Customer Churn ML to App copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "443200f0",
   "metadata": {},
   "source": [
    "**Goal: Predicting Customer Churn for Vodafone"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bef318e",
   "metadata": {},
   "source": [
    "Hypothesis\n",
    "1.Increases in monthly charges causes customers to churn\n",
    "2.Customers with multiple lines are less likely to churn\n",
    "3.Tech support for customers reduces churn rate for categories such as females and senior citizens\n",
    "\n",
    "Questions\n",
    " 1. what is the chrun rate by:\n",
    "i. Payment method\n",
    "ii. Gender\n",
    "iii. Patner Status\n",
    "iv. Number of Dependents\n",
    " 2. Does increase in monthly charges influence:\n",
    " a. churn rate by citizenship?\n",
    " b. churn rate paperless vs. non- paperless?\n",
    " 3. Which internet  service customers churn the most?\n",
    " 4. Does tech support influence customer churn in any way and by how much?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca16b9d",
   "metadata": {},
   "source": [
    "1.0 Importing Relevant Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be246e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# Set Training and Testing Data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Normalization\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "#Models\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import xgboost as xgb \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Model Evaluation\n",
    "from sklearn.metrics import accuracy_score, precision_score,recall_score, f1_score, fbeta_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "# Testing Features\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "# Cross Validation and Hyperparameter Tuning\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import  RandomizedSearchCV\n",
    "\n",
    "#Others\n",
    "import pickle, os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ad43b9",
   "metadata": {},
   "source": [
    "1.1 Loading and Exploring the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42c7d114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95        1889.5   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80        1990.5   \n",
       "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40         306.6   \n",
       "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
       "\n",
       "     Churn  \n",
       "0       No  \n",
       "1       No  \n",
       "2      Yes  \n",
       "3       No  \n",
       "4      Yes  \n",
       "...    ...  \n",
       "7038    No  \n",
       "7039    No  \n",
       "7040    No  \n",
       "7041   Yes  \n",
       "7042    No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data =pd.read_csv(\"C:/Users/HP/Documents/Telco-Customer-Churn.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ba445e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3973b91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     3641\n",
       "Yes    3402\n",
       "Name: Partner, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing unique columns\n",
    "unique_patner_status =(data.loc[:,'Partner']).value_counts()\n",
    "unique_patner_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f8ecec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     4933\n",
       "Yes    2110\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_dependent_numbers =(data.loc[:,'Dependents']).value_counts()\n",
    "unique_dependent_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2ef3ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5901\n",
       "1    1142\n",
       "Name: SeniorCitizen, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_SeniorCitizen_numbers =(data.loc[:,'SeniorCitizen']).value_counts()\n",
    "unique_SeniorCitizen_numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e35145",
   "metadata": {},
   "source": [
    "1.2 Data Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6559c722",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customerID           object\n",
      "gender               object\n",
      "SeniorCitizen         int64\n",
      "Partner              object\n",
      "Dependents           object\n",
      "tenure                int64\n",
      "PhoneService         object\n",
      "MultipleLines        object\n",
      "InternetService      object\n",
      "OnlineSecurity       object\n",
      "OnlineBackup         object\n",
      "DeviceProtection     object\n",
      "TechSupport          object\n",
      "StreamingTV          object\n",
      "StreamingMovies      object\n",
      "Contract             object\n",
      "PaperlessBilling     object\n",
      "PaymentMethod        object\n",
      "MonthlyCharges      float64\n",
      "TotalCharges        float64\n",
      "Churn                object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#convert totalcharges column to datatype; float\n",
    "data['TotalCharges'] = pd.to_numeric(data['TotalCharges'], errors = 'coerce')\n",
    "print (data.dtypes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9000eea4",
   "metadata": {},
   "source": [
    "1.3 Pandas Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96557bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from pandas_profiling import ProfileReport\n",
    "#profile = ProfileReport(data, title=\"Pandas Profiling Report\")\n",
    "#profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f5bded3",
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95       1889.50   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80       1990.50   \n",
       "7039              Yes    Credit card (automatic)         103.20       7362.90   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40        306.60   \n",
       "7042              Yes  Bank transfer (automatic)         105.65       6844.50   \n",
       "\n",
       "      Churn  \n",
       "0        No  \n",
       "1        No  \n",
       "2       Yes  \n",
       "3        No  \n",
       "4       Yes  \n",
       "...     ...  \n",
       "7038     No  \n",
       "7039     No  \n",
       "7040     No  \n",
       "7041    Yes  \n",
       "7042     No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping duplicates\n",
    "data.drop_duplicates(inplace =True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd42fd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           0\n",
       "gender               0\n",
       "SeniorCitizen        0\n",
       "Partner              0\n",
       "Dependents           0\n",
       "tenure               0\n",
       "PhoneService         0\n",
       "MultipleLines        0\n",
       "InternetService      0\n",
       "OnlineSecurity       0\n",
       "OnlineBackup         0\n",
       "DeviceProtection     0\n",
       "TechSupport          0\n",
       "StreamingTV          0\n",
       "StreamingMovies      0\n",
       "Contract             0\n",
       "PaperlessBilling     0\n",
       "PaymentMethod        0\n",
       "MonthlyCharges       0\n",
       "TotalCharges        11\n",
       "Churn                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dealing with missing cells\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5150d9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imputer = imputer.fit(data[['TotalCharges']])\n",
    "data['TotalCharges'] = imputer.transform(data[['TotalCharges']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "687685ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for changes\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c399fe7c",
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95       1889.50   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80       1990.50   \n",
       "7039              Yes    Credit card (automatic)         103.20       7362.90   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40        306.60   \n",
       "7042              Yes  Bank transfer (automatic)         105.65       6844.50   \n",
       "\n",
       "      Churn  \n",
       "0        No  \n",
       "1        No  \n",
       "2       Yes  \n",
       "3        No  \n",
       "4       Yes  \n",
       "...     ...  \n",
       "7038     No  \n",
       "7039     No  \n",
       "7040     No  \n",
       "7041    Yes  \n",
       "7042     No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "151da91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop customer ID column\n",
    "data.drop(columns = [\"customerID\"], inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "903634ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gender',\n",
       " 'Partner',\n",
       " 'Dependents',\n",
       " 'PhoneService',\n",
       " 'MultipleLines',\n",
       " 'InternetService',\n",
       " 'OnlineSecurity',\n",
       " 'OnlineBackup',\n",
       " 'DeviceProtection',\n",
       " 'TechSupport',\n",
       " 'StreamingTV',\n",
       " 'StreamingMovies',\n",
       " 'Contract',\n",
       " 'PaperlessBilling',\n",
       " 'PaymentMethod',\n",
       " 'Churn']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the categorical columns\n",
    "categoricals = data.select_dtypes(include=[\"object\"]).columns.to_list()\n",
    "categoricals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c8cf023",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>Churn_Yes</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>7038</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_Male  Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0             0.0          1.0             0.0               0.0   \n",
       "1             1.0          0.0             0.0               1.0   \n",
       "2             1.0          0.0             0.0               1.0   \n",
       "3             1.0          0.0             0.0               0.0   \n",
       "4             0.0          0.0             0.0               1.0   \n",
       "...           ...          ...             ...               ...   \n",
       "7038          1.0          1.0             1.0               1.0   \n",
       "7039          0.0          1.0             1.0               1.0   \n",
       "7040          0.0          1.0             1.0               0.0   \n",
       "7041          1.0          1.0             0.0               1.0   \n",
       "7042          1.0          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_No phone service  MultipleLines_Yes  \\\n",
       "0                                1.0                0.0   \n",
       "1                                0.0                0.0   \n",
       "2                                0.0                0.0   \n",
       "3                                1.0                0.0   \n",
       "4                                0.0                0.0   \n",
       "...                              ...                ...   \n",
       "7038                             0.0                1.0   \n",
       "7039                             0.0                1.0   \n",
       "7040                             1.0                0.0   \n",
       "7041                             0.0                1.0   \n",
       "7042                             0.0                0.0   \n",
       "\n",
       "      InternetService_Fiber optic  InternetService_No  \\\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                             1.0                 0.0   \n",
       "...                           ...                 ...   \n",
       "7038                          0.0                 0.0   \n",
       "7039                          1.0                 0.0   \n",
       "7040                          0.0                 0.0   \n",
       "7041                          1.0                 0.0   \n",
       "7042                          1.0                 0.0   \n",
       "\n",
       "      OnlineSecurity_No internet service  OnlineSecurity_Yes  ...  \\\n",
       "0                                    0.0                 0.0  ...   \n",
       "1                                    0.0                 1.0  ...   \n",
       "2                                    0.0                 1.0  ...   \n",
       "3                                    0.0                 1.0  ...   \n",
       "4                                    0.0                 0.0  ...   \n",
       "...                                  ...                 ...  ...   \n",
       "7038                                 0.0                 1.0  ...   \n",
       "7039                                 0.0                 0.0  ...   \n",
       "7040                                 0.0                 1.0  ...   \n",
       "7041                                 0.0                 0.0  ...   \n",
       "7042                                 0.0                 1.0  ...   \n",
       "\n",
       "      StreamingTV_Yes  StreamingMovies_No internet service  \\\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",
       "7038              1.0                                  0.0   \n",
       "7039              1.0                                  0.0   \n",
       "7040              0.0                                  0.0   \n",
       "7041              0.0                                  0.0   \n",
       "7042              1.0                                  0.0   \n",
       "\n",
       "      StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
       "0                     0.0                0.0                0.0   \n",
       "1                     0.0                1.0                0.0   \n",
       "2                     0.0                0.0                0.0   \n",
       "3                     0.0                1.0                0.0   \n",
       "4                     0.0                0.0                0.0   \n",
       "...                   ...                ...                ...   \n",
       "7038                  1.0                1.0                0.0   \n",
       "7039                  1.0                1.0                0.0   \n",
       "7040                  0.0                0.0                0.0   \n",
       "7041                  0.0                0.0                0.0   \n",
       "7042                  1.0                0.0                1.0   \n",
       "\n",
       "      PaperlessBilling_Yes  PaymentMethod_Credit card (automatic)  \\\n",
       "0                      1.0                                    0.0   \n",
       "1                      0.0                                    0.0   \n",
       "2                      1.0                                    0.0   \n",
       "3                      0.0                                    0.0   \n",
       "4                      1.0                                    0.0   \n",
       "...                    ...                                    ...   \n",
       "7038                   1.0                                    0.0   \n",
       "7039                   1.0                                    1.0   \n",
       "7040                   1.0                                    0.0   \n",
       "7041                   1.0                                    0.0   \n",
       "7042                   1.0                                    0.0   \n",
       "\n",
       "      PaymentMethod_Electronic check  PaymentMethod_Mailed check  Churn_Yes  \n",
       "0                                1.0                         0.0        0.0  \n",
       "1                                0.0                         1.0        0.0  \n",
       "2                                0.0                         1.0        1.0  \n",
       "3                                0.0                         0.0        0.0  \n",
       "4                                1.0                         0.0        1.0  \n",
       "...                              ...                         ...        ...  \n",
       "7038                             0.0                         1.0        0.0  \n",
       "7039                             0.0                         0.0        0.0  \n",
       "7040                             1.0                         0.0        0.0  \n",
       "7041                             0.0                         1.0        1.0  \n",
       "7042                             0.0                         0.0        0.0  \n",
       "\n",
       "[7043 rows x 27 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the categorical variables\n",
    "encoder = OneHotEncoder(drop = \"first\", sparse = False)\n",
    "encoder.fit(data[categoricals])\n",
    "encoded_categoricals = encoder.transform(data[categoricals])\n",
    "encoded_categoricals = pd.DataFrame(encoded_categoricals, columns = encoder.get_feature_names_out().tolist())\n",
    "encoded_categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d71869d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "      <th>3</th>\n",
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       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "0                 0       1           29.85         29.85          0.0   \n",
       "1                 0      34           56.95       1889.50          1.0   \n",
       "2                 0       2           53.85        108.15          1.0   \n",
       "3                 0      45           42.30       1840.75          1.0   \n",
       "4                 0       2           70.70        151.65          0.0   \n",
       "...             ...     ...             ...           ...          ...   \n",
       "7038              0      24           84.80       1990.50          1.0   \n",
       "7039              0      72          103.20       7362.90          0.0   \n",
       "7040              0      11           29.60        346.45          0.0   \n",
       "7041              1       4           74.40        306.60          1.0   \n",
       "7042              0      66          105.65       6844.50          1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0             1.0             0.0               0.0   \n",
       "1             0.0             0.0               1.0   \n",
       "2             0.0             0.0               1.0   \n",
       "3             0.0             0.0               0.0   \n",
       "4             0.0             0.0               1.0   \n",
       "...           ...             ...               ...   \n",
       "7038          1.0             1.0               1.0   \n",
       "7039          1.0             1.0               1.0   \n",
       "7040          1.0             1.0               0.0   \n",
       "7041          1.0             0.0               1.0   \n",
       "7042          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
       "0                                1.0                0.0  ...              0.0   \n",
       "1                                0.0                0.0  ...              0.0   \n",
       "2                                0.0                0.0  ...              0.0   \n",
       "3                                1.0                0.0  ...              0.0   \n",
       "4                                0.0                0.0  ...              0.0   \n",
       "...                              ...                ...  ...              ...   \n",
       "7038                             0.0                1.0  ...              1.0   \n",
       "7039                             0.0                1.0  ...              1.0   \n",
       "7040                             1.0                0.0  ...              0.0   \n",
       "7041                             0.0                1.0  ...              0.0   \n",
       "7042                             0.0                0.0  ...              1.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\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",
       "7038                                  0.0                  1.0   \n",
       "7039                                  0.0                  1.0   \n",
       "7040                                  0.0                  0.0   \n",
       "7041                                  0.0                  0.0   \n",
       "7042                                  0.0                  1.0   \n",
       "\n",
       "      Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                   0.0                0.0                   1.0   \n",
       "1                   1.0                0.0                   0.0   \n",
       "2                   0.0                0.0                   1.0   \n",
       "3                   1.0                0.0                   0.0   \n",
       "4                   0.0                0.0                   1.0   \n",
       "...                 ...                ...                   ...   \n",
       "7038                1.0                0.0                   1.0   \n",
       "7039                1.0                0.0                   1.0   \n",
       "7040                0.0                0.0                   1.0   \n",
       "7041                0.0                0.0                   1.0   \n",
       "7042                0.0                1.0                   1.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                       0.0                             1.0   \n",
       "1                                       0.0                             0.0   \n",
       "2                                       0.0                             0.0   \n",
       "3                                       0.0                             0.0   \n",
       "4                                       0.0                             1.0   \n",
       "...                                     ...                             ...   \n",
       "7038                                    0.0                             0.0   \n",
       "7039                                    1.0                             0.0   \n",
       "7040                                    0.0                             1.0   \n",
       "7041                                    0.0                             0.0   \n",
       "7042                                    0.0                             0.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  Churn_Yes  \n",
       "0                            0.0        0.0  \n",
       "1                            1.0        0.0  \n",
       "2                            1.0        1.0  \n",
       "3                            0.0        0.0  \n",
       "4                            0.0        1.0  \n",
       "...                          ...        ...  \n",
       "7038                         1.0        0.0  \n",
       "7039                         0.0        0.0  \n",
       "7040                         0.0        0.0  \n",
       "7041                         1.0        1.0  \n",
       "7042                         0.0        0.0  \n",
       "\n",
       "[7043 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the encoded categoricals to the DataFrame and dropping the original columns\n",
    "data = data.join(encoded_categoricals)\n",
    "data.drop(columns= categoricals, inplace= True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3659fe28",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_sample = data.sample(n= int(0.5*len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "45664e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89840358",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_sample.to_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7493fc5b",
   "metadata": {},
   "source": [
    "1.6.**Feature Engineering and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9763666a",
   "metadata": {},
   "source": [
    "1.6.1.Split data into Predictor and Response Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50489140",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7043 entries, 0 to 7042\n",
      "Data columns (total 31 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   SeniorCitizen                          7043 non-null   int64  \n",
      " 1   tenure                                 7043 non-null   int64  \n",
      " 2   MonthlyCharges                         7043 non-null   float64\n",
      " 3   TotalCharges                           7043 non-null   float64\n",
      " 4   gender_Male                            7043 non-null   float64\n",
      " 5   Partner_Yes                            7043 non-null   float64\n",
      " 6   Dependents_Yes                         7043 non-null   float64\n",
      " 7   PhoneService_Yes                       7043 non-null   float64\n",
      " 8   MultipleLines_No phone service         7043 non-null   float64\n",
      " 9   MultipleLines_Yes                      7043 non-null   float64\n",
      " 10  InternetService_Fiber optic            7043 non-null   float64\n",
      " 11  InternetService_No                     7043 non-null   float64\n",
      " 12  OnlineSecurity_No internet service     7043 non-null   float64\n",
      " 13  OnlineSecurity_Yes                     7043 non-null   float64\n",
      " 14  OnlineBackup_No internet service       7043 non-null   float64\n",
      " 15  OnlineBackup_Yes                       7043 non-null   float64\n",
      " 16  DeviceProtection_No internet service   7043 non-null   float64\n",
      " 17  DeviceProtection_Yes                   7043 non-null   float64\n",
      " 18  TechSupport_No internet service        7043 non-null   float64\n",
      " 19  TechSupport_Yes                        7043 non-null   float64\n",
      " 20  StreamingTV_No internet service        7043 non-null   float64\n",
      " 21  StreamingTV_Yes                        7043 non-null   float64\n",
      " 22  StreamingMovies_No internet service    7043 non-null   float64\n",
      " 23  StreamingMovies_Yes                    7043 non-null   float64\n",
      " 24  Contract_One year                      7043 non-null   float64\n",
      " 25  Contract_Two year                      7043 non-null   float64\n",
      " 26  PaperlessBilling_Yes                   7043 non-null   float64\n",
      " 27  PaymentMethod_Credit card (automatic)  7043 non-null   float64\n",
      " 28  PaymentMethod_Electronic check         7043 non-null   float64\n",
      " 29  PaymentMethod_Mailed check             7043 non-null   float64\n",
      " 30  Churn_Yes                              7043 non-null   float64\n",
      "dtypes: float64(29), int64(2)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2f636455",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate input features and target\n",
    "X= data.drop(['Churn_Yes'], axis=1, inplace =False)\n",
    "\n",
    "# Select Target\n",
    "y = data['Churn_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd31319a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training feature: (5282, 30)\n",
      "Shape of testing feature: (1761, 30)\n",
      "Shape of training label: (5282,)\n",
      "Shape of test label: (1761,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)\n",
    "\n",
    "# Show the Training and Testing Data\n",
    "print('Shape of training feature:', X_train.shape)\n",
    "print('Shape of testing feature:', X_test.shape)\n",
    "print('Shape of training label:', y_train.shape)\n",
    "print('Shape of test label:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a839ff49",
   "metadata": {},
   "source": [
    "1.6.2 Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8140138c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization\n",
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e2333a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transform =['tenure', 'MonthlyCharges', 'TotalCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99f28c39",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Male</th>\n",
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       "      <th>PaymentMethod_Electronic check</th>\n",
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       "      <th>Churn_Yes</th>\n",
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       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.845274</td>\n",
       "      <td>0.847461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0</td>\n",
       "      <td>0.152778</td>\n",
       "      <td>0.112935</td>\n",
       "      <td>0.037809</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>1</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.558706</td>\n",
       "      <td>0.033210</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.869652</td>\n",
       "      <td>0.787641</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen    tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "0                 0  0.013889        0.115423      0.001275          0.0   \n",
       "1                 0  0.472222        0.385075      0.215867          1.0   \n",
       "2                 0  0.027778        0.354229      0.010310          1.0   \n",
       "3                 0  0.625000        0.239303      0.210241          1.0   \n",
       "4                 0  0.027778        0.521891      0.015330          0.0   \n",
       "...             ...       ...             ...           ...          ...   \n",
       "7038              0  0.333333        0.662189      0.227521          1.0   \n",
       "7039              0  1.000000        0.845274      0.847461          0.0   \n",
       "7040              0  0.152778        0.112935      0.037809          0.0   \n",
       "7041              1  0.055556        0.558706      0.033210          1.0   \n",
       "7042              0  0.916667        0.869652      0.787641          1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0             1.0             0.0               0.0   \n",
       "1             0.0             0.0               1.0   \n",
       "2             0.0             0.0               1.0   \n",
       "3             0.0             0.0               0.0   \n",
       "4             0.0             0.0               1.0   \n",
       "...           ...             ...               ...   \n",
       "7038          1.0             1.0               1.0   \n",
       "7039          1.0             1.0               1.0   \n",
       "7040          1.0             1.0               0.0   \n",
       "7041          1.0             0.0               1.0   \n",
       "7042          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
       "0                                1.0                0.0  ...              0.0   \n",
       "1                                0.0                0.0  ...              0.0   \n",
       "2                                0.0                0.0  ...              0.0   \n",
       "3                                1.0                0.0  ...              0.0   \n",
       "4                                0.0                0.0  ...              0.0   \n",
       "...                              ...                ...  ...              ...   \n",
       "7038                             0.0                1.0  ...              1.0   \n",
       "7039                             0.0                1.0  ...              1.0   \n",
       "7040                             1.0                0.0  ...              0.0   \n",
       "7041                             0.0                1.0  ...              0.0   \n",
       "7042                             0.0                0.0  ...              1.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\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",
       "7038                                  0.0                  1.0   \n",
       "7039                                  0.0                  1.0   \n",
       "7040                                  0.0                  0.0   \n",
       "7041                                  0.0                  0.0   \n",
       "7042                                  0.0                  1.0   \n",
       "\n",
       "      Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                   0.0                0.0                   1.0   \n",
       "1                   1.0                0.0                   0.0   \n",
       "2                   0.0                0.0                   1.0   \n",
       "3                   1.0                0.0                   0.0   \n",
       "4                   0.0                0.0                   1.0   \n",
       "...                 ...                ...                   ...   \n",
       "7038                1.0                0.0                   1.0   \n",
       "7039                1.0                0.0                   1.0   \n",
       "7040                0.0                0.0                   1.0   \n",
       "7041                0.0                0.0                   1.0   \n",
       "7042                0.0                1.0                   1.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                       0.0                             1.0   \n",
       "1                                       0.0                             0.0   \n",
       "2                                       0.0                             0.0   \n",
       "3                                       0.0                             0.0   \n",
       "4                                       0.0                             1.0   \n",
       "...                                     ...                             ...   \n",
       "7038                                    0.0                             0.0   \n",
       "7039                                    1.0                             0.0   \n",
       "7040                                    0.0                             1.0   \n",
       "7041                                    0.0                             0.0   \n",
       "7042                                    0.0                             0.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  Churn_Yes  \n",
       "0                            0.0        0.0  \n",
       "1                            1.0        0.0  \n",
       "2                            1.0        1.0  \n",
       "3                            0.0        0.0  \n",
       "4                            0.0        1.0  \n",
       "...                          ...        ...  \n",
       "7038                         1.0        0.0  \n",
       "7039                         0.0        0.0  \n",
       "7040                         0.0        0.0  \n",
       "7041                         1.0        1.0  \n",
       "7042                         0.0        0.0  \n",
       "\n",
       "[7043 rows x 31 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data_transform]=scaler.fit_transform(data[data_transform])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd81e533",
   "metadata": {},
   "source": [
    "1.6.3.Check for Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59488624",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "labels =['Churn_Yes','No_Churn']\n",
    "sns.color_palette()\n",
    "sns.countplot(data.Churn_Yes)\n",
    "sns.set(rc={'figure.figsize':(8,8)})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ddad87",
   "metadata": {},
   "source": [
    "1.6.4.Perform SMOTE Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a749615a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 1.]), array([3900, 3900], dtype=int64))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm = SMOTE(random_state=27, sampling_strategy=1.0)\n",
    "X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "np.unique(y_train, return_counts =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f152c3cc",
   "metadata": {},
   "source": [
    "1.6.5. Model Building with (SMOTE) Balancing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747d12be",
   "metadata": {},
   "source": [
    "1.Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "56fb8b6b",
   "metadata": {},
   "outputs": [
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       "      <th>SeniorCitizen</th>\n",
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       "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "0              0       1           59.20          59.2          0.0   \n",
       "1              0      29           58.75        1696.2          1.0   \n",
       "2              0      72           65.50        4919.7          0.0   \n",
       "3              0      23           20.30         470.6          0.0   \n",
       "4              0      72           92.40        6786.1          0.0   \n",
       "\n",
       "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0          0.0             1.0               1.0   \n",
       "1          0.0             0.0               1.0   \n",
       "2          1.0             0.0               0.0   \n",
       "3          0.0             0.0               1.0   \n",
       "4          1.0             0.0               1.0   \n",
       "\n",
       "   MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
       "0                             0.0                0.0  ...              0.0   \n",
       "1                             0.0                0.0  ...              0.0   \n",
       "2                             1.0                0.0  ...              1.0   \n",
       "3                             0.0                0.0  ...              0.0   \n",
       "4                             0.0                1.0  ...              1.0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                  0.0                  1.0   \n",
       "1                                  0.0                  0.0   \n",
       "2                                  0.0                  1.0   \n",
       "3                                  1.0                  0.0   \n",
       "4                                  0.0                  1.0   \n",
       "\n",
       "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "0                0.0                0.0                   0.0   \n",
       "1                1.0                0.0                   1.0   \n",
       "2                0.0                1.0                   0.0   \n",
       "3                0.0                0.0                   1.0   \n",
       "4                0.0                1.0                   1.0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                    0.0                             1.0   \n",
       "1                                    0.0                             1.0   \n",
       "2                                    0.0                             0.0   \n",
       "3                                    0.0                             0.0   \n",
       "4                                    0.0                             1.0   \n",
       "\n",
       "   PaymentMethod_Mailed check  Churn_Yes  \n",
       "0                         0.0        1.0  \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",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining the trainset for modelling\n",
    "train_set = X_train.join(y_train, on = X_train.index)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fb6f68b3",
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "      SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "4903              0       5           90.80        455.50          0.0   \n",
       "2695              0      52           81.40       4354.45          1.0   \n",
       "2184              1       2           88.55        179.25          0.0   \n",
       "6024              0       3           91.50        242.95          0.0   \n",
       "5861              0      35           19.25        677.90          1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "4903          0.0             0.0               1.0   \n",
       "2695          1.0             0.0               1.0   \n",
       "2184          0.0             0.0               1.0   \n",
       "6024          1.0             0.0               1.0   \n",
       "5861          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_No phone service  MultipleLines_Yes  ...  StreamingTV_Yes  \\\n",
       "4903                             0.0                1.0  ...              1.0   \n",
       "2695                             0.0                1.0  ...              0.0   \n",
       "2184                             0.0                1.0  ...              0.0   \n",
       "6024                             0.0                1.0  ...              0.0   \n",
       "5861                             0.0                0.0  ...              0.0   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "4903                                  0.0                  0.0   \n",
       "2695                                  0.0                  1.0   \n",
       "2184                                  0.0                  1.0   \n",
       "6024                                  0.0                  1.0   \n",
       "5861                                  1.0                  0.0   \n",
       "\n",
       "      Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
       "4903                0.0                0.0                   1.0   \n",
       "2695                1.0                0.0                   1.0   \n",
       "2184                0.0                0.0                   1.0   \n",
       "6024                0.0                0.0                   1.0   \n",
       "5861                0.0                1.0                   0.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "4903                                    0.0                             1.0   \n",
       "2695                                    1.0                             0.0   \n",
       "2184                                    0.0                             1.0   \n",
       "6024                                    1.0                             0.0   \n",
       "5861                                    0.0                             0.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  Churn_Yes  \n",
       "4903                         0.0        1.0  \n",
       "2695                         0.0        0.0  \n",
       "2184                         0.0        1.0  \n",
       "6024                         0.0        1.0  \n",
       "5861                         1.0        0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining the testset for modelling\n",
    "test_set = X_test.join(y_test, on = X_test.index)\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "574c7be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate input features and target\n",
    "X= train_set.drop(['Churn_Yes'], axis=1, inplace =False)\n",
    "\n",
    "# Select Target\n",
    "y = train_set['Churn_Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "da162792",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=27)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "54b26667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt =DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d91b2a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4afd72e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Decision Tree  0.807692   0.797872  0.802139       0.8  0.801282\n"
     ]
    }
   ],
   "source": [
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['Decision Tree',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21f173ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(max_depth =10, random_state = 101, max_features= None, min_samples_leaf = 15)\n",
    "dtree_smote =dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b26bb642",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree_pred = dtree_smote.predict(X_test)\n",
    "dtree_true = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "12091cdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Confusion Matrix\n",
    "cm_dtree = confusion_matrix(dtree_true, dtree_pred)\n",
    "f,ax = plt.subplots(figsize =(8,8))\n",
    "sns.heatmap(cm_dtree, annot =True, linewidth =0.5, fmt=\".0f\",cmap ='RdPu', ax =ax)\n",
    "plt.xlabel = ('dtree_pred')\n",
    "plt.ylabel =('dtree_true')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b16da740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances: [0.0164252  0.15627794 0.11436109 0.12538311 0.01416391 0.01683471\n",
      " 0.01345562 0.01265938 0.00901971 0.01017043 0.04755296 0.00051438\n",
      " 0.00051438 0.01045899 0.         0.01389094 0.00034292 0.0130367\n",
      " 0.         0.01468302 0.         0.01111831 0.00317167 0.01721656\n",
      " 0.03013472 0.02921251 0.04531274 0.00566413 0.25893117 0.00949278]\n"
     ]
    }
   ],
   "source": [
    "dt =DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "print('Feature Importances:',dt.feature_importances_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a9cbe332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot feature importance\n",
    "\n",
    "feature_importance = dt.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(X_test.columns)[sorted_idx])\n",
    "plt.title('Feature Importance')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea13977",
   "metadata": {},
   "source": [
    "2.Logistic Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "22590d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "33d1ff62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.76\n"
     ]
    }
   ],
   "source": [
    "#predicting test results and calculating accuracy\n",
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f6a0be41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.79      0.72      0.76      1207\n",
      "         1.0       0.73      0.80      0.76      1133\n",
      "\n",
      "    accuracy                           0.76      2340\n",
      "   macro avg       0.76      0.76      0.76      2340\n",
      "weighted avg       0.76      0.76      0.76      2340\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf3efd85",
   "metadata": {},
   "source": [
    "3.Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "372e29b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=42, ...)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgc =xgb.XGBClassifier(n_estimators = 500, max_depth =5, random_state =42)\n",
    "xgc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c5073879",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the testset results\n",
    "y_pred =xgc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b406f9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  XGBClassifier  0.833761   0.825744  0.832304  0.829011  0.830983\n"
     ]
    }
   ],
   "source": [
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['XGBClassifier',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66972c85",
   "metadata": {},
   "source": [
    "4.Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c0a76172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC =SVC(kernel ='rbf')\n",
    "SVC.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "168dcef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the testset results\n",
    "y_pred =SVC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "632269bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0   SVC  0.641026   0.640191  0.590468  0.614325  0.599785\n"
     ]
    }
   ],
   "source": [
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['SVC',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c765389",
   "metadata": {},
   "source": [
    "5.Random Forest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f44e44b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier()   \n",
    "rf_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b1f054ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the testset results\n",
    "y_pred =rf_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5b8f9e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Random Forest  0.855128   0.848246  0.853486  0.850858  0.852433\n"
     ]
    }
   ],
   "source": [
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['Random Forest',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784bce9f",
   "metadata": {},
   "source": [
    "1.6.7.Summarizing the performance of models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652d4f9f",
   "metadata": {},
   "source": [
    "Observations: From the five models(SMOTE) performed to train the model, the Random Forest and Xgboost models have the highest F1 score of 85.2% and 82.5% respectivelyand since higher F1 scores are generally better, we go ahead to do some iterations to choose the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c156d46",
   "metadata": {},
   "source": [
    "1.6.8.** Iteration using Important Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3c7ac9f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances: [0.01036717 0.02716832 0.00797075 0.00915977 0.01507764 0.0182956\n",
      " 0.00973231 0.03937767 0.         0.02601804 0.132918   0.02553219\n",
      " 0.         0.03549222 0.         0.02512234 0.         0.01236963\n",
      " 0.         0.02835647 0.         0.02275716 0.         0.02445823\n",
      " 0.08320194 0.1764734  0.04001008 0.01055958 0.2010519  0.01852961]\n"
     ]
    }
   ],
   "source": [
    "# evaluating model by using important features\n",
    "# first visualize important features\n",
    "xgc =xgb.XGBClassifier(random_state =42)\n",
    "xgc.fit(X_train, y_train)\n",
    "print('Feature Importances:',xgc.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d576dbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature Importance')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot feature importance\n",
    "feature_importance = xgc.feature_importances_\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center')\n",
    "plt.yticks(range(len(sorted_idx)), np.array(X_test.columns)[sorted_idx])\n",
    "plt.title('Feature Importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "62ad1006",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                        callbacks=None, colsample_bylevel=1,\n",
       "                                        colsample_bynode=1, colsample_bytree=1,\n",
       "                                        early_stopping_rounds=None,\n",
       "                                        enable_categorical=False,\n",
       "                                        eval_metric=None, feature_types=None,\n",
       "                                        gamma=0, gpu_id=-1,\n",
       "                                        grow_policy=&#x27;depthwise&#x27;,\n",
       "                                        importance_type=None,\n",
       "                                        interaction_constraints=&#x27;&#x27;,\n",
       "                                        learning_rate=0.300000012, max_bin=256,\n",
       "                                        max_cat_threshold=64,\n",
       "                                        max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                        max_depth=6, max_leaves=0,\n",
       "                                        min_child_weight=1, missing=nan,\n",
       "                                        monotone_constraints=&#x27;()&#x27;,\n",
       "                                        n_estimators=100, n_jobs=0,\n",
       "                                        num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                        random_state=42, ...))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;,\n",
       "                                        callbacks=None, colsample_bylevel=1,\n",
       "                                        colsample_bynode=1, colsample_bytree=1,\n",
       "                                        early_stopping_rounds=None,\n",
       "                                        enable_categorical=False,\n",
       "                                        eval_metric=None, feature_types=None,\n",
       "                                        gamma=0, gpu_id=-1,\n",
       "                                        grow_policy=&#x27;depthwise&#x27;,\n",
       "                                        importance_type=None,\n",
       "                                        interaction_constraints=&#x27;&#x27;,\n",
       "                                        learning_rate=0.300000012, max_bin=256,\n",
       "                                        max_cat_threshold=64,\n",
       "                                        max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                        max_depth=6, max_leaves=0,\n",
       "                                        min_child_weight=1, missing=nan,\n",
       "                                        monotone_constraints=&#x27;()&#x27;,\n",
       "                                        n_estimators=100, n_jobs=0,\n",
       "                                        num_parallel_tree=1, predictor=&#x27;auto&#x27;,\n",
       "                                        random_state=42, ...))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
       "                                        callbacks=None, colsample_bylevel=1,\n",
       "                                        colsample_bynode=1, colsample_bytree=1,\n",
       "                                        early_stopping_rounds=None,\n",
       "                                        enable_categorical=False,\n",
       "                                        eval_metric=None, feature_types=None,\n",
       "                                        gamma=0, gpu_id=-1,\n",
       "                                        grow_policy='depthwise',\n",
       "                                        importance_type=None,\n",
       "                                        interaction_constraints='',\n",
       "                                        learning_rate=0.300000012, max_bin=256,\n",
       "                                        max_cat_threshold=64,\n",
       "                                        max_cat_to_onehot=4, max_delta_step=0,\n",
       "                                        max_depth=6, max_leaves=0,\n",
       "                                        min_child_weight=1, missing=nan,\n",
       "                                        monotone_constraints='()',\n",
       "                                        n_estimators=100, n_jobs=0,\n",
       "                                        num_parallel_tree=1, predictor='auto',\n",
       "                                        random_state=42, ...))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection=SelectFromModel(xgc)\n",
    "selection.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "76930314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=42, ...)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform the train and test features\n",
    "select_X_train = selection.transform(X_train)\n",
    "select_X_test = selection.transform(X_test)\n",
    "\n",
    "#train model\n",
    "xgc.fit(select_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6796376a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limited Features XGBoost Model Accuracy: 0.8179487179487179\n",
      "Limited Features XGBoost Model F1 Score: 0.8144599303135888\n",
      "Limited Features XGBoost Model F2 Score: 0.8208955223880596\n"
     ]
    }
   ],
   "source": [
    "#predicting test results\n",
    "y_pred = xgc.predict(select_X_test)\n",
    "\n",
    "#evaluating the model\n",
    "xgc_acc=accuracy_score(y_test,y_pred)\n",
    "xgc_fscore = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "\n",
    "print('Limited Features XGBoost Model Accuracy:', xgc_acc)\n",
    "print('Limited Features XGBoost Model F1 Score:', xgc_fscore)\n",
    "print('Limited Features XGBoost Model F2 Score:', f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e5343ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectFromModel(estimator=RandomForestClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectFromModel</label><div class=\"sk-toggleable__content\"><pre>SelectFromModel(estimator=RandomForestClassifier())</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectFromModel(estimator=RandomForestClassifier())"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "selection=SelectFromModel(rf_clf)\n",
    "selection.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a5a32be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform the train and test features\n",
    "select_X_train = selection.transform(X_train)\n",
    "select_X_test = selection.transform(X_test)\n",
    "\n",
    "#train model\n",
    "rf_clf.fit(select_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "856f5842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limited Features RandomForest Model Accuracy: 0.8405982905982906\n",
      "Limited Features RandomForest Model F1 Score: 0.8374727668845315\n",
      "Limited Features RandomForest Model F2 Score: 0.843870741131015\n"
     ]
    }
   ],
   "source": [
    "#predicting test results\n",
    "y_pred = rf_clf.predict(select_X_test)\n",
    "\n",
    "#evaluating the model\n",
    "rf_clf_acc=accuracy_score(y_test,y_pred)\n",
    "rf_clf_fscore = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "\n",
    "print('Limited Features RandomForest Model Accuracy:', rf_clf_acc)\n",
    "print('Limited Features RandomForest Model F1 Score:', rf_clf_fscore)\n",
    "print('Limited Features RandomForest Model F2 Score:', f2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b94338",
   "metadata": {},
   "source": [
    "Notes: the first model without the limited features for both XGBoost and RandomForest models perform better that with limited features therefore we stick to the first models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70726a50",
   "metadata": {},
   "source": [
    "2.0. **Model Buidling without Smote Balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "28749d15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate input features and target\n",
    "wsb_X= train_set.drop(['Churn_Yes'], axis=1, inplace =False)\n",
    "\n",
    "# Select Target\n",
    "wsb_y = train_set['Churn_Yes']\n",
    "\n",
    "wsb_X_train, wsb_X_test, wsb_y_train, wsb_y_test = train_test_split(wsb_X, wsb_y, test_size=0.25, random_state=27)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b365aa9",
   "metadata": {},
   "source": [
    "i.Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "265b89cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Decision Tree  0.805128   0.796158  0.797861  0.797009   0.79752\n"
     ]
    }
   ],
   "source": [
    "dt =DecisionTreeClassifier()\n",
    "dt.fit(wsb_X_train, wsb_y_train)\n",
    "wsb_y_pred= dt.predict(wsb_X_test)\n",
    "# evaluating the model\n",
    "acc=accuracy_score(wsb_y_test,wsb_y_pred)\n",
    "prec= precision_score(wsb_y_test,wsb_y_pred)\n",
    "rec= recall_score(wsb_y_test,wsb_y_pred)\n",
    "f1 = f1_score(wsb_y_test,wsb_y_pred)\n",
    "f2 = fbeta_score(wsb_y_test,wsb_y_pred, beta =2.0)\n",
    "dt_results_wsb = pd.DataFrame([['Decision Tree',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(dt_results_wsb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce342848",
   "metadata": {},
   "source": [
    "ii.Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "20a318e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsb_X_train, wsb_X_test, wsb_y_train, wsb_y_test = train_test_split(wsb_X, wsb_y, test_size=0.3, random_state=27)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(wsb_X_train, wsb_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "fd69dae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.79\n"
     ]
    }
   ],
   "source": [
    "#predicting test results and calculating accuracy\n",
    "wsb_y_pred = logreg.predict(wsb_X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(wsb_X_test, wsb_y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ae5cc9e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.81      0.76      0.78      1186\n",
      "         1.0       0.77      0.82      0.79      1154\n",
      "\n",
      "    accuracy                           0.79      2340\n",
      "   macro avg       0.79      0.79      0.79      2340\n",
      "weighted avg       0.79      0.79      0.79      2340\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(wsb_y_test, wsb_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e780f57",
   "metadata": {},
   "source": [
    "iii.  Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "25388d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_bin=256, max_cat_threshold=64, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=5, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=500,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=42, ...)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgc_wsb =xgb.XGBClassifier(n_estimators = 500, max_depth =5, random_state =42)\n",
    "xgc_wsb.fit(wsb_X_train, wsb_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "360e64ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  XGBClassifier  0.851282   0.860465  0.833622  0.846831  0.838856\n"
     ]
    }
   ],
   "source": [
    "#predicting the testset results\n",
    "wsb_y_pred =xgc_wsb.predict(wsb_X_test)\n",
    "\n",
    "# evaluating the model\n",
    "acc=accuracy_score(wsb_y_test,wsb_y_pred)\n",
    "prec= precision_score(wsb_y_test,wsb_y_pred)\n",
    "rec= recall_score(wsb_y_test,wsb_y_pred)\n",
    "f1 = f1_score(wsb_y_test,wsb_y_pred)\n",
    "f2 = fbeta_score(wsb_y_test,wsb_y_pred, beta =2.0)\n",
    "wsb_results2 = pd.DataFrame([['XGBClassifier',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(wsb_results2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d340e25",
   "metadata": {},
   "source": [
    "iv. Random Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b916c664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier()   \n",
    "rf_clf.fit(wsb_X_train, wsb_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "98f02815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Random Forest  0.860256   0.870852  0.841421  0.855884  0.847147\n"
     ]
    }
   ],
   "source": [
    "#predicting the testset results\n",
    "wsb_y_pred =rf_clf.predict(wsb_X_test)\n",
    "# evaluating the model\n",
    "acc=accuracy_score(wsb_y_test,wsb_y_pred)\n",
    "prec= precision_score(wsb_y_test,wsb_y_pred)\n",
    "rec= recall_score(wsb_y_test,wsb_y_pred)\n",
    "f1 = f1_score(wsb_y_test,wsb_y_pred)\n",
    "f2 = fbeta_score(wsb_y_test,wsb_y_pred, beta =2.0)\n",
    "wsb_results3 = pd.DataFrame([['Random Forest',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(wsb_results3)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9533c8ba",
   "metadata": {},
   "source": [
    "3.0. **Model Improvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "64f11862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Random Forest  0.851709   0.844737  0.849956  0.847338  0.848907\n"
     ]
    }
   ],
   "source": [
    "#for randomforestclassifier (SMOTE)\n",
    "rf_cl =RandomForestClassifier(n_estimators =33)\n",
    "rf_cl.fit(X_train, y_train)\n",
    "#predicting test results\n",
    "y_pred = rf_cl.predict(X_test)\n",
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['Random Forest',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "9a70117e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  Random Forest  0.860256   0.870852  0.841421  0.855884  0.847147\n"
     ]
    }
   ],
   "source": [
    "#for randomforestclassifier ( Without SMOTE)\n",
    "rf_cl_wsb =RandomForestClassifier(n_estimators =33)\n",
    "rf_cl_wsb.fit(wsb_X_train, wsb_y_train)\n",
    "#predicting the testset results\n",
    "wsb_y_pred =rf_clf.predict(wsb_X_test)\n",
    "# evaluating the model\n",
    "acc=accuracy_score(wsb_y_test,wsb_y_pred)\n",
    "prec= precision_score(wsb_y_test,wsb_y_pred)\n",
    "rec= recall_score(wsb_y_test,wsb_y_pred)\n",
    "f1 = f1_score(wsb_y_test,wsb_y_pred)\n",
    "f2 = fbeta_score(wsb_y_test,wsb_y_pred, beta =2.0)\n",
    "wsb_results= pd.DataFrame([['Random Forest',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(wsb_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "10be2695",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:04:46] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-03de431ba26204c4d-1/xgboost/xgboost-ci-windows/src/learner.cc:767: \n",
      "Parameters: { \"max_depths\" } are not used.\n",
      "\n",
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  XGBClassifier   0.83547   0.826353  0.835834  0.831066   0.83392\n"
     ]
    }
   ],
   "source": [
    "#for XGBClassifier(SMOTE)\n",
    "xgc =xgb.XGBClassifier(n_estimators =500,max_depths =5, random_state=42)\n",
    "xgc.fit(X_train, y_train)\n",
    "#predicting test results\n",
    "y_pred = xgc.predict(X_test)\n",
    "# evaluating the model\n",
    "acc=accuracy_score(y_test,y_pred)\n",
    "prec= precision_score(y_test,y_pred)\n",
    "rec= recall_score(y_test,y_pred)\n",
    "f1 = f1_score(y_test,y_pred)\n",
    "f2 = fbeta_score(y_test,y_pred, beta =2.0)\n",
    "results = pd.DataFrame([['XGBClassifier',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b850ac84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:04:52] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-03de431ba26204c4d-1/xgboost/xgboost-ci-windows/src/learner.cc:767: \n",
      "Parameters: { \"max_depths\" } are not used.\n",
      "\n",
      "           Model  Accuracy  Presicion    Recall  F1 Score  F2 Score\n",
      "0  XGBClassifier  0.954274   0.950904  0.956672   0.95378  0.955513\n"
     ]
    }
   ],
   "source": [
    "#for XGBClassifier(Without SMOTE)\n",
    "xgc_wsb =xgb.XGBClassifier(n_estimators =500,max_depths =5, random_state=42)\n",
    "xgc_wsb.fit(wsb_X_train, wsb_y_train)\n",
    "\n",
    "#predicting the testset results\n",
    "wsb_y_pred =xgc.predict(wsb_X_test)\n",
    "\n",
    "# evaluating the model\n",
    "acc=accuracy_score(wsb_y_test,wsb_y_pred)\n",
    "prec= precision_score(wsb_y_test,wsb_y_pred)\n",
    "rec= recall_score(wsb_y_test,wsb_y_pred)\n",
    "f1 = f1_score(wsb_y_test,wsb_y_pred)\n",
    "f2 = fbeta_score(wsb_y_test,wsb_y_pred, beta =2.0)\n",
    "wsb_results2 = pd.DataFrame([['XGBClassifier',acc, prec, rec, f1, f2]], columns =['Model','Accuracy','Presicion','Recall','F1 Score', 'F2 Score'])\n",
    "print(wsb_results2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "edc0ce17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9542735042735043"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(wsb_y_test, wsb_y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f6ea3f",
   "metadata": {},
   "source": [
    "4.0.**Evaluate Chosen model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27720a15",
   "metadata": {},
   "source": [
    "4.1.Cross Validation and Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dfb7085",
   "metadata": {},
   "source": [
    " XGboost models with smote with f1 score of 95% is selected, to perform cross validation and hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d7fc62cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8443223443223443\n"
     ]
    }
   ],
   "source": [
    "#model on which to use CV with SMOTE\n",
    "xgc =xgb.XGBClassifier(n_estimators =500, random_state =42)\n",
    "#define cross-validation method\n",
    "kfold =KFold(n_splits=5) \n",
    "#evaluate model\n",
    "results =cross_val_score(xgc, X_train, y_train, cv=kfold)\n",
    "\n",
    "scores = cross_val_score(xgc, X_train,y_train, cv=kfold, n_jobs=-1)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ace9bb63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8554945054945055\n"
     ]
    }
   ],
   "source": [
    "#model on which to use CV with Smote\n",
    "rf_cl =RandomForestClassifier(n_estimators =500, random_state =42)\n",
    "#define cross-validation method\n",
    "kfold =KFold(n_splits=5) \n",
    "#evaluate model\n",
    "results =cross_val_score(rf_cl, X_train, y_train, cv=kfold)\n",
    "\n",
    "scores = cross_val_score(rf_cl, X_train,y_train, cv=kfold, n_jobs=-1)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2e3aa83",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d98116d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 342 candidates, totalling 1710 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hyperparameter(Grid Search Method) tuning for Random Forest Classifier\n",
    "grid_search = GridSearchCV(RandomForestClassifier(random_state=42), \n",
    "{ 'n_estimators':np.arange(5,100,5), 'max_features':np.arange(0.1,1.0,0.05), }\n",
    ",cv=5,verbose=1,n_jobs=-1 ) \n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "22414307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 0.20000000000000004, 'n_estimators': 95}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2963caf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8575091575091577"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "17bfc04e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 342 candidates, totalling 1710 fits\n",
      "[11:41:22] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-03de431ba26204c4d-1/xgboost/xgboost-ci-windows/src/learner.cc:767: \n",
      "Parameters: { \"max_features\" } are not used.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     gpu_id=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     gpu_id=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     gpu_id=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                         'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hyperparameter(Grid Search Method) tuning for XGB Classifier\n",
    "grid_search = GridSearchCV(xgb.XGBClassifier(random_state=42), \n",
    "{ 'n_estimators':np.arange(5,100,5), 'max_features':np.arange(0.1,1.0,0.05), }\n",
    ",cv=5, verbose=1,n_jobs=-1 ) \n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ceaae57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 0.1, 'n_estimators': 25}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fd72b3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8593406593406595"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91dad821",
   "metadata": {},
   "source": [
    "Model's best score for Random Classifier and XGB is  0.8569 and 0.8589 respectively using GridSearch method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "da268ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "[11:44:20] WARNING: C:/buildkite-agent/builds/buildkite-windows-cpu-autoscaling-group-i-03de431ba26204c4d-1/xgboost/xgboost-ci-windows/src/learner.cc:767: \n",
      "Parameters: { \"max_features\" } are not used.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           predictor=None, random_state=42, ...),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           predictor=None, random_state=42, ...),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100, n_jobs=None,\n",
       "                                           num_parallel_tree=None,\n",
       "                                           predictor=None, random_state=42, ...),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameter tuning (Randomized search method)n forXGBClassifier\n",
    "random_search = RandomizedSearchCV(xgb.XGBClassifier(random_state=42), \n",
    "{ 'n_estimators':np.arange(5,100,5), 'max_features':np.arange(0.1,1.0,0.05), }\n",
    ",cv=5, verbose=1,n_jobs=-1, n_iter=50, random_state = 0 ) \n",
    "random_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "548a112d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 25, 'max_features': 0.8500000000000002}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f70f55f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8593406593406595"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "56243288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={&#x27;max_features&#x27;: array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "                   n_iter=50, n_jobs=-1,\n",
       "                   param_distributions={'max_features': array([0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55, 0.6 ,\n",
       "       0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]),\n",
       "                                        'n_estimators': array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85,\n",
       "       90, 95])},\n",
       "                   random_state=0, verbose=1)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameter tuning (Randomized search method) for RandomForest Classifier\n",
    "random_search = RandomizedSearchCV(RandomForestClassifier(random_state=42), \n",
    "{ 'n_estimators':np.arange(5,100,5), 'max_features':np.arange(0.1,1.0,0.05), }\n",
    ",cv=5,verbose=1,n_jobs=-1, n_iter=50, random_state = 0 ) \n",
    "random_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "fb056207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 95, 'max_features': 0.20000000000000004}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b47e3a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8575091575091577"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73595143",
   "metadata": {},
   "source": [
    "models best score for Randomclassifier model is 0.8558 and Model's best score for XGBClassifier is 0.8589 using Randomsearch Method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01073fb1",
   "metadata": {},
   "source": [
    "From both the Gridsearch and Randomized search,XGBoost model still performs a little higher  than Randomclassifier model and so we settle on XGBoost to build an optimized version of the model using the combination of hyperparameters  from the Randomizedsearch method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2450a8c3",
   "metadata": {},
   "source": [
    "5.0.**  Future Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "86ebef8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.86      0.86      1207\n",
      "         1.0       0.85      0.86      0.85      1133\n",
      "\n",
      "    accuracy                           0.86      2340\n",
      "   macro avg       0.86      0.86      0.86      2340\n",
      "weighted avg       0.86      0.86      0.86      2340\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#fitting the best model to the train data\n",
    "best_XGBoost_model =random_search.fit(X_train,y_train)\n",
    "#predicting the testset results\n",
    "best_XGBoost_pred =best_XGBoost_model.predict(X_test)\n",
    "#evaluating the model\n",
    "print(classification_report(y_test, best_XGBoost_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "586ecbdf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing Confusion Matrix\n",
    "cm_best_XGBoost = confusion_matrix(y_test, best_XGBoost_pred)\n",
    "f,ax = plt.subplots(figsize =(8,8))\n",
    "sns.heatmap(cm_best_XGBoost, annot =True, linewidth =0.5, fmt=\".0f\",cmap ='RdPu', ax =ax)\n",
    "plt.xlabel = ('best_XGBoost_pred')\n",
    "plt.ylabel =('y_test')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fe670a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "0.854029304029304\n"
     ]
    }
   ],
   "source": [
    "#get final_cv_score\n",
    "#define cross-validation method\n",
    "kfold =KFold(n_splits=5) \n",
    "final_cv_score =cross_val_score(best_XGBoost_model, X_train, y_train, cv=kfold)\n",
    "\n",
    "final_cv_score = cross_val_score(best_XGBoost_model, X_train,y_train, cv=kfold, n_jobs=-1)\n",
    "print(final_cv_score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb34706f",
   "metadata": {},
   "source": [
    "conclusion: From the scores and confusion matrix above, we see that this version of the model (Xgboost with SMOTE) is the best for now with an average cross validation score of 85.40% and F1(0.85). We can expect that this model will reliably predict which customers are likely to churn and inform Vodafone what strategies to implement for customer retention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53f1b452",
   "metadata": {},
   "source": [
    "** Save Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "931ca6db",
   "metadata": {},
   "outputs": [],
   "source": [
    "requirements = '\\n'.join(f'{m.__name__}=={m.__version__}' for m in globals().values() if getattr(m, '__version__', None))\n",
    "\n",
    "with open('requirements.txt', 'w') as f:\n",
    "    f.write(requirements)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f80d3698",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_export = {\n",
    "    \"encoder\": encoder,\n",
    "    \"scaler\": scaler,\n",
    "    \"model\": logreg,\n",
    "    \"pipeline\": None,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c856c18",
   "metadata": {},
   "source": [
    "** Exportation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "bc7960ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('ML_tools', 'wb') as file:\n",
    "    pickle.dump(to_export, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fda55f",
   "metadata": {},
   "source": [
    "** Loading after Exportation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2ea05e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('ML_tools', 'rb') as file:\n",
    "    loaded_object = pickle.load(file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14dffec",
   "metadata": {},
   "source": [
    "loaded_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "93a6b8f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'encoder': OneHotEncoder(drop='first', sparse=False),\n",
       " 'scaler': MinMaxScaler(),\n",
       " 'model': LogisticRegression()}"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'encoder': encoder,\n",
    " 'scaler': scaler,\n",
    " 'model':logreg}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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