Churn_Modeling_Telecom.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: plotly in ./venv/lib/python3.12/site-packages (5.24.1)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in ./venv/lib/python3.12/site-packages (from plotly) (9.0.0)\n",
      "Requirement already satisfied: packaging in ./venv/lib/python3.12/site-packages (from plotly) (24.1)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>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>OnlineBackup</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>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>Yes</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>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>No</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>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>Yes</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>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>No</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>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>No</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",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           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              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets/IT_customer_churn.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
       "       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
       "       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',\n",
       "       'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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   object \n",
      " 19  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# df.dtypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "No     5174\n",
       "Yes    1869\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['TotalCharges'], errors='coerce').isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Remove rows with space in TotalCharges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df['TotalCharges']!=' ']\n",
    "# df1.shape\n",
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.TotalCharges = pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7032, 20)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges        float64\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MonthlyCharges_churn_no = df1[df1.Churn=='No'].MonthlyCharges\n",
    "MonthlyCharges_churn_yes = df1[df1.Churn=='Yes'].MonthlyCharges\n",
    "\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Customer Churn Prediction Visualization')\n",
    "\n",
    "plt.hist([MonthlyCharges_churn_yes, MonthlyCharges_churn_no], color=['green', 'black'], label=['Churn = Yes', 'Churn = No'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tenure_churn_no = df1[df1.Churn=='No'].tenure\n",
    "tenure_churn_yes = df1[df1.Churn=='Yes'].tenure\n",
    "\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Customer Churn Prediction Visualization')\n",
    "\n",
    "plt.hist([tenure_churn_yes, tenure_churn_no], color=['blue', 'red'], label=['Churn = Yes', 'Churn = No'])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  See unique values in object columns \n",
    "\n",
    "def show_unique_value_col(df):\n",
    "    for column in df :\n",
    "        if df[column].dtype=='object':\n",
    "            print(f'{column}: {df[column].unique()}')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No phone service' 'No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup: ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection: ['No' 'Yes' 'No internet service']\n",
      "TechSupport: ['No' 'Yes' 'No internet service']\n",
      "StreamingTV: ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies: ['No' 'Yes' 'No internet service']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "show_unique_value_col(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.replace('No internet service', 'No', inplace=True)\n",
    "df1.replace('No phone service', 'No', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>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>OnlineBackup</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>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</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</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>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>No</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>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>Yes</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>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</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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>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>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7032 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0     Female              0     Yes         No       1           No   \n",
       "1       Male              0      No         No      34          Yes   \n",
       "2       Male              0      No         No       2          Yes   \n",
       "3       Male              0      No         No      45           No   \n",
       "4     Female              0      No         No       2          Yes   \n",
       "...      ...            ...     ...        ...     ...          ...   \n",
       "7038    Male              0     Yes        Yes      24          Yes   \n",
       "7039  Female              0     Yes        Yes      72          Yes   \n",
       "7040  Female              0     Yes        Yes      11           No   \n",
       "7041    Male              1     Yes         No       4          Yes   \n",
       "7042    Male              0      No         No      66          Yes   \n",
       "\n",
       "     MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0               No             DSL             No          Yes   \n",
       "1               No             DSL            Yes           No   \n",
       "2               No             DSL            Yes          Yes   \n",
       "3               No             DSL            Yes           No   \n",
       "4               No     Fiber optic             No           No   \n",
       "...            ...             ...            ...          ...   \n",
       "7038           Yes             DSL            Yes           No   \n",
       "7039           Yes     Fiber optic             No          Yes   \n",
       "7040            No             DSL            Yes           No   \n",
       "7041           Yes     Fiber optic             No           No   \n",
       "7042            No     Fiber optic            Yes           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",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "0                 Yes           Electronic check           29.85   \n",
       "1                  No               Mailed check           56.95   \n",
       "2                 Yes               Mailed check           53.85   \n",
       "3                  No  Bank transfer (automatic)           42.30   \n",
       "4                 Yes           Electronic check           70.70   \n",
       "...               ...                        ...             ...   \n",
       "7038              Yes               Mailed check           84.80   \n",
       "7039              Yes    Credit card (automatic)          103.20   \n",
       "7040              Yes           Electronic check           29.60   \n",
       "7041              Yes               Mailed check           74.40   \n",
       "7042              Yes  Bank transfer (automatic)          105.65   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "1          1889.50    No  \n",
       "2           108.15   Yes  \n",
       "3          1840.75    No  \n",
       "4           151.65   Yes  \n",
       "...            ...   ...  \n",
       "7038       1990.50    No  \n",
       "7039       7362.90    No  \n",
       "7040        346.45    No  \n",
       "7041        306.60   Yes  \n",
       "7042       6844.50    No  \n",
       "\n",
       "[7032 rows x 20 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "Partner: ['Yes' 'No']\n",
      "Dependents: ['No' 'Yes']\n",
      "PhoneService: ['No' 'Yes']\n",
      "MultipleLines: ['No' 'Yes']\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: ['No' 'Yes']\n",
      "OnlineBackup: ['Yes' 'No']\n",
      "DeviceProtection: ['No' 'Yes']\n",
      "TechSupport: ['No' 'Yes']\n",
      "StreamingTV: ['No' 'Yes']\n",
      "StreamingMovies: ['No' 'Yes']\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: ['Yes' 'No']\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Churn: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "show_unique_value_col(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_yes_no = [\n",
    "    'Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
    "    'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn'\n",
    "]\n",
    "\n",
    "for col in columns_yes_no:\n",
    "    df1[col].replace({'Yes': 1, 'No': 0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: ['Female' 'Male']\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\n",
      "InternetService: ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "Contract: ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling: [1 0]\n",
      "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "Churn: [0 1]\n"
     ]
    }
   ],
   "source": [
    "# show_unique_value_col(df1)\n",
    "for col in df1:\n",
    "    print(f'{col}: {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OnHotEncoding categorical columns\n",
    "df1['gender'].replace({'Male': 1, 'Female': 0}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: [0 1]\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\n",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "PaperlessBilling: [1 0]\n",
      "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
      "Churn: [0 1]\n",
      "InternetService_DSL: [1. 0.]\n",
      "InternetService_Fiber optic: [0. 1.]\n",
      "InternetService_No: [0. 1.]\n",
      "Contract_Month-to-month: [1. 0.]\n",
      "Contract_One year: [0. 1.]\n",
      "Contract_Two year: [0. 1.]\n",
      "PaymentMethod_Bank transfer (automatic): [0. 1.]\n",
      "PaymentMethod_Credit card (automatic): [0. 1.]\n",
      "PaymentMethod_Electronic check: [1. 0.]\n",
      "PaymentMethod_Mailed check: [0. 1.]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "    print(f'{col}: {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.get_dummies(df1, ['InternetService','Contract','PaymentMethod'], dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>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>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</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",
       "      <th>6899</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4452</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4426</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "6899       1              0        0           0       1             1   \n",
       "327        0              1        0           0       2             1   \n",
       "4452       0              0        1           0      70             1   \n",
       "717        1              0        0           0      32             1   \n",
       "4426       0              0        0           1      16             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "6899              0               0             0                 0  ...   \n",
       "327               0               0             0                 0  ...   \n",
       "4452              0               1             1                 1  ...   \n",
       "717               1               0             0                 0  ...   \n",
       "4426              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "6899                  1.0                          0.0                 0.0   \n",
       "327                   0.0                          1.0                 0.0   \n",
       "4452                  1.0                          0.0                 0.0   \n",
       "717                   0.0                          1.0                 0.0   \n",
       "4426                  0.0                          1.0                 0.0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "6899                      1.0                0.0                0.0   \n",
       "327                       1.0                0.0                0.0   \n",
       "4452                      0.0                0.0                1.0   \n",
       "717                       1.0                0.0                0.0   \n",
       "4426                      0.0                1.0                0.0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "6899                                      1.0   \n",
       "327                                       0.0   \n",
       "4452                                      0.0   \n",
       "717                                       1.0   \n",
       "4426                                      0.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "6899                                    0.0                             0.0   \n",
       "327                                     0.0                             1.0   \n",
       "4452                                    1.0                             0.0   \n",
       "717                                     0.0                             0.0   \n",
       "4426                                    1.0                             0.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "6899                         0.0  \n",
       "327                          0.0  \n",
       "4452                         0.0  \n",
       "717                          0.0  \n",
       "4426                         0.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
       "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_scal = ['tenure','MonthlyCharges','TotalCharges']\n",
    "scaler = MinMaxScaler()\n",
    "df1[cols_to_scal] = scaler.fit_transform(df1[cols_to_scal])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender: [0 1]\n",
      "SeniorCitizen: [0 1]\n",
      "Partner: [1 0]\n",
      "Dependents: [0 1]\n",
      "tenure: [0.         0.46478873 0.01408451 0.61971831 0.09859155 0.29577465\n",
      " 0.12676056 0.38028169 0.85915493 0.16901408 0.21126761 0.8028169\n",
      " 0.67605634 0.33802817 0.95774648 0.71830986 0.98591549 0.28169014\n",
      " 0.15492958 0.4084507  0.64788732 1.         0.22535211 0.36619718\n",
      " 0.05633803 0.63380282 0.14084507 0.97183099 0.87323944 0.5915493\n",
      " 0.1971831  0.83098592 0.23943662 0.91549296 0.11267606 0.02816901\n",
      " 0.42253521 0.69014085 0.88732394 0.77464789 0.08450704 0.57746479\n",
      " 0.47887324 0.66197183 0.3943662  0.90140845 0.52112676 0.94366197\n",
      " 0.43661972 0.76056338 0.50704225 0.49295775 0.56338028 0.07042254\n",
      " 0.04225352 0.45070423 0.92957746 0.30985915 0.78873239 0.84507042\n",
      " 0.18309859 0.26760563 0.73239437 0.54929577 0.81690141 0.32394366\n",
      " 0.6056338  0.25352113 0.74647887 0.70422535 0.35211268 0.53521127]\n",
      "PhoneService: [0 1]\n",
      "MultipleLines: [0 1]\n",
      "OnlineSecurity: [0 1]\n",
      "OnlineBackup: [1 0]\n",
      "DeviceProtection: [0 1]\n",
      "TechSupport: [0 1]\n",
      "StreamingTV: [0 1]\n",
      "StreamingMovies: [0 1]\n",
      "PaperlessBilling: [1 0]\n",
      "MonthlyCharges: [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
      "TotalCharges: [0.0012751  0.21586661 0.01031041 ... 0.03780868 0.03321025 0.78764136]\n",
      "Churn: [0 1]\n",
      "InternetService_DSL: [1. 0.]\n",
      "InternetService_Fiber optic: [0. 1.]\n",
      "InternetService_No: [0. 1.]\n",
      "Contract_Month-to-month: [1. 0.]\n",
      "Contract_One year: [0. 1.]\n",
      "Contract_Two year: [0. 1.]\n",
      "PaymentMethod_Bank transfer (automatic): [0. 1.]\n",
      "PaymentMethod_Credit card (automatic): [0. 1.]\n",
      "PaymentMethod_Electronic check: [1. 0.]\n",
      "PaymentMethod_Mailed check: [0. 1.]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "    print(f'{col}: {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop('Churn', axis='columns')\n",
    "y = df1.Churn.astype(np.float32)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train,  y_test = train_test_split(X, y, test_size=0.2, random_state=15, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0.0    4130\n",
       "1.0    1495\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0.0    5163\n",
       "1.0    1869\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0.0    1033\n",
       "1.0     374\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5625, 26)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1407, 26)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>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>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>...</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</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",
       "      <th>684</th>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2446</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.239437</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.774648</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2842</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4372</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.028169</td>\n",
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       "    <tr>\n",
       "      <th>6773</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.507042</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "684        0              0        0           0  0.000000             1   \n",
       "2446       0              0        0           0  0.239437             1   \n",
       "1680       1              0        1           1  0.774648             1   \n",
       "2220       1              0        1           0  1.000000             1   \n",
       "2842       0              0        0           0  0.042254             0   \n",
       "5131       0              0        1           0  0.605634             1   \n",
       "4372       1              0        1           1  0.000000             1   \n",
       "4687       1              0        1           0  0.295775             1   \n",
       "2385       0              0        0           1  0.028169             0   \n",
       "6773       1              1        1           0  0.507042             1   \n",
       "\n",
       "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "684               0               0             0                 0  ...   \n",
       "2446              1               0             1                 0  ...   \n",
       "1680              1               0             0                 0  ...   \n",
       "2220              0               1             1                 0  ...   \n",
       "2842              0               1             0                 1  ...   \n",
       "5131              1               0             0                 0  ...   \n",
       "4372              0               0             0                 0  ...   \n",
       "4687              0               0             0                 1  ...   \n",
       "2385              0               0             1                 0  ...   \n",
       "6773              1               0             0                 0  ...   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "684                   0.0                          1.0                 0.0   \n",
       "2446                  0.0                          1.0                 0.0   \n",
       "1680                  0.0                          0.0                 1.0   \n",
       "2220                  1.0                          0.0                 0.0   \n",
       "2842                  1.0                          0.0                 0.0   \n",
       "5131                  0.0                          1.0                 0.0   \n",
       "4372                  0.0                          0.0                 1.0   \n",
       "4687                  0.0                          1.0                 0.0   \n",
       "2385                  1.0                          0.0                 0.0   \n",
       "6773                  0.0                          1.0                 0.0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "684                       1.0                0.0                0.0   \n",
       "2446                      1.0                0.0                0.0   \n",
       "1680                      0.0                1.0                0.0   \n",
       "2220                      0.0                0.0                1.0   \n",
       "2842                      1.0                0.0                0.0   \n",
       "5131                      1.0                0.0                0.0   \n",
       "4372                      1.0                0.0                0.0   \n",
       "4687                      1.0                0.0                0.0   \n",
       "2385                      1.0                0.0                0.0   \n",
       "6773                      1.0                0.0                0.0   \n",
       "\n",
       "      PaymentMethod_Bank transfer (automatic)  \\\n",
       "684                                       0.0   \n",
       "2446                                      0.0   \n",
       "1680                                      0.0   \n",
       "2220                                      1.0   \n",
       "2842                                      0.0   \n",
       "5131                                      0.0   \n",
       "4372                                      1.0   \n",
       "4687                                      0.0   \n",
       "2385                                      0.0   \n",
       "6773                                      0.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "684                                     0.0                             0.0   \n",
       "2446                                    1.0                             0.0   \n",
       "1680                                    0.0                             0.0   \n",
       "2220                                    0.0                             0.0   \n",
       "2842                                    0.0                             0.0   \n",
       "5131                                    0.0                             1.0   \n",
       "4372                                    0.0                             0.0   \n",
       "4687                                    1.0                             0.0   \n",
       "2385                                    0.0                             0.0   \n",
       "6773                                    0.0                             1.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "684                          1.0  \n",
       "2446                         0.0  \n",
       "1680                         1.0  \n",
       "2220                         0.0  \n",
       "2842                         1.0  \n",
       "5131                         0.0  \n",
       "4372                         0.0  \n",
       "4687                         0.0  \n",
       "2385                         1.0  \n",
       "6773                         0.0  \n",
       "\n",
       "[10 rows x 26 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_reg(X_train, X_test, y_train,  y_test, weights):\n",
    "    if weights==-1:\n",
    "        model = LogisticRegression()\n",
    "    else:\n",
    "        model = LogisticRegression(class_weight={0:weights[0], 1:weights[1]})\n",
    "\n",
    "    model.fit(X_train, y_train)\n",
    "    acc = model.score(X_test, y_test)\n",
    "    print('Accuracy', acc, '\\n')\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    print('preds', y_pred[:5], '\\n')\n",
    "\n",
    "    cl_rep = classification_report(y_test, y_pred)\n",
    "    print(cl_rep)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8031272210376688 \n",
      "\n",
      "preds [0. 0. 0. 0. 1.] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.84      0.90      0.87      1033\n",
      "         1.0       0.66      0.54      0.59       374\n",
      "\n",
      "    accuracy                           0.80      1407\n",
      "   macro avg       0.75      0.72      0.73      1407\n",
      "weighted avg       0.79      0.80      0.80      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = -1\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7931769722814499 \n",
      "\n",
      "preds [0. 0. 0. 0. 1.] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.88      0.83      0.86      1033\n",
      "         1.0       0.60      0.68      0.64       374\n",
      "\n",
      "    accuracy                           0.79      1407\n",
      "   macro avg       0.74      0.76      0.75      1407\n",
      "weighted avg       0.80      0.79      0.80      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = [1, 1.5]\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7739872068230277 \n",
      "\n",
      "preds [0. 0. 0. 0. 1.] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.89      0.79      0.84      1033\n",
      "         1.0       0.56      0.74      0.63       374\n",
      "\n",
      "    accuracy                           0.77      1407\n",
      "   macro avg       0.72      0.76      0.74      1407\n",
      "weighted avg       0.80      0.77      0.78      1407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = [1, 2]\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imbalanced Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Method1 : Undersampling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Class Count\n",
    "count_class_0, count_class_1 = df1.Churn.value_counts()\n",
    "\n",
    "# Divide by class\n",
    "df_class_0 = df1[df1['Churn'] == 0]\n",
    "df_class_1 = df1[df1['Churn'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5163"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_class_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1869"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_class_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Under-Sampling\n",
      "Churn\n",
      "0    1869\n",
      "1    1869\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#  Undersampling 0-class and concat DataFrames of voth class\n",
    "df_class_0_under = df_class_0.sample(count_class_1)\n",
    "df_test_under = pd.concat([df_class_0_under, df_class_1], axis=0)\n",
    "\n",
    "print('Random Under-Sampling')\n",
    "print(df_test_under.Churn.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_test_under.drop('Churn', axis='columns')\n",
    "y = df_test_under['Churn']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y ,test_size=0.2, random_state=15, stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0    1495\n",
       "1    1495\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of classes in training data\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trainig Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7419786096256684 \n",
      "\n",
      "preds [0 1 1 1 0] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.71      0.73       374\n",
      "           1       0.73      0.78      0.75       374\n",
      "\n",
      "    accuracy                           0.74       748\n",
      "   macro avg       0.74      0.74      0.74       748\n",
      "weighted avg       0.74      0.74      0.74       748\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = -1\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Method 2 : Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random over- sampling:\n",
      "Churn\n",
      "0    5163\n",
      "1    5163\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# oversampling 1-class and concat the DataFrames of both classes\n",
    "df_class_1_over = df_class_1.sample(count_class_0, replace=True)\n",
    "df_test_over = pd.concat([df_class_0, df_class_1_over], axis=0)\n",
    "\n",
    "print('Random over- sampling:')\n",
    "print(df_test_over.Churn.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_test_over.drop('Churn', axis='columns')\n",
    "y = df_test_over['Churn']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=15, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "1    4130\n",
       "0    4130\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Number of classes in training data\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7637947725072604 \n",
      "\n",
      "preds [1 0 1 1 0] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.73      0.76      1033\n",
      "           1       0.75      0.80      0.77      1033\n",
      "\n",
      "    accuracy                           0.76      2066\n",
      "   macro avg       0.77      0.76      0.76      2066\n",
      "weighted avg       0.77      0.76      0.76      2066\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = -1\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Method 3 : SMOTE***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install imbalanced-learn library use 'pip install imbalanced-learn' command\n",
    "X = df1.drop('Churn', axis='columns')\n",
    "y = df1['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0    5163\n",
       "1    5163\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X_sm, y_sm = smote.fit_resample(X,y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train,  y_test = train_test_split(X_sm, y_sm, test_size=0.2, random_state=15, stratify=y_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "1    4130\n",
       "0    4130\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Number of classes in training data\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Result : Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7904162633107454 \n",
      "\n",
      "preds [1 0 1 1 0] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.76      0.78      1033\n",
      "           1       0.77      0.82      0.80      1033\n",
      "\n",
      "    accuracy                           0.79      2066\n",
      "   macro avg       0.79      0.79      0.79      2066\n",
      "weighted avg       0.79      0.79      0.79      2066\n",
      "\n"
     ]
    }
   ],
   "source": [
    "weights = -1\n",
    "log_reg(X_train, X_test, y_train,  y_test, weights)"
   ]
  }
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