Bank_churn_pred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f303e1b",
   "metadata": {},
   "source": [
    "# Bank Customer Churn prediction ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aaea87d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb688068",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c4916de",
   "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>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",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\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",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "<p>10000 rows × 14 columns</p>\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",
       "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",
       "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",
       "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",
       "0           101348.88       1  \n",
       "1           112542.58       0  \n",
       "2           113931.57       1  \n",
       "3            93826.63       0  \n",
       "4            79084.10       0  \n",
       "...               ...     ...  \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  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Churn_Modelling.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e63ee29d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87017468",
   "metadata": {},
   "source": [
    "# Descriptive Statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ed94e9a",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dec528b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "460"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CreditScore'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b3622b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2edfa0f3",
   "metadata": {},
   "source": [
    "# Checking Outliers through box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1d27f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='EstimatedSalary'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "559b5d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Balance'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['Balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b654eda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "9995    False\n",
       "9996    False\n",
       "9997    False\n",
       "9998    False\n",
       "9999    False\n",
       "Length: 10000, dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a106a14",
   "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>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",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\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",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "<p>10000 rows × 14 columns</p>\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",
       "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",
       "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",
       "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",
       "0           101348.88       1  \n",
       "1           112542.58       0  \n",
       "2           113931.57       1  \n",
       "3            93826.63       0  \n",
       "4            79084.10       0  \n",
       "...               ...     ...  \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  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b532e228",
   "metadata": {},
   "source": [
    "# Droping Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2dec09dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
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       "      <th>2</th>\n",
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       "      <td>15619304</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
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       "      <td>France</td>\n",
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       "      <td>2</td>\n",
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       "      <td>93826.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</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",
       "      <td>2</td>\n",
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       "      <td>96270.64</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</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",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</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",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
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       "      <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",
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       "      <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>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",
       "<p>10000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore Geography  Gender  Age  Tenure  \\\n",
       "0             1    15634602          619    France  Female   42       2   \n",
       "1             2    15647311          608     Spain  Female   41       1   \n",
       "2             3    15619304          502    France  Female   42       8   \n",
       "3             4    15701354          699    France  Female   39       1   \n",
       "4             5    15737888          850     Spain  Female   43       2   \n",
       "...         ...         ...          ...       ...     ...  ...     ...   \n",
       "9995       9996    15606229          771    France    Male   39       5   \n",
       "9996       9997    15569892          516    France    Male   35      10   \n",
       "9997       9998    15584532          709    France  Female   36       7   \n",
       "9998       9999    15682355          772   Germany    Male   42       3   \n",
       "9999      10000    15628319          792    France  Female   28       4   \n",
       "\n",
       "        Balance  NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "0          0.00              1          1               1        101348.88   \n",
       "1      83807.86              1          0               1        112542.58   \n",
       "2     159660.80              3          1               0        113931.57   \n",
       "3          0.00              2          0               0         93826.63   \n",
       "4     125510.82              1          1               1         79084.10   \n",
       "...         ...            ...        ...             ...              ...   \n",
       "9995       0.00              2          1               0         96270.64   \n",
       "9996   57369.61              1          1               1        101699.77   \n",
       "9997       0.00              1          0               1         42085.58   \n",
       "9998   75075.31              2          1               0         92888.52   \n",
       "9999  130142.79              1          1               0         38190.78   \n",
       "\n",
       "      Exited  \n",
       "0          1  \n",
       "1          0  \n",
       "2          1  \n",
       "3          0  \n",
       "4          0  \n",
       "...      ...  \n",
       "9995       0  \n",
       "9996       0  \n",
       "9997       1  \n",
       "9998       1  \n",
       "9999       0  \n",
       "\n",
       "[10000 rows x 13 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:, :2].join(df.iloc[:, 3:])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98e7b0e",
   "metadata": {},
   "source": [
    "# Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3bdd82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>619</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>608</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>502</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>699</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>850</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>771</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>516</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>709</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>772</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>792</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  \\\n",
       "0             1    15634602          619   42       2       0.00   \n",
       "1             2    15647311          608   41       1   83807.86   \n",
       "2             3    15619304          502   42       8  159660.80   \n",
       "3             4    15701354          699   39       1       0.00   \n",
       "4             5    15737888          850   43       2  125510.82   \n",
       "...         ...         ...          ...  ...     ...        ...   \n",
       "9995       9996    15606229          771   39       5       0.00   \n",
       "9996       9997    15569892          516   35      10   57369.61   \n",
       "9997       9998    15584532          709   36       7       0.00   \n",
       "9998       9999    15682355          772   42       3   75075.31   \n",
       "9999      10000    15628319          792   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0                 1          1               1        101348.88       1   \n",
       "1                 1          0               1        112542.58       0   \n",
       "2                 3          1               0        113931.57       1   \n",
       "3                 2          0               0         93826.63       0   \n",
       "4                 1          1               1         79084.10       0   \n",
       "...             ...        ...             ...              ...     ...   \n",
       "9995              2          1               0         96270.64       0   \n",
       "9996              1          1               1        101699.77       0   \n",
       "9997              1          0               1         42085.58       1   \n",
       "9998              2          1               0         92888.52       1   \n",
       "9999              1          1               0         38190.78       0   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "0                     0                0            0  \n",
       "1                     0                1            0  \n",
       "2                     0                0            0  \n",
       "3                     0                0            0  \n",
       "4                     0                1            0  \n",
       "...                 ...              ...          ...  \n",
       "9995                  0                0            1  \n",
       "9996                  0                0            1  \n",
       "9997                  0                0            0  \n",
       "9998                  1                0            1  \n",
       "9999                  0                0            0  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.get_dummies(df,columns=['Geography','Gender'],drop_first=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f9590c",
   "metadata": {},
   "source": [
    "# Checking Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "833d3366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14110871094154384"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Balance'].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6908298f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0020853576615585162"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['EstimatedSalary'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f58a54",
   "metadata": {},
   "source": [
    "# Selecting Independent variable(X) and Dependent variable(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61d1b451",
   "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>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>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>619</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>608</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>502</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>699</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>850</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>771</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>516</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>709</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>772</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>792</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  \\\n",
       "0             1    15634602          619   42       2       0.00   \n",
       "1             2    15647311          608   41       1   83807.86   \n",
       "2             3    15619304          502   42       8  159660.80   \n",
       "3             4    15701354          699   39       1       0.00   \n",
       "4             5    15737888          850   43       2  125510.82   \n",
       "...         ...         ...          ...  ...     ...        ...   \n",
       "9995       9996    15606229          771   39       5       0.00   \n",
       "9996       9997    15569892          516   35      10   57369.61   \n",
       "9997       9998    15584532          709   36       7       0.00   \n",
       "9998       9999    15682355          772   42       3   75075.31   \n",
       "9999      10000    15628319          792   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "0                 1          1               1        101348.88   \n",
       "1                 1          0               1        112542.58   \n",
       "2                 3          1               0        113931.57   \n",
       "3                 2          0               0         93826.63   \n",
       "4                 1          1               1         79084.10   \n",
       "...             ...        ...             ...              ...   \n",
       "9995              2          1               0         96270.64   \n",
       "9996              1          1               1        101699.77   \n",
       "9997              1          0               1         42085.58   \n",
       "9998              2          1               0         92888.52   \n",
       "9999              1          1               0         38190.78   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "0                     0                0            0  \n",
       "1                     0                1            0  \n",
       "2                     0                0            0  \n",
       "3                     0                0            0  \n",
       "4                     0                1            0  \n",
       "...                 ...              ...          ...  \n",
       "9995                  0                0            1  \n",
       "9996                  0                0            1  \n",
       "9997                  0                0            0  \n",
       "9998                  1                0            1  \n",
       "9999                  0                0            0  \n",
       "\n",
       "[10000 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df1.drop('Exited',axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e38b3c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "9995    0\n",
       "9996    0\n",
       "9997    1\n",
       "9998    1\n",
       "9999    0\n",
       "Name: Exited, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df1['Exited']\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcbe2cc",
   "metadata": {},
   "source": [
    "# Spliting Data into Train and Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43e8c371",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5b88200",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>15598175</td>\n",
       "      <td>592</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>850</td>\n",
       "      <td>36</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>8000 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  \\\n",
       "8856       8857    15759733          774   26       5       0.00   \n",
       "1545       1546    15673908          602   42       6  158414.85   \n",
       "8136       8137    15757425          716   38       1       0.00   \n",
       "7768       7769    15726695          601   20       9  122446.61   \n",
       "7782       7783    15595991          585   54       8   87105.32   \n",
       "...         ...         ...          ...  ...     ...        ...   \n",
       "1099       1100    15673055          494   38       7       0.00   \n",
       "2514       2515    15598175          592   26       4  105082.07   \n",
       "6637       6638    15668767          850   36       3       0.00   \n",
       "2575       2576    15592736          551   54       5  102994.04   \n",
       "7336       7337    15801072          654   28       7       0.00   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "8856              2          1               1         64716.08   \n",
       "1545              1          1               1        131886.46   \n",
       "8136              2          1               1         99661.46   \n",
       "7768              2          1               0         86791.90   \n",
       "7782              1          1               1         55346.14   \n",
       "...             ...        ...             ...              ...   \n",
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       "2575              1          1               0        176680.16   \n",
       "7336              2          1               0        151316.37   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "8856                  0                0            0  \n",
       "1545                  1                0            0  \n",
       "8136                  0                0            0  \n",
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       "7782                  0                0            1  \n",
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       "6637                  0                0            1  \n",
       "2575                  1                0            1  \n",
       "7336                  0                0            0  \n",
       "\n",
       "[8000 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "787df854",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>29217.37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>9073</th>\n",
       "      <td>9074</td>\n",
       "      <td>15611058</td>\n",
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       "      <td>60</td>\n",
       "      <td>5</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>8585</td>\n",
       "      <td>15732967</td>\n",
       "      <td>731</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>39</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>46</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  \\\n",
       "7878       7879    15760456          731   38      10  123711.73   \n",
       "3224       3225    15713463          645   41       2  138881.04   \n",
       "1919       1920    15739858          618   56       7       0.00   \n",
       "4432       4433    15751193          621   33       4       0.00   \n",
       "4835       4836    15590623          561   34       4   85141.79   \n",
       "...         ...         ...          ...  ...     ...        ...   \n",
       "9073       9074    15611058          702   60       5  138597.54   \n",
       "8584       8585    15732967          731   19       6       0.00   \n",
       "5390       5391    15573851          735   38       1       0.00   \n",
       "5092       5093    15596303          688   39       0       0.00   \n",
       "2964       2965    15679760          721   46       1  115764.32   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "7878              2          1               0        171340.68   \n",
       "3224              1          1               0        129936.53   \n",
       "1919              1          1               1        142400.27   \n",
       "4432              2          1               1         40299.23   \n",
       "4835              2          1               1         29217.37   \n",
       "...             ...        ...             ...              ...   \n",
       "9073              2          1               1         41536.59   \n",
       "8584              2          1               1        151581.79   \n",
       "5390              3          0               0         92220.12   \n",
       "5092              2          1               0         53222.15   \n",
       "2964              2          0               0        102950.79   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "7878                  0                0            0  \n",
       "3224                  1                0            0  \n",
       "1919                  0                0            1  \n",
       "4432                  0                1            1  \n",
       "4835                  0                1            1  \n",
       "...                 ...              ...          ...  \n",
       "9073                  1                0            0  \n",
       "8584                  0                0            1  \n",
       "5390                  0                0            0  \n",
       "5092                  0                0            0  \n",
       "2964                  0                0            1  \n",
       "\n",
       "[2000 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "24f5c8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8856    0\n",
       "1545    0\n",
       "8136    0\n",
       "7768    0\n",
       "7782    0\n",
       "       ..\n",
       "1099    0\n",
       "2514    0\n",
       "6637    0\n",
       "2575    1\n",
       "7336    0\n",
       "Name: Exited, Length: 8000, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d074f9a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7878    1\n",
       "3224    1\n",
       "1919    1\n",
       "4432    0\n",
       "4835    0\n",
       "       ..\n",
       "9073    1\n",
       "8584    0\n",
       "5390    1\n",
       "5092    1\n",
       "2964    0\n",
       "Name: Exited, Length: 2000, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81885f3c",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ff3ac09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc=StandardScaler()\n",
    "\n",
    "X_train.iloc[:,[2,3,4,5,9]]=sc.fit_transform(X_train.iloc[:,[2,3,4,5,9]])\n",
    "X_test.iloc[:,[2,3,4,5,9]]=sc.transform(X_test.iloc[:,[2,3,4,5,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "400b1240",
   "metadata": {},
   "outputs": [
    {
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       "\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>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>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>15595991</td>\n",
       "      <td>-0.675242</td>\n",
       "      <td>1.421668</td>\n",
       "      <td>1.023976</td>\n",
       "      <td>0.160226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.783044</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1099</th>\n",
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       "      <td>15673055</td>\n",
       "      <td>-1.615474</td>\n",
       "      <td>-0.095081</td>\n",
       "      <td>0.678403</td>\n",
       "      <td>-1.239615</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>2515</td>\n",
       "      <td>15598175</td>\n",
       "      <td>-0.602917</td>\n",
       "      <td>-1.232643</td>\n",
       "      <td>-0.358316</td>\n",
       "      <td>0.449125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.562495</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6637</th>\n",
       "      <td>6638</td>\n",
       "      <td>15668767</td>\n",
       "      <td>2.062794</td>\n",
       "      <td>-0.284675</td>\n",
       "      <td>-0.703889</td>\n",
       "      <td>-1.239615</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.643567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2576</td>\n",
       "      <td>15592736</td>\n",
       "      <td>-1.026538</td>\n",
       "      <td>1.421668</td>\n",
       "      <td>-0.012743</td>\n",
       "      <td>0.415569</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.324744</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>15801072</td>\n",
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       "      <td>-1.043050</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.884130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8000 rows × 13 columns</p>\n",
       "</div>"
      ],
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       "      RowNumber  CustomerId  CreditScore       Age    Tenure   Balance  \\\n",
       "8856       8857    15759733     1.277546 -1.232643 -0.012743 -1.239615   \n",
       "1545       1546    15673908    -0.499595  0.284106  0.332830  1.306219   \n",
       "8136       8137    15757425     0.678277 -0.095081 -1.395034 -1.239615   \n",
       "7768       7769    15726695    -0.509927 -1.801425  1.369548  0.728185   \n",
       "7782       7783    15595991    -0.675242  1.421668  1.023976  0.160226   \n",
       "...         ...         ...          ...       ...       ...       ...   \n",
       "1099       1100    15673055    -1.615474 -0.095081  0.678403 -1.239615   \n",
       "2514       2515    15598175    -0.602917 -1.232643 -0.358316  0.449125   \n",
       "6637       6638    15668767     2.062794 -0.284675 -0.703889 -1.239615   \n",
       "2575       2576    15592736    -1.026538  1.421668 -0.012743  0.415569   \n",
       "7336       7337    15801072     0.037680 -1.043050  0.678403 -1.239615   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "8856              2          1               1        -0.620271   \n",
       "1545              1          1               1         0.546598   \n",
       "8136              2          1               1        -0.013208   \n",
       "7768              2          1               0        -0.236775   \n",
       "7782              1          1               1        -0.783044   \n",
       "...             ...        ...             ...              ...   \n",
       "1099              2          1               1        -1.636736   \n",
       "2514              2          1               0         0.562495   \n",
       "6637              2          1               0         1.643567   \n",
       "2575              1          1               0         1.324744   \n",
       "7336              2          1               0         0.884130   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "8856                  0                0            0  \n",
       "1545                  1                0            0  \n",
       "8136                  0                0            0  \n",
       "7768                  0                1            0  \n",
       "7782                  0                0            1  \n",
       "...                 ...              ...          ...  \n",
       "1099                  0                1            1  \n",
       "2514                  1                0            0  \n",
       "6637                  0                0            1  \n",
       "2575                  1                0            1  \n",
       "7336                  0                0            0  \n",
       "\n",
       "[8000 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "753e9a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>15590623</td>\n",
       "      <td>-0.923215</td>\n",
       "      <td>-0.474269</td>\n",
       "      <td>-0.358316</td>\n",
       "      <td>0.128671</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.236947</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9073</th>\n",
       "      <td>9074</td>\n",
       "      <td>15611058</td>\n",
       "      <td>0.533626</td>\n",
       "      <td>1.990449</td>\n",
       "      <td>-0.012743</td>\n",
       "      <td>0.987741</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.022940</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8584</th>\n",
       "      <td>8585</td>\n",
       "      <td>15732967</td>\n",
       "      <td>0.833261</td>\n",
       "      <td>-1.896221</td>\n",
       "      <td>0.332830</td>\n",
       "      <td>-1.239615</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.888741</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>5391</td>\n",
       "      <td>15573851</td>\n",
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       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>5093</td>\n",
       "      <td>15596303</td>\n",
       "      <td>0.388976</td>\n",
       "      <td>-0.000284</td>\n",
       "      <td>-1.740607</td>\n",
       "      <td>-1.239615</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.819941</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2964</th>\n",
       "      <td>2965</td>\n",
       "      <td>15679760</td>\n",
       "      <td>0.729939</td>\n",
       "      <td>0.663294</td>\n",
       "      <td>-1.395034</td>\n",
       "      <td>0.620796</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore       Age    Tenure   Balance  \\\n",
       "7878       7879    15760456     0.833261 -0.095081  1.715121  0.748516   \n",
       "3224       3225    15713463    -0.055310  0.189309 -1.049462  0.992297   \n",
       "1919       1920    15739858    -0.334279  1.611262  0.678403 -1.239615   \n",
       "4432       4433    15751193    -0.303283 -0.569065 -0.358316 -1.239615   \n",
       "4835       4836    15590623    -0.923215 -0.474269 -0.358316  0.128671   \n",
       "...         ...         ...          ...       ...       ...       ...   \n",
       "9073       9074    15611058     0.533626  1.990449 -0.012743  0.987741   \n",
       "8584       8585    15732967     0.833261 -1.896221  0.332830 -1.239615   \n",
       "5390       5391    15573851     0.874590 -0.095081 -1.395034 -1.239615   \n",
       "5092       5093    15596303     0.388976 -0.000284 -1.740607 -1.239615   \n",
       "2964       2965    15679760     0.729939  0.663294 -1.395034  0.620796   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
       "7878              2          1               0         1.231988   \n",
       "3224              1          1               0         0.512724   \n",
       "1919              1          1               1         0.729242   \n",
       "4432              2          1               1        -1.044435   \n",
       "4835              2          1               1        -1.236947   \n",
       "...             ...        ...             ...              ...   \n",
       "9073              2          1               1        -1.022940   \n",
       "8584              2          1               1         0.888741   \n",
       "5390              3          0               0        -0.142477   \n",
       "5092              2          1               0        -0.819941   \n",
       "2964              2          0               0         0.043934   \n",
       "\n",
       "      Geography_Germany  Geography_Spain  Gender_Male  \n",
       "7878                  0                0            0  \n",
       "3224                  1                0            0  \n",
       "1919                  0                0            1  \n",
       "4432                  0                1            1  \n",
       "4835                  0                1            1  \n",
       "...                 ...              ...          ...  \n",
       "9073                  1                0            0  \n",
       "8584                  0                0            1  \n",
       "5390                  0                0            0  \n",
       "5092                  0                0            0  \n",
       "2964                  0                0            1  \n",
       "\n",
       "[2000 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3305edbe",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7fe9860c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.79325\n",
      "Test Accuracy score: 0.8085\n",
      "Cross Validation Score: 0.7963\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_reg= LogisticRegression(random_state=0)\n",
    "logistic_reg.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "logistic_reg_pred_train=logistic_reg.predict(X_train)\n",
    "logistic_reg_pred_test=logistic_reg.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,logistic_reg_pred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,logistic_reg_pred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(logistic_reg,X,y,cv=5,scoring=\"accuracy\").mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752fa848",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "88f741fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.80525\n",
      "Test Accuracy score: 0.7655\n",
      "Cross Validation Score: 0.7605999999999999\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model=KNeighborsClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=model.predict(X_train)\n",
    "ypred_test=model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(model,X,y,cv=5,scoring=\"accuracy\").mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f204da",
   "metadata": {},
   "source": [
    "# hyper parameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e5e58d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 18}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator=KNeighborsClassifier()\n",
    "\n",
    "param_grid={'n_neighbors':list(range(1,41))}\n",
    "\n",
    "cv_classifier=GridSearchCV(estimator,param_grid,cv=5,scoring=\"accuracy\")\n",
    "\n",
    "cv_classifier.fit(X_train,y_train)\n",
    "\n",
    "cv_classifier.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b9674399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.79375\n",
      "Test Accuracy score: 0.8075\n",
      "Cross Validation Score: 0.7935999999999999\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model=KNeighborsClassifier(n_neighbors=18)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=model.predict(X_train)\n",
    "ypred_test=model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(model,X,y,cv=5,scoring=\"accuracy\").mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34dc27c0",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2aef01bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.79375\n",
      "Test Accuracy score: 0.8085\n",
      "Cross Validation Score: 0.7935999999999999\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.svm import SVC\n",
    "svm_model=SVC()\n",
    "svm_model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=model.predict(X_train)\n",
    "ypred_test=svm_model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(model,X,y,cv=5,scoring=\"accuracy\").mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ec806e",
   "metadata": {},
   "source": [
    "# Hyper Parameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "946887cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kernel': 'linear'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator=SVC()\n",
    "\n",
    "param_grid={'kernel':['linear','rbf','sigmoid','poly']}\n",
    "\n",
    "grid=GridSearchCV(estimator,param_grid,cv=5)\n",
    "\n",
    "grid.fit(X,y)\n",
    "\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57bc7c0",
   "metadata": {},
   "source": [
    "# DecisionTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a478ca98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 1.0\n",
      "Test Accuracy score: 0.786\n",
      "Cross Validation Score: 0.7434000000000001\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_model=DecisionTreeClassifier(random_state=2)\n",
    "\n",
    "dt_model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=dt_model.predict(X_train)\n",
    "ypred_test=dt_model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(dt_model,X,y,cv=5,scoring=\"accuracy\").mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f21d24",
   "metadata": {},
   "source": [
    "# Hyper parameter tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dd0ac01b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 5}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator=DecisionTreeClassifier(random_state=2)\n",
    "\n",
    "param_grid={'criterion':['gini','entropy'],'max_depth':list(range(1,20))}\n",
    "\n",
    "grid=GridSearchCV(estimator,param_grid,cv=5)\n",
    "\n",
    "grid.fit(X,y)\n",
    "\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e520291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.857375\n",
      "Test Accuracy score: 0.8575\n",
      "Cross Validation Score: 0.8503000000000001\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_model=DecisionTreeClassifier(random_state=2,max_depth=5,criterion=\"entropy\")\n",
    "\n",
    "dt_model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=dt_model.predict(X_train)\n",
    "ypred_test=dt_model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(dt_model,X,y,cv=5,scoring=\"accuracy\").mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb80a166",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9dca7a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 1.0\n",
      "Test Accuracy score: 0.858\n",
      "Cross Validation Score: 0.8564999999999999\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(random_state=2)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=model.predict(X_train)\n",
    "ypred_test=model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(model,X,y,cv=5,scoring=\"accuracy\").mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cacff8",
   "metadata": {},
   "source": [
    "# Hyper Parameter Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "30ee26d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 86}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "estimator=RandomForestClassifier(random_state=2)\n",
    "\n",
    "param_grid={'n_estimators':list(range(1,101))}\n",
    "\n",
    "grid=GridSearchCV(estimator,param_grid,cv=5)\n",
    "\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "353b20c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.99975\n",
      "Test Accuracy score: 0.86\n",
      "Cross Validation Score: 0.8550000000000001\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(random_state=2,n_estimators=86)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "ypred_train=model.predict(X_train)\n",
    "ypred_test=model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(model,X,y,cv=5,scoring=\"accuracy\").mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ebf69f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in c:\\users\\ts895\\anaconda3\\anaconda4\\lib\\site-packages (1.7.6)\n",
      "Requirement already satisfied: numpy in c:\\users\\ts895\\anaconda3\\anaconda4\\lib\\site-packages (from xgboost) (1.21.5)\n",
      "Requirement already satisfied: scipy in c:\\users\\ts895\\anaconda3\\anaconda4\\lib\\site-packages (from xgboost) (1.7.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808e9fee",
   "metadata": {},
   "source": [
    "# Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "15d45100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy score: 0.964875\n",
      "Test Accuracy score: 0.8515\n",
      "Cross Validation Score: 0.7916\n"
     ]
    }
   ],
   "source": [
    "#Modelling\n",
    "from xgboost import XGBClassifier\n",
    "xgb_model=XGBClassifier()\n",
    "xgb_model.fit(X_train,y_train)\n",
    "\n",
    "#Prediction\n",
    "ypred_train=xgb_model.predict(X_train)\n",
    "ypred_test=xgb_model.predict(X_test)\n",
    "\n",
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Train Accuracy score:',accuracy_score(y_train,ypred_train))\n",
    "print('Test Accuracy score:',accuracy_score(y_test,ypred_test))\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print('Cross Validation Score:',cross_val_score(estimator=xgb_model,X=X,y=y,cv=5).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4efce49",
   "metadata": {},
   "source": [
    "# Model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "30d29c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model=DecisionTreeClassifier(random_state=2,max_depth=5,criterion=\"entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8b523129",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree is my final model because it has no overfitting or underfitting problem(Train accuracy=Test accuracy)\n",
    "#And Test accuracy=cross validation score and also it has highest cross validation score"
   ]
  }
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