model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "- Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>1889.5</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the dataset\n",
    "df = pd.read_csv(\"telecom_churn.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Check the structure of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Churn\n",
      "No     5174\n",
      "Yes    1869\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Distribution of the target variable (Churn)\n",
    "print(df['Churn'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Handle Missing Data\n",
    "\n",
    "Check for missing values and handle them by either filling them with the median, mean, or dropping rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values for numerical columns\n",
    "#df.fillna(df.mean(numeric_only=True), inplace=True)\n",
    "\n",
    "# Drop rows with missing categorical values (if necessary)\n",
    "#df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting data from text to numeric may results into NAN, to make sure recheck missing value and if there, drop or imputes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(11)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.TotalCharges.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have 11 missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute missing value with mean\n",
    "df['TotalCharges'] = df['TotalCharges'].fillna(df['TotalCharges'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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   float64\n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
       "0           Electronic check          29.85         29.85     No  \n",
       "1               Mailed check          56.95       1889.50     No  \n",
       "2               Mailed check          53.85        108.15    Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75     No  \n",
       "4           Electronic check          70.70        151.65    Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='customerID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7043 non-null   object \n",
      " 1   SeniorCitizen     7043 non-null   int64  \n",
      " 2   Partner           7043 non-null   object \n",
      " 3   Dependents        7043 non-null   object \n",
      " 4   tenure            7043 non-null   int64  \n",
      " 5   PhoneService      7043 non-null   object \n",
      " 6   MultipleLines     7043 non-null   object \n",
      " 7   InternetService   7043 non-null   object \n",
      " 8   OnlineSecurity    7043 non-null   object \n",
      " 9   OnlineBackup      7043 non-null   object \n",
      " 10  DeviceProtection  7043 non-null   object \n",
      " 11  TechSupport       7043 non-null   object \n",
      " 12  StreamingTV       7043 non-null   object \n",
      " 13  StreamingMovies   7043 non-null   object \n",
      " 14  Contract          7043 non-null   object \n",
      " 15  PaperlessBilling  7043 non-null   object \n",
      " 16  PaymentMethod     7043 non-null   object \n",
      " 17  MonthlyCharges    7043 non-null   float64\n",
      " 18  TotalCharges      7043 non-null   float64\n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(2), int64(2), object(16)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply Label Encoding for binary categorical features\n",
    "- convert yes/no to 1/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoding for binary categorical features\n",
    "label_encoder = LabelEncoder()\n",
    "df['gender'] = label_encoder.fit_transform(df['gender'])\n",
    "df['Partner'] = label_encoder.fit_transform(df['Partner'])\n",
    "df['Dependents'] = label_encoder.fit_transform(df['Dependents'])\n",
    "df['PhoneService'] = label_encoder.fit_transform(df['PhoneService'])\n",
    "df['PaperlessBilling'] = label_encoder.fit_transform(df['PaperlessBilling'])\n",
    "df['Churn'] = label_encoder.fit_transform(df['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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   int64  \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   int64  \n",
      " 4   Dependents        7043 non-null   int64  \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   int64  \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   int64  \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   float64\n",
      " 20  Churn             7043 non-null   int64  \n",
      "dtypes: float64(2), int64(8), object(11)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Encoding Categorical Variables\n",
    "Categorical variables like gender, Contract, PaymentMethod, etc., need to be encoded into numeric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0             0  No phone service             DSL             No  ...   \n",
       "1             1                No             DSL            Yes  ...   \n",
       "2             1                No             DSL            Yes  ...   \n",
       "3             0  No phone service             DSL            Yes  ...   \n",
       "4             1                No     Fiber optic             No  ...   \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",
       "  PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                1           Electronic check          29.85         29.85   \n",
       "1                0               Mailed check          56.95       1889.50   \n",
       "2                1               Mailed check          53.85        108.15   \n",
       "3                0  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                1           Electronic check          70.70        151.65   \n",
       "\n",
       "   Churn  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      0  \n",
       "4      1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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   int64  \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   int64  \n",
      " 4   Dependents        7043 non-null   int64  \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   int64  \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   int64  \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   float64\n",
      " 20  Churn             7043 non-null   int64  \n",
      "dtypes: float64(2), int64(8), object(11)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Apply one-hot encoding for multi-class categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding for multi-class categorical features\n",
    "df = pd.get_dummies(df, columns=['MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup','DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
    "                                 'Contract', 'PaymentMethod'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 32 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   customerID                             7043 non-null   object \n",
      " 1   gender                                 7043 non-null   int64  \n",
      " 2   SeniorCitizen                          7043 non-null   int64  \n",
      " 3   Partner                                7043 non-null   int64  \n",
      " 4   Dependents                             7043 non-null   int64  \n",
      " 5   tenure                                 7043 non-null   int64  \n",
      " 6   PhoneService                           7043 non-null   int64  \n",
      " 7   PaperlessBilling                       7043 non-null   int64  \n",
      " 8   MonthlyCharges                         7043 non-null   float64\n",
      " 9   TotalCharges                           7043 non-null   float64\n",
      " 10  Churn                                  7043 non-null   int64  \n",
      " 11  MultipleLines_No phone service         7043 non-null   bool   \n",
      " 12  MultipleLines_Yes                      7043 non-null   bool   \n",
      " 13  InternetService_Fiber optic            7043 non-null   bool   \n",
      " 14  InternetService_No                     7043 non-null   bool   \n",
      " 15  OnlineSecurity_No internet service     7043 non-null   bool   \n",
      " 16  OnlineSecurity_Yes                     7043 non-null   bool   \n",
      " 17  OnlineBackup_No internet service       7043 non-null   bool   \n",
      " 18  OnlineBackup_Yes                       7043 non-null   bool   \n",
      " 19  DeviceProtection_No internet service   7043 non-null   bool   \n",
      " 20  DeviceProtection_Yes                   7043 non-null   bool   \n",
      " 21  TechSupport_No internet service        7043 non-null   bool   \n",
      " 22  TechSupport_Yes                        7043 non-null   bool   \n",
      " 23  StreamingTV_No internet service        7043 non-null   bool   \n",
      " 24  StreamingTV_Yes                        7043 non-null   bool   \n",
      " 25  StreamingMovies_No internet service    7043 non-null   bool   \n",
      " 26  StreamingMovies_Yes                    7043 non-null   bool   \n",
      " 27  Contract_One year                      7043 non-null   bool   \n",
      " 28  Contract_Two year                      7043 non-null   bool   \n",
      " 29  PaymentMethod_Credit card (automatic)  7043 non-null   bool   \n",
      " 30  PaymentMethod_Electronic check         7043 non-null   bool   \n",
      " 31  PaymentMethod_Mailed check             7043 non-null   bool   \n",
      "dtypes: bool(21), float64(2), int64(8), object(1)\n",
      "memory usage: 749.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>45</td>\n",
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      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1           29.85         29.85  ...   \n",
       "1             1                 0           56.95       1889.50  ...   \n",
       "2             1                 1           53.85        108.15  ...   \n",
       "3             0                 0           42.30       1840.75  ...   \n",
       "4             1                 1           70.70        151.65  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Feature Engineering\n",
    "Feature engineering allows us to create new features that help the model make better predictions.\n",
    "\n",
    "### 1. Binning Continuous Variables\n",
    "For instance, the tenure feature could be categorized into groups (bins) such as New customers (0-12 months), Medium-term (12-24 months), and Long-term (24+ months).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1           29.85         29.85  ...   \n",
       "1             1                 0           56.95       1889.50  ...   \n",
       "2             1                 1           53.85        108.15  ...   \n",
       "3             0                 0           42.30       1840.75  ...   \n",
       "4             1                 1           70.70        151.65  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tenure\n",
       "1     613\n",
       "72    362\n",
       "2     238\n",
       "3     200\n",
       "4     176\n",
       "     ... \n",
       "28     57\n",
       "39     56\n",
       "44     51\n",
       "36     50\n",
       "0      11\n",
       "Name: count, Length: 73, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tenure.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binning tenure into categories\n",
    "#df['tenure_bin'] = pd.cut(df['tenure'], bins=[0, 12, 24, np.inf], labels=['New', 'Medium', 'Long-term'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After bining, we need to encode tenure bin as we all we need to drop original tenure column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.drop(columns='tenure', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1           29.85         29.85  ...   \n",
       "1             1                 0           56.95       1889.50  ...   \n",
       "2             1                 1           53.85        108.15  ...   \n",
       "3             0                 0           42.30       1840.75  ...   \n",
       "4             1                 1           70.70        151.65  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding for multi-class categorical features\n",
    "#df = pd.get_dummies(df, columns=['tenure_bin'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1           29.85         29.85  ...   \n",
       "1             1                 0           56.95       1889.50  ...   \n",
       "2             1                 1           53.85        108.15  ...   \n",
       "3             0                 0           42.30       1840.75  ...   \n",
       "4             1                 1           70.70        151.65  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Feature Interaction\n",
    "We could create an interaction between MonthlyCharges and Contract type, assuming customers with higher monthly charges and a short contract are more likely to churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interaction between MonthlyCharges and Contract type\n",
    "#df['Monthly_Contract_Interaction'] = df['MonthlyCharges'] * df['Contract_Two year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7590-VHVEG</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0       1   \n",
       "1  5575-GNVDE       1              0        0           0      34   \n",
       "2  3668-QPYBK       1              0        0           0       2   \n",
       "3  7795-CFOCW       1              0        0           0      45   \n",
       "4  9237-HQITU       0              0        0           0       2   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1           29.85         29.85  ...   \n",
       "1             1                 0           56.95       1889.50  ...   \n",
       "2             1                 1           53.85        108.15  ...   \n",
       "3             0                 0           42.30       1840.75  ...   \n",
       "4             1                 1           70.70        151.65  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Scaling Numerical Features\n",
    "Some models like Logistic Regression and Neural Networks require features to be on a similar scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Scaling numerical features\n",
    "scaler = StandardScaler()\n",
    "df[['MonthlyCharges', 'TotalCharges', 'tenure']] = scaler.fit_transform(df[['MonthlyCharges', 'TotalCharges', 'tenure']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
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       "  <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",
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       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>...</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No internet service</th>\n",
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       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>7590-VHVEG</td>\n",
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       "      <td>-0.994971</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066327</td>\n",
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       "      <td>0</td>\n",
       "      <td>-0.259629</td>\n",
       "      <td>-0.173876</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>-0.960399</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514251</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.746535</td>\n",
       "      <td>-0.195400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.236724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197365</td>\n",
       "      <td>-0.941193</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents    tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0 -1.277445   \n",
       "1  5575-GNVDE       1              0        0           0  0.066327   \n",
       "2  3668-QPYBK       1              0        0           0 -1.236724   \n",
       "3  7795-CFOCW       1              0        0           0  0.514251   \n",
       "4  9237-HQITU       0              0        0           0 -1.236724   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1       -1.160323     -0.994971  ...   \n",
       "1             1                 0       -0.259629     -0.173876  ...   \n",
       "2             1                 1       -0.362660     -0.960399  ...   \n",
       "3             0                 0       -0.746535     -0.195400  ...   \n",
       "4             1                 1        0.197365     -0.941193  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Splitting\n",
    "Before model training, split the data into training and testing sets to evaluate performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Features and target\n",
    "X = df.drop(columns=['customerID', 'Churn']) # customerID not usefull \n",
    "y = df['Churn']\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 30)\n",
      "(7043,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5634, 30)\n",
      "(5634,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7999432060201619"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5634/7043"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1409, 30)\n",
      "(1409,)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.066327</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.259629</td>\n",
       "      <td>-0.173876</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.236724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.362660</td>\n",
       "      <td>-0.960399</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514251</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.746535</td>\n",
       "      <td>-0.195400</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.236724</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.197365</td>\n",
       "      <td>-0.941193</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents    tenure  \\\n",
       "0  7590-VHVEG       0              0        1           0 -1.277445   \n",
       "1  5575-GNVDE       1              0        0           0  0.066327   \n",
       "2  3668-QPYBK       1              0        0           0 -1.236724   \n",
       "3  7795-CFOCW       1              0        0           0  0.514251   \n",
       "4  9237-HQITU       0              0        0           0 -1.236724   \n",
       "\n",
       "   PhoneService  PaperlessBilling  MonthlyCharges  TotalCharges  ...  \\\n",
       "0             0                 1       -1.160323     -0.994971  ...   \n",
       "1             1                 0       -0.259629     -0.173876  ...   \n",
       "2             1                 1       -0.362660     -0.960399  ...   \n",
       "3             0                 0       -0.746535     -0.195400  ...   \n",
       "4             1                 1        0.197365     -0.941193  ...   \n",
       "\n",
       "   TechSupport_Yes  StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0            False                            False            False   \n",
       "1            False                            False            False   \n",
       "2            False                            False            False   \n",
       "3             True                            False            False   \n",
       "4            False                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   Contract_One year  Contract_Two year  \\\n",
       "0              False              False   \n",
       "1               True              False   \n",
       "2              False              False   \n",
       "3               True              False   \n",
       "4              False              False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Model Building\n",
    "We’ll use Logistic Regression as our baseline model for predicting customer churn.\n",
    "\n",
    "- Why Logistic Regression?\n",
    "Logistic regression is simple, interpretable, and works well for binary classification tasks like churn prediction. It will serve as a good starting point, but we can try more advanced models later.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 82.11%\n",
      "[[934 102]\n",
      " [150 223]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.90      0.88      1036\n",
      "           1       0.69      0.60      0.64       373\n",
      "\n",
      "    accuracy                           0.82      1409\n",
      "   macro avg       0.77      0.75      0.76      1409\n",
      "weighted avg       0.82      0.82      0.82      1409\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "# Initialize the model\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy * 100:.2f}%')\n",
    "\n",
    "# Confusion matrix and classification report\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Model Evaluation\n",
    "\n",
    "**Confusion Matrix**\n",
    "The confusion matrix shows how many churn and non-churn predictions were correct and incorrect.\n",
    "\n",
    "**Classification Report**\n",
    "The classification report includes important metrics:\n",
    "\n",
    "    - Precision: How many predicted churns were correct.    \n",
    "    - Recall: How many actual churns were correctly identified.\n",
    "    - F1-Score: Harmonic mean of precision and recall.\n",
    "\n",
    "\n",
    "**ROC-AUC Curve**\n",
    "The ROC curve is used to visualize model performance, especially with imbalanced data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC Score: 0.86\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get predicted probabilities\n",
    "y_pred_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate ROC-AUC score\n",
    "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
    "print(f'ROC-AUC Score: {roc_auc:.2f}')\n",
    "\n",
    "# Plot ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "plt.plot(fpr, tpr, label=f'ROC Curve (AUC = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Model Tuning\n",
    "We can try more advanced models like Random Forest or XGBoost to improve performance, followed by hyperparameter tuning using GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 81.48%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Initialize RandomForestClassifier\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Define hyperparameters grid for tuning\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200],\n",
    "    'max_depth': [10, 20, None],\n",
    "    'min_samples_split': [2, 5],\n",
    "    'min_samples_leaf': [1, 2]\n",
    "}\n",
    "\n",
    "# Initialize GridSearchCV\n",
    "grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='accuracy')\n",
    "\n",
    "# Fit the model\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Best model evaluation\n",
    "best_model = grid_search.best_estimator_\n",
    "y_pred_rf = best_model.predict(X_test)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Random Forest Accuracy: {accuracy_rf * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "185     1\n",
       "2715    0\n",
       "3825    0\n",
       "1807    1\n",
       "132     0\n",
       "       ..\n",
       "6366    0\n",
       "315     0\n",
       "2439    0\n",
       "5002    0\n",
       "1161    1\n",
       "Name: Churn, Length: 1409, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>PaperlessBilling</th>\n",
       "      <th>MonthlyCharges</th>\n",
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       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>-0.472339</td>\n",
       "      <td>0.431289</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "185        0              0        1           0 -1.277445             0   \n",
       "2715       1              0        0           0  0.351370             1   \n",
       "3825       0              0        1           1  0.799294             1   \n",
       "1807       0              0        0           0 -1.277445             1   \n",
       "132        1              0        0           0  1.410099             1   \n",
       "\n",
       "      PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "185                  1       -1.328164     -0.997201   \n",
       "2715                 1       -1.313208     -0.568186   \n",
       "3825                 0       -1.509300     -0.552622   \n",
       "1807                 0        0.385148     -0.974440   \n",
       "132                  0       -0.472339      0.431289   \n",
       "\n",
       "      MultipleLines_No phone service  ...  TechSupport_Yes  \\\n",
       "185                             True  ...            False   \n",
       "2715                           False  ...            False   \n",
       "3825                           False  ...            False   \n",
       "1807                           False  ...            False   \n",
       "132                            False  ...             True   \n",
       "\n",
       "      StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "185                             False            False   \n",
       "2715                             True            False   \n",
       "3825                             True            False   \n",
       "1807                            False            False   \n",
       "132                             False            False   \n",
       "\n",
       "      StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "185                                 False                False   \n",
       "2715                                 True                False   \n",
       "3825                                 True                False   \n",
       "1807                                False                False   \n",
       "132                                 False                False   \n",
       "\n",
       "      Contract_One year  Contract_Two year  \\\n",
       "185               False              False   \n",
       "2715              False              False   \n",
       "3825              False               True   \n",
       "1807              False              False   \n",
       "132               False               True   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "185                                   False                            True   \n",
       "2715                                  False                           False   \n",
       "3825                                  False                           False   \n",
       "1807                                  False                            True   \n",
       "132                                   False                           False   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "185                        False  \n",
       "2715                       False  \n",
       "3825                        True  \n",
       "1807                       False  \n",
       "132                        False  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                          0\n",
       "SeniorCitizen                                   0\n",
       "Partner                                         1\n",
       "Dependents                                      0\n",
       "tenure                                  -1.277445\n",
       "PhoneService                                    0\n",
       "PaperlessBilling                                1\n",
       "MonthlyCharges                          -1.328164\n",
       "TotalCharges                            -0.997201\n",
       "MultipleLines_No phone service               True\n",
       "MultipleLines_Yes                           False\n",
       "InternetService_Fiber optic                 False\n",
       "InternetService_No                          False\n",
       "OnlineSecurity_No internet service          False\n",
       "OnlineSecurity_Yes                          False\n",
       "OnlineBackup_No internet service            False\n",
       "OnlineBackup_Yes                            False\n",
       "DeviceProtection_No internet service        False\n",
       "DeviceProtection_Yes                        False\n",
       "TechSupport_No internet service             False\n",
       "TechSupport_Yes                             False\n",
       "StreamingTV_No internet service             False\n",
       "StreamingTV_Yes                             False\n",
       "StreamingMovies_No internet service         False\n",
       "StreamingMovies_Yes                         False\n",
       "Contract_One year                           False\n",
       "Contract_Two year                           False\n",
       "PaymentMethod_Credit card (automatic)       False\n",
       "PaymentMethod_Electronic check               True\n",
       "PaymentMethod_Mailed check                  False\n",
       "Name: 185, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.loc[185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Documents\\Data Analyst Portfolio Projects\\ML_projects\\churn_prediction\\venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.predict([X_test.loc[185,:]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the Model\n",
    "Before deploying, save the model using Pickle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the best trained model to a file\n",
    "pickle.dump(best_model, open('models/model.pkl', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save other transformation model like transformation and label conding\n",
    "\n",
    "# Scaling numerical features\n",
    "pickle.dump(scaler, open('models/scaler.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \n",
    "\n",
    "\n",
    "#label conding\n",
    "pickle.dump(label_encoder, open('models/label_encoder.pkl','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Model Deployment Using Flask\n",
    "\n",
    "Now that we have a trained model, let’s deploy it as a REST API using Flask.\n",
    "\n",
    "**Flask App (app.py)**\n",
    "\n",
    "Create a simple API where you can input customer data and get a churn prediction.\n",
    "\n",
    "\n",
    "**Run the Flask App**\n",
    "\n",
    "To run the Flask app, use the following command in the terminal:\n",
    "\n",
    "**Testing the API**\n",
    "Open 'http://localhost:5000/' in your browser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"input.csv\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_df = df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cols in [\"SeniorCitizen\", \"tenure\", \"MonthlyCharges\", \"TotalCharges\"]:\n",
    "        user_input_df[cols] = pd.to_numeric(user_input_df[cols],  errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cols in ['gender','Partner', 'Dependents', 'PhoneService','PaperlessBilling']:\n",
    "    user_input_df[cols] = label_encoder.fit_transform(user_input_df[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoded_columns = ['MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup','DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
    "                                 'Contract', 'PaymentMethod']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_df = pd.get_dummies(user_input_df, columns=label_encoded_columns, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_frame = pd.read_csv(\"x_test.csv\")\n",
    "\n",
    "    \n",
    "user_input_df = user_input_df.reindex(columns = dummies_frame.columns, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.preprocess import preprocess_user_input\n",
    "\n",
    "df2 = preprocess_user_input(pd.read_csv(\"input.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cols in ['gender','Partner', 'Dependents', 'PhoneService','PaperlessBilling']:\n",
    "        user_input_df[cols] = label_encoder.fit_transform(user_input_df[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test[y_test == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.iloc[185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf = best_model.predict(X_test)\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.loc[185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoded_columns = ['MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup','DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
    "                                 'Contract', 'PaymentMethod']\n",
    "    \n",
    "    # Ensure that the input is in the same format as training data\n",
    "    # for col in label_encoded_columns:\n",
    "    #     if col not in user_input_df:\n",
    "    #         user_input_df[col] = 0  # Default or fallback value for missing columns\n",
    "\n",
    "user_input_df = pd.get_dummies(user_input_df, columns=label_encoded_columns, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_frame = X_test\n",
    "df2 = df2.reindex(columns = dummies_frame.columns, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head(1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.to_csv(\"x_test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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