XGBoost_Hyperparameter_Tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f9d2a48",
   "metadata": {},
   "source": [
    "### Hyperparameter Optimization For Xgboost using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3a458b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import cross_val_score\n",
    "## Hyperparameter optimization using RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, MinMaxScaler, scale"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa039e08",
   "metadata": {},
   "source": [
    "#### importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36646147",
   "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",
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       "    <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Hyperparameter-Optimization/master/Churn_Modelling.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c73c55b3",
   "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": "code",
   "execution_count": 8,
   "id": "11d650fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</th>\n",
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       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2e04b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f53536d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          False\n",
       "CustomerId         False\n",
       "Surname            False\n",
       "CreditScore        False\n",
       "Geography          False\n",
       "Gender             False\n",
       "Age                False\n",
       "Tenure             False\n",
       "Balance            False\n",
       "NumOfProducts      False\n",
       "HasCrCard          False\n",
       "IsActiveMember     False\n",
       "EstimatedSalary    False\n",
       "Exited             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da2055d1",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RowNumber, CustomerId, Surname, CreditScore, Geography, Gender, Age, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4e409d",
   "metadata": {},
   "source": [
    "### Types of variables\n",
    "\n",
    "\n",
    "In this section, I segregate the dataset into categorical and numerical variables. There are a mixture of categorical and numerical variables in the dataset. Categorical variables have data type object. Numerical variables have data type int64.\n",
    "\n",
    "\n",
    "First of all, I will explore Numerical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299bed56",
   "metadata": {},
   "source": [
    "### Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b41520ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of numerical variables: 11\n"
     ]
    },
    {
     "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>Exited</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          1    15634602          619   42       2       0.00              1   \n",
       "1          2    15647311          608   41       1   83807.86              1   \n",
       "2          3    15619304          502   42       8  159660.80              3   \n",
       "3          4    15701354          699   39       1       0.00              2   \n",
       "4          5    15737888          850   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of Numerical Variables\n",
    "numerical_features=[feature for feature in df.columns if df[feature].dtypes != 'O']\n",
    "\n",
    "print('Number of numerical variables:', len(numerical_features))\n",
    "\n",
    "# Visualize the numerical variables\n",
    "df[numerical_features].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37d52ae",
   "metadata": {},
   "source": [
    "#### Discrete Feature\n",
    "+ Numerical variables are usually of 2 types \n",
    "+ Continuous variable and discrete variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aef9f846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 5\n"
     ]
    }
   ],
   "source": [
    "discrete_feature=[feature for feature in numerical_features if len(df[feature].unique())<25]\n",
    "print('Discrete Variables Count: {}'.format(len(discrete_feature)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1efec0",
   "metadata": {},
   "source": [
    "#### Continuous Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0ba6f1b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous Feature Count 6\n"
     ]
    }
   ],
   "source": [
    "continuous_feature=[feature for feature in numerical_features if feature not in discrete_feature]\n",
    "print('Continuous Feature Count {}'.format(len(continuous_feature)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89f817d",
   "metadata": {},
   "source": [
    "#### Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6b5a5980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 categorical variables\n",
      "\n",
      "The categorical variables are :\n",
      "\n",
      " ['Surname', 'Geography', 'Gender']\n"
     ]
    }
   ],
   "source": [
    "# find categorical variables\n",
    "\n",
    "categorical = [var for var in df.columns if df[var].dtype=='O']\n",
    "\n",
    "print('There are {} categorical variables\\n'.format(len(categorical)))\n",
    "\n",
    "print('The categorical variables are :\\n\\n', categorical)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d43f8d4",
   "metadata": {},
   "source": [
    "#### Frequency counts of categorical variables\n",
    "\n",
    "\n",
    "Now, I will check the frequency counts of categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "847fc786",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smith       32\n",
      "Scott       29\n",
      "Martin      29\n",
      "Walker      28\n",
      "Brown       26\n",
      "            ..\n",
      "Izmailov     1\n",
      "Bold         1\n",
      "Bonham       1\n",
      "Poninski     1\n",
      "Burbidge     1\n",
      "Name: Surname, Length: 2932, dtype: int64\n",
      "France     5014\n",
      "Germany    2509\n",
      "Spain      2477\n",
      "Name: Geography, dtype: int64\n",
      "Male      5457\n",
      "Female    4543\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# view frequency counts of values in categorical variables\n",
    "\n",
    "for var in categorical: \n",
    "    \n",
    "    print(df[var].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3ff3155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smith       0.0032\n",
      "Scott       0.0029\n",
      "Martin      0.0029\n",
      "Walker      0.0028\n",
      "Brown       0.0026\n",
      "             ...  \n",
      "Izmailov    0.0001\n",
      "Bold        0.0001\n",
      "Bonham      0.0001\n",
      "Poninski    0.0001\n",
      "Burbidge    0.0001\n",
      "Name: Surname, Length: 2932, dtype: float64\n",
      "France     0.5014\n",
      "Germany    0.2509\n",
      "Spain      0.2477\n",
      "Name: Geography, dtype: float64\n",
      "Male      0.5457\n",
      "Female    0.4543\n",
      "Name: Gender, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# view frequency distribution of categorical variables\n",
    "\n",
    "for var in categorical: \n",
    "    \n",
    "    print(df[var].value_counts()/np.float(len(df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ddf174e",
   "metadata": {},
   "source": [
    "#### Number of labels: cardinality\n",
    "\n",
    "\n",
    "The number of labels within a categorical variable is known as **cardinality**. A high number of labels within a variable is known as **high cardinality**. High cardinality may pose some serious problems in the machine learning model. So, I will check for high cardinality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5228b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surname  contains  2932  labels\n",
      "Geography  contains  3  labels\n",
      "Gender  contains  2  labels\n"
     ]
    }
   ],
   "source": [
    "# check for cardinality in categorical variables\n",
    "\n",
    "for var in categorical:\n",
    "    \n",
    "    print(var, ' contains ', len(df[var].unique()), ' labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93266610",
   "metadata": {},
   "source": [
    "##### We can see that **`Surname  `** column contains relatively large number of labels as compared to other columns. I will check for cardinality after train-test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56ab268a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e21a8aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(18, 12))\n",
    "sns.heatmap(df.corr(), annot=True);\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c6b45c",
   "metadata": {},
   "source": [
    "### Analysis of the response column<a class=\"anchor\" id=\"3.6\"></a>\n",
    "It is very important to look at the response column, which holds the information, which we are going to predict. In our case we should look at 'Salary' column and compare its values to other columns.\n",
    "First of all we should look at the number of Employees with salary '<=50K' and '>50K' in the Dependent column 'Salary'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1088fe26",
   "metadata": {},
   "source": [
    "#### Checking for Imbalanced or balanced dataset with regards to the Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d19f9819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Customer Churn: 20.37%\n",
      "Percentage of Customer that did not Churn: 79.63%\n"
     ]
    }
   ],
   "source": [
    "customer_not_churn = len(df[df.Exited == 0])\n",
    "customer_churn = len(df[df.Exited == 1])\n",
    "print(\"Percentage of Customer Churn: {:.2f}%\".format((customer_churn / (len(df.Exited))*100)))\n",
    "print(\"Percentage of Customer that did not Churn: {:.2f}%\".format((customer_not_churn / (len(df.Exited))*100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb95d5c5",
   "metadata": {},
   "source": [
    "Imbalanced data refers to those types of datasets where the target class has an uneven distribution of observations, i.e one class label has a very high number of observations and the other has a very low number of observations.\n",
    "\n",
    "Here, the class “0” is called the majority class, and the much smaller in size “1” class is called the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "32966d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Exited.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2abc3dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Exited')"
      ]
     },
     "execution_count": 80,
     "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": [
    "pal = sns.color_palette('rainbow')\n",
    "sns.countplot(x='Exited', data=df, palette = pal, \n",
    "              order=df['Exited'].value_counts().index, );\n",
    "plt.xticks(fontsize = 12)\n",
    "plt.title('Exited')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d6880e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1], dtype='int64')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exited'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ea32bfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "plt.pie(df['Exited'].value_counts(),\n",
    "       labels=df['Exited'].value_counts().index,\n",
    "       explode = [0.07,0.0],\n",
    "       autopct= '%.2f%%',\n",
    "        colors = [\"#F96167\", \"#FCE77D\"],\n",
    "       shadow= True,\n",
    "       startangle= 190,\n",
    "       textprops = {'size':'large',\n",
    "                   'fontweight':'bold',\n",
    "                    'rotation':'0',\n",
    "                   'color':'black'})\n",
    "plt.legend(loc= 'upper right')\n",
    "plt.title(\"Class Type Distribution Pie Chart\", fontsize = 18, fontweight = 'bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2755165",
   "metadata": {},
   "source": [
    "### Problems with imbalanced data classification\n",
    "If we explain it in a very simple manner, the main problem with imbalanced dataset prediction is how accurately are we actually predicting both majority and minority class?\n",
    "\n",
    "Sometimes when the records of a certain class are much more than the other class, our classifier may get biased towards the prediction. In this case, the confusion matrix for the classification problem shows how well our model classifies the target classes and we arrive at the accuracy of the model from the confusion matrix.\n",
    "\n",
    "It is calculated based on the total no of correct predictions by the model divided by the total no of predictions. In the an example case it is (0+95)/(0+95+0+5)=0.95 or 95%. It means that the model fails to identify the minority class yet the accuracy score of the model will be 95%\n",
    "\n",
    "Thus our traditional approach of classification and model accuracy calculation is not useful in the case of the imbalanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b40ae0c",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9414b9",
   "metadata": {},
   "source": [
    "#### Applying some Data Transformation to increase the linear realtionship and improve our model prediction as well it scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce2f808",
   "metadata": {},
   "source": [
    "#### Applying Standard Scaler\n",
    "+ For each feature, the Standard Scaler scales the values such that the mean is 0 and the standard deviation is 1(or the variance).\n",
    "+ x_scaled = x – mean/std_dev\n",
    "+ However, Standard Scaler assumes that the distribution of the variable is normal. Thus, in case, the variables are not normally distributed, we either choose a different scaler or first, convert the variables to a normal distribution and then apply this scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "c3b987d4",
   "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>Balance</th>\n",
       "      <th>EstimatedSalary</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>0.00</td>\n",
       "      <td>101348.88</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>83807.86</td>\n",
       "      <td>112542.58</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>159660.80</td>\n",
       "      <td>113931.57</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>0.00</td>\n",
       "      <td>93826.63</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>125510.82</td>\n",
       "      <td>79084.10</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",
       "    </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>0.00</td>\n",
       "      <td>96270.64</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>57369.61</td>\n",
       "      <td>101699.77</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>0.00</td>\n",
       "      <td>42085.58</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>75075.31</td>\n",
       "      <td>92888.52</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>130142.79</td>\n",
       "      <td>38190.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  CreditScore  Age    Balance  EstimatedSalary\n",
       "0             1    15634602          619   42       0.00        101348.88\n",
       "1             2    15647311          608   41   83807.86        112542.58\n",
       "2             3    15619304          502   42  159660.80        113931.57\n",
       "3             4    15701354          699   39       0.00         93826.63\n",
       "4             5    15737888          850   43  125510.82         79084.10\n",
       "...         ...         ...          ...  ...        ...              ...\n",
       "9995       9996    15606229          771   39       0.00         96270.64\n",
       "9996       9997    15569892          516   35   57369.61        101699.77\n",
       "9997       9998    15584532          709   36       0.00         42085.58\n",
       "9998       9999    15682355          772   42   75075.31         92888.52\n",
       "9999      10000    15628319          792   28  130142.79         38190.78\n",
       "\n",
       "[10000 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[continuous_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b4399763",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_standard_scaled = df.copy()\n",
    "features = df_standard_scaled[continuous_feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "204cf610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>-1.731878</td>\n",
       "      <td>-0.783213</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>-0.326221</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021886</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.731531</td>\n",
       "      <td>-0.606534</td>\n",
       "      <td>Hill</td>\n",
       "      <td>-0.440036</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.198164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.117350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.731185</td>\n",
       "      <td>-0.995885</td>\n",
       "      <td>Onio</td>\n",
       "      <td>-1.536794</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>8</td>\n",
       "      <td>1.333053</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.240687</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.730838</td>\n",
       "      <td>0.144767</td>\n",
       "      <td>Boni</td>\n",
       "      <td>0.501521</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.108918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.730492</td>\n",
       "      <td>0.652659</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>2.063884</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.388871</td>\n",
       "      <td>2</td>\n",
       "      <td>0.785728</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.365276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender       Age  \\\n",
       "0  -1.731878   -0.783213  Hargrave    -0.326221    France  Female  0.293517   \n",
       "1  -1.731531   -0.606534      Hill    -0.440036     Spain  Female  0.198164   \n",
       "2  -1.731185   -0.995885      Onio    -1.536794    France  Female  0.293517   \n",
       "3  -1.730838    0.144767      Boni     0.501521    France  Female  0.007457   \n",
       "4  -1.730492    0.652659  Mitchell     2.063884     Spain  Female  0.388871   \n",
       "\n",
       "   Tenure   Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2 -1.225848              1          1               1   \n",
       "1       1  0.117350              1          0               1   \n",
       "2       8  1.333053              3          1               0   \n",
       "3       1 -1.225848              2          0               0   \n",
       "4       2  0.785728              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0         0.021886       1  \n",
       "1         0.216534       0  \n",
       "2         0.240687       1  \n",
       "3        -0.108918       0  \n",
       "4        -0.365276       0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "df_standard_scaled[continuous_feature] = scaler.fit_transform(features.values)\n",
    "df_standard_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c97dcd6",
   "metadata": {},
   "source": [
    "#### Now if we check the mean and standard deviation of our scaled data it should have a Mean '0' and Standard deviation '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "292a94ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n",
      " RowNumber         -0.0\n",
      "CustomerId         0.0\n",
      "CreditScore       -0.0\n",
      "Age                0.0\n",
      "Tenure             5.0\n",
      "Balance           -0.0\n",
      "NumOfProducts      1.5\n",
      "HasCrCard          0.7\n",
      "IsActiveMember     0.5\n",
      "EstimatedSalary   -0.0\n",
      "Exited             0.2\n",
      "dtype: float64 \n",
      "Standard Devaition \n",
      " RowNumber          1.0\n",
      "CustomerId         1.0\n",
      "CreditScore        1.0\n",
      "Age                1.0\n",
      "Tenure             3.0\n",
      "Balance            1.0\n",
      "NumOfProducts      1.0\n",
      "HasCrCard          0.0\n",
      "IsActiveMember     0.0\n",
      "EstimatedSalary    1.0\n",
      "Exited             0.0\n",
      "dtype: float64 1\n"
     ]
    }
   ],
   "source": [
    "print('Mean' '\\n',np.round(df_standard_scaled.mean(),1),'\\n' 'Standard Devaition','\\n',np.round(df_standard_scaled.std()),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "83f3ee1f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</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>...</th>\n",
       "      <th>Surname_Zubareva</th>\n",
       "      <th>Surname_Zuev</th>\n",
       "      <th>Surname_Zuyev</th>\n",
       "      <th>Surname_Zuyeva</th>\n",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.731878</td>\n",
       "      <td>-0.783213</td>\n",
       "      <td>-0.326221</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021886</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.731531</td>\n",
       "      <td>-0.606534</td>\n",
       "      <td>-0.440036</td>\n",
       "      <td>0.198164</td>\n",
       "      <td>1</td>\n",
       "      <td>0.117350</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216534</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.731185</td>\n",
       "      <td>-0.995885</td>\n",
       "      <td>-1.536794</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>8</td>\n",
       "      <td>1.333053</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.240687</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.730838</td>\n",
       "      <td>0.144767</td>\n",
       "      <td>0.501521</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.108918</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.730492</td>\n",
       "      <td>0.652659</td>\n",
       "      <td>2.063884</td>\n",
       "      <td>0.388871</td>\n",
       "      <td>2</td>\n",
       "      <td>0.785728</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.365276</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2948 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId  CreditScore       Age  Tenure   Balance  \\\n",
       "0  -1.731878   -0.783213    -0.326221  0.293517       2 -1.225848   \n",
       "1  -1.731531   -0.606534    -0.440036  0.198164       1  0.117350   \n",
       "2  -1.731185   -0.995885    -1.536794  0.293517       8  1.333053   \n",
       "3  -1.730838    0.144767     0.501521  0.007457       1 -1.225848   \n",
       "4  -1.730492    0.652659     2.063884  0.388871       2  0.785728   \n",
       "\n",
       "   NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  ...  \\\n",
       "0              1          1               1         0.021886  ...   \n",
       "1              1          0               1         0.216534  ...   \n",
       "2              3          1               0         0.240687  ...   \n",
       "3              2          0               0        -0.108918  ...   \n",
       "4              1          1               1        -0.365276  ...   \n",
       "\n",
       "   Surname_Zubareva  Surname_Zuev  Surname_Zuyev  Surname_Zuyeva  \\\n",
       "0                 0             0              0               0   \n",
       "1                 0             0              0               0   \n",
       "2                 0             0              0               0   \n",
       "3                 0             0              0               0   \n",
       "4                 0             0              0               0   \n",
       "\n",
       "   Geography_France  Geography_Germany  Geography_Spain  Gender_Female  \\\n",
       "0                 1                  0                0              1   \n",
       "1                 0                  0                1              1   \n",
       "2                 1                  0                0              1   \n",
       "3                 1                  0                0              1   \n",
       "4                 0                  0                1              1   \n",
       "\n",
       "   Gender_Male  Exited  \n",
       "0            0       1  \n",
       "1            0       0  \n",
       "2            0       1  \n",
       "3            0       0  \n",
       "4            0       0  \n",
       "\n",
       "[5 rows x 2948 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_standard_scaled.copy()\n",
    "data.drop('Exited',axis=1, inplace =True)\n",
    "data = pd.get_dummies(data.iloc[:,:])\n",
    "data['Exited'] = df.Exited\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f650879",
   "metadata": {},
   "source": [
    "### 5.2 Label Encoding <a class=\"anchor\" id=\"5.2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "99de4c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
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       "      <th>CreditScore</th>\n",
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       "      <th>Tenure</th>\n",
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       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.731878</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.731531</td>\n",
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       "      <td>1177</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.731185</td>\n",
       "      <td>-0.995885</td>\n",
       "      <td>2040</td>\n",
       "      <td>-1.536794</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>8</td>\n",
       "      <td>1.333053</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.240687</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.730838</td>\n",
       "      <td>0.144767</td>\n",
       "      <td>289</td>\n",
       "      <td>0.501521</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.108918</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.730492</td>\n",
       "      <td>0.652659</td>\n",
       "      <td>1822</td>\n",
       "      <td>2.063884</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.388871</td>\n",
       "      <td>2</td>\n",
       "      <td>0.785728</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.365276</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>1.730492</td>\n",
       "      <td>-1.177652</td>\n",
       "      <td>1999</td>\n",
       "      <td>1.246488</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.007457</td>\n",
       "      <td>5</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.066419</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>1.730838</td>\n",
       "      <td>-1.682806</td>\n",
       "      <td>1336</td>\n",
       "      <td>-1.391939</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.373958</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.306379</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.027988</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1.731185</td>\n",
       "      <td>-1.479282</td>\n",
       "      <td>1570</td>\n",
       "      <td>0.604988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.278604</td>\n",
       "      <td>7</td>\n",
       "      <td>-1.225848</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.008643</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>1.731531</td>\n",
       "      <td>-0.119356</td>\n",
       "      <td>2345</td>\n",
       "      <td>1.256835</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.293517</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.022608</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.125231</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>1.731878</td>\n",
       "      <td>-0.870559</td>\n",
       "      <td>2751</td>\n",
       "      <td>1.463771</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.041433</td>\n",
       "      <td>4</td>\n",
       "      <td>0.859965</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.076370</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  \\\n",
       "0     -1.731878   -0.783213     1115    -0.326221          0       0   \n",
       "1     -1.731531   -0.606534     1177    -0.440036          2       0   \n",
       "2     -1.731185   -0.995885     2040    -1.536794          0       0   \n",
       "3     -1.730838    0.144767      289     0.501521          0       0   \n",
       "4     -1.730492    0.652659     1822     2.063884          2       0   \n",
       "...         ...         ...      ...          ...        ...     ...   \n",
       "9995   1.730492   -1.177652     1999     1.246488          0       1   \n",
       "9996   1.730838   -1.682806     1336    -1.391939          0       1   \n",
       "9997   1.731185   -1.479282     1570     0.604988          0       0   \n",
       "9998   1.731531   -0.119356     2345     1.256835          1       1   \n",
       "9999   1.731878   -0.870559     2751     1.463771          0       0   \n",
       "\n",
       "           Age  Tenure   Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0     0.293517       2 -1.225848              1          1               1   \n",
       "1     0.198164       1  0.117350              1          0               1   \n",
       "2     0.293517       8  1.333053              3          1               0   \n",
       "3     0.007457       1 -1.225848              2          0               0   \n",
       "4     0.388871       2  0.785728              1          1               1   \n",
       "...        ...     ...       ...            ...        ...             ...   \n",
       "9995  0.007457       5 -1.225848              2          1               0   \n",
       "9996 -0.373958      10 -0.306379              1          1               1   \n",
       "9997 -0.278604       7 -1.225848              1          0               1   \n",
       "9998  0.293517       3 -0.022608              2          1               0   \n",
       "9999 -1.041433       4  0.859965              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "0            0.021886       1  \n",
       "1            0.216534       0  \n",
       "2            0.240687       1  \n",
       "3           -0.108918       0  \n",
       "4           -0.365276       0  \n",
       "...               ...     ...  \n",
       "9995        -0.066419       0  \n",
       "9996         0.027988       0  \n",
       "9997        -1.008643       1  \n",
       "9998        -0.125231       1  \n",
       "9999        -1.076370       0  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_standard_scaled.copy()\n",
    "label_encoder = LabelEncoder()\n",
    "data[\"Surname\"]=label_encoder.fit_transform(data[\"Surname\"])\n",
    "data[\"Geography\"]=label_encoder.fit_transform(data[\"Geography\"])\n",
    "data[\"Gender\"]=label_encoder.fit_transform(data[\"Gender\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b13741",
   "metadata": {},
   "source": [
    "#### splitting the data into independent and dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6ff5bd07",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,3:13]\n",
    "y = data.iloc[:,13]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e0c289",
   "metadata": {},
   "source": [
    "### Hyper Parameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5f600633",
   "metadata": {},
   "outputs": [],
   "source": [
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b79b2818",
   "metadata": {},
   "outputs": [],
   "source": [
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time = datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\n",
    "        tmin, tsec = divmod(temp_sec, 60)\n",
    "        print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "86f8de52",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9316605f",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search=RandomizedSearchCV(classifier,param_distributions=params,n_iter=5,scoring='roc_auc',n_jobs=-1,cv=5,verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "60114291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
      "\n",
      " Time taken: 0 hours 0 minutes and 20.0 seconds.\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "# Here we go\n",
    "start_time = timer(None) # timing starts from this point for \"start_time\" variable\n",
    "random_search.fit(x,y)\n",
    "timer(start_time) # timing ends here for \"start_time\" variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6e98ced6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.4,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0.3, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.1, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=3,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "04f1949b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 3,\n",
       " 'max_depth': 6,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': 0.3,\n",
       " 'colsample_bytree': 0.4}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8889c94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=xgboost.XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
    "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.4,\n",
    "              early_stopping_rounds=None, enable_categorical=False,\n",
    "              eval_metric=None, gamma=0.3, gpu_id=-1, grow_policy='depthwise',\n",
    "              importance_type=None, interaction_constraints='',\n",
    "              learning_rate=0.1, max_bin=256, max_cat_to_onehot=4,\n",
    "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=3,\n",
    "              monotone_constraints='()', n_estimators=100,\n",
    "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
    "              reg_alpha=0, reg_lambda=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "39cf4c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=cross_val_score(classifier,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ccc059cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87 , 0.868, 0.874, 0.867, 0.866, 0.858, 0.872, 0.876, 0.857,\n",
       "       0.861])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "45332739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8669"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "234b4f80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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