churning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e9eff1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import roc_auc_score, recall_score, confusion_matrix, classification_report \n",
    "import subprocess\n",
    "import joblib\n",
    "# Get multiple outputs in the same cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "# Ignore all warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(action='ignore', category=DeprecationWarning)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91e6e622",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the dataset\n",
    "dc = pd.read_csv(\"Churn_Modelling.csv\")\n",
    "dc.head(5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2b7d99f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display tail\n",
    "data_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "19b68853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber            int64\n",
       "CustomerId           int64\n",
       "Surname             object\n",
       "CreditScore          int64\n",
       "Geography           object\n",
       "Gender              object\n",
       "Age                  int64\n",
       "Tenure               int64\n",
       "Balance            float64\n",
       "NumOfProducts        int64\n",
       "HasCrCard            int64\n",
       "IsActiveMember       int64\n",
       "EstimatedSalary    float64\n",
       "Exited               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check data types\n",
    "data_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "da9be95d",
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
       "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
       "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
       "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
       "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
       "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
       "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
       "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
       "\n",
       "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
       "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
       "mean    76485.889288       1.530200      0.70550        0.515100   \n",
       "std     62397.405202       0.581654      0.45584        0.499797   \n",
       "min         0.000000       1.000000      0.00000        0.000000   \n",
       "25%         0.000000       1.000000      0.00000        0.000000   \n",
       "50%     97198.540000       1.000000      1.00000        1.000000   \n",
       "75%    127644.240000       2.000000      1.00000        1.000000   \n",
       "max    250898.090000       4.000000      1.00000        1.000000   \n",
       "\n",
       "       EstimatedSalary        Exited  \n",
       "count     10000.000000  10000.000000  \n",
       "mean     100090.239881      0.203700  \n",
       "std       57510.492818      0.402769  \n",
       "min          11.580000      0.000000  \n",
       "25%       51002.110000      0.000000  \n",
       "50%      100193.915000      0.000000  \n",
       "75%      149388.247500      0.000000  \n",
       "max      199992.480000      1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>Surname</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2932</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Smith</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>32</td>\n",
       "      <td>5014</td>\n",
       "      <td>5457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Surname Geography Gender\n",
       "count    10000     10000  10000\n",
       "unique    2932         3      2\n",
       "top      Smith    France   Male\n",
       "freq        32      5014   5457"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe all numerical columns\n",
    "data_raw.describe(exclude= ['O'])\n",
    "# Describe all categorical columns\n",
    "data_raw.describe(include = ['O']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2f8d45c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of unique customers in the dataset\n",
    "dc.shape[0], dc.CustomerId.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9fa1c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# churn value Distribution\n",
    "dc[\"Exited\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "086250c2",
   "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>Surname</th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>Smith</td>\n",
       "      <td>32</td>\n",
       "      <td>0.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>Martin</td>\n",
       "      <td>29</td>\n",
       "      <td>0.310345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2389</th>\n",
       "      <td>Scott</td>\n",
       "      <td>29</td>\n",
       "      <td>0.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2751</th>\n",
       "      <td>Walker</td>\n",
       "      <td>28</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Brown</td>\n",
       "      <td>26</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Surname  RowNumber    Exited\n",
       "2473   Smith         32  0.281250\n",
       "1689  Martin         29  0.310345\n",
       "2389   Scott         29  0.103448\n",
       "2751  Walker         28  0.142857\n",
       "336    Brown         26  0.192308"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping by Surname to see the average churn value:\n",
    "dc.groupby(['Surname']).agg({'RowNumber':'count', 'Exited':'mean'}\n",
    "                                  ).reset_index().sort_values(by='RowNumber', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79ec9fed",
   "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>Geography</th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>5014</td>\n",
       "      <td>0.161548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2509</td>\n",
       "      <td>0.324432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2477</td>\n",
       "      <td>0.166734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geography  RowNumber    Exited\n",
       "0    France       5014  0.161548\n",
       "1   Germany       2509  0.324432\n",
       "2     Spain       2477  0.166734"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping with geography\n",
    "dc.groupby(['Geography']).agg({'RowNumber':'count', 'Exited':'mean'}\n",
    "                                  ).reset_index().sort_values(by='RowNumber', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c5a06dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='CreditScore'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "#Plotting CreditScore as a boxplot:\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.boxplot(y=dc['CreditScore'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d987c76a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Age'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "#Plotting age as a boxplot:\n",
    "sns.boxplot(y=dc['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "598fe3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Tenure'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "#Tenure violin plot\n",
    "sns.violinplot(y = dc.Tenure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c7f235e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Balance'>"
      ]
     },
     "execution_count": 37,
     "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": [
    "sns.violinplot(y = dc['Balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "94c1ed1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NumOfProducts'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEJCAYAAAB8Pye7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcpElEQVR4nO3df1CVZf7/8Seeg5j54Vt8OydYMCprtXRKi83sU4e0CU4eWZRtdk02MLdGndLWLYqQZHSy1FgZXRanVtch19mWNMVYOrZrjbMbZoZbLi21jakTIHD8kQjGr8P9/aOvZ0TQgyYoXq/HDAP3dV33ua+3V724vT33fUIsy7IQERGjDLjYExARkb6n8BcRMZDCX0TEQAp/EREDKfxFRAxkv9gTCKa5uZmKigocDgc2m+1iT0dEpF/w+/34fD5GjRrFoEGDuvRf8uFfUVFBamrqxZ6GiEi/tH79euLi4rq0X/Lh73A4gO8LiIyMvMizERHpH2pra0lNTQ1k6Oku+fA/eaknMjKSmJiYizwbEZH+5UyXy/UPviIiBlL4i4gYSOEvImIghb+IiIEU/iIiBlL4i4gYSOEvImKgHr3P/9FHH+XIkSPY7d8PX7RoEU1NTbzyyiu0tLTw0EMPMW/ePAAqKyuZP38+TU1NxMXFsXDhQux2OzU1NWRkZHD48GFuuOEGcnNzufLKK3uvsv/v+IlWvmtuDzruikF2/mfwwF6fj4jIpSBo+FuWxf79+/nggw8C4d/c3Izb7WbdunVERUUxc+ZMtm/fTnx8PBkZGbz00kuMHj2arKwsioqKmDZtGgsXLmTatGl4PB5+//vfU1BQQEZGRq8X+F1zO7u/rA867o7hToW/iBgj6GWfr7/+GoAZM2bw05/+lD/96U/s2bOH2NhYhg4dit1uJykpCa/XS3V1Nc3NzYwePRqAlJQUvF4vbW1t7Nq1i8TExE7tIiJycQQ9829oaGDcuHG8+OKLtLW1kZaWxuOPP97peRFOp5O6ujrq6+s7tTscDurq6jh69ChDhgwJ/M3hZHt3x2poaOjUVltbe97FiYhI94KG/5gxYxgzZkxg++GHH2blypXceeedgTbLsggJCaGjo4OQkJAu7Se/n+r0bYDCwkLy8/PPqxAREem5oOH/ySef0NbWxrhx44DvAz06OhqfzxcY4/P5cDqdREZGdmo/dOgQTqeTiIgIjh8/jt/vx2azBcafLj09nSlTpnRqO/lkOhERuXCCXvM/fvw4y5Yto6WlhcbGRjZt2sRvfvMb9u3bx4EDB/D7/ZSUlOByuYiOjiYsLIzy8nIAiouLcblchIaGEhcXR2lpKQCbN2/G5XJ1OVZ4eDgxMTGdvvQYZxGRCy/omf/48eP57LPPmDx5Mh0dHUybNo0xY8awZMkS5syZQ0tLC/Hx8bjdbgByc3PJzs6msbGRkSNHkpaWBkBOTg6ZmZmsWrWKqKgoli9f3ruViYjIGYVYlmVd7EmcTVVVFQ888ADbtm07r+f51x850eO3ejojBp/PFEVELjnBslN3+IqIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBevQZviIXWk8+W1mfqyzSexT+clH05LOV9bnKIr1Hl31ERAyk8BcRMZDCX0TEQAp/EREDKfxFRAyk8BcRMZDCX0TEQAp/EREDKfxFRAyk8BcRMZDCX0TEQAp/EREDKfxFRAyk8BcRMZDCX0TEQAp/EREDKfxFRAyk8BcRMZDCX0TEQD0O/6VLl5KZmQlAWVkZSUlJJCQkkJeXFxhTWVlJSkoKiYmJzJ8/n/b27z+gu6amhtTUVNxuN7Nnz6apqekClyEiIueiR+G/Y8cONm3aBEBzczNZWVkUFBRQWlpKRUUF27dvByAjI4MFCxawdetWLMuiqKgIgIULFzJt2jS8Xi+jRo2ioKCgl8oREZGeCBr+3377LXl5ecyaNQuAPXv2EBsby9ChQ7Hb7SQlJeH1eqmurqa5uZnRo0cDkJKSgtfrpa2tjV27dpGYmNipvTsNDQ1UVVV1+qqtrb1ApYqIyEn2YAMWLFjAvHnzOHjwIAD19fU4HI5Av9PppK6urku7w+Ggrq6Oo0ePMmTIEOx2e6f27hQWFpKfn/+DChIRkeDOGv5vvfUWUVFRjBs3jrfffhuAjo4OQkJCAmMsyyIkJOSM7Se/n+r07ZPS09OZMmVKp7ba2lpSU1PPrSoRETmrs4Z/aWkpPp+P5ORkjh07xokTJ6iursZmswXG+Hw+nE4nkZGR+Hy+QPuhQ4dwOp1ERERw/Phx/H4/NpstML474eHhhIeHX6DSRETkTM56zX/t2rWUlJRQXFzM3LlzmTBhAqtXr2bfvn0cOHAAv99PSUkJLpeL6OhowsLCKC8vB6C4uBiXy0VoaChxcXGUlpYCsHnzZlwuV+9XJiIiZxT0mv/pwsLCWLJkCXPmzKGlpYX4+HjcbjcAubm5ZGdn09jYyMiRI0lLSwMgJyeHzMxMVq1aRVRUFMuXL7+wVYiIyDnpcfinpKSQkpICwLhx49iyZUuXMSNGjGDDhg1d2qOjo1m3bt0PmKaIiFxIusNXRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDNSj8F+xYgUTJ07E4/Gwdu1aAMrKykhKSiIhIYG8vLzA2MrKSlJSUkhMTGT+/Pm0t7cDUFNTQ2pqKm63m9mzZ9PU1NQL5YiISE8EDf+PP/6Yjz76iC1btrBx40bWrVvHF198QVZWFgUFBZSWllJRUcH27dsByMjIYMGCBWzduhXLsigqKgJg4cKFTJs2Da/Xy6hRoygoKOjdykRE5IyChv9dd93FG2+8gd1u5/Dhw/j9fhoaGoiNjWXo0KHY7XaSkpLwer1UV1fT3NzM6NGjAUhJScHr9dLW1sauXbtITEzs1C4iIheHvSeDQkNDWblyJX/84x9xu93U19fjcDgC/U6nk7q6ui7tDoeDuro6jh49ypAhQ7Db7Z3aT9fQ0EBDQ0Onttra2vMqTEREzqxH4Q8wd+5cnnjiCWbNmsX+/fsJCQkJ9FmWRUhICB0dHd22n/x+qtO3AQoLC8nPzz+fOkRE5BwEDf+9e/fS2trKLbfcwhVXXEFCQgJerxebzRYY4/P5cDqdREZG4vP5Au2HDh3C6XQSERHB8ePH8fv92Gy2wPjTpaenM2XKlE5ttbW1pKam/pAaRUTkNEGv+VdVVZGdnU1rayutra1s27aNqVOnsm/fPg4cOIDf76ekpASXy0V0dDRhYWGUl5cDUFxcjMvlIjQ0lLi4OEpLSwHYvHkzLpery7HCw8OJiYnp9BUZGXmBSxYRkaBn/vHx8ezZs4fJkydjs9lISEjA4/EQERHBnDlzaGlpIT4+HrfbDUBubi7Z2dk0NjYycuRI0tLSAMjJySEzM5NVq1YRFRXF8uXLe7cyERE5oxDLsqyLPYmzqaqq4oEHHmDbtm3ExMSc8/71R06w+8v6oOPuGO7EGTH4fKYo56En66I1ETl/wbJTd/iKiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIG6lH45+fn4/F48Hg8LFu2DICysjKSkpJISEggLy8vMLayspKUlBQSExOZP38+7e3tANTU1JCamorb7Wb27Nk0NTX1QjkiItITQcO/rKyMf/7zn2zatInNmzfz+eefU1JSQlZWFgUFBZSWllJRUcH27dsByMjIYMGCBWzduhXLsigqKgJg4cKFTJs2Da/Xy6hRoygoKOjdykRE5IyChr/D4SAzM5OBAwcSGhrKsGHD2L9/P7GxsQwdOhS73U5SUhJer5fq6mqam5sZPXo0ACkpKXi9Xtra2ti1axeJiYmd2kVE5OKwBxtw8803B37ev38/7777Lr/85S9xOByBdqfTSV1dHfX19Z3aHQ4HdXV1HD16lCFDhmC32zu1n66hoYGGhoZObbW1tedelYiInFXQ8D/pq6++YubMmTz33HPYbDb2798f6LMsi5CQEDo6OggJCenSfvL7qU7fBigsLCQ/P/88yhARkXPRo/AvLy9n7ty5ZGVl4fF4+Pjjj/H5fIF+n8+H0+kkMjKyU/uhQ4dwOp1ERERw/Phx/H4/NpstMP506enpTJkypVNbbW0tqamp51ufiIh0I+g1/4MHD/Lkk0+Sm5uLx+MB4Pbbb2ffvn0cOHAAv99PSUkJLpeL6OhowsLCKC8vB6C4uBiXy0VoaChxcXGUlpYCsHnzZlwuV5djhYeHExMT0+krMjLyQtYrIiL04Mx/zZo1tLS0sGTJkkDb1KlTWbJkCXPmzKGlpYX4+HjcbjcAubm5ZGdn09jYyMiRI0lLSwMgJyeHzMxMVq1aRVRUFMuXL++lkkREJJig4Z+dnU12dna3fVu2bOnSNmLECDZs2NClPTo6mnXr1p3HFEVE5ELTHb4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBFP4iIgZS+IuIGEjhLyJiIIW/iIiBehT+jY2NTJo0iaqqKgDKyspISkoiISGBvLy8wLjKykpSUlJITExk/vz5tLe3A1BTU0Nqaiput5vZs2fT1NTUC6WIiEhPBQ3/zz77jEceeYT9+/cD0NzcTFZWFgUFBZSWllJRUcH27dsByMjIYMGCBWzduhXLsigqKgJg4cKFTJs2Da/Xy6hRoygoKOi9ikREJKig4V9UVEROTg5OpxOAPXv2EBsby9ChQ7Hb7SQlJeH1eqmurqa5uZnRo0cDkJKSgtfrpa2tjV27dpGYmNipXURELh57sAGLFy/utF1fX4/D4QhsO51O6urqurQ7HA7q6uo4evQoQ4YMwW63d2rvTkNDAw0NDZ3aamtre16NiIj0SNDwP11HRwchISGBbcuyCAkJOWP7ye+nOn37pMLCQvLz8891SiIico7OOfwjIyPx+XyBbZ/Ph9Pp7NJ+6NAhnE4nERERHD9+HL/fj81mC4zvTnp6OlOmTOnUVltbS2pq6rlOU0REzuKc3+p5++23s2/fPg4cOIDf76ekpASXy0V0dDRhYWGUl5cDUFxcjMvlIjQ0lLi4OEpLSwHYvHkzLper29cODw8nJiam01dkZOQPKE9ERLpzzmf+YWFhLFmyhDlz5tDS0kJ8fDxutxuA3NxcsrOzaWxsZOTIkaSlpQGQk5NDZmYmq1atIioqiuXLl1/YKkRE5Jz0OPzff//9wM/jxo1jy5YtXcaMGDGCDRs2dGmPjo5m3bp15zlFERG50HSHr4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiCFv4iIgRT+IiIGUviLiBhI4S8iYiD7xZ6AiFwajp9o5bvm9qDjrhhk538GD+yDGUlvUviLCADfNbez+8v6oOPuGO5U+F8GdNlHRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQP1afi/8847TJw4kYSEBNavX9+XhxYRkVP02fv86+rqyMvL4+2332bgwIFMnTqVsWPHctNNN/XVFERELik9ubGut26q67PwLysr4+677+aqq64CIDExEa/Xy1NPPRUY09DQQENDQ6f9qqurAaitrT2v4x4+1sxh36Gg4w6Gt9N6YtB5HUPOXU/WRWvSt/T/St87fKyZir1n/zMfNewa/u//Ofc/75OZ6ff7u+3vs/Cvr6/H4XAEtp1OJ3v27Ok0prCwkPz8/G73T01N7dX5iYhcjnw+H7GxsV3a+yz8Ozo6CAkJCWxbltVpGyA9PZ0pU6Z0amttbeWbb77h+uuvx2azndMxa2trSU1NZf369URGRp7/5C8BquXSdLnUcrnUAarlJL/fj8/nY9SoUd3291n4R0ZG8sknnwS2fT4fTqez05jw8HDCw8O77HvjjTf+4GPHxMT8oNe4VKiWS9PlUsvlUgeoFqDbM/6T+uzdPvfccw87duzgyJEjfPfdd7z33nu4XK6+OryIiJyiz878r732WubNm0daWhptbW08/PDD3HbbbX11eBEROUWfPtI5KSmJpKSkvjykiIh047K+wzc8PJynnnqq239H6G9Uy6XpcqnlcqkDVEtPhViWZV3wVxURkUvaZX3mLyIi3VP4i4gY6LIK/8bGRiZNmkRVVVWXvsrKSlJSUkhMTGT+/Pm0twf/oOqL6Wy15OfnM378eJKTk0lOTr5kH5KXn5+Px+PB4/GwbNmyLv39aU2C1dJf1gRgxYoVTJw4EY/Hw9q1a7v096d1CVZLf1oXgKVLl5KZmdmlvVfWxLpMfPrpp9akSZOskSNHWt98802Xfo/HY/3rX/+yLMuyXnjhBWv9+vV9PMOeC1bLzJkzrd27d1+EmfXchx9+aP3iF7+wWlparNbWVistLc167733Oo3pL2vSk1r6w5pYlmXt3LnTmjp1qtXW1mZ999131vjx4629e/d2GtNf1qUntfSXdbEsyyorK7PGjh1rPf/88136emNNLpsz/6KiInJycrrcNQzfPxyuubmZ0aNHA5CSkoLX6+3jGfbc2WoBqKio4LXXXiMpKYlFixbR0tLSxzMMzuFwkJmZycCBAwkNDWXYsGHU1NQE+vvTmgSrBfrHmgDcddddvPHGG9jtdg4fPozf72fw4MGB/v60LsFqgf6zLt9++y15eXnMmjWrS19vrcllE/6LFy8mLi6u277THyrncDioq6vrq6mds7PV0tTUxC233EJGRgabNm2ioaGBgoKCPp5hcDfffHPgP9b9+/fz7rvvEh8fH+jvT2sSrJb+siYnhYaGsnLlSjweD+PGjePaa68N9PWndYGz19Kf1mXBggXMmzev27d09taaXDbhfzY9eahcf3HllVfyhz/8gWHDhmG325kxYwbbt2+/2NM6o6+++ooZM2bw3HPPcf311wfa++OanKmW/rYmAHPnzmXHjh0cPHiQoqKiQHt/XJcz1dJf1uWtt94iKiqKcePGddvfW2tiRPhHRkbi8/kC24cOHTrjJZVLXU1NDRs2bAhsW5aF3d6nN2r3WHl5OdOnT+eZZ57p8rTW/rYmZ6ulP63J3r17qaysBOCKK64gISGBL7/8MtDfn9YlWC39ZV1KS0v58MMPSU5OZuXKlbz//vu8/PLLgf7eWhMjwj86OpqwsDDKy8sBKC4u7rcPlRs0aBCvvvoq33zzDZZlsX79eh588MGLPa0uDh48yJNPPklubi4ej6dLf39ak2C19Jc1AaiqqiI7O5vW1lZaW1vZtm0bd955Z6C/P61LsFr6y7qsXbuWkpISiouLmTt3LhMmTCArKyvQ31trclmH/xNPPMG///1vAHJzc3nllVdwu92cOHGCtLS0izy7c3OyloiICBYtWsTs2bNxu91YlsVjjz12safXxZo1a2hpaWHJkiWBt9n9+c9/7pdrEqyW/rImAPHx8dx///1MnjyZn/3sZ4wZMwaPx9Mv1yVYLf1pXbrT22uixzuIiBjosj7zFxGR7in8RUQMpPAXETGQwl9ExEAKfxERAyn85ZJVVVXF8OHDeeuttzq1r1mzptsnH56v//znP8ycOZMHH3yQ5ORkHnnkEf7+978H+hsbG5k6dSoej4f33nuP4cOHk5SURHJyMpMnT2bSpEm89tprP3geM2fO5O233z7v/WfMmMGRI0d+8DzEDJfe7W4ipxgwYABLly7lzjvv5MYbb7zgr79nzx6eeuopFi1axP333w98f+fo008/zaFDh5g6dSqVlZUcPnyYv/3tb4H9CgsLiYiIAL7/5ZCcnMyPf/xjxo8ff8Hn2FMffvjhRTu29D8Kf7mkDRo0iMcee4xnn32WN998k4EDBwb6MjMzufnmm/nVr37VZXvChAlMmjSJjz76iGPHjvH444+ze/duPv/8c+x2O6tWreLaa69lxYoVzJo1KxD8AMOGDWPZsmVMnz6du+66i6ysLOrq6khOTuYvf/lLlzkOGTKEUaNG8fXXXzN48GAWL17M4MGDaWpqYuPGjWzatIl169YxYMAArrnmGl588UVuuOEG6urqyMzMpL6+nh/96EccPnw48JrDhw9nx44dgV8wp25v2LCBtWvXMmDAAK6++mqWLl3KypUrAUhPT+f111/ngw8+4M033yQ0NJSwsDAWLVrETTfd1BtLJP2ULvvIJW/27NkMHjyYvLy8c9qvpaWFoqIinn76aRYsWEB6ejpbtmwhKiqKTZs2AbB7925+8pOfdNn31ltvJSQkhObmZl566SWuu+46iouLGTRoUJexX3/9Nbt27Qq8zldffcVvf/tb3nnnHcrLy1m9ejVvvPEGW7ZsYdKkSTz55JNYlsWiRYu4/fbb+etf/0p2djb79u0LWtMXX3xBbm4uq1ev5p133mHChAmsWrWKV155Bfj+byROp5OXX36Z1atXs3HjRn7+858HHg0gcpLO/OWSN2DAAF599VUmT57Mvffe2+P9EhISABg6dCjXXHMNI0aMAOC6667j2LFjgXFn+lSk1tbWMz49MT09nQEDBtDR0cEVV1zBc889x2233cbOnTuJiooiOjoagH/84x9MnDgxcAafkpLC4sWLqaqqoqysjOeffx6A2NhYxo4dG7SmHTt2cO+99xIVFQXA9OnTu4yx2Wy43W6mTp3K/fffz7333tvpEdQioPCXfiIqKoqFCxfy/PPPM3nyZABCQkI49ekkbW1tnfY59RJRaGhot697xx13sHPnTm655ZZO7Xv27CE0NJQbb7yRTz/9tMt+p17zP92pHyjS0dHRpd+yLNrb27vM/0xPnGxtbQ38bLPZOv1Cam5uprq6mmHDhnXaJzc3l//+97+UlZXx+uuvU1xczIoVK7p9fTGTLvtIv+F2u3G5XBQWFgJw9dVXU1FRAUBdXR0ff/zxOb/mM888w+rVqzs9533v3r288MILPP3004SFhf2gOd93332UlpYG3oWzceNGrrrqKmJjY7nvvvsC/4ZQU1PDzp07A/tFREQEHupVUlISaB87diw7duygvr4egDfffJNXX30V+P4XQ3t7O0eOHCE+Pp6rrrqK6dOn8+tf/zrwWiIn6cxf+pXs7OzA9etHH32UZ599lsTERGJiYrj77rvP+fVuvfVW1qxZw4oVK3j55Zex2WyEh4czZ84c3G73D57v//7v/zJ9+nTS09Pp6OggIiKC1157jQEDBpCTk8MLL7zAQw89RGRkZOCy1Mk6Fy1aRHh4OPfcc0/gk5yGDx9ORkYGjz/+OPD9pzqdfPa72+3m0Ucf5Xe/+x2zZ89m+vTpDBo0CJvNxksvvfSDa5HLi57qKSJiIF32ERExkMJfRMRACn8REQMp/EVEDKTwFxExkMJfRMRACn8REQMp/EVEDPT/AC7CJfuHftejAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a distribution plot of NumOfProducts:\n",
    "sns.set(style = 'ticks')\n",
    "sns.distplot(dc.NumOfProducts, hist=True, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1f73554e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EstimatedSalary', ylabel='Density'>"
      ]
     },
     "execution_count": 39,
     "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": [
    "#Kernel density estimation plot for EstimatedSalary:\n",
    "# When dealing with numerical characteristics, one of the most useful statistics to examine is the data distribution.\n",
    "# we can use Kernel-Density-Estimation plot for that purpose. \n",
    "sns.kdeplot(dc.EstimatedSalary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1a27c565",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating out different columns into various categories as defined above\n",
    "target_var = ['Exited']\n",
    "cols_to_remove = ['RowNumber', 'CustomerId']\n",
    "# numerical columns\n",
    "num_feats = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n",
    "# categorical columns\n",
    "cat_feats = ['Surname', 'Geography', 'Gender', 'HasCrCard', 'IsActiveMember']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0b1dd698",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dc[target_var].values\n",
    "dc.drop(cols_to_remove, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "87fda77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7920, 12), (1080, 12), (1000, 12), (7920,), (1080,), (1000,))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.20303030303030303, 0.22037037037037038, 0.191)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping aside a test/holdout set\n",
    "dc_train_val, dc_test, y_train_val, y_test = train_test_split(dc, y.ravel(), test_size = 0.1, random_state = 42)\n",
    "\n",
    "# Splitting into train and validation set\n",
    "dc_train, dc_val, y_train, y_val = train_test_split(dc_train_val, y_train_val, test_size = 0.12, random_state = 42)\n",
    "dc_train.shape, dc_val.shape, dc_test.shape, y_train.shape, y_val.shape, y_test.shape\n",
    "np.mean(y_train), np.mean(y_val), np.mean(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0eb46fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Female': 0, 'Male': 1}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label encoding With  the sklearn method\n",
    "le = LabelEncoder()\n",
    "# Label encoding of Gender variable\n",
    "dc_train['Gender'] = le.fit_transform(dc_train['Gender'])\n",
    "le_gender_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
    "le_gender_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eae5b9b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding Gender feature for validation and test set\n",
    "dc_val['Gender'] = dc_val.Gender.map(le_gender_mapping)\n",
    "dc_test['Gender'] = dc_test.Gender.map(le_gender_mapping)\n",
    "\n",
    "# Filling missing/NaN values created due to new categorical levels\n",
    "dc_val['Gender'].fillna(-1, inplace=True)\n",
    "dc_test['Gender'].fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "70ded086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 0]), array([1, 0]), array([1, 0]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_train.Gender.unique(), dc_val.Gender.unique(), dc_test.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a36bb4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       ...,\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With the sklearn method(LabelEncoder())\n",
    "le_ohe = LabelEncoder()\n",
    "ohe = OneHotEncoder(handle_unknown = 'ignore', sparse=False)\n",
    "enc_train = le_ohe.fit_transform(dc_train.Geography).reshape(dc_train.shape[0],1)\n",
    "ohe_train = ohe.fit_transform(enc_train)\n",
    "ohe_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bf944d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'France': 0, 'Germany': 1, 'Spain': 2}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mapping between classes\n",
    "le_ohe_geography_mapping = dict(zip(le_ohe.classes_, le_ohe.transform(le_ohe.classes_)))\n",
    "le_ohe_geography_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ce52a4e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding Geography feature for validation and test set\n",
    "enc_val = dc_val.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n",
    "enc_test = dc_test.Geography.map(le_ohe_geography_mapping).ravel().reshape(-1,1)\n",
    "\n",
    "# Filling missing/NaN values created due to new categorical levels\n",
    "enc_val[np.isnan(enc_val)] = 9999\n",
    "enc_test[np.isnan(enc_test)] = 9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "331a10e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_val = ohe.transform(enc_val)\n",
    "ohe_test = ohe.transform(enc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8f07ab8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show what happens when a new value is inputted into the OHE \n",
    "ohe.transform(np.array([[9999]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "471a6cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['country_France', 'country_Germany', 'country_Spain']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['country_' + str(x) for x in le_ohe_geography_mapping.keys()]\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bdba617e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
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       "      <th>country_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yermakova</td>\n",
       "      <td>678</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>117864.85</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Warlow-Davies</td>\n",
       "      <td>613</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>125167.74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>199104.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fu</td>\n",
       "      <td>628</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96862.56</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shih</td>\n",
       "      <td>513</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162523.66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahmood</td>\n",
       "      <td>639</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28188.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0      Yermakova          678   Germany       1   36       1  117864.85   \n",
       "1  Warlow-Davies          613    France       0   27       5  125167.74   \n",
       "2             Fu          628    France       1   45       9       0.00   \n",
       "3           Shih          513    France       1   30       5       0.00   \n",
       "4        Mahmood          639    France       1   22       4       0.00   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              2          1               0         27619.06       0   \n",
       "1              1          1               0        199104.52       0   \n",
       "2              2          1               1         96862.56       0   \n",
       "3              2          1               0        162523.66       0   \n",
       "4              2          1               0         28188.96       0   \n",
       "\n",
       "   country_France  country_Germany  country_Spain  \n",
       "0             0.0              1.0            0.0  \n",
       "1             1.0              0.0            0.0  \n",
       "2             1.0              0.0            0.0  \n",
       "3             1.0              0.0            0.0  \n",
       "4             1.0              0.0            0.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Validation set\n"
     ]
    },
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       "      <td>Russo</td>\n",
       "      <td>552</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12186.83</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Munro</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>70729.17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>160948.87</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Perkins</td>\n",
       "      <td>633</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
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       "      <td>0</td>\n",
       "      <td>65675.47</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aliyeva</td>\n",
       "      <td>698</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>95010.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>105227.86</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0      Sun          757    France       1   36       7  144852.06   \n",
       "1    Russo          552    France       1   29      10       0.00   \n",
       "2    Munro          619    France       0   30       7   70729.17   \n",
       "3  Perkins          633    France       1   35      10       0.00   \n",
       "4  Aliyeva          698     Spain       1   38      10   95010.92   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              1          0               0        130861.95       0   \n",
       "1              2          1               0         12186.83       0   \n",
       "2              1          1               1        160948.87       0   \n",
       "3              2          1               0         65675.47       0   \n",
       "4              1          1               1        105227.86       0   \n",
       "\n",
       "   country_France  country_Germany  country_Spain  \n",
       "0             1.0              0.0            0.0  \n",
       "1             1.0              0.0            0.0  \n",
       "2             1.0              0.0            0.0  \n",
       "3             1.0              0.0            0.0  \n",
       "4             0.0              0.0            1.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Test set\n"
     ]
    },
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       "      <th>Age</th>\n",
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       "      <th>0</th>\n",
       "      <td>Anderson</td>\n",
       "      <td>596</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Herring</td>\n",
       "      <td>623</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>146379.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amechi</td>\n",
       "      <td>601</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>58561.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Liang</td>\n",
       "      <td>506</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>119152.10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>170679.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chuang</td>\n",
       "      <td>560</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>124995.98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>114669.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Surname  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
       "0  Anderson          596   Germany       1   32       3   96709.07   \n",
       "1   Herring          623    France       1   43       1       0.00   \n",
       "2    Amechi          601     Spain       0   44       4       0.00   \n",
       "3     Liang          506   Germany       1   59       8  119152.10   \n",
       "4    Chuang          560     Spain       0   27       7  124995.98   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0              2          0               0         41788.37       0   \n",
       "1              2          1               1        146379.30       0   \n",
       "2              2          1               0         58561.31       0   \n",
       "3              2          1               1        170679.74       0   \n",
       "4              1          1               1        114669.79       0   \n",
       "\n",
       "   country_France  country_Germany  country_Spain  \n",
       "0             0.0              1.0            0.0  \n",
       "1             1.0              0.0            0.0  \n",
       "2             0.0              0.0            1.0  \n",
       "3             0.0              1.0            0.0  \n",
       "4             0.0              0.0            1.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding to the respective dataframes\n",
    "dc_train = pd.concat([dc_train.reset_index(), pd.DataFrame(ohe_train, columns = cols)], axis = 1).drop(['index'], axis=1)\n",
    "dc_val = pd.concat([dc_val.reset_index(), pd.DataFrame(ohe_val, columns = cols)], axis = 1).drop(['index'], axis=1)\n",
    "dc_test = pd.concat([dc_test.reset_index(), pd.DataFrame(ohe_test, columns = cols)], axis = 1).drop(['index'], axis=1)\n",
    "print(\"Training set\")\n",
    "dc_train.head()\n",
    "print(\"\\n\\nValidation set\")\n",
    "dc_val.head()\n",
    "print(\"\\n\\nTest set\")\n",
    "dc_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fa1f7a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_train.drop(['Geography'], axis=1, inplace=True)\n",
    "dc_val.drop(['Geography'], axis=1, inplace=True)\n",
    "dc_test.drop(['Geography'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ea482584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname\n",
       "Abazu       0.00\n",
       "Abbie       0.00\n",
       "Abbott      0.25\n",
       "Abdullah    1.00\n",
       "Abdulov     0.00\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Surname\n",
       "Zubarev     0.0\n",
       "Zubareva    0.0\n",
       "Zuev        0.0\n",
       "Zuyev       0.0\n",
       "Zuyeva      0.0\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#target encoding\n",
    "means = dc_train.groupby(['Surname']).Exited.mean()\n",
    "means.head()\n",
    "means.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e2b1999b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20303030303030303"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#global mean of Exited column:\n",
    "global_mean = y_train.mean()\n",
    "global_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8ba9c0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new encoded features for surname - Target (mean) encoding\n",
    "dc_train['Surname_mean_churn'] = dc_train.Surname.map(means)\n",
    "dc_train['Surname_mean_churn'].fillna(global_mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a4101cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Surname\n",
       "Abazu       2\n",
       "Abbie       1\n",
       "Abbott      4\n",
       "Abdullah    1\n",
       "Abdulov     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating the frequency of each Surname:\n",
    "freqs = dc_train.groupby(['Surname']).size()\n",
    "freqs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9e0fc3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create frequency encoding - Number of instances of each category in the data\n",
    "dc_train['Surname_freq'] = dc_train.Surname.map(freqs)\n",
    "dc_train['Surname_freq'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "66affe61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>country_France</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_Spain</th>\n",
       "      <th>Surname_mean_churn</th>\n",
       "      <th>Surname_freq</th>\n",
       "      <th>Surname_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yermakova</td>\n",
       "      <td>678</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>117864.85</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27619.06</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Warlow-Davies</td>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>125167.74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>199104.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fu</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96862.56</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shih</td>\n",
       "      <td>513</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162523.66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>21</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mahmood</td>\n",
       "      <td>639</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28188.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Surname  CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0      Yermakova          678       1   36       1  117864.85              2   \n",
       "1  Warlow-Davies          613       0   27       5  125167.74              1   \n",
       "2             Fu          628       1   45       9       0.00              2   \n",
       "3           Shih          513       1   30       5       0.00              2   \n",
       "4        Mahmood          639       1   22       4       0.00              2   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  country_France  \\\n",
       "0          1               0         27619.06       0             0.0   \n",
       "1          1               0        199104.52       0             1.0   \n",
       "2          1               1         96862.56       0             1.0   \n",
       "3          1               0        162523.66       0             1.0   \n",
       "4          1               0         28188.96       0             1.0   \n",
       "\n",
       "   country_Germany  country_Spain  Surname_mean_churn  Surname_freq  \\\n",
       "0              1.0            0.0            0.000000             4   \n",
       "1              0.0            0.0            0.000000             2   \n",
       "2              0.0            0.0            0.200000            10   \n",
       "3              0.0            0.0            0.285714            21   \n",
       "4              0.0            0.0            0.333333             3   \n",
       "\n",
       "   Surname_enc  \n",
       "0     0.000000  \n",
       "1     0.000000  \n",
       "2     0.222222  \n",
       "3     0.300000  \n",
       "4     0.500000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating Leave-one-out target encoding for Surname:\n",
    "dc_train['Surname_enc'] = ((dc_train.Surname_freq * dc_train.Surname_mean_churn) - dc_train.Exited)/(dc_train.Surname_freq - 1)\n",
    "# Fill NaNs occuring due to category frequency being 1 or less\n",
    "dc_train['Surname_enc'].fillna((((dc_train.shape[0] * global_mean) - dc_train.Exited) / (dc_train.shape[0] - 1)), inplace=True)\n",
    "dc_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a38bfd22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Surname_mean_churn</th>\n",
       "      <th>Surname_enc</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Surname_mean_churn</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.54823</td>\n",
       "      <td>0.562677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surname_enc</th>\n",
       "      <td>0.548230</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>-0.026440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>0.562677</td>\n",
       "      <td>-0.02644</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Surname_mean_churn  Surname_enc    Exited\n",
       "Surname_mean_churn            1.000000      0.54823  0.562677\n",
       "Surname_enc                   0.548230      1.00000 -0.026440\n",
       "Exited                        0.562677     -0.02644  1.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing by category means and new category levels by global mean\n",
    "dc_val['Surname_enc'] = dc_val.Surname.map(means)\n",
    "dc_val['Surname_enc'].fillna(global_mean, inplace=True)\n",
    "dc_test['Surname_enc'] = dc_test.Surname.map(means)\n",
    "dc_test['Surname_enc'].fillna(global_mean, inplace=True)\n",
    "# Show that using LOO Target encoding decorrelates features\n",
    "dc_train[['Surname_mean_churn', 'Surname_enc', 'Exited']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f7f4cd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>country_France</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_Spain</th>\n",
       "      <th>Surname_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>678</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>117864.85</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27619.06</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>125167.74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>199104.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96862.56</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>513</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162523.66</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>639</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28188.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          678       1   36       1  117864.85              2          1   \n",
       "1          613       0   27       5  125167.74              1          1   \n",
       "2          628       1   45       9       0.00              2          1   \n",
       "3          513       1   30       5       0.00              2          1   \n",
       "4          639       1   22       4       0.00              2          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  country_France  country_Germany  \\\n",
       "0               0         27619.06       0             0.0              1.0   \n",
       "1               0        199104.52       0             1.0              0.0   \n",
       "2               1         96862.56       0             1.0              0.0   \n",
       "3               0        162523.66       0             1.0              0.0   \n",
       "4               0         28188.96       0             1.0              0.0   \n",
       "\n",
       "   country_Spain  Surname_enc  \n",
       "0            0.0     0.000000  \n",
       "1            0.0     0.000000  \n",
       "2            0.0     0.222222  \n",
       "3            0.0     0.300000  \n",
       "4            0.0     0.500000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_train.drop(['Surname_mean_churn'], axis=1, inplace=True)\n",
    "dc_train.drop(['Surname_freq'], axis=1, inplace=True)\n",
    "dc_train.drop(['Surname'], axis=1, inplace=True)\n",
    "dc_val.drop(['Surname'], axis=1, inplace=True)\n",
    "dc_test.drop(['Surname'], axis=1, inplace=True)\n",
    "dc_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3dd8d5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#bivariate analysis\n",
    "corr = dc_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5d274a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, cmap = 'coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f1f4ebde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Exited', ylabel='Age'>"
      ]
     },
     "execution_count": 65,
     "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": [
    "#boxplot for Age column\n",
    "sns.boxplot(x=\"Exited\", y=\"Age\", data=dc_train, palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5276166c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Exited', ylabel='Balance'>"
      ]
     },
     "execution_count": 66,
     "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": [
    "#violin plot for the Balance:\n",
    "sns.violinplot(x=\"Exited\", y=\"Balance\", data=dc_train, palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "36815e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "0    0.248191\n",
       "1    0.165511\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "IsActiveMember\n",
       "0    0.266285\n",
       "1    0.143557\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country_Germany\n",
       "0.0    0.163091\n",
       "1.0    0.324974\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "country_France\n",
       "0.0    0.245877\n",
       "1.0    0.160593\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Checking association of categorical features with the target variable:\n",
    "cat_vars_bv = ['Gender', 'IsActiveMember', 'country_Germany', 'country_France']\n",
    "\n",
    "for col in cat_vars_bv:\n",
    "    dc_train.groupby([col]).Exited.mean()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "44ffc909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NumOfProducts\n",
       "1    0.273428\n",
       "2    0.076881\n",
       "3    0.825112\n",
       "4    1.000000\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1    4023\n",
       "2    3629\n",
       "3     223\n",
       "4      45\n",
       "Name: NumOfProducts, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computed mean on churned or non chuned custmers group by number of product on training data\n",
    "col = 'NumOfProducts'\n",
    "dc_train.groupby([col]).Exited.mean()\n",
    "# unique \"NumOfProducts\" on training data\n",
    "dc_train[col].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f29c243b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating some new features based on simple interactions between the existing features:\n",
    "eps = 1e-6\n",
    "\n",
    "dc_train['bal_per_product'] = dc_train.Balance/(dc_train.NumOfProducts + eps)\n",
    "dc_train['bal_by_est_salary'] = dc_train.Balance/(dc_train.EstimatedSalary + eps)\n",
    "dc_train['tenure_age_ratio'] = dc_train.Tenure/(dc_train.Age + eps)\n",
    "dc_train['age_surname_mean_churn'] = np.sqrt(dc_train.Age) * dc_train.Surname_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e331e8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bal_per_product           0\n",
       "bal_by_est_salary         0\n",
       "tenure_age_ratio          0\n",
       "age_surname_mean_churn    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_cols = ['bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn']\n",
    "# Ensuring that the new column doesn't have any missing values\n",
    "dc_train[new_cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ca24a3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Linear association of new columns with target variables to judge importance\n",
    "sns.heatmap(dc_train[new_cols + ['Exited']].corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "444611ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating them on the validation and testing sets\n",
    "dc_val['bal_per_product'] = dc_val.Balance/(dc_val.NumOfProducts + eps)\n",
    "dc_val['bal_by_est_salary'] = dc_val.Balance/(dc_val.EstimatedSalary + eps)\n",
    "dc_val['tenure_age_ratio'] = dc_val.Tenure/(dc_val.Age + eps)\n",
    "dc_val['age_surname_mean_churn'] = np.sqrt(dc_val.Age) * dc_val.Surname_enc\n",
    "dc_test['bal_per_product'] = dc_test.Balance/(dc_test.NumOfProducts + eps)\n",
    "dc_test['bal_by_est_salary'] = dc_test.Balance/(dc_test.EstimatedSalary + eps)\n",
    "dc_test['tenure_age_ratio'] = dc_test.Tenure/(dc_test.Age + eps)\n",
    "dc_test['age_surname_mean_churn'] = np.sqrt(dc_test.Age) * dc_test.Surname_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "51e8b25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7920, 11)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Surname_enc</th>\n",
       "      <th>bal_per_product</th>\n",
       "      <th>bal_by_est_salary</th>\n",
       "      <th>tenure_age_ratio</th>\n",
       "      <th>age_surname_mean_churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.284761</td>\n",
       "      <td>-0.274383</td>\n",
       "      <td>-1.389130</td>\n",
       "      <td>0.670778</td>\n",
       "      <td>0.804059</td>\n",
       "      <td>-1.254732</td>\n",
       "      <td>-1.079210</td>\n",
       "      <td>-0.062389</td>\n",
       "      <td>0.095448</td>\n",
       "      <td>-1.232035</td>\n",
       "      <td>-1.062507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.389351</td>\n",
       "      <td>-1.128482</td>\n",
       "      <td>-0.004763</td>\n",
       "      <td>0.787860</td>\n",
       "      <td>-0.912423</td>\n",
       "      <td>1.731950</td>\n",
       "      <td>-1.079210</td>\n",
       "      <td>1.104840</td>\n",
       "      <td>-0.118834</td>\n",
       "      <td>0.525547</td>\n",
       "      <td>-1.062507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.233786</td>\n",
       "      <td>0.579716</td>\n",
       "      <td>1.379604</td>\n",
       "      <td>-1.218873</td>\n",
       "      <td>0.804059</td>\n",
       "      <td>-0.048751</td>\n",
       "      <td>0.094549</td>\n",
       "      <td>-1.100925</td>\n",
       "      <td>-0.155854</td>\n",
       "      <td>0.690966</td>\n",
       "      <td>0.193191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.426446</td>\n",
       "      <td>-0.843782</td>\n",
       "      <td>-0.004763</td>\n",
       "      <td>-1.218873</td>\n",
       "      <td>0.804059</td>\n",
       "      <td>1.094838</td>\n",
       "      <td>0.505364</td>\n",
       "      <td>-1.100925</td>\n",
       "      <td>-0.155854</td>\n",
       "      <td>0.318773</td>\n",
       "      <td>0.321611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.119706</td>\n",
       "      <td>-1.602981</td>\n",
       "      <td>-0.350855</td>\n",
       "      <td>-1.218873</td>\n",
       "      <td>0.804059</td>\n",
       "      <td>-1.244806</td>\n",
       "      <td>1.561746</td>\n",
       "      <td>-1.100925</td>\n",
       "      <td>-0.155854</td>\n",
       "      <td>0.487952</td>\n",
       "      <td>0.912973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore       Age    Tenure   Balance  NumOfProducts  EstimatedSalary  \\\n",
       "0     0.284761 -0.274383 -1.389130  0.670778       0.804059        -1.254732   \n",
       "1    -0.389351 -1.128482 -0.004763  0.787860      -0.912423         1.731950   \n",
       "2    -0.233786  0.579716  1.379604 -1.218873       0.804059        -0.048751   \n",
       "3    -1.426446 -0.843782 -0.004763 -1.218873       0.804059         1.094838   \n",
       "4    -0.119706 -1.602981 -0.350855 -1.218873       0.804059        -1.244806   \n",
       "\n",
       "   Surname_enc  bal_per_product  bal_by_est_salary  tenure_age_ratio  \\\n",
       "0    -1.079210        -0.062389           0.095448         -1.232035   \n",
       "1    -1.079210         1.104840          -0.118834          0.525547   \n",
       "2     0.094549        -1.100925          -0.155854          0.690966   \n",
       "3     0.505364        -1.100925          -0.155854          0.318773   \n",
       "4     1.561746        -1.100925          -0.155854          0.487952   \n",
       "\n",
       "   age_surname_mean_churn  \n",
       "0               -1.062507  \n",
       "1               -1.062507  \n",
       "2                0.193191  \n",
       "3                0.321611  \n",
       "4                0.912973  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize the standard scaler\n",
    "sc = StandardScaler()\n",
    "cont_vars = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc', 'bal_per_product'\n",
    "             , 'bal_by_est_salary', 'tenure_age_ratio', 'age_surname_mean_churn']\n",
    "cat_vars = ['Gender', 'HasCrCard', 'IsActiveMember', 'country_France', 'country_Germany', 'country_Spain']\n",
    "# Scaling only continuous columns\n",
    "cols_to_scale = cont_vars\n",
    "sc_X_train = sc.fit_transform(dc_train[cols_to_scale])\n",
    "# Converting from array to dataframe and naming the respective features/columns\n",
    "sc_X_train = pd.DataFrame(data=sc_X_train, columns=cols_to_scale)\n",
    "sc_X_train.shape\n",
    "sc_X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "07b325ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling validation and test sets by transforming the mapping obtained through the training set\n",
    "sc_X_val = sc.transform(dc_val[cols_to_scale])\n",
    "sc_X_test = sc.transform(dc_test[cols_to_scale])\n",
    "# Converting val and test arrays to dataframes for re-usability\n",
    "sc_X_val = pd.DataFrame(data=sc_X_val, columns=cols_to_scale)\n",
    "sc_X_test = pd.DataFrame(data=sc_X_test, columns=cols_to_scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7952c658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'HasCrCard', 'IsActiveMember', 'country_France',\n",
       "       'country_Germany', 'country_Spain', 'CreditScore', 'Age', 'Tenure',\n",
       "       'Balance', 'NumOfProducts', 'EstimatedSalary', 'Surname_enc',\n",
       "       'bal_per_product', 'bal_by_est_salary', 'tenure_age_ratio',\n",
       "       'age_surname_mean_churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Selection using RFE\n",
    "# Creating feature-set and target for RFE model\n",
    "y = dc_train['Exited'].values\n",
    "X = dc_train[cat_vars + cont_vars]\n",
    "X.columns = cat_vars + cont_vars\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e0b5cb90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True False  True False False  True False\n",
      "  True False False  True False]\n",
      "[1 1 1 1 1 1 4 1 3 6 1 8 1 7 5 1 2]\n"
     ]
    }
   ],
   "source": [
    "# for logistics regression\n",
    "rfe = RFE(estimator=LogisticRegression(), n_features_to_select=10) \n",
    "rfe = rfe.fit(X.values, y) \n",
    "# mask of selected features\n",
    "print(rfe.support_)\n",
    "# The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature \n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b53320fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'HasCrCard',\n",
       " 'IsActiveMember',\n",
       " 'country_France',\n",
       " 'country_Germany',\n",
       " 'country_Spain',\n",
       " 'Age',\n",
       " 'NumOfProducts',\n",
       " 'Surname_enc',\n",
       " 'tenure_age_ratio']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic regression (linear)\n",
    "mask = rfe.support_.tolist()\n",
    "selected_feats = [b for a,b in zip(mask, X.columns) if a]\n",
    "selected_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "224e647b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#decision tree model:\n",
    "rfe_dt = RFE(estimator=DecisionTreeClassifier(max_depth = 4, criterion = 'entropy'), n_features_to_select=10) \n",
    "rfe_dt = rfe_dt.fit(X.values, y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0e5e1287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['IsActiveMember',\n",
       " 'country_Germany',\n",
       " 'Age',\n",
       " 'NumOfProducts',\n",
       " 'EstimatedSalary',\n",
       " 'Surname_enc',\n",
       " 'bal_per_product',\n",
       " 'bal_by_est_salary',\n",
       " 'tenure_age_ratio',\n",
       " 'age_surname_mean_churn']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = rfe_dt.support_.tolist()\n",
    "selected_feats_dt = [b for a,b in zip(mask, X.columns) if a]\n",
    "selected_feats_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "020f0c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7920, 10), (1080, 10), (1000, 10))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Baseline Model 1: Logistic Regression\n",
    "selected_cat_vars = [x for x in selected_feats if x in cat_vars]\n",
    "selected_cont_vars = [x for x in selected_feats if x in cont_vars]\n",
    "# Using categorical features and scaled numerical features\n",
    "X_train = np.concatenate((dc_train[selected_cat_vars].values, sc_X_train[selected_cont_vars].values), axis=1)\n",
    "X_val = np.concatenate((dc_val[selected_cat_vars].values, sc_X_val[selected_cont_vars].values), axis=1)\n",
    "X_test = np.concatenate((dc_test[selected_cat_vars].values, sc_X_test[selected_cont_vars].values), axis=1)\n",
    "# print the shapes\n",
    "X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b77b7302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.0, 1: 3.925373134328358}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtaining class weights based on the class samples imbalance ratio\n",
    "_, num_samples = np.unique(y_train, return_counts=True)\n",
    "weights = np.max(num_samples)/num_samples\n",
    "# Define weight dictionnary\n",
    "weights_dict = dict()\n",
    "class_labels = [0,1]\n",
    "# Weights associated with classes\n",
    "for a,b in zip(class_labels,weights):\n",
    "    weights_dict[a] = b\n",
    "\n",
    "weights_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f8c907e5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "[[541 301]\n",
      " [ 79 159]]\n",
      "Area Under Curve: 0.6552925208087986\n",
      "Recall score: 0.6680672268907563\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.64      0.74       842\n",
      "           1       0.35      0.67      0.46       238\n",
      "\n",
      "    accuracy                           0.65      1080\n",
      "   macro avg       0.61      0.66      0.60      1080\n",
      "weighted avg       0.76      0.65      0.68      1080\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Defining model\n",
    "lr = LogisticRegression(C=1.0, penalty='l2', class_weight=weights_dict, n_jobs=-1)\n",
    "# train\n",
    "lr.fit(X_train, y_train)\n",
    "print(f'Confusion Matrix: \\n{confusion_matrix(y_val, lr.predict(X_val))}')\n",
    "print(f'Area Under Curve: {roc_auc_score(y_val, lr.predict(X_val))}')\n",
    "print(f'Recall score: {recall_score(y_val,lr.predict(X_val))}')\n",
    "print(f'Classification report: \\n{classification_report(y_val,lr.predict(X_val))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5653ec71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7920, 2), (7920,))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "# Transforming the dataset using PCA\n",
    "X_pca = pca.fit_transform(X_train)\n",
    "y = y_train\n",
    "X_pca.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6d546d3f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight={0: 1.0, 1: 3.925373134328358}, n_jobs=-1)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "NameError",
     "evalue": "name 'svm' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [108]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m lr\u001b[38;5;241m.\u001b[39mfit(X_pca, y)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Fitting SVM model on 2 features\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m \u001b[43msvm\u001b[49m\u001b[38;5;241m.\u001b[39mfit(X_pca, y)\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Plotting decision boundary for LR\u001b[39;00m\n\u001b[0;32m     12\u001b[0m z1 \u001b[38;5;241m=\u001b[39m lr\u001b[38;5;241m.\u001b[39mpredict(np\u001b[38;5;241m.\u001b[39mc_[xx\u001b[38;5;241m.\u001b[39mravel(), yy\u001b[38;5;241m.\u001b[39mravel()])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'svm' is not defined"
     ]
    }
   ],
   "source": [
    "# min and max values \n",
    "xmin, xmax = X_pca[:, 0].min() - 2, X_pca[:, 0].max() + 2\n",
    "ymin, ymax = X_pca[:, 1].min() - 2, X_pca[:, 1].max() + 2\n",
    "# Creating a mesh region where the boundary will be plotted\n",
    "xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.2),\n",
    "                     np.arange(ymin, ymax, 0.2))\n",
    "# Fitting LR model on 2 features\n",
    "lr.fit(X_pca, y)\n",
    "# Fitting SVM model on 2 features\n",
    "svm.fit(X_pca, y)\n",
    "# Plotting decision boundary for LR\n",
    "z1 = lr.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "z1 = z1.reshape(xx.shape)\n",
    "# Plotting decision boundary for SVM\n",
    "z2 = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "z2 = z2.reshape(xx.shape)\n",
    "# Displaying the result\n",
    "plt.contourf(xx, yy, z1, alpha=0.4) # LR\n",
    "plt.contour(xx, yy, z2, alpha=0.4, colors='blue') # SVM\n",
    "sns.scatterplot(X_pca[:,0], X_pca[:,1], hue=y_train, s=50, alpha=0.8)\n",
    "plt.title('Linear models - LogReg and SVM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5605babc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['IsActiveMember',\n",
       " 'country_Germany',\n",
       " 'Age',\n",
       " 'NumOfProducts',\n",
       " 'EstimatedSalary',\n",
       " 'Surname_enc',\n",
       " 'bal_per_product',\n",
       " 'bal_by_est_salary',\n",
       " 'tenure_age_ratio',\n",
       " 'age_surname_mean_churn']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Features selected from the RFE process\n",
    "selected_feats_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ebd9cd7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.925373134328358},\n",
       "                       criterion='entropy', max_depth=4, min_samples_leaf=15,\n",
       "                       min_samples_split=25)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IsActiveMember</td>\n",
       "      <td>0.476841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>country_France</td>\n",
       "      <td>0.351863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gender</td>\n",
       "      <td>0.096402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.032268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HasCrCard</td>\n",
       "      <td>0.028361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumOfProducts</td>\n",
       "      <td>0.010400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>country_Germany</td>\n",
       "      <td>0.003865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>country_Spain</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Surname_enc</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tenure_age_ratio</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           features  importance\n",
       "2    IsActiveMember    0.476841\n",
       "3    country_France    0.351863\n",
       "0            Gender    0.096402\n",
       "6               Age    0.032268\n",
       "1         HasCrCard    0.028361\n",
       "7     NumOfProducts    0.010400\n",
       "4   country_Germany    0.003865\n",
       "5     country_Spain    0.000000\n",
       "8       Surname_enc    0.000000\n",
       "9  tenure_age_ratio    0.000000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-defining X_train and X_val to consider original unscaled continuous features. y_train and y_val remain unaffected\n",
    "X_train = dc_train[selected_feats_dt].values\n",
    "X_val = dc_val[selected_feats_dt].values\n",
    "# Decision tree classiier model\n",
    "clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, max_depth=4, max_features=None\n",
    "                            , min_samples_split=25, min_samples_leaf=15)\n",
    "# Fit the model\n",
    "clf.fit(X_train, y_train)\n",
    "# Checking the importance of different features of the model\n",
    "pd.DataFrame({'features': selected_feats,\n",
    "              'importance': clf.feature_importances_\n",
    "             }).sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b9601769",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: [[622 220]\n",
      " [ 78 160]]\n",
      "Area Under Curve: 0.7054931236152419\n",
      "Recall score: 0.6722689075630253\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.74      0.81       842\n",
      "           1       0.42      0.67      0.52       238\n",
      "\n",
      "    accuracy                           0.72      1080\n",
      "   macro avg       0.65      0.71      0.66      1080\n",
      "weighted avg       0.79      0.72      0.74      1080\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Validation metrics\n",
    "print(f'Confusion Matrix: {confusion_matrix(y_val, clf.predict(X_val))}')\n",
    "print(f'Area Under Curve: {roc_auc_score(y_val, clf.predict(X_val))}')\n",
    "print(f'Recall score: {recall_score(y_val,clf.predict(X_val))}')\n",
    "print(f'Classification report: \\n{classification_report(y_val,clf.predict(X_val))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3f5d95a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight={0: 1.0, 1: 3.925373134328358},\n",
       "                       criterion='entropy', max_depth=3, min_samples_leaf=15,\n",
       "                       min_samples_split=25)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "ValueError",
     "evalue": "Length of feature_names, 10 does not match number of features, 17",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [109]\u001b[0m, in \u001b[0;36m<cell line: 8>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m clf\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# Export now as a dot file\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m dot_data \u001b[38;5;241m=\u001b[39m \u001b[43mexport_graphviz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtree.dot\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mfeature_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mselected_feats_dt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mclass_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDid not churn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mChurned\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mrounded\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproportion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m                          \u001b[49m\u001b[43mprecision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Convert to png using system command (requires Graphviz)\u001b[39;00m\n\u001b[0;32m     14\u001b[0m subprocess\u001b[38;5;241m.\u001b[39mrun([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdot\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-Tpng\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtree.dot\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-o\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtree.png\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-Gdpi=600\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:889\u001b[0m, in \u001b[0;36mexport_graphviz\u001b[1;34m(decision_tree, out_file, max_depth, feature_names, class_names, label, filled, leaves_parallel, impurity, node_ids, proportion, rotate, rounded, special_characters, precision, fontname)\u001b[0m\n\u001b[0;32m    870\u001b[0m     out_file \u001b[38;5;241m=\u001b[39m StringIO()\n\u001b[0;32m    872\u001b[0m exporter \u001b[38;5;241m=\u001b[39m _DOTTreeExporter(\n\u001b[0;32m    873\u001b[0m     out_file\u001b[38;5;241m=\u001b[39mout_file,\n\u001b[0;32m    874\u001b[0m     max_depth\u001b[38;5;241m=\u001b[39mmax_depth,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    887\u001b[0m     fontname\u001b[38;5;241m=\u001b[39mfontname,\n\u001b[0;32m    888\u001b[0m )\n\u001b[1;32m--> 889\u001b[0m \u001b[43mexporter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecision_tree\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_string:\n\u001b[0;32m    892\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m exporter\u001b[38;5;241m.\u001b[39mout_file\u001b[38;5;241m.\u001b[39mgetvalue()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_export.py:452\u001b[0m, in \u001b[0;36m_DOTTreeExporter.export\u001b[1;34m(self, decision_tree)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    451\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names) \u001b[38;5;241m!=\u001b[39m decision_tree\u001b[38;5;241m.\u001b[39mn_features_in_:\n\u001b[1;32m--> 452\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    453\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of feature_names, \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m does not match number of features, \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    454\u001b[0m             \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeature_names), decision_tree\u001b[38;5;241m.\u001b[39mn_features_in_)\n\u001b[0;32m    455\u001b[0m         )\n\u001b[0;32m    456\u001b[0m \u001b[38;5;66;03m# each part writes to out_file\u001b[39;00m\n\u001b[0;32m    457\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhead()\n",
      "\u001b[1;31mValueError\u001b[0m: Length of feature_names, 10 does not match number of features, 17"
     ]
    }
   ],
   "source": [
    "# Decision tree Classifier\n",
    "clf = DecisionTreeClassifier(criterion='entropy', class_weight=weights_dict, \n",
    "                            max_depth=3, max_features=None,\n",
    "                            min_samples_split=25, min_samples_leaf=15)\n",
    "# We fit the model\n",
    "clf.fit(X_train, y_train)\n",
    "# Export now as a dot file\n",
    "dot_data = export_graphviz(clf, out_file='tree.dot',\n",
    "                          feature_names=selected_feats_dt,\n",
    "                          class_names=['Did not churn', 'Churned'],\n",
    "                          rounded=True, proportion=False,\n",
    "                          precision=2, filled=True)\n",
    "# Convert to png using system command (requires Graphviz)\n",
    "subprocess.run(['dot', '-Tpng','tree.dot', '-o', 'tree.png', '-Gdpi=600'])\n",
    "# Display the rule-set of a single tree\n",
    "from IPython.display import Image\n",
    "Image(filename='tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "308c1b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('categorical_encoding',\n",
       "                 CategoricalEncoder(cols=[], lcols=[], ohecols=[], tcols=[])),\n",
       "                ('add_new_features', AddFeatures()),\n",
       "                ('classifier',\n",
       "                 LGBMClassifier(boosting_type='dart',\n",
       "                                class_weight={0: 1, 1: 3.0},\n",
       "                                colsample_bytree=0.6, importance_type='gain',\n",
       "                                max_depth=6, n_estimators=201, num_leaves=63,\n",
       "                                reg_alpha=1, reg_lambda=1))])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: [[740 102]\n",
      " [ 86 152]]\n",
      "Area Under Curve: 0.7587576598335297\n",
      "Recall score: 0.6386554621848739\n",
      "Classification report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.88      0.89       842\n",
      "           1       0.60      0.64      0.62       238\n",
      "\n",
      "    accuracy                           0.83      1080\n",
      "   macro avg       0.75      0.76      0.75      1080\n",
      "weighted avg       0.83      0.83      0.83      1080\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "\n",
    "# Preparing data and a few common model parameters\n",
    "# Unscaled features will be used since it's a tree model\n",
    "\n",
    "X_train = dc_train.drop(columns = ['Exited'], axis=1)\n",
    "X_val = dc_val.drop(columns = ['Exited'], axis=1)\n",
    "\n",
    "best_f1_lgb = LGBMClassifier(boosting_type='dart', class_weight={0: 1, 1: 3.0}, min_child_samples=20, n_jobs=-1, importance_type='gain', max_depth=6, num_leaves=63, colsample_bytree=0.6, learning_rate=0.1, n_estimators=201, reg_alpha=1, reg_lambda=1)\n",
    "best_recall_lgb = LGBMClassifier(boosting_type='dart', num_leaves=31, max_depth=6, learning_rate=0.1, n_estimators=21, class_weight={0: 1, 1: 3.93}, min_child_samples=2, colsample_bytree=0.6, reg_alpha=0.3, reg_lambda=1.0, n_jobs=-1, importance_type='gain')\n",
    "model = Pipeline(steps = [('categorical_encoding', CategoricalEncoder()),\n",
    "                          ('add_new_features', AddFeatures()),\n",
    "                          ('classifier', best_f1_lgb)\n",
    "                         ])\n",
    "# Fitting final model on train dataset\n",
    "model.fit(X_train, y_train)\n",
    "# Predict target probabilities\n",
    "val_probs = model.predict_proba(X_val)[:,1]\n",
    "# Predict target values on val data\n",
    "val_preds = np.where(val_probs > 0.45, 1, 0) # The probability threshold can be tweaked\n",
    "# Validation metrics\n",
    "print(f'Confusion Matrix: {confusion_matrix(y_val,val_preds)}')\n",
    "print(f'Area Under Curve: {roc_auc_score(y_val,val_preds)}')\n",
    "print(f'Recall score: {recall_score(y_val,val_preds)}')\n",
    "print(f'Classification report: \\n{classification_report(y_val,val_preds)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1431aa91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['final_churn_model_f1_0_45.sav']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save model object\n",
    "joblib.dump(model, 'final_churn_model_f1_0_45.sav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "464df1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7678570272911421"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.675392670157068"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[696, 113],\n",
       "       [ 62, 129]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.86      0.89       809\n",
      "           1       0.53      0.68      0.60       191\n",
      "\n",
      "    accuracy                           0.82      1000\n",
      "   macro avg       0.73      0.77      0.74      1000\n",
      "weighted avg       0.84      0.82      0.83      1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Load model object\n",
    "model = joblib.load('final_churn_model_f1_0_45.sav')\n",
    "X_test = dc_test.drop(columns=['Exited'], axis=1)\n",
    "# Predict target probabilities\n",
    "test_probs = model.predict_proba(X_test)[:,1]\n",
    "# Predict target values on test data\n",
    "test_preds = np.where(test_probs > 0.45, 1, 0) # Flexibility to tweak the probability threshold\n",
    "#test_preds = model.predict(X_test)\n",
    "# Test set metrics\n",
    "roc_auc_score(y_test, test_preds)\n",
    "recall_score(y_test, test_preds)\n",
    "confusion_matrix(y_test, test_preds)\n",
    "print(classification_report(y_test, test_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3b49f2ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>country_France</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_Spain</th>\n",
       "      <th>Surname_enc</th>\n",
       "      <th>bal_per_product</th>\n",
       "      <th>bal_by_est_salary</th>\n",
       "      <th>tenure_age_ratio</th>\n",
       "      <th>age_surname_mean_churn</th>\n",
       "      <th>predictions</th>\n",
       "      <th>pred_probabilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>632</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>111589.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>170382.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>111589.218411</td>\n",
       "      <td>0.654932</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>5.656854</td>\n",
       "      <td>0</td>\n",
       "      <td>0.073268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>547</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56908.41</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.280414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>832</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>178779.52</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>1.538619</td>\n",
       "      <td>0</td>\n",
       "      <td>0.111404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>785</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96832.82</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.790569</td>\n",
       "      <td>0</td>\n",
       "      <td>0.190693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>613</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>136491.72</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>1.658312</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "74           632       1   32       6  111589.33              1          1   \n",
       "325          547       0   51       1       0.00              2          1   \n",
       "415          832       0   29       5       0.00              2          1   \n",
       "289          785       0   40       3       0.00              2          1   \n",
       "873          613       1   44       3       0.00              2          0   \n",
       "\n",
       "     IsActiveMember  EstimatedSalary  Exited  country_France  country_Germany  \\\n",
       "74                1        170382.99       0             1.0              0.0   \n",
       "325               1         56908.41       0             1.0              0.0   \n",
       "415               0        178779.52       0             1.0              0.0   \n",
       "289               1         96832.82       0             1.0              0.0   \n",
       "873               1        136491.72       0             1.0              0.0   \n",
       "\n",
       "     country_Spain  Surname_enc  bal_per_product  bal_by_est_salary  \\\n",
       "74             0.0     1.000000    111589.218411           0.654932   \n",
       "325            0.0     0.000000         0.000000           0.000000   \n",
       "415            0.0     0.285714         0.000000           0.000000   \n",
       "289            0.0     0.125000         0.000000           0.000000   \n",
       "873            0.0     0.250000         0.000000           0.000000   \n",
       "\n",
       "     tenure_age_ratio  age_surname_mean_churn  predictions  pred_probabilities  \n",
       "74           0.187500                5.656854            0            0.073268  \n",
       "325          0.019608                0.000000            0            0.280414  \n",
       "415          0.172414                1.538619            0            0.111404  \n",
       "289          0.075000                0.790569            0            0.190693  \n",
       "873          0.068182                1.658312            0            0.094890  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding predictions and their probabilities in the original test dataframe\n",
    "test = dc_test.copy()\n",
    "test['predictions'] = test_preds\n",
    "test['pred_probabilities'] = test_probs\n",
    "test.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2f7f3aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 18)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>country_France</th>\n",
       "      <th>country_Germany</th>\n",
       "      <th>country_Spain</th>\n",
       "      <th>Surname_enc</th>\n",
       "      <th>bal_per_product</th>\n",
       "      <th>bal_by_est_salary</th>\n",
       "      <th>tenure_age_ratio</th>\n",
       "      <th>age_surname_mean_churn</th>\n",
       "      <th>pred_probabilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>546</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>26614.570846</td>\n",
       "      <td>0.826015</td>\n",
       "      <td>0.051724</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.992935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>479</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>107714.74</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>86128.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>35904.901365</td>\n",
       "      <td>1.250633</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>2.380476</td>\n",
       "      <td>0.979605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>745</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>117231.63</td>\n",
       "      <td>3</td>\n",
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       "      <td>122381.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>39077.196974</td>\n",
       "      <td>0.957923</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>1.677051</td>\n",
       "      <td>0.976361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>515</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>120961.50</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39288.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>40320.486560</td>\n",
       "      <td>3.078832</td>\n",
       "      <td>0.155556</td>\n",
       "      <td>1.341641</td>\n",
       "      <td>0.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>169719.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>1.677741</td>\n",
       "      <td>0.965838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          546       0   58       3  106458.31              4          1   \n",
       "1          479       1   51       1  107714.74              3          1   \n",
       "2          745       1   45      10  117231.63              3          1   \n",
       "3          515       1   45       7  120961.50              3          1   \n",
       "4          481       0   57       9       0.00              3          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  country_France  country_Germany  \\\n",
       "0               0        128881.87             0.0              1.0   \n",
       "1               0         86128.21             0.0              1.0   \n",
       "2               1        122381.02             0.0              1.0   \n",
       "3               1         39288.11             0.0              1.0   \n",
       "4               1        169719.35             1.0              0.0   \n",
       "\n",
       "   country_Spain  Surname_enc  bal_per_product  bal_by_est_salary  \\\n",
       "0            0.0     0.000000     26614.570846           0.826015   \n",
       "1            0.0     0.333333     35904.901365           1.250633   \n",
       "2            0.0     0.250000     39077.196974           0.957923   \n",
       "3            0.0     0.200000     40320.486560           3.078832   \n",
       "4            0.0     0.222222         0.000000           0.000000   \n",
       "\n",
       "   tenure_age_ratio  age_surname_mean_churn  pred_probabilities  \n",
       "0          0.051724                0.000000            0.992935  \n",
       "1          0.019608                2.380476            0.979605  \n",
       "2          0.222222                1.677051            0.976361  \n",
       "3          0.155556                1.341641            0.970001  \n",
       "4          0.157895                1.677741            0.965838  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_churn_list = test[test.pred_probabilities > 0.7].sort_values(by=['pred_probabilities'], ascending=False\n",
    "                                                                 ).reset_index().drop(columns=['index', 'Exited', 'predictions'], axis=1)\n",
    "high_churn_list.shape\n",
    "high_churn_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "608d7479",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_churn_list.to_csv('high_churn_list.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3593f7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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