy4/Pz95e3s3Gm+Nrl39WrX+mQoKJLu9U5PxLl0kh8O3zdu9GBwOu7sjtAg525en5JQ8Jys5m9eiUvi+Xr16afXq1WZ54sSJysrKUmRkpGw2mxm3LEs2m00NDQ1nHf/u55m+v3w+JSUVamiw2rIbkuwqL69qMnrqlE0uV00bt9n+HA67XK5yd8c4L3K2L0/JKXlOVnJKXl62Zn+ZbtO7j/bu3atNmzaZZcuy5O3trcDAQLlcLjPucrnkdDqbjBcXF8vpdCogIEDl5eWqr69vtD4AwD3aVAqWZWnJkiU6efKkamtrtW7dOo0cOVJBQUE6cOCACgoKVF9frw0bNig0NFQ9e/aUr6+v8vPzJUnZ2dkKDQ2Vj4+PQkJClJOTI0nKyspSaGho++0dAKBV2jR9dPPNN2vq1KkaN26c6urqFBERodjYWElSSkqKZs6cqerqaoWFhSkqKkqSlJaWpvnz56uiokL9+/dXQkKCJCk5OVmJiYlas2aNevTooWXLlrXTrgEAWstmWVZbJ+QvCxdyTuHUKbvee6/pOYXoaJu6deOcQmuRs315Sk7Jc7KS8yKdUwAAXJkoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAaFEpVFRUKDY2VocPH5Yk5eXlKS4uThEREUpPTzfr7dmzR/Hx8YqMjNS8efNUV1cnSSosLNSECRMUFRWl6dOnq7KyUpJUVlamqVOnatSoUZowYYJcLld77x8AoBXOWwo7d+7UuHHjdPDgQUlSVVWVkpKSlJGRoZycHO3evVtbt26VJM2dO1cLFizQpk2bZFmWMjMzJUmLFi3S+PHjlZubqwEDBigjI0OStHz5coWEhGjjxo164IEHtHjx4ou0mwCAljhvKWRmZio5OVlOp1OStGvXLvXu3Vu9evWSt7e34uLilJubqyNHjqiqqkqDBg2SJMXHxys3N1e1tbXavn27IiMjG41L0pYtWxQXFydJio2N1UcffaTa2tqLsqMAgPPzPt8K3//t/dixY3I4HGbZ6XSqqKioybjD4VBRUZGOHz8uPz8/eXt7Nxr//ra8vb3l5+en0tJSde/e/cL3DADQaucthe9raGiQzWYzy5ZlyWaznXP8u59n+v7ymffx8mrdue+uXf1atf6ZCgoku71Tk/EuXSSHw7fN270YHA67uyO0CDnbl6fklDwnKzmb1+pSCAwMbHRC2OVyyel0NhkvLi6W0+lUQECAysvLVV9frw4dOpj1pW+PMoqLixUYGKi6ujpVVlbK39+/VXlKSirU0GC1djf+P7vKy6uajJ46ZZPLVdPGbbY/h8Mul6vc3THOi5zty1NySp6TlZySl5et2V+mW/2W1KCgIB04cEAFBQWqr6/Xhg0bFBoaqp49e8rX11f5+fmSpOzsbIWGhsrHx0chISHKycmRJGVlZSk0NFSSFBYWpqysLElSTk6OQkJC5OPj0+qdBAC0j1YfKfj6+iolJUUzZ85UdXW1wsLCFBUVJUlKS0vT/PnzVVFRof79+yshIUGSlJycrMTERK1Zs0Y9evTQsmXLJEmzZ89WYmKiYmJiZLfblZaW1o67BgBoLZtlWW2de7ksXMj00alTdr33XtPpo+hom7p1Y/qotcjZvjwlp+Q5Wcl5EaaPAABXLkoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADC8L+TOEydOVGlpqby9v93Mc889p2+++UZr1qxRXV2dJk2apAkTJkiS8vLytHTpUlVXV2vUqFGaM2eOJGnPnj2aN2+eKisrFRISokWLFpntAQAurTYfKViWpYMHDyo7O9v8FxgYqPT0dL399tvKysrSunXr9NVXX6mqqkpJSUnKyMhQTk6Odu/era1bt0qS5s6dqwULFmjTpk2yLEuZmZnttnMAgNZpcyns379fkjR58mTde++9evPNN5WXl6dhw4bJ399fXbp0UWRkpHJzc7Vr1y717t1bvXr1kre3t+Li4pSbm6sjR46oqqpKgwYNkiTFx8crNze3ffYMANBqbS6FsrIyDR8+XKtXr9Zrr72md955R4WFhXI4HGYdp9OpoqIiHTt2rEXjDodDRUVFbY0EALhAbZ68Dw4OVnBwsFkeM2aMli5dqunTp5sxy7Jks9nU0NAgm83W4vHW6NrVr627oIICyW7v1GS8SxfJ4fBt83YvBofD7u4ILULO9uUpOSXPyUrO5rW5FHbs2KHa2loNHz5c0rcv6D179pTL5TLruFwuOZ1OBQYGtmi8uLhYTqezVTlKSirU0GC1cS/sKi+vajJ66pRNLldNG7fZ/hwOu1yucnfHOC9yti9PySl5TlZySl5etmZ/mW7z9FF5eblSU1NVXV2tiooKrV+/Xr/97W+1bds2lZaW6vTp09q8ebNCQ0MVFBSkAwcOqKCgQPX19dqwYYNCQ0PVs2dP+fr6Kj8/X5KUnZ2t0NDQtkYCAFygNh8phIeHa+fOnbrvvvvU0NCg8ePHa8iQIZozZ44SEhJUW1urMWPG6NZbb5UkpaSkaObMmaqurlZYWJiioqIkSWlpaZo/f74qKirUv39/JSQktM+eAQBazWZZVlvnXi4LFzJ9dOqUXe+913T6KDrapm7dmD5qLXK2L0/JKXlOVnJexOkjAMCVh1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIDh7e4AaH+nT/uostLWZPyaayx17lzrhkQAPAWlcAWqrLQpJ8dqMh4dbVPnzm4IBMBjMH0EADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYvCX1MnKuzxd487cE4BLh5eYycq7PF4wZI3Xp4oZAAK46TB8BAAxKAQBgUAoAAINSAAAYnGhGs7jiKnB1oRTQLK64ClxdmD4CABiUAgDAuCxK4W9/+5uio6MVERGht956y91xAOCq5fZzCkVFRUpPT9f777+vjh07auzYsbrtttv0k5/8xN3R0CwvFRd3bDRy6pRks/lwAhrwYG4vhby8PA0bNkz+/v6SpMjISOXm5mrGjBktur+XV9N3xrRUhw7Stdc2vb+Pz4Vtt618fM6ep0OH1uU513basl/n2lZdnU3/+lfjsWuukYYOtemaay79c9da7vj7bQtPySl5TtarPef5tuv2Ujh27JgcDodZdjqd2rVrV4vvf91111zQ4//yl77nuOVc4xdP167SuQ+Q/NppO63br+a2NXDg2f75+Lb6Mdyha9eWP5/u5Ck5Jc/JSs7muf2cQkNDg2y2/2suy7IaLQMALh23l0JgYKBcLpdZdrlccjqdbkwEAFcvt5fC7bffrm3btqm0tFSnT5/W5s2bFRoa6u5YAHBVcvs5he7du2vOnDlKSEhQbW2txowZo1tvvdXdsQDgqmSzLKvpNQwAAFclt08fAQAuH5QCAMCgFAAABqUAADCuylLwpAvwVVRUKDY2VocPH3Z3lHNatWqVYmJiFBMTo9TUVHfHadbvfvc7RUdHKyYmRq+++qq74zTrhRdeUGJiortjNGvixImKiYnR6NGjNXr0aO3cudPdkc7qww8/VHx8vEaNGqXnn3/e3XHO6t133zXP4+jRozVkyBA999xzlz6IdZX53//+Z4WHh1vHjx+3Kisrrbi4OGvfvn3ujnVWX3zxhRUbG2v179/fOnTokLvjnNUnn3xiPfjgg1Z1dbVVU1NjJSQkWJs3b3Z3rLP69NNPrbFjx1q1tbXW6dOnrfDwcOvrr792d6yzysvLs2677TbrmWeecXeUc2poaLDuuOMOq7a21t1RmvXNN99Yd9xxh3X06FGrpqbGGjdunLVlyxZ3x2rWf//7X2vkyJFWSUnJJX/sq+5I4cwL8HXp0sVcgO9ylJmZqeTk5Mv6E94Oh0OJiYnq2LGjfHx8dOONN6qwsNDdsc5q6NChev311+Xt7a2SkhLV19erS5cu7o7VxIkTJ5Senq5HH33U3VGatX//fknS5MmTde+99+rNN990c6Kz++CDDxQdHa3AwED5+PgoPT1dQUFB7o7VrIULF2rOnDkKCAi45I/t9g+vXWoXegG+S2nx4sXujnBeN910k/nzwYMHtXHjRq1du9aNiZrn4+OjFStW6JVXXlFUVJS6d+/u7khNLFiwQHPmzNHRo0fdHaVZZWVlGj58uH7961+rtrZWCQkJuuGGGzRixAh3R2ukoKBAPj4+evTRR3X06FHdddddeuKJJ9wd65zy8vJUVVWlUaNGueXxr7ojBS7Ad3Hs27dPkydP1tNPP63rr7/e3XGaNWvWLG3btk1Hjx5VZmamu+M08u6776pHjx4aPny4u6OcV3BwsFJTU2W32xUQEKAxY8Zo69at7o7VRH19vbZt26YlS5Zo3bp12rVrl9avX+/uWOf0zjvv6JFHHnHb4191pcAF+Npffn6+Hn74Yf3qV7/Sz372M3fHOaevv/5ae/bskSR17txZERER2rt3r5tTNZaTk6NPPvlEo0eP1ooVK/Thhx9qyZIl7o51Vjt27NC2bdvMsmVZ8va+/CYfunXrpuHDhysgIECdOnXST3/608t2dqCmpkbbt2/X3Xff7bYMV10pcAG+9nX06FE9/vjjSktLU0xMjLvjNOvw4cOaP3++ampqVFNTo3/84x8aMmSIu2M18uqrr2rDhg3Kzs7WrFmzdPfddyspKcndsc6qvLxcqampqq6uVkVFhdavX6+RI0e6O1YT4eHh+vjjj1VWVqb6+nr985//VP/+/d0d66z27t2r66+/3q3nui6/Wr/IuABf+3r55ZdVXV2tlJQUMzZ27FiNGzfOjanOLiwsTLt27dJ9992nDh06KCIi4rIvsstZeHi4du7cqfvuu08NDQ0aP368goOD3R2riaCgIP3iF7/Q+PHjVVtbqxEjRuj+++93d6yzOnTokAIDA92agQviAQCMq276CABwbpQCAMCgFAAABqUAADAoBeAKxXtI0BZX3VtS4fkmTpyozz77zCx36NBBdrtdAwcO1JQpUzzi08DfSUxM1O7du7Vhw4Z23e6qVat03XXXacKECe26XVz5OFKARxo8eLDWrVundevW6fXXX1dycrJqamr0yCOPtPsLrCdauXKlqqqq3B0DHogjBXika6+9VoMGDWo0FhUVpUmTJmnhwoW688479YMf/MBN6QDPxZECrhheXl567LHHVF5ebi6HXlJSoqefflpDhw5VcHCwHn30UR06dMjcZ+XKlYqNjVV2drbCw8MVHBysadOmNflSo927d2vSpEkKCgrSsGHD9Jvf/EanT582t0+cOFFLly5Venq6RowYoaCgID322GMqKioy69TV1SktLU0jRozQ4MGDtXTpUtXX1zfZj9dff10REREaMGCAYmJilJOTY247fPiw+vbtqw8//FBTpkxRUFCQ7rzzTq1Zs8as07dvX0lSamqquYaOy+XS7NmzddtttykoKEjjx49vNAUHfIdSwBVl6NCh6tChgz7//HNVVVUpISFB+fn5mj9/vlJTU1VcXKyHHnpIJ0+eNPc5cuSIUlNTNWPGDC1dulT79+/Xww8/rJqaGknSV199pYceekg2m03Lly/XU089pZycnCaXX/7LX/6inTt3asmSJVq4cKE+/fRTLV261Ny+ZMkSvfHGG/rlL3+pZcuW6csvv9TGjRsbbWPVqlV64YUXFB0drZdeekm33367nnzyySbrPfvsswoKCtJLL72k8PBwLV++3FyhdN26dZK+LapVq1aZ9b/55hstXbpUGRkZ6ty5s6ZNm6YTJ0600zOPK8Yl/1of4AI99NBD1tSpU895+/Dhw60pU6ZYa9eutfr162d99dVX5rby8nIrJCTEWrlypWVZlrVixQqrT58+1tatW806e/futfr27Wu9//77lmVZ1pw5c6y7777bqq6uNuts377d6tOnj/XZZ5+ZTEOHDrWqqqrMOkuWLLEGDRpkWZZlHT9+3OrXr5/1+9//3txeVVVlDR8+3IqJibEsy7JOnjxpDRw40Fq2bFmj/Xn22Wete+65x7Isyzp06JDVp08fKzk52dxeX19vDR061HruuefMWJ8+faw//elPZjkoKMh66aWXzHJRUZGVkpJiFRYWnvN5xNWJIwVcsT799FP17t1bvXv3Vl1dnerq6tSpUycNGTJE//rXv8x6dru90ZVy+/Tpo169emnHjh1mOyNGjJCXl5fZzqBBg+Tn59fo0tF9+/aVr6+vWQ4MDDRTTDt37lR9fX2jx/H19VVYWJhZ/uKLL1RdXa277rrLPE5dXZ1CQ0N16NChRtNeZ55P8fLyktPp1KlTp875XAQHB2vFihV68sknlZ2drY4dO+qZZ55Rjx49WvWc4srHiWZcUaqrq3Xy5El1795dhYWF2r9//1kvk3zmFwGd+U183wkICDBTTCdOnDDvdPq+M7+bo3Pnzo1us9ls5rMCZWVlkqTrrruu0TrdunUzf/5uKmfs2LFn3bczv/ujU6dOjW7z8vJq9nMJ6enpWr16tTZu3Ki///3v8vHxUXx8vObPn6+OHTue8364+lAKuKLs2LFDdXV1GjJkiMrLy3XzzTfr+eefb7LemS+EZ5tXLykpMSds/fz8dM8995z1cuDff5E/F39/f0lSaWlpo68APfOx7Xa7JGn16tVn/ZrQG264oc3nAPz9/TVv3jzNmzdPe/bs0V//+le9+uqr+tGPfqSpU6e2aZu4MjF9hCuGZVn6wx/+IH9/f0VERGjw4ME6fPiwevbsqYEDB2rgwIEaMGCAXnvtNW3ZssXcr7S0tNE3ce3du1eHDh3SsGHDJElDhgzR/v37NWDAALOdHj166MUXX9S+fftalC04OFgdO3bU5s2bzVhdXZ0++eQTsxwUFCQfHx+VlJSYxxk4cKD27dun1atXt+q58PL6v/+1S0tLddddd+mDDz6QJPXr10/PPPOMfvjDH1723wONS48jBXiksrIyffHFF5K+fXEtKirSu+++q+3btystLU1+fn4aM2aM3njjDU2ePFlTp06Vv7+/1q1bp82bN+vee+8127LZbHriiSf01FNPSfp2qqVfv36KiIiQJD322GMaO3asZs+erfvvv181NTXKyMjQ0aNHdcstt7Qor5+fn6ZMmaI//vGP8vX11S233KK1a9equLhYP/7xjyV9O2U1ceJEpaSk6OTJk7r11lv15ZdfKj09Xffcc4/8/PxafKRw7bXXKj8/XyEhIQoKClLv3r31/PPPq7KyUj169NCWLVt05MiRy/Kb0uBelAI80r///W89+OCDkiQfHx85HA4NHDhQa9euVVBQkKRvX4jfeustpaamauHChaqpqdFNN92kjIyMRid4O3furBkzZmjJkiWqqqpSeHi4kpKSzPcNDxgwQH/+85+1fPlyzZo1S76+vho8eLBSU1PPOs1zLrNnz1anTp309ttvq6ysTBEREfr5z3/e6KT33LlzFRAQoMzMTK1YsUJOp1OTJk3SjBkzWvX8zJgxQ8uXL9eOHTuUl5enZcuWKTU1VWlpaTpx4oRuuOEGvfjii7r99ttbtV1c+fjmNVzVVq5cqVdeeUWff/65u6MAlwXOKQAADEoBAGAwfQQAMDhSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAAjP8HAeJo9UN0RMQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#treating 'dependents'\n",
    "#Removing right skewness\n",
    "sn.distplot(np.power(cp_frame['dependents'], 1/2), kde = False, color = 'blue')\n",
    "plt.xlabel('Dependents', fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After Plotting the histogram of the 'dependents' variable, we observe that it is having categorical properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Days since last transaction')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#treating 'days_since_last_transaction'\n",
    "#Removing right skewness\n",
    "temps = np.log(cp_frame['days_since_last_transaction']+0.1)\n",
    "#Adding 0.1 so that a zero value could be irradicated for log operation\n",
    "sn.distplot(np.square(temps), kde = False, color = 'purple')\n",
    "cp_frame['days_since_last_transaction'] = np.square(temps) #Changing it with the original value of dataframe\n",
    "plt.xlabel('Days since last transaction', fontsize = 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now plotting graphs for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#After visualising the histogram we can make sure that\n",
    "#the variables\n",
    "#'customer_nw_category'\n",
    "#'dependents'\n",
    "#are categorical in nature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender', 'occupation', 'customer_nw_category', 'dependents']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding them up in the category variable list\n",
    "categ.append('customer_nw_category')\n",
    "categ.append('dependents')\n",
    "categ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#As we have only 4 variables so slicing the index variables\n",
    "indexc1 = index1[0:4]\n",
    "indexc2 = index2[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 2, figsize = (16, 10))\n",
    "for j in range(len(categ)):\n",
    "    cp_frame[categ[j]].value_counts().plot.bar(ax = axes[indexc1[j]][indexc2[j]], title = categ[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Value Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                          0\n",
       "vintage                              0\n",
       "age                                  0\n",
       "gender                             525\n",
       "dependents                        2463\n",
       "occupation                          80\n",
       "city                               803\n",
       "customer_nw_category                 0\n",
       "branch_code                          0\n",
       "days_since_last_transaction       3223\n",
       "current_balance                      0\n",
       "previous_month_end_balance           0\n",
       "average_monthly_balance_prevQ        0\n",
       "average_monthly_balance_prevQ2       0\n",
       "current_month_credit                 0\n",
       "previous_month_credit                0\n",
       "current_month_debit                  0\n",
       "previous_month_debit                 0\n",
       "current_month_balance                0\n",
       "previous_month_balance               0\n",
       "churn                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Examining missing values \n",
    "cp_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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_id</th>\n",
       "      <th>vintage</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>dependents</th>\n",
       "      <th>occupation</th>\n",
       "      <th>city</th>\n",
       "      <th>customer_nw_category</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>days_since_last_transaction</th>\n",
       "      <th>...</th>\n",
       "      <th>previous_month_end_balance</th>\n",
       "      <th>average_monthly_balance_prevQ</th>\n",
       "      <th>average_monthly_balance_prevQ2</th>\n",
       "      <th>current_month_credit</th>\n",
       "      <th>previous_month_credit</th>\n",
       "      <th>current_month_debit</th>\n",
       "      <th>previous_month_debit</th>\n",
       "      <th>current_month_balance</th>\n",
       "      <th>previous_month_balance</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>55.991071</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>187.0</td>\n",
       "      <td>2</td>\n",
       "      <td>43.913388</td>\n",
       "      <td>29.290744</td>\n",
       "      <td>...</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1449.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>17.606817</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>65.210011</td>\n",
       "      <td>16.777297</td>\n",
       "      <td>...</td>\n",
       "      <td>8704.66</td>\n",
       "      <td>7799.26</td>\n",
       "      <td>12419.41</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5486.27</td>\n",
       "      <td>100.56</td>\n",
       "      <td>6496.78</td>\n",
       "      <td>8787.61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>48.538644</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>salaried</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.790617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5815.29</td>\n",
       "      <td>4910.17</td>\n",
       "      <td>2815.94</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>6046.73</td>\n",
       "      <td>259.23</td>\n",
       "      <td>5006.28</td>\n",
       "      <td>5070.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>21.863211</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>40.531946</td>\n",
       "      <td>24.911205</td>\n",
       "      <td>...</td>\n",
       "      <td>2291.91</td>\n",
       "      <td>2084.54</td>\n",
       "      <td>1006.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2143.33</td>\n",
       "      <td>2291.91</td>\n",
       "      <td>1669.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>50.309045</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.533585</td>\n",
       "      <td>16.501190</td>\n",
       "      <td>...</td>\n",
       "      <td>1401.72</td>\n",
       "      <td>1643.31</td>\n",
       "      <td>1871.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>714.61</td>\n",
       "      <td>588.62</td>\n",
       "      <td>1538.06</td>\n",
       "      <td>1157.15</td>\n",
       "      <td>1677.16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28377</th>\n",
       "      <td>30297</td>\n",
       "      <td>42.953463</td>\n",
       "      <td>10</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>student</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2</td>\n",
       "      <td>50.351701</td>\n",
       "      <td>18.061844</td>\n",
       "      <td>...</td>\n",
       "      <td>1076.43</td>\n",
       "      <td>2282.19</td>\n",
       "      <td>2787.70</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1076.43</td>\n",
       "      <td>1076.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28378</th>\n",
       "      <td>30298</td>\n",
       "      <td>70.135583</td>\n",
       "      <td>34</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>2</td>\n",
       "      <td>29.237507</td>\n",
       "      <td>7.002241</td>\n",
       "      <td>...</td>\n",
       "      <td>4069.21</td>\n",
       "      <td>3668.83</td>\n",
       "      <td>3865.55</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.29</td>\n",
       "      <td>901.00</td>\n",
       "      <td>1014.07</td>\n",
       "      <td>3738.54</td>\n",
       "      <td>3690.32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28379</th>\n",
       "      <td>30299</td>\n",
       "      <td>17.233688</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>salaried</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>2</td>\n",
       "      <td>40.662647</td>\n",
       "      <td>5.301898</td>\n",
       "      <td>...</td>\n",
       "      <td>61017.55</td>\n",
       "      <td>53444.81</td>\n",
       "      <td>21925.81</td>\n",
       "      <td>4666.84</td>\n",
       "      <td>3883.06</td>\n",
       "      <td>168.23</td>\n",
       "      <td>71.80</td>\n",
       "      <td>61078.50</td>\n",
       "      <td>57564.24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28380</th>\n",
       "      <td>30300</td>\n",
       "      <td>50.842895</td>\n",
       "      <td>50</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31.507207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1625.55</td>\n",
       "      <td>1683.20</td>\n",
       "      <td>1857.42</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1625.55</td>\n",
       "      <td>1625.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28381</th>\n",
       "      <td>30301</td>\n",
       "      <td>48.466483</td>\n",
       "      <td>18</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>student</td>\n",
       "      <td>1232.0</td>\n",
       "      <td>2</td>\n",
       "      <td>37.960476</td>\n",
       "      <td>16.640125</td>\n",
       "      <td>...</td>\n",
       "      <td>2821.34</td>\n",
       "      <td>3213.44</td>\n",
       "      <td>4447.45</td>\n",
       "      <td>0.11</td>\n",
       "      <td>7.44</td>\n",
       "      <td>714.40</td>\n",
       "      <td>1094.09</td>\n",
       "      <td>2402.62</td>\n",
       "      <td>3260.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28382 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id    vintage  age  gender  dependents     occupation    city  \\\n",
       "0                1  55.991071   66    Male         0.0  self_employed   187.0   \n",
       "1                2  17.606817   35    Male         0.0  self_employed     NaN   \n",
       "2                4  48.538644   31    Male         0.0       salaried   146.0   \n",
       "3                5  21.863211   90     NaN         NaN  self_employed  1020.0   \n",
       "4                6  50.309045   42    Male         2.0  self_employed  1494.0   \n",
       "...            ...        ...  ...     ...         ...            ...     ...   \n",
       "28377        30297  42.953463   10  Female         0.0        student  1020.0   \n",
       "28378        30298  70.135583   34  Female         0.0  self_employed  1046.0   \n",
       "28379        30299  17.233688   47    Male         0.0       salaried  1096.0   \n",
       "28380        30300  50.842895   50    Male         3.0  self_employed  1219.0   \n",
       "28381        30301  48.466483   18    Male         0.0        student  1232.0   \n",
       "\n",
       "       customer_nw_category  branch_code  days_since_last_transaction  ...  \\\n",
       "0                         2    43.913388                    29.290744  ...   \n",
       "1                         2    65.210011                    16.777297  ...   \n",
       "2                         2    13.790617                          NaN  ...   \n",
       "3                         2    40.531946                    24.911205  ...   \n",
       "4                         3    35.533585                    16.501190  ...   \n",
       "...                     ...          ...                          ...  ...   \n",
       "28377                     2    50.351701                    18.061844  ...   \n",
       "28378                     2    29.237507                     7.002241  ...   \n",
       "28379                     2    40.662647                     5.301898  ...   \n",
       "28380                     3    31.507207                          NaN  ...   \n",
       "28381                     2    37.960476                    16.640125  ...   \n",
       "\n",
       "       previous_month_end_balance  average_monthly_balance_prevQ  \\\n",
       "0                         1458.71                        1458.71   \n",
       "1                         8704.66                        7799.26   \n",
       "2                         5815.29                        4910.17   \n",
       "3                         2291.91                        2084.54   \n",
       "4                         1401.72                        1643.31   \n",
       "...                           ...                            ...   \n",
       "28377                     1076.43                        2282.19   \n",
       "28378                     4069.21                        3668.83   \n",
       "28379                    61017.55                       53444.81   \n",
       "28380                     1625.55                        1683.20   \n",
       "28381                     2821.34                        3213.44   \n",
       "\n",
       "       average_monthly_balance_prevQ2  current_month_credit  \\\n",
       "0                             1449.07                  0.20   \n",
       "1                            12419.41                  0.56   \n",
       "2                             2815.94                  0.61   \n",
       "3                             1006.54                  0.47   \n",
       "4                             1871.12                  0.33   \n",
       "...                               ...                   ...   \n",
       "28377                         2787.70                  0.30   \n",
       "28378                         3865.55                  1.71   \n",
       "28379                        21925.81               4666.84   \n",
       "28380                         1857.42                  0.20   \n",
       "28381                         4447.45                  0.11   \n",
       "\n",
       "       previous_month_credit  current_month_debit  previous_month_debit  \\\n",
       "0                       0.20                 0.20                  0.20   \n",
       "1                       0.56              5486.27                100.56   \n",
       "2                       0.61              6046.73                259.23   \n",
       "3                       0.47                 0.47               2143.33   \n",
       "4                     714.61               588.62               1538.06   \n",
       "...                      ...                  ...                   ...   \n",
       "28377                   0.30                 0.30                  0.30   \n",
       "28378                   2.29               901.00               1014.07   \n",
       "28379                3883.06               168.23                 71.80   \n",
       "28380                   0.20                 0.20                  0.20   \n",
       "28381                   7.44               714.40               1094.09   \n",
       "\n",
       "       current_month_balance  previous_month_balance  churn  \n",
       "0                    1458.71                 1458.71      0  \n",
       "1                    6496.78                 8787.61      0  \n",
       "2                    5006.28                 5070.14      0  \n",
       "3                    2291.91                 1669.79      1  \n",
       "4                    1157.15                 1677.16      1  \n",
       "...                      ...                     ...    ...  \n",
       "28377                1076.43                 1076.43      0  \n",
       "28378                3738.54                 3690.32      0  \n",
       "28379               61078.50                57564.24      1  \n",
       "28380                1625.55                 1625.55      0  \n",
       "28381                2402.62                 3260.58      1  \n",
       "\n",
       "[28382 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Appending all the missing categorical variables in a separate list and doing the same for continuous variables \n",
    "categ_missing = []\n",
    "contin_missing = []\n",
    "for i in cp_frame.columns:\n",
    "    if cp_frame[i].isnull().sum()!=0:\n",
    "        if cp_frame[i].dtypes == 'object':\n",
    "            categ_missing.append(i)\n",
    "        else:\n",
    "            contin_missing.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['gender', 'occupation'],\n",
       " ['dependents', 'city', 'days_since_last_transaction'])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categ_missing, contin_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------dependents------\n",
      "Median =  0.0\n",
      "\n",
      "------city------\n",
      "Median =  834.0\n",
      "\n",
      "------days_since_last_transaction------\n",
      "Median =  11.590791645112255\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in contin_missing:\n",
    "    print('------'+i+'------')\n",
    "    print('Median = ',cp_frame[i].median())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Treating the missing values for Continuous variables\n",
    "#Filling it with the median of that column\n",
    "\n",
    "for i in contin_missing:\n",
    "    cp_frame[i].fillna(cp_frame[i].median(),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------gender----------\n",
      "Male      16548\n",
      "Female    11309\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "\n",
      "----------occupation----------\n",
      "self_employed    17476\n",
      "salaried          6704\n",
      "student           2058\n",
      "retired           2024\n",
      "company             40\n",
      "Name: occupation, dtype: int64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for j in categ_missing:\n",
    "    print('----------'+j+'----------')\n",
    "    print(cp_frame[j].value_counts())\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Treating the missing values for Categorical variables\n",
    "#Filling it with the mode of that column\n",
    "\n",
    "for j in categ_missing:\n",
    "    cp_frame[j].fillna(cp_frame[j].mode()[0],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                       0\n",
       "vintage                           0\n",
       "age                               0\n",
       "gender                            0\n",
       "dependents                        0\n",
       "occupation                        0\n",
       "city                              0\n",
       "customer_nw_category              0\n",
       "branch_code                       0\n",
       "days_since_last_transaction       0\n",
       "current_balance                   0\n",
       "previous_month_end_balance        0\n",
       "average_monthly_balance_prevQ     0\n",
       "average_monthly_balance_prevQ2    0\n",
       "current_month_credit              0\n",
       "previous_month_credit             0\n",
       "current_month_debit               0\n",
       "previous_month_debit              0\n",
       "current_month_balance             0\n",
       "previous_month_balance            0\n",
       "churn                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                         int64\n",
       "vintage                           float64\n",
       "age                                 int64\n",
       "gender                             object\n",
       "dependents                        float64\n",
       "occupation                         object\n",
       "city                              float64\n",
       "customer_nw_category                int64\n",
       "branch_code                       float64\n",
       "days_since_last_transaction       float64\n",
       "current_balance                   float64\n",
       "previous_month_end_balance        float64\n",
       "average_monthly_balance_prevQ     float64\n",
       "average_monthly_balance_prevQ2    float64\n",
       "current_month_credit              float64\n",
       "previous_month_credit             float64\n",
       "current_month_debit               float64\n",
       "previous_month_debit              float64\n",
       "current_month_balance             float64\n",
       "previous_month_balance            float64\n",
       "churn                               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now according to the histogram plot and the dataset values we see that \n",
    "#the variables\n",
    "#'vintage'\n",
    "#'city'\n",
    "#'branch_code'\n",
    "#'days_since_last_transaction'\n",
    "#should have a integer datatype\n",
    "\n",
    "#whereas \n",
    "#the variables\n",
    "#'dependents'\n",
    "#'customer_nw_category'\n",
    "#should be categorical in nature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_frame = cp_frame.astype({'dependents':'object', 'customer_nw_category': 'object', \n",
    "                            'vintage': 'int64', 'city': 'int64', 'branch_code': 'int64', \n",
    "                            'days_since_last_transaction':'int64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                         int64\n",
       "vintage                             int64\n",
       "age                                 int64\n",
       "gender                             object\n",
       "dependents                         object\n",
       "occupation                         object\n",
       "city                                int64\n",
       "customer_nw_category               object\n",
       "branch_code                         int64\n",
       "days_since_last_transaction         int64\n",
       "current_balance                   float64\n",
       "previous_month_end_balance        float64\n",
       "average_monthly_balance_prevQ     float64\n",
       "average_monthly_balance_prevQ2    float64\n",
       "current_month_credit              float64\n",
       "previous_month_credit             float64\n",
       "current_month_debit               float64\n",
       "previous_month_debit              float64\n",
       "current_month_balance             float64\n",
       "previous_month_balance            float64\n",
       "churn                               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------Correlation---------------------\n",
      "\n",
      "current_balance and  previous_month_end_balance\n",
      "0.9472755077987904\n",
      "\n",
      "current_balance and  average_monthly_balance_prevQ\n",
      "0.9583070699816738\n",
      "\n",
      "current_balance and  average_monthly_balance_prevQ2\n",
      "0.7145997660188935\n",
      "\n",
      "current_balance and  current_month_balance\n",
      "0.9834121512153574\n",
      "\n",
      "current_balance and  previous_month_balance\n",
      "0.94220714530421\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Performing Calculations on Variables\n",
    "#Creating a list of items relating to balance and checking correlation of these with 'current_balance'\n",
    "bal = ['previous_month_end_balance', 'average_monthly_balance_prevQ', 'average_monthly_balance_prevQ2',\n",
    "       'current_month_balance', 'previous_month_balance']\n",
    "\n",
    "print('---------------------Correlation---------------------\\n')\n",
    "for i in bal:\n",
    "    print('current_balance and ',i)\n",
    "    print(cp_frame['current_balance'].corr(cp_frame[i]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So, we see that all these variables are highly correlated with 'current_balance' variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['orangered', 'green', 'dodgerblue', 'red', 'purple']\n",
    "index1 = [0,0,1,1,2]\n",
    "index2 = [0,1,0,1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BzzcMBi10sNcdjN1euY9bdc8XERERkZrjsNto8uvPOHv2wH7euTh79qDJrz/jsNtq9BoiIiLH8nsGITc3l8TERHbt2gXAhg0bSEpKIi4ujmnTpvmO27p1K8nJyfTu3ZtHH30Ut9sNwJ49exgyZAjx8fHcd9995OXlAXDkyBHuuece+vTpw5AhQ8jMzASgqKiI4cOH06dPHwYMGMC2bdsAME2TyZMnEx8fT0JCAl9++aW/X3qts9sNXM5g8p0huJw1lzAqMAK5J81gV461vSsH7kmzRmzVxPkiIiINnfpf4k8Ou42mBUcgPZ2mBUcIPZqN0b8/pKdbB6SnY/TvT2hedqWvGZpX/WuIiIgcy68ZkG+//Zabb76ZHTt2AFBQUMDo0aOZMWMGS5cuZcuWLaxZswaA4cOHM27cOD744ANM02Tu3LkATJgwgVtuuYXly5fTpUsXZsyYAcBzzz1HdHQ0y5YtY9CgQUyaNAmA2bNnExwczLJlyxg9ejSjRo0C4IMPPmDbtm0sXbqUf/7zn4waNcrXqauPanOUlJuSZFWxXTnW/po4X0REpCFT/0v86diRWJxzDs6ePXDkHy1JVBVLT8dwuyp9XcNVVO1riIiIHMuvGYS5c+cyfvx4IiMjAdi8eTNRUVG0a9cOh8NBUlISy5cvZ/fu3RQUFNC1a1cAkpOTWb58OS6Xi02bNtG7d+9S+wFWr15NUlISAImJiaxduxaXy8Xq1au5/vrrAbjssss4ePAge/bsYc2aNSQkJGAYBu3bt6dVq1Z8/fXX/nz5tao2R0k58NI2rPS+tmHW/po4X0REpCFT/0v8qbyRWLaffoKoqNIHRkXhdTgrfV2vM6Da1xARETmWX4vWF9/1K5aRkUFERIRvOzIykv3795fZHxERwf79+zl06BChoaE4HI5S+4+/lsPhIDQ0lIMHD5Z7rX379pGRkeHr+B27/1Q0axZ6SsefqoiIsJMfVEm7j1DuKCnTcJzW5ymP14SXk+DuJdZztg2ztls2cWDYTv7c1T2/svwdBylN8a45inXNUrxrluJ9cup/nRp9pk5R+sGyI7EefxwWLYIBA6zHoqJg8WKcbVoRYVTy/ro3BBYvhn79qn6NM4w+e9Wj+FWP4lc9il/V1WTsanSVRq/Xi81WUnjSNE1sNluF+4t/Huv47WPPMQyjzDnF+8t7DuMU/3geOJCL1+uflWIiIsLIzMw5+YGVZHMG0zbMUSrp1TYMbF43mZn5p+15KtLCbjAvufQqiweyKj9Cq7rnn8zpjrecmOJdcxTrmqV41yx/xtswbH5PrNQW9b8qpt/hU9fUcOCMiiqd9Nq3D3fL1pjr1mO4XXgdTmuFxQN5p3RtR7vzCK3mNc4U+uxVj+JXPYpf9Sh+VVde7PzZB6vR2yUtW7b0FTcFyMzMJDIyssz+rKwsIiMjCQ8PJycnB4/HU+p4sO5OZmVlAeB2u8nLy6Np06a0aNGCjIyMMtdq2bJlufvrqyBvIbMSS6YGtg2DWYlW4qgmeDxenK58gl15OF35eDynlqyq7vkiIiJiUf9LTqfckCZ4U1NLph9GReFNTSUnMITDQY05GNqMw0GNcXtOPUnp9pjVvoaIiEixGk14XXzxxWzfvp309HQ8Hg9paWnExMTQpk0bAgMDfSv3LF68mJiYGJxOJ9HR0SxduhSA1NRUYmJiAIiNjSU1NRWApUuXEh0djdPpJDY2lsWLFwPwxRdfEBgYSOvWrYmJiWHJkiV4PB7S09PZsWMHF154YU2+/Cqp6kqLHo+XVo585iW7WXebl3nJblo5Tpw4qq1VHUVERMR/1P+SihSvthiek0XTgiMEOo1S2w572ZF9bo9J9tkdcK1bDzt24Fq3nuyzOyg5JSIidU6NTmkMDAzk6aef5i9/+QuFhYXExsYSHx8PQEpKCmPGjCE3N5cLLriAoUOHAjB+/HhGjhzJv/71L1q1asXUqVMBePjhhxk5ciR9+/YlLCyMlJQUAG699VbGjRtH3759CQgIYMqUKQDEx8ezefNmX0HVSZMmERQUVJMv/5QVr7RYXHy+bZjBrETjpImrYh6PF6cnn+JSnx4/PpeIiIjUTep/SXmKV1ssLkBv79cPx9ix2AYOtLajomiSmlpuMqt4JFZERBiHM3NAyS4REamDbKZp6i9UJdV0DQmXM5hBC8vW4ZqX7MbpOr11uGryueoCzbuuWYp3zVGsa5biXbNUw6thUg0v/2hacARnzx4ltbgWLoRhw0rX5oqKwrVuPYeDGpd7jYYcv+pS7KpH8asexa96FL+qq9c1vOTUuDHKXWnR7Ye3rSafS0RERERql+EqKp3cCg8vu/piejqG21WzDRMRETlNlM2owxyUFJ0v1jbM2n8mP5eIiIiI1C6vM6Ck8DzAwYOlt8EqSO9wIiIiciZSwqsOq8mVFmt7VUcRERERqTllVlt84w3MBQvKrL6YG9Kk9hopIiJSDTVatF5OTclKi4G4MXBgJaD8UUS+Jp9LRERERGpX8WqLoevWY7hdeB1O8sOaEnzMdm5IE62+KCIiZywlvOq4U1lpsa49l91uUGAogSYiIiJyOjnsNkLzsjFcRXidAVVOTBWvtujj8lJ47LaSXSIicgZTwquOK04a2QwDLzY8XvOMSB7Z7QZ73cHck2YVw28bZjAr0aCVI79Ot1tERESkKk6UhDpdCariazX59WeM/v0hPR17VBRNUlPJPruDRmOJiIgcQzW86rDipNH4tQ5+yTa4cb6Nnm8YDFroYK87GLu97r59BUagL9kF1oqP96RZyTsRERGR+qQ4CeXs2QP7eefi7NmDJr/+jMNuO+FjVRGal+1LdgHWSor9+xOal30aX5GIiMiZr+5mTBogu93A5Qwm3xmCyxmM22EljQZ2hhEfckYlj9yUJLuK7cqx9ouIiIjUJydKQp3uBJXhKiq51uWXw8KF8MYbOLzuKifRRERE6iNNaawjvCZlpgC+lGgSEQKRIVSYPKqrC0U78NI2rHTSq22YtV9ERESkPimVhCqWno7hdoFpVvxYBU40BdLrDMAeFQUtW8KkSXDXXZCejk1TG0VERErRcJs64sBRykwB/HOajZE9oEmQlSw6lr+SR8ePMqvqtMkgbyGzEr2+drcNg1mJVu0xERERkfrE6wyAqKjSO6Oi8DqcJ3ysmMNuo2nBEcJzsjjLffSEUyBzQ5rgTU2FceN8yS5AUxtFRESOo4RXHVHkKX8UV6tQk6fXwZTrOC55ZPqSR6eSpDrRscU1wwYtdFS7VpjH46WVI595yW7W3eZlXrJbBetFRESkXvIloYoTW1FReFNTyQ1pUvaxfv0wP/4YR1EhzfMOcpbnKE0ydvkSXI6vvzzhFEi3xyT77A6YHTud8sgxERGRhkRTGuuIALuVyDp+CqDdgBXbITMfxsZA0yA4XADhQSaeQu8prYZ4smMrKjQ/LzkQpyf/lF+Tx+PF6cn3Tbv0VDE2IiIiInWZ22OSe875hK5di83lwnQ6yW3cDLfL6otln92B0HXrMWxgZGZg69XLSlZFReF47TUIDbWmKKanQ0jISRNZbo+JOyAQZ1RU6WOPGzkmIiLSkGmEVx3RrBFlpgDOTDTZk2OjbRh8vQ/+/D7ctAAmrgXTa3WgTmU1xJMdq0LzIiIi0tAdO72wacGRShWCd9hthO74CSMmBluHDhgxMYTu+Ml3rttjcjioMV4TbAMGlBq9xR13QGYmPPKIte/gwZNOgYQTjyoTERERjfCqE+x2g8P50DTIxjvJJnYDnAaMWWUjM9+azli8SmPxdMZGNhcuTpykOv7+XkXHen479kSF5u12KzHmxsCBVYtL0xNFRESkPvAViXe7MLwebH//OyxejP24QvDHHmezG5g2Ay82sFHuNMSmq9fgCQzC9LgxioowDKNkJFex4lFdISHW9uTJ8NprViLst1FgvkTWMcXoi6c2hq5bj+F24XU4SxW3FxERaeiU8KplzgAHRwjiUA7c/77Nl9R6KdGkaTAM7AzBDni9H+S74awgmP2tjX6dAmnl8BJkeHnteoNGTmuq48wvITMP7IaNfGdIqeSU3bCVO23SZtiw2w3s3kJmJRrHTHm0Rp01srnYXclpkyIiIiJnEofdRpNffy5JWEVFwSuvQLNmkJiIkZdHk9yD5DZuZo3iOva4N97AHhqK2bQpTJsGaWmQmAjh4XDwILbAABy7d8INN5Sc89prMGoUbNxoNSAqCvLyShq0bx/eVq3wfLIew3XiRFbxyDEfJbtERER8bKZp6i9jJR04kIvXe/rCZbcbFDgaUeCxceuisomo2QPw7Y9rD6N6Wo85DWtVx/BgExN4Yq2NFdutc1LioHkjkymflOx7o7+XELuJaTPIzLfx4kYrkda8EZwVbF3PbjPxeE2cNhO7zaTAWzKSq8AIZNBCR5n2zUt243Sdem2vuiAiIozMzJyTHyinheJdcxTrmqV41yx/xtswbDRrFuqXa0v1nO7+17EiIsJw7dyNs2eP0qOu+vWDRx+FQYN8iSpz5Ups11xTpmYWb70F+fnw+9+DaUJBAbjdkJEB7dtDTEzZc2bMgL59fQkwb6tWeJucha0g/4waqaXvwKpT7KpH8asexa96FL+qKy92/uyDaYRXLSowAin02MgpLJ3suqQl3HspGDZ4+lp470fo38lKfkWEwCM94B8rYNdv9b2mXGcVtf96n7X/2Tgr2QXW8ZlHDSZ/ZSW52jWGMTHwxFqrGH5ce3joCrg3zea73qxEr2/0lgdwG5WfNikiIiJyxvB6cRQVliSkLr/cqqXVqRNs3w7XXOMbsWXzestOR2zZEoKDISUFJk2yklzHTEVkwYJypzCanTrBtm1gt+MJCOJocCjBOYex6T60iIjIaaOEVy1yY+A0oElQyQqNl7SE4X8sXbPrX33hhY3W9tiY4mSXdY1dOdaxY2Osova7cqBFCMwZaE1xDAmAV7+C2y8pfc3iJNnAznBvGmUK2R+7MuOJanuJiIiInIkCnQZs347N5YL337eSU7fcAnfdVZKwmj8fnngCFi+2tmfPtkZvGQYUFcF558HVV1vTGdPT4f77SxekHziwZDRXsago3M5A31REh91Gk2OmSh5fN0xERESqRgmvWhTitKYk5rrgzQGQfhiCHDD8w9IJqPvetxJaK7ZD06DyR4Od3wxe6gsL/muNDPt8N3SJtEZ0jegBU9aXnyQ7/nrFjx87eiuogtpeQd5CPH6Iiwrki4iIiD8FOg3Ctv8PildMjIqCDz6A3r2t7eKRXoWF8OSTsG+fdaLNBuecY01bzMiwfrZsadXsgtIjuYq3O3Swrl88NXLRolIF6EPzsssteB+6bn3p+lwiIiJySpTwqkUmBocL4ddsaPRbdikyxJqGGBFiJbKaBlkjtdr91t85XHDy0WCrfoGr28Of08qO6Pr6t/7arpySa5dXyP7Y0VsejzXFcV6y/5NQdrvBXhXIFxERET9x2G2EHdiHrTjZBdbPI0esn3feaRWVLx7JlZEB//wnBAZCZib06lWSJJs925rOmJlpPV6c2CoWFWU9Nm2ar5C9NyKy1Mgtw1VUbqLMcLtqIBoiIiL1l1HbDWjITOBgPoz9GG5aYP08mA//SoDHr4KJa639E9dax667HX4XDi8lWkmpey8tSXZByWiwK6NKkl3F+0d8aB1frG2Ylexa8F8rSdY2rGR/8eitY3k8XpyufIJdeThd/ks+FRiBvmRXcdvvSbNGfImIiIhUV2heNrZ9+6z6XD/9BD//DDt2QJs2VrH6+++Ha6+1itDHxYHLZY3sCgwsqc8F1s9bb7XOe+MNaN7cWoExKsp6PCoK3n0X/v53SE6Gq66CYcPwAk0LjhCek0XTgiN4AwJKzikWFYXXoUqpIiIi1aGEVy0q8sCwD0pGa42NsaYjesySml1g/fxzGni81o1GG/DuQOgSUf50RLtR/v7mjax/tw2DGX2hdZhVw2v2Zuu5195mMn+gp1ZHU7mpuEC+iIiISHUZriJrWuKDD1qJrQ4d4MUXwW6H55+3pilOm2ZNa0xPt5Jc+/ZZnbDypiwWFsJtt0FAAHTsCKtXW0m01autaxZPh4yKwpu6GHtuDs6ePbCfd671MzcHb2pqqUSZNzXVmvYoIiIiVaYpjbXIbVpTF99OhrwiuOeYKYhT48pOQTSBPy0sOealRGuVxeIVGcHaH2Avf5piixBYd5uJ3YDHVttKnbdhJ0zsZaPTWWatTh08lQL5qvUlIiIip8oMP8sqOH/WWfD555CfbyWz9uwpXdPrlVfg0Udh40YICbGSV+VNWdy5E4YNs4reJydbxwP064c5Y4aV+PJ48AQGg92Oo/vlpet1xcfj/nQjnnXrMdwubHYD02YQmpdNbkgTFa4XERGpogY3bGbJkiUkJCQQFxfH22+/XattCbTD2/0g2GHV8Hq9P7w1wEqC/W0FPNfbWm3xpb5WYmvH4bKjvkb2PH46ojUSLCWuZH9ce3gnGbyYOA0vIWYBD19hEtf+t0L3g6yi+ct+LjuSym43cDmDyXeG4HIGY7f79yMT5C3kjf5eXrveeu2vXQ9v9C87xbK41teghQ56vmEwaKGDvW7/t09EpL6o6e93adjqUv/L7vVaCSybDXJ+61g1alSS7ALr5113WYXro6IgLw+czrJTFhcssGpzzZiBGRlZajSXOXYstvvvx9a+PbZrrsHIysBWkF/uKDFbQT65IU2wZR/GiInBfnY7nD170OTXn3HYbTUTGBERkXqmQY3w2r9/P9OmTWPhwoUEBAQwePBgLr/8cjp06FDjbQkIcuJwwN6jkHkU/rGi9Oiup9aDacLT6yEzD2Ymwpvflr7GrhzILYQ3+lt9tsw8CA+GG+ZZSbOxMda0RRO4ZSHsyrHRNszOrMRA2oR4ePByB/e/X/K8KXEQZJSMkKqtAvKFbqueWcmKkJT5pFZU62teciBOT77f2iYiUh9ogRCpSXWp/+Ww/1aL64cfYODAktFcCxZYNb1efbXk4PR0iIy0HvN6rVFhTqdvxBa7dsEDD8C+fdYUxLBwgo8ZpWU89BAsXuy7ltG/P961a8sdJeZ1OLVao4iIyGnWoG7nbtiwgSuuuIKmTZvSqFEjevfuzfLly2ulLblmIIeKbPyaXZLsAuvn31bAQ91g+2Gr0PyuHLg3Dfoc1y9sGwahgfCPD2HoIivJVeC2jv96H/z5fdiTgy+pVXz9e9IMjnrsZfb/YwV4zJK7iLVRQL6yz6laXyIiVacFQqQm1aX+V2heNrasrJJkF1g/Bw6Ef/yj9MFRUdborRYtIDQUgoKgbVtramNYGOa55+L5z7u41q0n++wOFLq8HA5qzMHQZphuT0myq1h6OqZhr7Bel1ZrFBEROb0a1AivjIwMIiIifNuRkZFs3ry50uc3axZ62tqSftj62chZfoH59mdZBe1H9ijZd07TktpcxYXnZ35RUucrtxCaNSpdv6tpUPnXd5u2cvd7bXYiIqy5kLuPlH+uaTh8x5xulX3OzLzy65QFOw0imlaubf56DVI+xbvmKNY160yMd218v58udb19UlZd6n+RfhDc7vKLzxevllg86mvePGsIfUCAlfhyu63h9w4HjBqFbdw47O3PwQ6cdfzzePLKHclld9jhoovgs8+sYveBgRiRkZxlGFCUW/45wUF1+nNfl9tW1yl21aP4VY/iVz2KX9XVZOwaVMLL6/Vis5WMYDJNs9T2yRw4kIvXe3oKhzoCQwEbR13lJ24C7FZS53BByb7dOdY0xeaNrETWPz4sSXa1DbP2Bdit6Y/3/lYAv6LrO2wmbcNsZfbbvG4yM60pgTZnMG3DHCc85nSr7HPa7QazEo+djgOzEr3YC/PJPHry6TgREWFkZuac9Dg5PRTvmqNY16wzNd618f1+Ovgz3oZhO72JFfGpS/2vpoYDp4Pyi887HPDxx9bURYfDGtXl9VpF6Vu0gJ9+slZsXLoUVq7E9djjHK7g8+gICKVJamrJFMXfRnJlB4TiPpAH9hBoFPLbC8w7+Tl19HvmTP0OrAsUu+pR/KpH8asexa/qyoudP/tgDWr+V8uWLcnMzPRtZ2ZmEhkZWSttCbUV0jTA5OwmpQvMF6++2Nhp7Z/5pbVvWm949lOYuBYCHeAxrYTYsecEO60VFls3cjP3BpO1t5l0jjCZHn98YXurcP2sRG+Z/ccWhw/yFp70mNOtss/p8Xhp5chnXrKbdbd5mZfsVu0ZEZFKqo3vd2m46lL/KzekCWbz5lZdruOLzzdvbm03bmwlug4dslZubNQIDh+G55+H+fPhtdd80xAr4vaYZJ/dAde69Xh+2e6b9niiFRerco6IiIhUzGaaZoP5K7p//35uvvlm5s+fT3BwMIMHD2bixIlcdNFFlTr/dN5hhN8K1zsDyfNAkceGxzRxGtDECUdcYNigyAMOo+Tfhg1sWKtnmya4vGC3QSO7B7OwsEzCx243cDsCKcKO1wtOm5dATwEejxe73arX4sbAgfU/OuWdf7JjTreaeE5l5WuW4l1zFOuadSbHuza+36tLI7zOTHWt/+Ww22gaaIOsLGxuN6bDYSW7CgqsovRZWdYIL6fTmsYYGGhNP/R68QYG4XW5yQ1pokQUZ/Z3YG1T7KpH8asexa96FL+qq+kRXg1qSmOLFi0YNmwYQ4cOxeVyccMNN1S6s+UPRQUuigpc2IC2EWFkZuYCcLSg5I0JOub4oOMvQMlx7gqew+PxYvPkc2wZYs8xjzk9+TiP23/8+Sc75nSrjecUEWlo9F0rNaWu9b/cHpOsoyY0CiciIoyszBw46gUCoBAIK6k3VtIhCy7ZZ8caai8iIiJ1WoNKeAEkJSWRlJRU280QERERaTDU/xIREZGa1qBqeImIiIiIiIiISP2nhJeIiIiIiIiIiNQrSniJiIiIiIiIiEi90uBqeFWHYdjO6OtLaYp3zVK8a45iXbMU75rlr3jrfay71P+q2xS/qlPsqkfxqx7Fr3oUv6o7Pnb+jKXNNE0tMyMiIiIiIiIiIvWGpjSKiIiIiIiIiEi9ooSXiIiIiIiIiIjUK0p4iYiIiIiIiIhIvaKEl4iIiIiIiIiI1CtKeImIiIiIiIiISL2ihJeIiIiIiIiIiNQrSniJiIiIiIiIiEi9ooSXiIiIiIiIiIjUK0p4iYiIiIiIiIhIvaKEVx2wZMkSEhISiIuL4+23367t5tRZubm5JCYmsmvXLgA2bNhAUlIScXFxTJs2zXfc1q1bSU5Opnfv3jz66KO43W4A9uzZw5AhQ4iPj+e+++4jLy8PgCNHjnDPPffQp08fhgwZQmZmJgBFRUUMHz6cPn36MGDAALZt2waAaZpMnjyZ+Ph4EhIS+PLLL2syDDXixRdfpG/fvvTt25cpU6YAirc/Pf/88yQkJNC3b19ee+01QPH2t8mTJzNy5EigdmP66quvEh8fT+/evVmxYkVNhqBG3HrrrfTt25d+/frRr18/vv322wr/5tXWZ14apoba91JfqurUN6oe9XWqT32XqlN/pOpWrVpFcnIyffr04YknngDOsN9dU2rVvn37zF69epmHDh0y8/LyzKSkJPOnn36q7WbVOd98842ZmJhoXnDBBebOnTvN/Px8MzY21vz1119Nl8tl3nnnnebq1atN0zTNvn37ml9//bVpmqY5atQo8+233zZN0zTvueceMy0tzTRN03zxxRfNKVOmmKZpmhMmTDBfeukl0zRNc9GiRebDDz9smqZpvvzyy+bYsWNN0zTNzz//3Bw0aJBpmqa5bNky8//+7/9Mj8dj/vLLL+Z1111nulyuGoqE/61fv9686aabzMLCQrOoqMgcOnSouWTJEsXbTzZu3GgOHjzYdLlcZn5+vtmrVy9z69atircfsoqjugAAIABJREFUbdiwwbz88svNRx55xDTN2ovpt99+a/br188sKCgws7KyzGuuucY8dOhQDUXB/7xer3nllVeW+vxU9DevNr/TpeFpqH0v9aWqTn2j6lFfp/rUd6k69Ueq7tdffzWvvPJKc+/evWZRUZF58803m6tXrz6jfnc1wquWbdiwgSuuuIKmTZvSqFEjevfuzfLly2u7WXXO3LlzGT9+PJGRkQBs3ryZqKgo2rVrh8PhICkpieXLl7N7924KCgro2rUrAMnJySxfvhyXy8WmTZvo3bt3qf0Aq1evJikpCYDExETWrl2Ly+Vi9erVXH/99QBcdtllHDx4kD179rBmzRoSEhIwDIP27dvTqlUrvv7665oOid9EREQwcuRIAgICcDqdnHfeeezYsUPx9pNu3brx5ptv4nA4OHDgAB6PhyNHjijefnL48GGmTZvGvffeC1CrMV27di3XXXcdgYGBNGvWjG7durF69eoajoj//PLLLwDceeedXH/99bz11lsV/s2rze90aXgaat9LfamqU9+oetTXqR71XapH/ZGq+/DDD0lISKBly5Y4nU6mTZtGcHDwGfW7q4RXLcvIyCAiIsK3HRkZyf79+2uxRXXTpEmTiI6O9m1XFLfj90dERLB//34OHTpEaGgoDoej1P7jr+VwOAgNDeXgwYPlXmvfvn1kZGT4OovH7q8vzj//fN8X1Y4dO1i2bBk2m03x9iOn08n06dPp27cv3bt31+fbj8aNG8ewYcNo3LgxUPa7pCZjWt9jfeTIEbp3784///lPXn/9dd5991327NlTqc92TX7mpeFpqH0v9aWqTn2j6lNfp+rUd6ke9UeqLj09HY/Hw7333ku/fv145513zrjfXSW8apnX68Vms/m2TdMstS3lqyhuFe0vL64Vxdk0TQzDKHNO8f7ynsMw6t+v0k8//cSdd97JiBEjaNeuneLtZw899BCffvope/fuZceOHYq3H8ybN49WrVrRvXt3377ajKnX6y1zrfoSa4BLLrmEKVOmEBYWRnh4ODfccAPTp08/pc92Tbw/0vCo72VRX+rUqW9UPerrnDr1XapP/ZGq83g8fPrppzz55JPMmTOHzZs3s3PnzjPqd7duRrYBadmypa84G0BmZmaprKWUr6K4Hb8/KyuLyMhIwsPDycnJwePxlDoerKx0VlYWAG63m7y8PJo2bUqLFi3IyMgoc62WLVuWu78++fLLL7n99tv5+9//zoABAxRvP9q2bRtbt24FIDg4mLi4ODZu3Kh4+8HSpUtZv349/fr1Y/r06axatYr58+fXWkzr+/f/F198waeffurbNk2TNm3aVOqzXZOfeWl46vvvXmXpb/upUd+o6tTXqTr1XapP/ZGqa968Od27dyc8PJygoCCuvfZaNmzYcEb97irhVcv++Mc/8umnn3Lw4EHy8/NZsWIFMTExtd2sOu/iiy9m+/btvmGWaWlpxMTE0KZNGwIDA30rNixevJiYmBicTifR0dEsXboUgNTUVF+cY2NjSU1NBaw/KtHR0TidTmJjY1m8eDFgfVEGBgbSunVrYmJiWLJkCR6Ph/T0dHbs2MGFF15YC1Hwj7179/LAAw+QkpJC3759AcXbn3bt2sWYMWMoKiqiqKiIlStXMnjwYMXbD1577TXS0tJYvHgxDz30EFdffTVPPfVUrcU0JiaGFStWkJ+fz8GDB/nss89K3cE90+Xk5DBlyhQKCwvJzc1l0aJFPPPMM+X+zavN7xhpeNT3suhve+Wpb1Q96utUnfou1af+SNX16tWLTz75hCNHjuDxeFi3bh3x8fFn1u/uCUvaS4147733zL59+5pxcXHmrFmzars5dVqvXr3MnTt3mqZprVaSlJRkxsXFmZMmTTK9Xq9pmqa5detWc+DAgWbv3r3Nv/3tb2ZhYaFpmqa5a9cu809/+pPZp08f88477zQPHz5smqZpHjp0yPzzn/9sJiQkmDfddJPv+gUFBeaIESPMhIQEs3///uaWLVtM07RW+nj66afNhIQEMyEhwVy3bl1Nh8GvJk6caHbt2tW8/vrrff+98847ircfTZ8+3ezTp4+ZmJhoTp8+3TRNfb79bcGCBb6Vjmozpq+88oqZkJBgxsXFmYsWLarJENSIadOmmfHx8WZcXJz5+uuvm6ZZ8d+82vrMS8PUkPte6kudOvWNqk99nepT36Xq1B+punnz5vniNGHCBNPj8ZxRv7s20zRNPyUERUREREREREREapymNIqIiIiIiIiISL2ihJeIiIiIiIiIiNQrSniJiIiIiIiIiEi9ooSXiIiIiIiIiIjUK0p4iYiIiIiIiIhIvaKEl4jUCy+++CIfffQRACNHjuSVV14p97iOHTty8ODBKj3HrbfeyvLly0/pnBdeeIHHH3+8Ss93Jtq4cSN33HEHffr04frrr+eOO+5g06ZNvse9Xi9Tpkyhb9++JCUl8eCDD1b5/RAREREREamIEl4iUi9s3LgRt9td281o0NasWcPIkSN5+OGHWbZsGe+99x4PP/wwI0aM4OOPPwZgwYIFfP/99yxatIglS5Zw9tln8/TTT9dyy0VERKQiY8aMYcuWLbXahnnz5vH2228DVbuZuHHjRhITE0/5eatzo1REap+jthsgIifn9Xp58skn+fbbb8nLy8M0TSZMmMC9997LBx98QEREBACDBg3iwQcfpHv37qSkpLBp0yY8Hg+dO3dmzJgxhIaGcvXVV3PRRRfx448/8re//Q2Hw8FLL71EUVERBw8epH///vz1r38FYNasWcyfP5+QkBCio6NZuXIlq1atoqioqMLrV2Tjxo1MnTqVVq1asX37doKDg7nnnnuYPXs227dvJy4ujtGjRwMwZ84cZs+ejWEYNG/enLFjx9K+fXtGjhxJaGgoP/74I/v27aNjx45MnjyZ1NRUtmzZwpQpU7Db7QB8/fXXDB48mKysLM4//3yeffZZGjVq5GtP8SikG2+8EYAZM2Zw+PBhXxsq8uGHHzJr1iwKCgpISkrivvvuA2DmzJmsXLmSgoIC8vPzeeSRR7juuutKnfvxxx+XG+uNGzcybdo02rVrx08//YTb7WbChAlceuml5OXl8cQTT/DVV19ht9u59tprGTZsGC6Xq0rvQUpKCq1bt+aXX34hKCiIp59+mvPOO4+RI0dy+PBhdu7cyVVXXcXDDz9c7vW/+eYbJk+ezJIlSwA4cuQI11xzDR999BFTpkxh1KhRdO3a1fecXbt2ZfTo0TzzzDP06tWLDh06MGLECAICAgDo0qUL77zzzgljLiIiIrVnw4YN3HTTTbXahi+//JLzzz+/VtsgImceJbxEzgDffvstGRkZzJkzB8MwmDVrFq+++irXXXcd7733HnfddRfbtm0jKyuLnj17MmPGDOx2OwsXLsRmszF16lRSUlJ47LHHADj//PN57rnnME2ToUOH8vTTT3POOeewf/9+evXqxdChQ/n+++9ZuHAh8+fPJywsjEcffdTXnlmzZp3w+hX57rvvGD9+PJ07d+buu+9m1qxZvPnmm+Tm5hITE8Ndd93FL7/8wssvv8ycOXMIDw9n4cKFPPDAA7z//vsAbNmyhTfffBObzcaNN97I8uXLGTJkiO/nddddx8qVK9m/fz9vvvkmAQEBDBo0iBUrVtC/f39fW4YMGcLMmTO58cYb8Xq9zJ8/n5dffvmk70VeXh5z586loKCAQYMG0blzZzp06MCGDRuYPXs2QUFBvP/++0yfPr1Uwss0TV599dVyYw2wefNmxo8fz+9//3teffVVpk2bxltvvcX06dMpLCxk6dKleDwe7rzzTj7//HM2bdpUpfdgy5YtPPLII0RHR/Of//yH4cOHs3DhQgAKCgp8cX7xxRfLvf748ePJy8vju+++48ILLyQtLY3Y2FgAfv75Zy677LIyz/nHP/6RBx98kOzsbC655BLf/uzsbGbMmMHgwYNPGncREZGGaP78+bz22msYhsFZZ51FcnIyL7/8MmlpaYB1M2vixImkpaXxwgsv8M0335CRkUHHjh2JiooqtZ2SksK//vUvVqxYgdfrpU2bNowfP54WLVpw66230rVrV7766iv27t1L9+7dmThxIs8//zwZGRn84x//YMqUKVx88cXltnPXrl3cdttt9OjRgy1btuDxeHjooYeYM2cOv/zyC126dGHq1KkYhsFHH33Eiy++iNfrJSQkhFGjRnHRRRfxwgsvsHv3bjIzM9m9ezctWrTgmWee4dtvv2XVqlWsX7+eoKAgAH755RduvfVWMjMzad68OVOnTiUyMvKEsTx69CgPPfQQ6enpNG7cmMcff5z27duzfft2Hn/8cfLy8sjMzKRTp04899xzBAYGljr3scceIz09ncOHDxMSEkJKSgrnnntuhbEzDIOPP/6Y5557Dq/XS6NGjZgwYQKdOnXiq6++IiUlhfz8fAzD4MEHH6RXr16n6VMjIsWU8BI5A1xyySU0adKEd999l507d7Jx40ZCQkK4++67mTBhAnfddRcLFixg4MCBGIbB6tWrycnJYcOGDQC4XC6aNWvmu150dDQANpuNmTNnsnr1atLS0ti2bRumaZKfn8+aNWuIj4+ncePGgJUg+uyzzwBOev2KtG3bls6dOwNw9tlnExYWRkBAAOHh4YSEhJCdnc26detISEggPDwcgOTkZCZNmsSuXbsA6Nmzp2900O9+9zuys7PLfa5rr72W4OBgwErwHT8cvVevXkyaNIkffviB/fv307ZtW84999yTvoYbbrgBh8NBaGgovXv3ZsOGDcTGxjJlyhSWLFlCenq6byTesU4Ua4DWrVvz+9//HoDOnTuzaNEiwLqrOmrUKOx2O3a7nbfeeguAZ555pkrvQadOnXzv/8CBA3n88cc5dOgQAJdeeqnvuIreY5vNxsCBA1m0aBEXXnghCxcuZMSIESd9XrBGKhb79ddfeeCBB/jDH/7AkCFDKnW+iIhIQ/LDDz+QkpLCokWLaNWqFa+//jozZ87E4aj4f+F2795NWloaDofDl0Aq3k5NTeV///sf8+bNw+FwMGfOHMaMGcO///1vwPrbPHv2bI4ePUqfPn34/PPPGTZsGEuWLCElJYULL7zwhO3dtWsXsbGxPP7444wfP55Jkybx3nvv4XQ6ueaaa/jmm29o0qQJ48eP591336Vdu3Z8+umn3H///b4aqV988QWpqamEhoZy77338u677/LQQw+xcuVKzj//fIYMGcILL7zAzp07mTdvHuHh4dx///3MmzePBx544ITt27t3LykpKfzhD39gzpw5jBgxgnnz5jF37lz69+9Pv379cLlcJCcns3r1anr37u07d+3atTRu3Jg5c+YAMG7cON5++23Gjh1bYew6dOjA8OHDefPNN+ncuTMrVqwgJSWFZ599llGjRvHKK6/Qtm1b9u/fz4033kjHjh1p3br1yT8YIlJpSniJnAFWr17NpEmTuOOOO7jmmms499xzee+994iOjsbtdrN582bS0tJ8f4S9Xi+jR4/2jbzJy8ujsLDQd73iqX1Hjx5lwIABXHvttURHRzNw4EA++ugjTNPE4XBgmqbvnOKpgpW5fkWKE1XFyuuwHZsUKWaapq8+V/GdPbCSSMe2saJrl3ec3W7npptuYv78+WRkZFR6lNGxcSiO0/fff8/999/P7bffTo8ePbjsssuYMGFCqfNOFOsTvS6Hw4HNZvM9tnfvXoKCgqr8Hhzb/uP3HTvl80TXv+GGGxgwYACDBg0iJyeHbt26AXDeeefx+eef+zqI+/fvp0WLFnz22WdERUVx1llnAfDZZ58xbNgw7r77bu66666TtllERKQh+vTTT7nyyitp1aoVALfffju///3vmThxYoXndO3atVQf6Njtjz/+mO+++46BAwcC1t/64htvYN0MNAyD0NBQoqKiKrypWBGn08nVV18NWDc2L7nkEl+phcjISLKzs9m6dStXXHEF7dq1A6B79+6Eh4f7aoR169bNd07nzp0rbEOPHj18N0c7depUqTpbHTt25A9/+AMAAwYM4LHHHiMnJ4fhw4ezfv16/v3vf7Njxw4yMjI4evRoqXPj4+Np164ds2fPJj09nc8//7zUqPXyYvfVV19x/vnn+272xsXFERcXx5o1a8jMzCyVoLPZbPz4449KeImcZipaL3IGWL9+Pb169eKWW26hS5cufPTRR3g8HsCq2zVx4kQ6duzo6xBdeeWVvP322xQVFeH1ehk7dixTp04tc9309HRyc3P561//ytVXX83GjRt958TGxrJixQpycnIAa0h9scpevyp69uzJ0qVLfR2XBQsW0LRpU6Kiok54nt1uP+Wi9YMGDeKjjz7i+++/L1NvqyKpqamYpkl2djbLli2jZ8+ebNq0iS5dunDHHXfQrVs3Vq5c6Xt/ip0o1ifSvXt3Fi1ahNfrpaioiIceeohNmzZV+T344Ycf+OGHHwCrVtoll1ziG8V3rBNdv0WLFlx00UWMGzeOG264wXfOI488wuTJk/nmm28AmDJlCkOGDGHSpEm+UWDff/89Dz74IJMnT1ayS0RE5ATsdnupm14FBQVlbuK5XK5S5xx78+r4ba/Xy913383ixYtZvHgxCxYs4D//+Y/v8creVKyI0+ks1V6n01nmGK/XW+oY8M+NzfIYRun/9bXZbDgcDv72t78xd+5c2rRpw+23384FF1xQ5nrvvPMOjz76KEFBQSQlJZGYmFjqmPLaffz7Z5omP/zwAx6Ph/POO8/3PixevJg5c+Zw5ZVXnvQ1iMipUcJL5AwwePBgPv/8c5KSkhgwYADt2rVj165deL1e+vfvz9atWxk0aJDv+Pvvv582bdowYMAAEhISME2TkSNHlrlux44dueqqq+jTpw99+vTh448/pkOHDqSnp9O9e3duvPFGbrrpJpKTk8nJyfFNEazs9auiR48e3H777dx222307duX1NRUXnrppTKdlONdffXVTJ061TcVsDKaNWtGly5dSExMLLdTVp6wsDCSk5MZPHgwf/rTn7jiiitITEzk0KFD9OnTh4SEBBo1akR2dja5ubm+804U6xN58MEHcTqd9OvXj/79+xMbG0tcXFyV34PmzZvz3HPPkZSU5Cs0X56TXX/QoEFs3bqVAQMG+PbFxsby9NNP8/zzzxMfH8/3338PWNM1169fz+HDh5k6dSqmafLss8/Sr18/+vXrd9IpCCIiIg3R5ZdfzqeffkpGRgYA7777LjNnzmTPnj0cOHAA0zR9tTcr48orr2T+/Pm+/snzzz9fqbIEVbmpWJHu3bvzySefsHPnTsAaxbZ3794Ka4Odzjb8+OOPbN26FbBu+l166aUEBwfzySef8MADD5CQkABYtXOPv3H5ySef+Ea3t2/fnlWrVpU55ngXX3wx27Zt46effgJg5cqVDB8+nK5du5Kens6mTZsA2Lp1K71792b//v3Ven0iUpamNIqcAc477zxSU1NL7RszZgxAqWHgxYKCghg/fny511q1apXv34Zh8OSTT5Z73HfffYfdbmfp0qUAvPbaa74pbSe6fkUuv/xyX4FVsGofHGvjxo2+fw8ZMqTcuk5PP/10hdtDhw71FYA/Nglz/HE//vij798HDx7kv//9b6mC/Ccye/bscvc3b968zEqDxcmhv/zlL759FcUaKBWbY2PVqFEjJk2aVOb4qrwHAKGhocycObPM/uNje7LrX3PNNeUuUd6tWzffFMdipmmydu1aAgMDeeWVV065zSIiIg1Rx44dGT58OHfffTcAERERPPXUU7z++usMHDiQiIgIrrrqKr777rtKXW/QoEG+elE2m41WrVqV+ftfnuuuu47hw4fz2GOPVXsUUocOHRg/fjwPPvggHo+HoKAgZs6cSVhY2AnPi4mJqVRbT+Tcc8/lxRdfZOfOnTRr1sx3vWHDhvHAAw/QqFEjQkNDueyyy/j1119LnXvnnXcybtw434yHrl278r///e+Ez9e8eXNSUlJ45JFH8Hg8hIaGMm3aNMLDw5k+fTpTpkyhsLAQ0zSZMmUKbdu2rdbrE5GybOapjlUVkQYhNzeX0aNH88svv/g6RRMnTqRFixYVnvPXv/6V7du3l/vYtGnTKlUUvqbMnTuXqVOn8pe//MWXXPvss8946qmnyj3+8ssvZ/To0TXZxCo50Xtw880389Zbb5VKromIiIiIiNRHSniJiIiIiIjICT355JOlRuQfa9SoUVxxxRU13KLSzqQbryJSM5TwEhERERERERGRekVF60VEREREREREpF5RwktEREREREREROoVJbxEREREpFpyc3NJTExk165dZR7bunUrycnJ9O7dm0cffRS3210LLRQREZGGRgkvEREREamyb7/9lptvvpkdO3aU+/jw4cMZN24cH3zwAaZpMnfu3JptoIiIiDRISniJiIiISJXNnTuX8ePHExkZWeax3bt3U1BQQNeuXQFITk5m+fLlNd1EERERaYActd0AERERETlzTZo0qcLHMjIyiIiI8G1HRESwf//+mmiWiIiINHBKeJ2CQ4fy8HrN037dZs1COXAg97RftyFRDKtH8as+xbB6FL/qUwyrp1mz0NpuQr3k9Xqx2Wy+bdM0S21Xhr/6X1J1+r6pe/Se1E16X+oevSd1j2HYOOusEL9cWwmvU+D1mn7rcKkjV32KYfUoftWnGFaP4ld9iqHUNS1btiQzM9O3nZWVVe7UxxPxZ/9Lqk7vSd2j96Ru0vtS9+g9aTj8WsNr1apVJCcn06dPH5544gkANmzYQFJSEnFxcUybNs13bEUr+OzZs4chQ4YQHx/PfffdR15eHgBHjhzhnnvuoU+fPgwZMsTXmSoqKmL48OH06dOHAQMGsG3bNsC6ozh58mTi4+NJSEjgyy+/9OdLFxEREWnw2rRpQ2BgoK/ftXjxYmJiYmq5VSIiItIQ+C3htXPnTsaPH8+MGTN47733+O9//8uaNWsYPXo0M2bMYOnSpWzZsoU1a9YAFa/gM2HCBG655RaWL19Oly5dmDFjBgDPPfcc0dHRLFu2jEGDBvnqR8yePZvg4GCWLVvG6NGjGTVqFAAffPAB27ZtY+nSpfzzn/9k1KhRWhZbRERExA/+7//+j++++w6AlJQUnnrqKeLj4zl69ChDhw6t5daJiIhIQ+C3hNeHH35IQkICLVu2xOl0Mm3aNIKDg4mKiqJdu3Y4HA6SkpJYvnx5hSv4uFwuNm3aRO/evUvtB1i9ejVJSUkAJCYmsnbtWlwuF6tXr+b6668H4LLLLuPgwYPs2bOHNWvWkJCQgGEYtG/fnlatWvH111/76+WLiIiINCirVq2ibdu2APz73//mwgsvBKBTp07Mnz+f5cuX8+yzzxIQEFCbzRQREZEGwm81vNLT03E6ndx7773s3buXq666ivPPP7/USj2RkZHs37+/whV8Dh06RGhoKA6Ho9R+KL3qj8PhIDQ0lIMHD5Z7rX379pGRkVGqZkTxfhERERERERERqV/8lvDyeDx88cUXzJ49m0aNGnHfffcRFBRU7ko9Fa3gU95KPhWt7GOaJoZhlDmneH95z2EYpzbAzZ8rOEVEhPnt2g2FYlg9il/1KYbVo/hVX32Ooek1ycvIw13oxhHoICQyBJtxaqv9iYiIiEjD4beEV/PmzenevTvh4eEAXHvttSxfvhy73e47JjMzk8jIyApX8AkPDycnJwePx4PdbvcdD9bosKysLFq2bInb7SYvL4+mTZvSokULMjIyOPvss0tdq2XLlmRkZJR5jlNx4ECuX1Z0iIgIIzMz57RftyFRDKtH8as+xbB6FL/qq88xtNsNCn89wpz+c8hOz6ZJVBNuSr2JwLMb4/F4T8tz1OdkoYiIiEhD5LcaXr169eKTTz7hyJEjeDwe1q1bR3x8PNu3byc9PR2Px0NaWhoxMTEVruDjdDqJjo5m6dKlAKSmpvpW9omNjSU1NRWApUuXEh0djdPpJDY2lsWLFwPwxRdfEBgYSOvWrYmJiWHJkiV4PB7S09PZsWOHr7aEiIiI1F22PLcv2QWQnZ7NnP5zsOVp8RkRERERKZ/fRnhdfPHF3H333dxyyy24XC569OjBzTffzLnnnstf/vIXCgsLiY2NJT4+HrBW8Bk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\n",
      "text/plain": [
       "<Figure size 1440x936 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting scatterplot to analyse the relationship further\n",
    "f, axes = plt.subplots(3,2, figsize = (20,13))\n",
    "for i in range(len(bal)):\n",
    "    sn.scatterplot(x = cp_frame[bal[i]], y = cp_frame['current_balance'], color = colors[i], ax = axes[index1[i],index2[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hence, we can see a linear relationship between all four variables versus 'current_balance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x285d3bdbb88>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Similarly plotting a scatterplot for credit and debit\n",
    "f, axes = plt.subplots(1,2, figsize = (15, 4))\n",
    "sn.scatterplot(x = cp_frame['previous_month_credit'], y = cp_frame['previous_month_debit'], color = 'crimson', ax = axes[0])\n",
    "sn.scatterplot(x = cp_frame['current_month_credit'], y = cp_frame['current_month_debit'], color = 'darkviolet', ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Based on scatterplots creating new columns\n",
    "net_bal = []\n",
    "net_bal = cp_frame['current_balance'] + cp_frame['previous_month_end_balance'] + cp_frame['average_monthly_balance_prevQ'] + cp_frame['average_monthly_balance_prevQ2'] + cp_frame['current_month_balance'] + cp_frame['previous_month_balance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Similarly for credit\n",
    "net_cred = []\n",
    "net_cred = cp_frame['current_month_credit'] + cp_frame['previous_month_credit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Similarly for debit\n",
    "net_deb = []\n",
    "net_deb = cp_frame['current_month_debit'] + cp_frame['previous_month_debit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_frame.insert(20,'net_balance',net_bal) #Adding a new column at index 20 of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_frame.insert(21,'net_credit',net_cred) #Adding a new column at index 21 of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_frame.insert(22,'net_debit',net_deb)   #Adding a new column at index 22 of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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_id</th>\n",
       "      <th>vintage</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>dependents</th>\n",
       "      <th>occupation</th>\n",
       "      <th>city</th>\n",
       "      <th>customer_nw_category</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>days_since_last_transaction</th>\n",
       "      <th>...</th>\n",
       "      <th>current_month_credit</th>\n",
       "      <th>previous_month_credit</th>\n",
       "      <th>current_month_debit</th>\n",
       "      <th>previous_month_debit</th>\n",
       "      <th>current_month_balance</th>\n",
       "      <th>previous_month_balance</th>\n",
       "      <th>net_balance</th>\n",
       "      <th>net_credit</th>\n",
       "      <th>net_debit</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>1458.71</td>\n",
       "      <td>8742.62</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>834</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5486.27</td>\n",
       "      <td>100.56</td>\n",
       "      <td>6496.78</td>\n",
       "      <td>8787.61</td>\n",
       "      <td>49598.09</td>\n",
       "      <td>1.12</td>\n",
       "      <td>5586.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>salaried</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.61</td>\n",
       "      <td>6046.73</td>\n",
       "      <td>259.23</td>\n",
       "      <td>5006.28</td>\n",
       "      <td>5070.14</td>\n",
       "      <td>27530.98</td>\n",
       "      <td>1.22</td>\n",
       "      <td>6305.96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1020</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2143.33</td>\n",
       "      <td>2291.91</td>\n",
       "      <td>1669.79</td>\n",
       "      <td>11636.60</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2143.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>self_employed</td>\n",
       "      <td>1494</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>714.61</td>\n",
       "      <td>588.62</td>\n",
       "      <td>1538.06</td>\n",
       "      <td>1157.15</td>\n",
       "      <td>1677.16</td>\n",
       "      <td>8678.18</td>\n",
       "      <td>714.94</td>\n",
       "      <td>2126.68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  vintage  age gender dependents     occupation  city  \\\n",
       "0            1       55   66   Male          0  self_employed   187   \n",
       "1            2       17   35   Male          0  self_employed   834   \n",
       "2            4       48   31   Male          0       salaried   146   \n",
       "3            5       21   90   Male          0  self_employed  1020   \n",
       "4            6       50   42   Male          2  self_employed  1494   \n",
       "\n",
       "  customer_nw_category  branch_code  days_since_last_transaction  ...  \\\n",
       "0                    2           43                           29  ...   \n",
       "1                    2           65                           16  ...   \n",
       "2                    2           13                           11  ...   \n",
       "3                    2           40                           24  ...   \n",
       "4                    3           35                           16  ...   \n",
       "\n",
       "   current_month_credit  previous_month_credit  current_month_debit  \\\n",
       "0                  0.20                   0.20                 0.20   \n",
       "1                  0.56                   0.56              5486.27   \n",
       "2                  0.61                   0.61              6046.73   \n",
       "3                  0.47                   0.47                 0.47   \n",
       "4                  0.33                 714.61               588.62   \n",
       "\n",
       "   previous_month_debit  current_month_balance  previous_month_balance  \\\n",
       "0                  0.20                1458.71                 1458.71   \n",
       "1                100.56                6496.78                 8787.61   \n",
       "2                259.23                5006.28                 5070.14   \n",
       "3               2143.33                2291.91                 1669.79   \n",
       "4               1538.06                1157.15                 1677.16   \n",
       "\n",
       "   net_balance  net_credit  net_debit  churn  \n",
       "0      8742.62        0.40       0.40      0  \n",
       "1     49598.09        1.12    5586.83      0  \n",
       "2     27530.98        1.22    6305.96      0  \n",
       "3     11636.60        0.94    2143.80      1  \n",
       "4      8678.18      714.94    2126.68      1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, converting all the categorical variable values to numerics to further perform operations of sklearn library\n",
    "cp_frame = pd.get_dummies(cp_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cp_frame.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting with Prediction process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Division of Independent and Dependent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = cp_frame.drop(['churn'], axis=1)\n",
    "y = cp_frame['churn']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As our datset is having outliers, therefore using the RobustScaler() which Scale features using statistics that are robust to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = RobustScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_x = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(scaled_x, columns = x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "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",
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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>customer_id</th>\n",
       "      <th>vintage</th>\n",
       "      <th>age</th>\n",
       "      <th>city</th>\n",
       "      <th>branch_code</th>\n",
       "      <th>days_since_last_transaction</th>\n",
       "      <th>current_balance</th>\n",
       "      <th>previous_month_end_balance</th>\n",
       "      <th>average_monthly_balance_prevQ</th>\n",
       "      <th>average_monthly_balance_prevQ2</th>\n",
       "      <th>...</th>\n",
       "      <th>dependents_50.0</th>\n",
       "      <th>dependents_52.0</th>\n",
       "      <th>occupation_company</th>\n",
       "      <th>occupation_retired</th>\n",
       "      <th>occupation_salaried</th>\n",
       "      <th>occupation_self_employed</th>\n",
       "      <th>occupation_student</th>\n",
       "      <th>customer_nw_category_1</th>\n",
       "      <th>customer_nw_category_2</th>\n",
       "      <th>customer_nw_category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>-0.941776</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>1.384615</td>\n",
       "      <td>-0.375678</td>\n",
       "      <td>-0.404419</td>\n",
       "      <td>-0.464597</td>\n",
       "      <td>-0.407758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.999934</td>\n",
       "      <td>-1.173913</td>\n",
       "      <td>-0.458333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.961538</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.434748</td>\n",
       "      <td>1.120873</td>\n",
       "      <td>0.948830</td>\n",
       "      <td>1.933604</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.999802</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>-0.625000</td>\n",
       "      <td>-1.001456</td>\n",
       "      <td>-1.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130253</td>\n",
       "      <td>0.512653</td>\n",
       "      <td>0.304798</td>\n",
       "      <td>-0.116031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.999736</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>0.270742</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.203932</td>\n",
       "      <td>-0.229028</td>\n",
       "      <td>-0.325088</td>\n",
       "      <td>-0.502205</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.999670</td>\n",
       "      <td>0.260870</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.960699</td>\n",
       "      <td>-0.192308</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>-0.485130</td>\n",
       "      <td>-0.416415</td>\n",
       "      <td>-0.423446</td>\n",
       "      <td>-0.317681</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id   vintage       age      city  branch_code  \\\n",
       "0    -1.000000  0.478261  0.833333 -0.941776     0.115385   \n",
       "1    -0.999934 -1.173913 -0.458333  0.000000     0.961538   \n",
       "2    -0.999802  0.173913 -0.625000 -1.001456    -1.038462   \n",
       "3    -0.999736 -1.000000  1.833333  0.270742     0.000000   \n",
       "4    -0.999670  0.260870 -0.166667  0.960699    -0.192308   \n",
       "\n",
       "   days_since_last_transaction  current_balance  previous_month_end_balance  \\\n",
       "0                     1.384615        -0.375678                   -0.404419   \n",
       "1                     0.384615         0.434748                    1.120873   \n",
       "2                     0.000000         0.130253                    0.512653   \n",
       "3                     1.000000        -0.203932                   -0.229028   \n",
       "4                     0.384615        -0.485130                   -0.416415   \n",
       "\n",
       "   average_monthly_balance_prevQ  average_monthly_balance_prevQ2  ...  \\\n",
       "0                      -0.464597                       -0.407758  ...   \n",
       "1                       0.948830                        1.933604  ...   \n",
       "2                       0.304798                       -0.116031  ...   \n",
       "3                      -0.325088                       -0.502205  ...   \n",
       "4                      -0.423446                       -0.317681  ...   \n",
       "\n",
       "   dependents_50.0  dependents_52.0  occupation_company  occupation_retired  \\\n",
       "0              0.0              0.0                 0.0                 0.0   \n",
       "1              0.0              0.0                 0.0                 0.0   \n",
       "2              0.0              0.0                 0.0                 0.0   \n",
       "3              0.0              0.0                 0.0                 0.0   \n",
       "4              0.0              0.0                 0.0                 0.0   \n",
       "\n",
       "   occupation_salaried  occupation_self_employed  occupation_student  \\\n",
       "0                  0.0                       0.0                 0.0   \n",
       "1                  0.0                       0.0                 0.0   \n",
       "2                  1.0                      -1.0                 0.0   \n",
       "3                  0.0                       0.0                 0.0   \n",
       "4                  0.0                       0.0                 0.0   \n",
       "\n",
       "   customer_nw_category_1  customer_nw_category_2  customer_nw_category_3  \n",
       "0                     0.0                     0.0                     0.0  \n",
       "1                     0.0                     0.0                     0.0  \n",
       "2                     0.0                     0.0                     0.0  \n",
       "3                     0.0                     0.0                     0.0  \n",
       "4                     0.0                    -1.0                     1.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#division of train and test data\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x, valid_x, train_y, valid_y = train_test_split(x, y, random_state = 101, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating an Ensemble Model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8244081172491544"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction based on Logistic Regression\n",
    "Model_1 = LogisticRegression()\n",
    "Model_1.fit(train_x,train_y)\n",
    "pred_1 = Model_1.predict(valid_x)\n",
    "Model_1.score(valid_x, valid_y) #Prediction Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8286358511837655"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction based on K-Nearest-Neighbors\n",
    "Model_2 = KNeighborsClassifier(n_neighbors = 6)\n",
    "Model_2.fit(train_x,train_y)\n",
    "pred_2 = Model_2.predict(valid_x)\n",
    "Model_2.score(valid_x, valid_y) #Prediction Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8532976324689966"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction based on Decision Tree\n",
    "Model_3 = DecisionTreeClassifier(max_depth = 8)\n",
    "Model_3.fit(train_x,train_y)\n",
    "pred_3 = Model_3.predict(valid_x)\n",
    "Model_3.score(valid_x, valid_y) #Prediction Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, creating a final array in which we append the mode of all the three models\n",
    "from statistics import mode\n",
    "Final_pred = np.array([])\n",
    "for i in range(0,len(valid_x)):\n",
    "    Final_pred = np.append(Final_pred, mode([pred_1[i], pred_2[i], pred_3[i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing evaluation metric\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.62457722660653"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(valid_y, Final_pred)*100 #Accuracy Score from all the three models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.6685456595265"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prediction based on Random Forest\n",
    "Model_4 = RandomForestClassifier(n_estimators = 100, max_depth = 20)\n",
    "Model_4.fit(train_x,train_y)\n",
    "pred_4 = Model_4.predict(valid_x)\n",
    "Model_4.score(valid_x, valid_y)*100 #Prediction Percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hence, we see that the best score comes from the model of random forest."
   ]
  }
 ],
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