Telco_Customer_Churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGtrWrLXcPgf"
   },
   "source": [
    "# **Introduction**\n",
    "Customer attrition is one of the biggest expenditures of any organization. Customer churn otherwise known as customer attrition or customer turnover is the percentage of customers that stopped using your company's product or service within a specified timeframe.\n",
    "For instance, if you began the year with 500 customers but later ended with 480 customers, the percentage of customers that left would be 4%. If we could figure out why a customer leaves and when they leave with reasonable accuracy, it would immensely help the organization to strategize their retention initiatives manifold.\n",
    "\n",
    "In this project, we aim to find the likelihood of a customer leaving the organization, the key indicators of churn as well as the retention strategies that can be implemented to avert this problem.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mbWdbAUvVErg"
   },
   "source": [
    "# Business Understanding\n",
    "Vodafone is a leadng telecommunication company in Europe and Africa, led by their mission to connect for a better future(Vodafone, 2023).Vodafone Corporation aims to reduce customer churn and improve retention strategies by leveraging the past behaviour and patterns of their customers. The primary objective is to accurately predict the likelihood of customer churn based on historical data and identify the key indicators that drive churn behavior. By understanding the factors influencing churn, Vodafone aims to proactively implement targeted retention strategies to mitigate customer attrition. The marketing and sales teams are eager to receive actionable insights and recommendations that will enable them to develop personalized retention campaigns and enhance customer satisfaction. The project's success will be measured by the reduction in churn rate, improved customer retention, and increased customer loyalty. The available resources include a comprehensive dataset provided by the marketing and sales teams, a designated budget for analysis and model development, and a timeline of one month for the completion of the project. The findings will be presented to the business development unit, marketing, and sales teams, with the expectation that the insights will guide strategic decision-making and contribute to Vodafone's overall growth and profitability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RpiNRp9VYiRo"
   },
   "source": [
    "# Hypothesis\n",
    "Null Hypothesis: There is no significant relationship between the customer's internet service provider and their likelihood of churn\n",
    "\n",
    "Alterntive Hypothesis : There is a significant relationship between the Customer's internet service provider and their likelihood of churn\n",
    "\n",
    "\n",
    "# Questions\n",
    "1. What is the overall churn rate?\n",
    "2. Is there a relationship between the tenure of a customer and their likelihood of churn?\n",
    "3. Does the type of internet service influence customer churn?\n",
    "4. Is there a correlation between the customer's payment method and churn rate?\n",
    "5. Does the presence of paperless billing affect customer churn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZA5PJHd6aEcm"
   },
   "source": [
    "# Data Understanding\n",
    "The first dataset consisted of the first 3000 records of the companies customer churn data. The dataset was stored in a remote database and to access this dataset, a connection to the Microsoft SQL Server database using an Open Database Connectivity (ODBC) library such as pyodbc or an Object-Relational Mapping (ORM) library like SQLAlchemy needed to be established. This allowed for querying the database and retrieving the first 3000 records.\n",
    "\n",
    "THe second dataset is a csv file, with 2000 records.\n",
    "THe third dataset is the test dataset.\n",
    "The column description of this datasets is:\n",
    "The data for this project is in a csv format. The following describes the columns present in the data.\n",
    "\n",
    "Gender -- Whether the customer is a male or a female\n",
    "\n",
    "SeniorCitizen -- Whether a customer is a senior citizen or not\n",
    "\n",
    "Partner -- Whether the customer has a partner or not (Yes, No)\n",
    "\n",
    "Dependents -- Whether the customer has dependents or not (Yes, No)\n",
    "\n",
    "Tenure -- Number of months the customer has stayed with the company\n",
    "\n",
    "Phone Service -- Whether the customer has a phone service or not (Yes, No)\n",
    "\n",
    "MultipleLines -- Whether the customer has multiple lines or not\n",
    "\n",
    "InternetService -- Customer's internet service provider (DSL, Fiber Optic, No)\n",
    "\n",
    "OnlineSecurity -- Whether the customer has online security or not (Yes, No, No Internet)\n",
    "\n",
    "OnlineBackup -- Whether the customer has online backup or not (Yes, No, No Internet)\n",
    "\n",
    "DeviceProtection -- Whether the customer has device protection or not (Yes, No, No internet service)\n",
    "\n",
    "TechSupport -- Whether the customer has tech support or not (Yes, No, No internet)\n",
    "\n",
    "StreamingTV -- Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
    "\n",
    "StreamingMovies -- Whether the customer has streaming movies or not (Yes, No, No Internet service)\n",
    "\n",
    "Contract -- The contract term of the customer (Month-to-Month, One year, Two year)\n",
    "\n",
    "PaperlessBilling -- Whether the customer has paperless billing or not (Yes, No)\n",
    "\n",
    "Payment Method -- The customer's payment method (Electronic check, mailed check, Bank transfer(automatic), Credit card(automatic))\n",
    "\n",
    "MonthlyCharges -- The amount charged to the customer monthly\n",
    "\n",
    "TotalCharges -- The total amount charged to the customer\n",
    "\n",
    "Churn -- Whether the customer churned or not (Yes or No)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FlVB1uwQcuFs"
   },
   "source": [
    "# Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6RXbXTsQVDeQ",
    "outputId": "2a836bb9-9c15-4582-8183-b5d6a4d8b40d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyodbc in c:\\users\\newton\\projects\\environment\\lib\\site-packages (4.0.39)\n"
     ]
    }
   ],
   "source": [
    "#For creating a connection with the remote database\n",
    "!pip install  pyodbc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in c:\\users\\newton\\projects\\environment\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\newton\\projects\\environment\\lib\\site-packages (from imbalanced-learn) (1.2.2)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\newton\\projects\\environment\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\newton\\projects\\environment\\lib\\site-packages (from imbalanced-learn) (3.1.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\newton\\projects\\environment\\lib\\site-packages (from imbalanced-learn) (1.23.1)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\newton\\projects\\environment\\lib\\site-packages (from imbalanced-learn) (1.8.1)\n"
     ]
    }
   ],
   "source": [
    "#For balancing the data\n",
    "!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyodbc in c:\\users\\newton\\projects\\environment\\lib\\site-packages (4.0.39)\n"
     ]
    }
   ],
   "source": [
    "#Package to create a connection string to the database\n",
    "!pip install  pyodbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python-dotenv in c:\\users\\newton\\projects\\environment\\lib\\site-packages (1.0.0)\n"
     ]
    }
   ],
   "source": [
    "#Package for creating environment variables \n",
    "!pip install python-dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xUXu2PLEdIqc"
   },
   "source": [
    "# Importations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "xdstaZ8pdMyu"
   },
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "#Data handling\n",
    "import pyodbc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#For creating connection\n",
    "import pyodbc\n",
    "\n",
    "#For loading environment variables\n",
    "from dotenv import dotenv_values\n",
    "import dotenv\n",
    "\n",
    "\n",
    "#Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#Stat\n",
    "import scipy.stats as stats\n",
    "import random\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "random.seed(42)\n",
    "\n",
    "\n",
    "#Imputing missing values library\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "#Importing SMOTE for data balancing\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "\n",
    "# Feature Processing (Scikit-learn processing, etc. )\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# Modelling\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import *\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "#Model Evaluation\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "\n",
    "# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Other packages\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#Exporting Key Components\n",
    "import pickle\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "GmFsKbhldTnN"
   },
   "outputs": [],
   "source": [
    "#Test dataset\n",
    "\n",
    "test = pd.read_excel('Test.xlsx')\n",
    "\n",
    "\n",
    "#The first set of dataset\n",
    "file_url ='https://raw.githubusercontent.com/Azubi-Africa/Career_Accelerator_LP2-Classifcation/main/LP2_Telco-churn-last-2000.csv'\n",
    "# Read the file into a DataFrame\n",
    "data1 = pd.read_csv(file_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second set of dataset is contained a remote Microsoft SQL Server.TO access it, we need to create a connection to the Server. The sensitive information like the server name and password are contained in the .env file, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Y2Qfa30I-y5S"
   },
   "outputs": [],
   "source": [
    "# Loading environment variables from .env file into a dictionary\n",
    "environment_variables = dotenv_values('.env')\n",
    "\n",
    "\n",
    "# Getting the values for the credentials you set in the '.env' file\n",
    "database = environment_variables.get(\"DATABASE\")\n",
    "server = environment_variables.get(\"SERVER\")\n",
    "username = environment_variables.get(\"USERNAME\")\n",
    "password = environment_variables.get(\"PASSWORD\")\n",
    "\n",
    "\n",
    "connection_string = f\"DRIVER={{SQL Server}};SERVER={server};DATABASE={database};UID={username};PWD={password}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "9tMZipZlDqFb",
    "outputId": "f5a557b8-b7f0-4e78-8283-3bcb9eef889c"
   },
   "outputs": [],
   "source": [
    "#Using the connect method of the pyodbc library to pass in the connection string to connect to the server\n",
    "\n",
    "connection = pyodbc.connect(connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sql query to get the data\n",
    "\n",
    "query = \"Select * from dbo.LP2_Telco_churn_first_3000\"\n",
    "data = pd.read_sql(query, connection)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>DSL</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>One year</td>\n",
       "      <td>False</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.950001</td>\n",
       "      <td>1889.500000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.849998</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>One year</td>\n",
       "      <td>False</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.299999</td>\n",
       "      <td>1840.750000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.699997</td>\n",
       "      <td>151.649994</td>\n",
       "      <td>True</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  Female          False     True       False       1   \n",
       "1  5575-GNVDE    Male          False    False       False      34   \n",
       "2  3668-QPYBK    Male          False    False       False       2   \n",
       "3  7795-CFOCW    Male          False    False       False      45   \n",
       "4  9237-HQITU  Female          False    False       False       2   \n",
       "\n",
       "   PhoneService MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0         False          None             DSL          False  ...   \n",
       "1          True         False             DSL           True  ...   \n",
       "2          True         False             DSL           True  ...   \n",
       "3         False          None             DSL           True  ...   \n",
       "4          True         False     Fiber optic          False  ...   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0            False       False       False           False  Month-to-month   \n",
       "1             True       False       False           False        One year   \n",
       "2            False       False       False           False  Month-to-month   \n",
       "3             True        True       False           False        One year   \n",
       "4            False       False       False           False  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0             True           Electronic check      29.850000     29.850000   \n",
       "1            False               Mailed check      56.950001   1889.500000   \n",
       "2             True               Mailed check      53.849998    108.150002   \n",
       "3            False  Bank transfer (automatic)      42.299999   1840.750000   \n",
       "4             True           Electronic check      70.699997    151.649994   \n",
       "\n",
       "   Churn  \n",
       "0  False  \n",
       "1  False  \n",
       "2   True  \n",
       "3  False  \n",
       "4   True  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 21)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        3000 non-null   object \n",
      " 1   gender            3000 non-null   object \n",
      " 2   SeniorCitizen     3000 non-null   bool   \n",
      " 3   Partner           3000 non-null   bool   \n",
      " 4   Dependents        3000 non-null   bool   \n",
      " 5   tenure            3000 non-null   int64  \n",
      " 6   PhoneService      3000 non-null   bool   \n",
      " 7   MultipleLines     2731 non-null   object \n",
      " 8   InternetService   3000 non-null   object \n",
      " 9   OnlineSecurity    2349 non-null   object \n",
      " 10  OnlineBackup      2349 non-null   object \n",
      " 11  DeviceProtection  2349 non-null   object \n",
      " 12  TechSupport       2349 non-null   object \n",
      " 13  StreamingTV       2349 non-null   object \n",
      " 14  StreamingMovies   2349 non-null   object \n",
      " 15  Contract          3000 non-null   object \n",
      " 16  PaperlessBilling  3000 non-null   bool   \n",
      " 17  PaymentMethod     3000 non-null   object \n",
      " 18  MonthlyCharges    3000 non-null   float64\n",
      " 19  TotalCharges      2995 non-null   float64\n",
      " 20  Churn             2999 non-null   object \n",
      "dtypes: bool(5), float64(2), int64(1), object(13)\n",
      "memory usage: 389.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combining the two datasets\n",
    "train = pd.concat([data, data1])\n",
    "#Saving the combined dataset.\n",
    "train.to_csv('telco_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>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>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>DSL</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>29.85</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>One year</td>\n",
       "      <td>False</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.950001</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.849998</td>\n",
       "      <td>108.150002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>45</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>DSL</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>One year</td>\n",
       "      <td>False</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.299999</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>True</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.699997</td>\n",
       "      <td>151.649994</td>\n",
       "      <td>True</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         False    True      False       1        False   \n",
       "1  5575-GNVDE    Male         False   False      False      34         True   \n",
       "2  3668-QPYBK    Male         False   False      False       2         True   \n",
       "3  7795-CFOCW    Male         False   False      False      45        False   \n",
       "4  9237-HQITU  Female         False   False      False       2         True   \n",
       "\n",
       "  MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0          None             DSL          False  ...            False   \n",
       "1         False             DSL           True  ...             True   \n",
       "2         False             DSL           True  ...            False   \n",
       "3          None             DSL           True  ...             True   \n",
       "4         False     Fiber optic          False  ...            False   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0       False       False           False  Month-to-month             True   \n",
       "1       False       False           False        One year            False   \n",
       "2       False       False           False  Month-to-month             True   \n",
       "3        True       False           False        One year            False   \n",
       "4       False       False           False  Month-to-month             True   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
       "0           Electronic check      29.850000         29.85  False  \n",
       "1               Mailed check      56.950001        1889.5  False  \n",
       "2               Mailed check      53.849998    108.150002   True  \n",
       "3  Bank transfer (automatic)      42.299999       1840.75  False  \n",
       "4           Electronic check      70.699997    151.649994   True  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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",
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7613-LLQFO</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>12</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>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>84.45</td>\n",
       "      <td>1059.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4568-TTZRT</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.40</td>\n",
       "      <td>181.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9513-DXHDA</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>27</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>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>81.70</td>\n",
       "      <td>2212.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2640-PMGFL</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>27</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>Yes</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>79.50</td>\n",
       "      <td>2180.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3801-HMYNL</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</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>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>89.15</td>\n",
       "      <td>89.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7613-LLQFO   Male              0      No         No      12          Yes   \n",
       "1  4568-TTZRT   Male              0      No         No       9          Yes   \n",
       "2  9513-DXHDA   Male              0      No         No      27          Yes   \n",
       "3  2640-PMGFL   Male              0      No        Yes      27          Yes   \n",
       "4  3801-HMYNL   Male              0     Yes        Yes       1          Yes   \n",
       "\n",
       "  MultipleLines InternetService       OnlineSecurity         OnlineBackup  \\\n",
       "0           Yes     Fiber optic                   No                   No   \n",
       "1            No              No  No internet service  No internet service   \n",
       "2            No             DSL                  Yes                   No   \n",
       "3           Yes     Fiber optic                   No                   No   \n",
       "4            No     Fiber optic                   No                   No   \n",
       "\n",
       "      DeviceProtection          TechSupport          StreamingTV  \\\n",
       "0                   No                   No                  Yes   \n",
       "1  No internet service  No internet service  No internet service   \n",
       "2                  Yes                  Yes                  Yes   \n",
       "3                   No                  Yes                   No   \n",
       "4                   No                   No                  Yes   \n",
       "\n",
       "       StreamingMovies        Contract PaperlessBilling     PaymentMethod  \\\n",
       "0                   No  Month-to-month              Yes  Electronic check   \n",
       "1  No internet service  Month-to-month               No      Mailed check   \n",
       "2                  Yes        One year               No  Electronic check   \n",
       "3                   No  Month-to-month              Yes  Electronic check   \n",
       "4                  Yes  Month-to-month               No      Mailed check   \n",
       "\n",
       "   MonthlyCharges TotalCharges  \n",
       "0           84.45      1059.55  \n",
       "1           20.40        181.8  \n",
       "2           81.70      2212.55  \n",
       "3           79.50      2180.55  \n",
       "4           89.15        89.15  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5043 entries, 0 to 2042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        5043 non-null   object \n",
      " 1   gender            5043 non-null   object \n",
      " 2   SeniorCitizen     5043 non-null   object \n",
      " 3   Partner           5043 non-null   object \n",
      " 4   Dependents        5043 non-null   object \n",
      " 5   tenure            5043 non-null   int64  \n",
      " 6   PhoneService      5043 non-null   object \n",
      " 7   MultipleLines     4774 non-null   object \n",
      " 8   InternetService   5043 non-null   object \n",
      " 9   OnlineSecurity    4392 non-null   object \n",
      " 10  OnlineBackup      4392 non-null   object \n",
      " 11  DeviceProtection  4392 non-null   object \n",
      " 12  TechSupport       4392 non-null   object \n",
      " 13  StreamingTV       4392 non-null   object \n",
      " 14  StreamingMovies   4392 non-null   object \n",
      " 15  Contract          5043 non-null   object \n",
      " 16  PaperlessBilling  5043 non-null   object \n",
      " 17  PaymentMethod     5043 non-null   object \n",
      " 18  MonthlyCharges    5043 non-null   float64\n",
      " 19  TotalCharges      5038 non-null   object \n",
      " 20  Churn             5042 non-null   object \n",
      "dtypes: float64(1), int64(1), object(19)\n",
      "memory usage: 866.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#THe number of rows and data types of each rows in the combined dataset\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combined dataser has 5043 rows and 20 columns. All the columns are object data type except for the tenure and MonthlyCharges. Some of the columns have got missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        2000 non-null   object \n",
      " 1   gender            2000 non-null   object \n",
      " 2   SeniorCitizen     2000 non-null   int64  \n",
      " 3   Partner           2000 non-null   object \n",
      " 4   Dependents        2000 non-null   object \n",
      " 5   tenure            2000 non-null   int64  \n",
      " 6   PhoneService      2000 non-null   object \n",
      " 7   MultipleLines     2000 non-null   object \n",
      " 8   InternetService   2000 non-null   object \n",
      " 9   OnlineSecurity    2000 non-null   object \n",
      " 10  OnlineBackup      2000 non-null   object \n",
      " 11  DeviceProtection  2000 non-null   object \n",
      " 12  TechSupport       2000 non-null   object \n",
      " 13  StreamingTV       2000 non-null   object \n",
      " 14  StreamingMovies   2000 non-null   object \n",
      " 15  Contract          2000 non-null   object \n",
      " 16  PaperlessBilling  2000 non-null   object \n",
      " 17  PaymentMethod     2000 non-null   object \n",
      " 18  MonthlyCharges    2000 non-null   float64\n",
      " 19  TotalCharges      2000 non-null   object \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 312.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THe test dataset has 2000 rows and 20 columns. The target column, Churn is not in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>5043.0</td>\n",
       "      <td>32.576641</td>\n",
       "      <td>24.529807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>5043.0</td>\n",
       "      <td>65.090214</td>\n",
       "      <td>30.068133</td>\n",
       "      <td>18.4</td>\n",
       "      <td>35.775</td>\n",
       "      <td>70.550003</td>\n",
       "      <td>90.050003</td>\n",
       "      <td>118.650002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count       mean        std   min     25%        50%  \\\n",
       "tenure          5043.0  32.576641  24.529807   0.0   9.000  29.000000   \n",
       "MonthlyCharges  5043.0  65.090214  30.068133  18.4  35.775  70.550003   \n",
       "\n",
       "                      75%         max  \n",
       "tenure          56.000000   72.000000  \n",
       "MonthlyCharges  90.050003  118.650002  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the tenure column:\n",
    "\n",
    "The average tenure is approximately 32.58 months, indicating that, on average, customers have been with the company for around 2.7 years.\n",
    "The standard deviation is relatively high (24.53), suggesting that there is a wide variation in customer tenure. This could indicate that there are both long-term and short-term customers in the dataset.\n",
    "The median value (50th percentile) of 29 months suggests that half of the customers have been with the company for less than 2.5 years.\n",
    "The maximum tenure of 72 months suggests the presence of long-term customers who have been with the company for the entire duration of the dataset.\n",
    "\n",
    "For Monthly Charges Column:\n",
    "\n",
    "The average monthly charge is approximately 65.09, which shows the average amount that customers are billed per month.\n",
    "The highest amount charged to a customer is 118.65 and the minimum is 18.4\n",
    "A quarter of the customers have relatively low monthly charges, that is below 35.77.half of the customers have a monthly charge below 71 and three-quarters of the customers have monthly charges below 90.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.161500</td>\n",
       "      <td>0.368084</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>31.853000</td>\n",
       "      <td>24.632677</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55.000</td>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>63.933325</td>\n",
       "      <td>30.136858</td>\n",
       "      <td>18.25</td>\n",
       "      <td>34.25</td>\n",
       "      <td>69.8</td>\n",
       "      <td>89.275</td>\n",
       "      <td>118.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count       mean        std    min    25%   50%     75%  \\\n",
       "SeniorCitizen   2000.0   0.161500   0.368084   0.00   0.00   0.0   0.000   \n",
       "tenure          2000.0  31.853000  24.632677   0.00   8.00  27.0  55.000   \n",
       "MonthlyCharges  2000.0  63.933325  30.136858  18.25  34.25  69.8  89.275   \n",
       "\n",
       "                   max  \n",
       "SeniorCitizen     1.00  \n",
       "tenure           72.00  \n",
       "MonthlyCharges  118.75  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for duplicates in the train dataset\n",
    "train.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for duplicates in the test data set\n",
    "test.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID            0\n",
       "gender                0\n",
       "SeniorCitizen         0\n",
       "Partner               0\n",
       "Dependents            0\n",
       "tenure                0\n",
       "PhoneService          0\n",
       "MultipleLines       269\n",
       "InternetService       0\n",
       "OnlineSecurity      651\n",
       "OnlineBackup        651\n",
       "DeviceProtection    651\n",
       "TechSupport         651\n",
       "StreamingTV         651\n",
       "StreamingMovies     651\n",
       "Contract              0\n",
       "PaperlessBilling      0\n",
       "PaymentMethod         0\n",
       "MonthlyCharges        0\n",
       "TotalCharges          5\n",
       "Churn                 1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values in the train dataset\n",
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Missing values in the test dataset\n",
    "test.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "THe test dataset does not have any missing values. However, the data types for some of it columns are not correct, and when changed, they might reveal some missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
       "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for columns in the train dataset\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The categories in the OnlineSecurity column are: \n",
      "False                  1478\n",
      "No                     1029\n",
      "True                    871\n",
      "Yes                     585\n",
      "No internet service     429\n",
      "Name: OnlineSecurity, dtype: int64\n",
      "\n",
      " The categories in the gender column are: \n",
      "Male      2559\n",
      "Female    2484\n",
      "Name: gender, dtype: int64\n",
      "\n",
      " The columns in the SeniorCitizen column are: \n",
      "False    4224\n",
      "True      819\n",
      "Name: SeniorCitizen, dtype: int64\n",
      "\n",
      " The categories in the partner column are: \n",
      "False    1538\n",
      "True     1462\n",
      "No       1047\n",
      "Yes       996\n",
      "Name: Partner, dtype: int64\n",
      "\n",
      " The categories in the paymentmethod column are: \n",
      "Electronic check             1700\n",
      "Mailed check                 1128\n",
      "Bank transfer (automatic)    1125\n",
      "Credit card (automatic)      1090\n",
      "Name: PaymentMethod, dtype: int64\n",
      "\n",
      " The categories in the contract column are: \n",
      "Month-to-month    2744\n",
      "Two year          1244\n",
      "One year          1055\n",
      "Name: Contract, dtype: int64\n",
      "\n",
      " The categories in the StreamingMovies column are: \n",
      "True                   1199\n",
      "False                  1150\n",
      "No                      831\n",
      "Yes                     783\n",
      "No internet service     429\n",
      "Name: StreamingMovies, dtype: int64\n",
      "\n",
      " The categories in the Techsupport column are: \n",
      "False                  1476\n",
      "No                      996\n",
      "True                    873\n",
      "Yes                     618\n",
      "No internet service     429\n",
      "Name: TechSupport, dtype: int64\n",
      "\n",
      " The categories in the paperless billing column are: \n",
      "True     1776\n",
      "False    1224\n",
      "Yes      1220\n",
      "No        823\n",
      "Name: PaperlessBilling, dtype: int64\n",
      "\n",
      " The categories in the streamming tv column are: \n",
      "False                  1190\n",
      "True                   1159\n",
      "No                      828\n",
      "Yes                     786\n",
      "No internet service     429\n",
      "Name: StreamingTV, dtype: int64\n",
      "\n",
      " The categories in the device protection column are: \n",
      "False                  1296\n",
      "True                   1053\n",
      "No                      923\n",
      "Yes                     691\n",
      "No internet service     429\n",
      "Name: DeviceProtection, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#The number each of the categories occurs\n",
    "print('The categories in the OnlineSecurity column are: ')\n",
    "print(train['OnlineSecurity'].value_counts())\n",
    "\n",
    "print('\\n The categories in the gender column are: ')\n",
    "print(train['gender'].value_counts())\n",
    "\n",
    "print('\\n The columns in the SeniorCitizen column are: ')\n",
    "print(train['SeniorCitizen'].value_counts())\n",
    "\n",
    "print('\\n The categories in the partner column are: ')\n",
    "print(train['Partner'].value_counts())\n",
    "\n",
    "print('\\n The categories in the paymentmethod column are: ')\n",
    "print(train['PaymentMethod'].value_counts())\n",
    "\n",
    "print('\\n The categories in the contract column are: ')\n",
    "print(train['Contract'].value_counts())\n",
    "\n",
    "print('\\n The categories in the StreamingMovies column are: ')\n",
    "print(train['StreamingMovies'].value_counts())\n",
    "\n",
    "print('\\n The categories in the Techsupport column are: ')\n",
    "print(train['TechSupport'].value_counts())\n",
    "\n",
    "print('\\n The categories in the paperless billing column are: ')\n",
    "print(train['PaperlessBilling'].value_counts())\n",
    "\n",
    "print('\\n The categories in the streamming tv column are: ')\n",
    "print(train['StreamingTV'].value_counts())\n",
    "\n",
    "print('\\n The categories in the device protection column are: ')\n",
    "print(train['DeviceProtection'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The categories in the OnlineSecurity column for test dataset are: \n",
      "No                     991\n",
      "Yes                    563\n",
      "No internet service    446\n",
      "Name: OnlineSecurity, dtype: int64\n",
      "\n",
      " The categories in the gender column for test dataset are: \n",
      "Female    1004\n",
      "Male       996\n",
      "Name: gender, dtype: int64\n",
      "\n",
      " The columns in the SeniorCitizen column for test dataset are: \n",
      "0    1677\n",
      "1     323\n",
      "Name: SeniorCitizen, dtype: int64\n",
      "\n",
      " The categories in the partner column for test dataset are: \n",
      "No     1056\n",
      "Yes     944\n",
      "Name: Partner, dtype: int64\n",
      "\n",
      " The categories in the paymentmethod column for test dataset are: \n",
      "Electronic check             665\n",
      "Mailed check                 484\n",
      "Credit card (automatic)      432\n",
      "Bank transfer (automatic)    419\n",
      "Name: PaymentMethod, dtype: int64\n",
      "\n",
      " The categories in the contract column for test dataset are: \n",
      "Month-to-month    1131\n",
      "Two year           451\n",
      "One year           418\n",
      "Name: Contract, dtype: int64\n",
      "\n",
      " The categories in the StreamingMovies column for test dataset are: \n",
      "No                     804\n",
      "Yes                    750\n",
      "No internet service    446\n",
      "Name: StreamingMovies, dtype: int64\n",
      "\n",
      " The categories in the Techsupport column for test dataset are: \n",
      "No                     1001\n",
      "Yes                     553\n",
      "No internet service     446\n",
      "Name: TechSupport, dtype: int64\n",
      "\n",
      " The categories in the paperless billing column for test dataset are: \n",
      "Yes    1175\n",
      "No      825\n",
      "Name: PaperlessBilling, dtype: int64\n",
      "\n",
      " The categories in the streamming tv column for test dataset are: \n",
      "No                     792\n",
      "Yes                    762\n",
      "No internet service    446\n",
      "Name: StreamingTV, dtype: int64\n",
      "\n",
      " The categories in the device protection column for test dataset are: \n",
      "No                     876\n",
      "Yes                    678\n",
      "No internet service    446\n",
      "Name: DeviceProtection, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#The number each of the categories occurs in the test dataset\n",
    "print('The categories in the OnlineSecurity column for test dataset are: ')\n",
    "print(test['OnlineSecurity'].value_counts())\n",
    "\n",
    "print('\\n The categories in the gender column for test dataset are: ')\n",
    "print(test['gender'].value_counts())\n",
    "\n",
    "print('\\n The columns in the SeniorCitizen column for test dataset are: ')\n",
    "print(test['SeniorCitizen'].value_counts())\n",
    "\n",
    "print('\\n The categories in the partner column for test dataset are: ')\n",
    "print(test['Partner'].value_counts())\n",
    "\n",
    "print('\\n The categories in the paymentmethod column for test dataset are: ')\n",
    "print(test['PaymentMethod'].value_counts())\n",
    "\n",
    "print('\\n The categories in the contract column for test dataset are: ')\n",
    "print(test['Contract'].value_counts())\n",
    "\n",
    "print('\\n The categories in the StreamingMovies column for test dataset are: ')\n",
    "print(test['StreamingMovies'].value_counts())\n",
    "\n",
    "print('\\n The categories in the Techsupport column for test dataset are: ')\n",
    "print(test['TechSupport'].value_counts())\n",
    "\n",
    "print('\\n The categories in the paperless billing column for test dataset are: ')\n",
    "print(test['PaperlessBilling'].value_counts())\n",
    "\n",
    "print('\\n The categories in the streamming tv column for test dataset are: ')\n",
    "print(test['StreamingTV'].value_counts())\n",
    "\n",
    "print('\\n The categories in the device protection column for test dataset are: ')\n",
    "print(test['DeviceProtection'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, True, None, 'No', 'Yes'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The number of categories in the target column\n",
    "train['Churn'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The responses should only be either Yes or No. In this case, True means Yes, while False means No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#THe distribution of the categories in the Churn column\n",
    "sns.countplot(x='Churn', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "True and Yes represent the customers that Churned while False and No represent the customer who did not Churn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.hist(bins=50, figsize = (15,4))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram indicates a non-linear distribution of values for the tenure column. It suggests that there are concentrations of values at both ends of the range, with fewer values in the middle.\n",
    "For the Monthly Charges column, it shows that the values are slightly right skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr_matrix = train.corr()\n",
    "\n",
    "# Plot a heatmap of the correlation matrix\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Churn report based on the type of contract\n",
    "sns.countplot(x='Churn', data=train, hue='Contract')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Churn report based on the payment method used\n",
    "sns.countplot(x='Churn', hue='PaymentMethod', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shouls the different payment methods and whether they churned or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#A stacked bar plot of Churn and Contract\n",
    "cross_tab = pd.crosstab(train['Contract'], train['Churn'])\n",
    "cross_tab.plot(kind='bar', stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SeniorCitizen  False  True\n",
      "Churn                     \n",
      "False           1936   283\n",
      "True             588   192\n",
      "No              1291   196\n",
      "Yes              408   148\n"
     ]
    }
   ],
   "source": [
    "# Cross-tabulate Churn and SeniorCitizen\n",
    "cross_tab = pd.crosstab(train['Churn'], train['SeniorCitizen'])\n",
    "print(cross_tab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the unnecesary columns\n",
    "train.drop('customerID', axis=1, inplace=True)\n",
    "test.drop('customerID', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing True/False into Yes and No respectively in the Churn Column\n",
    "train['Churn'] = train['Churn'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['Churn'] = train['Churn'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#WE replace None with No, which is the mode \n",
    "train['Churn'] = train['Churn'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "\n",
    "#We replace even for the Online securit column\n",
    "train['OnlineSecurity'] = train['OnlineSecurity'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['OnlineSecurity'] = train['OnlineSecurity'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['OnlineSecurity'] = train['OnlineSecurity'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "train['OnlineSecurity'] = train['OnlineSecurity'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "\n",
    "\n",
    "#Replace for the Partner Column\n",
    "train['Partner'] = train['Partner'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['Partner'] = train['Partner'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#Replacing for the Streaming movies column\n",
    "train['StreamingMovies'] = train['StreamingMovies'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['StreamingMovies'] = train['StreamingMovies'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#Replacing for the tech support column\n",
    "train['Techsupport'] = train['TechSupport'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['Techsupport'] = train['Techsupport'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#Replacing for the multiple lines column\n",
    "train['MultipleLines'] = train['MultipleLines'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['MultipleLines'] = train['MultipleLines'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['MultipleLines'] = train['MultipleLines'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "#Replacing for the device protection column\n",
    "train['DeviceProtection'] = train['DeviceProtection'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['DeviceProtection'] = train['DeviceProtection'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['DeviceProtection'] = train['DeviceProtection'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "#Replacing for the tech support column\n",
    "train['TechSupport'] = train['TechSupport'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['TechSupport'] = train['TechSupport'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['TechSupport'] = train['TechSupport'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "train['TechSupport'] = train['TechSupport'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "\n",
    "#Replacing for the Streaming TV column\n",
    "train['StreamingTV'] = train['StreamingTV'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['StreamingTV'] = train['StreamingTV'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['StreamingTV'] = train['StreamingTV'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "train['StreamingTV'] = train['StreamingTV'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "\n",
    "\n",
    "\n",
    "train['StreamingMovies'] = train['StreamingMovies'].apply(lambda x:str(x).replace(\"None\",\"No\"))\n",
    "train['StreamingMovies'] = train['StreamingMovies'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "\n",
    "#Replacing for the paperless billing column\n",
    "train['PaperlessBilling'] = train['PaperlessBilling'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['PaperlessBilling'] = train['PaperlessBilling'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "\n",
    "#Converting false and true into 0 and one respectively for the senior citizen column\n",
    "train['SeniorCitizen'] = train['SeniorCitizen'].apply(lambda x:str(x).replace(\"True\",\"1\"))\n",
    "train['SeniorCitizen'] = train['SeniorCitizen'].apply(lambda x:str(x).replace(\"False\",\"0\"))\n",
    "\n",
    "train['DeviceProtection'] = train['DeviceProtection'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "#Replacing for the Dependents column\n",
    "train['Dependents'] = train['Dependents'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['Dependents'] = train['Dependents'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#Replacing for the Dependents column\n",
    "train['PhoneService'] = train['PhoneService'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['PhoneService'] = train['PhoneService'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "#Replacing for the Dependents column\n",
    "train['MultipleLines'] = train['MultipleLines'].apply(lambda x:str(x).replace(\"No phone service\",\"No\"))\n",
    "#Replacing for the Online backup column\n",
    "train['OnlineBackup'] = train['OnlineBackup'].apply(lambda x:str(x).replace(\"True\",\"Yes\"))\n",
    "train['OnlineBackup'] = train['OnlineBackup'].apply(lambda x:str(x).replace(\"False\",\"No\"))\n",
    "train['OnlineBackup'] = train['OnlineBackup'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns in the test dataset\n",
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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', 'Techsupport'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns in the train dataset\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        No\n",
       "1        No\n",
       "2        No\n",
       "3       Yes\n",
       "4        No\n",
       "       ... \n",
       "2038    Yes\n",
       "2039     No\n",
       "2040     No\n",
       "2041     No\n",
       "2042    Yes\n",
       "Name: TechSupport, Length: 5043, dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['TechSupport']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        No\n",
       "1        No\n",
       "2        No\n",
       "3       Yes\n",
       "4        No\n",
       "       ... \n",
       "2038    Yes\n",
       "2039     No\n",
       "2040     No\n",
       "2041     No\n",
       "2042    Yes\n",
       "Name: Techsupport, Length: 5043, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Techsupport']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the train dataset, there are two Techsupport columns. Both TechSupport and Techsupport columns in the train dataset have the same values.We drop one of the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping one of the techsupport column\n",
    "train.drop('Techsupport', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace inconsistent values in the test dataset\n",
    "test['OnlineSecurity'] = test['OnlineSecurity'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "test['TechSupport'] = test['TechSupport'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "test['StreamingTV'] = test['StreamingTV'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "test['StreamingMovies'] = test['StreamingMovies'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n",
    "test['DeviceProtection'] = test['DeviceProtection'].apply(lambda x:str(x).replace(\"No internet service\",\"No\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CHange the data type of the Total Charges column\n",
    "train['TotalCharges'] = train['TotalCharges'].replace(' ', np.nan)\n",
    "#COnvert The total charges column into float\n",
    "train[\"TotalCharges\"]=train.TotalCharges.apply(lambda x:float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change the data type of the Total charges column in the test data set\n",
    "test['TotalCharges'] = test['TotalCharges'].replace(' ', np.nan)\n",
    "#COnvert The total charges column into float\n",
    "test[\"TotalCharges\"]=test.TotalCharges.apply(lambda x:float(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change the data type of the Senior Citizen column\n",
    "train[\"SeniorCitizen\"]=train.SeniorCitizen.apply(lambda x:int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "1914    1\n",
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      "1942    1\n",
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      "1947    0\n",
      "1948    0\n",
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      "1950    0\n",
      "1951    0\n",
      "1952    0\n",
      "1953    1\n",
      "1954    0\n",
      "1955    0\n",
      "1956    1\n",
      "1957    0\n",
      "1958    0\n",
      "1959    0\n",
      "1960    1\n",
      "1961    0\n",
      "1962    1\n",
      "1963    0\n",
      "1964    0\n",
      "1965    1\n",
      "1966    0\n",
      "1967    1\n",
      "1968    1\n",
      "1969    0\n",
      "1970    0\n",
      "1971    1\n",
      "1972    1\n",
      "1973    1\n",
      "1974    0\n",
      "1975    0\n",
      "1976    0\n",
      "1977    0\n",
      "1978    0\n",
      "1979    0\n",
      "1980    1\n",
      "1981    0\n",
      "1982    0\n",
      "1983    0\n",
      "1984    0\n",
      "1985    0\n",
      "1986    0\n",
      "1987    1\n",
      "1988    0\n",
      "1989    0\n",
      "1990    0\n",
      "1991    0\n",
      "1992    0\n",
      "1993    1\n",
      "1994    0\n",
      "1995    0\n",
      "1996    0\n",
      "1997    0\n",
      "1998    0\n",
      "1999    0\n",
      "2000    0\n",
      "2001    0\n",
      "2002    0\n",
      "2003    0\n",
      "2004    0\n",
      "2005    0\n",
      "2006    0\n",
      "2007    1\n",
      "2008    1\n",
      "2009    0\n",
      "2010    1\n",
      "2011    0\n",
      "2012    0\n",
      "2013    0\n",
      "2014    0\n",
      "2015    1\n",
      "2016    0\n",
      "2017    0\n",
      "2018    0\n",
      "2019    0\n",
      "2020    0\n",
      "2021    0\n",
      "2022    0\n",
      "2023    1\n",
      "2024    0\n",
      "2025    0\n",
      "2026    0\n",
      "2027    0\n",
      "2028    0\n",
      "2029    1\n",
      "2030    0\n",
      "2031    1\n",
      "2032    1\n",
      "2033    0\n",
      "2034    0\n",
      "2035    0\n",
      "2036    0\n",
      "2037    0\n",
      "2038    0\n",
      "2039    0\n",
      "2040    0\n",
      "2041    1\n",
      "2042    0\n",
      "Name: SeniorCitizen, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Custom rule check for the Senior Citizen Column\n",
    "with pd.option_context('display.max_rows',None,):\n",
    "    print(train['SeniorCitizen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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        8\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for missing values\n",
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0.000000\n",
       "SeniorCitizen       0.000000\n",
       "Partner             0.000000\n",
       "Dependents          0.000000\n",
       "tenure              0.000000\n",
       "PhoneService        0.000000\n",
       "MultipleLines       0.000000\n",
       "InternetService     0.000000\n",
       "OnlineSecurity      0.000000\n",
       "OnlineBackup        0.000000\n",
       "DeviceProtection    0.000000\n",
       "TechSupport         0.000000\n",
       "StreamingTV         0.000000\n",
       "StreamingMovies     0.000000\n",
       "Contract            0.000000\n",
       "PaperlessBilling    0.000000\n",
       "PaymentMethod       0.000000\n",
       "MonthlyCharges      0.000000\n",
       "TotalCharges        0.158636\n",
       "Churn               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The percentage of the missing values\n",
    "(train.isna().sum()*100)/ len(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing the Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing the numerical columns using mean\n",
    "numerical_imputer = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Numerical Columns\n",
    "numerical_columns = ['tenure', 'TotalCharges']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputing the numerical columns in the train dataset\n",
    "for column in numerical_columns:\n",
    "    train[column] = numerical_imputer.fit_transform(train[column].values.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#To Impute the Categorical columns\n",
    "#categorical_imputer = SimpleImputer(strategy='most_frequent')\n",
    "#categorical_columns = ['MultipleLines', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Churn']\n",
    "#for column in categorical_columns:\n",
    " #   train[column] = categorical_imputer.fit_transform(train[column].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing values in 'MultipleLines' column\n",
    "train['MultipleLines'].fillna('No', inplace=True)\n",
    "\n",
    "# Handling missing values in 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
    "# 'StreamingTV', and 'StreamingMovies' columns\n",
    "train['OnlineSecurity'].fillna('No', inplace=True)\n",
    "train['OnlineBackup'].fillna('No', inplace=True)\n",
    "train['DeviceProtection'].fillna('No', inplace=True)\n",
    "train['TechSupport'].fillna('No', inplace=True)\n",
    "train['StreamingTV'].fillna('No', inplace=True)\n",
    "train['StreamingMovies'].fillna('', inplace=True)\n",
    "train['Churn'].fillna('No', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5043 entries, 0 to 2042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            5043 non-null   object \n",
      " 1   SeniorCitizen     5043 non-null   int64  \n",
      " 2   Partner           5043 non-null   object \n",
      " 3   Dependents        5043 non-null   object \n",
      " 4   tenure            5043 non-null   float64\n",
      " 5   PhoneService      5043 non-null   object \n",
      " 6   MultipleLines     5043 non-null   object \n",
      " 7   InternetService   5043 non-null   object \n",
      " 8   OnlineSecurity    5043 non-null   object \n",
      " 9   OnlineBackup      5043 non-null   object \n",
      " 10  DeviceProtection  5043 non-null   object \n",
      " 11  TechSupport       5043 non-null   object \n",
      " 12  StreamingTV       5043 non-null   object \n",
      " 13  StreamingMovies   5043 non-null   object \n",
      " 14  Contract          5043 non-null   object \n",
      " 15  PaperlessBilling  5043 non-null   object \n",
      " 16  PaymentMethod     5043 non-null   object \n",
      " 17  MonthlyCharges    5043 non-null   float64\n",
      " 18  TotalCharges      5043 non-null   float64\n",
      " 19  Churn             5043 non-null   object \n",
      "dtypes: float64(3), int64(1), object(16)\n",
      "memory usage: 827.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            2000 non-null   object \n",
      " 1   SeniorCitizen     2000 non-null   int64  \n",
      " 2   Partner           2000 non-null   object \n",
      " 3   Dependents        2000 non-null   object \n",
      " 4   tenure            2000 non-null   int64  \n",
      " 5   PhoneService      2000 non-null   object \n",
      " 6   MultipleLines     2000 non-null   object \n",
      " 7   InternetService   2000 non-null   object \n",
      " 8   OnlineSecurity    2000 non-null   object \n",
      " 9   OnlineBackup      2000 non-null   object \n",
      " 10  DeviceProtection  2000 non-null   object \n",
      " 11  TechSupport       2000 non-null   object \n",
      " 12  StreamingTV       2000 non-null   object \n",
      " 13  StreamingMovies   2000 non-null   object \n",
      " 14  Contract          2000 non-null   object \n",
      " 15  PaperlessBilling  2000 non-null   object \n",
      " 16  PaymentMethod     2000 non-null   object \n",
      " 17  MonthlyCharges    2000 non-null   float64\n",
      " 18  TotalCharges      1997 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(15)\n",
      "memory usage: 297.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The distribution of the target variable\n",
    "sns.countplot(x='Churn', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution shows that the number of customers who said No is greater than those who said Yes, which suggests that a larger proportion of customers in the dataset did not churn.\n",
    "The higher number of customers who said \"No\" to churn indicates a relatively higher retention rate within the dataset. This suggests that a significant portion of customers did not terminate their services during the observed period.\n",
    "However, the higher number of the customers who churned indicates that efforts should be directed towards enhancing customer retention measures, targeting the specific needs and concerns of customers who are more likely to churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The distribution of Internet Service\n",
    "sns.countplot(x='InternetService', data=train)\n",
    "plt.xlabel('Internet Service')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Internet Service')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph, it appears that the majority of people in the dataset have Fiber optic as their internet service provider. This is indicated by the higher count or frequency of the \"Fiber optic\" category compared to other categories.\n",
    "The second most common internet service provider is DSL (Digital Subscriber Line), which has a lower count compared to Fiber optic but still represents a significant portion of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram for 'tenure'\n",
    "plt.hist(train['tenure'], bins=20)\n",
    "plt.xlabel('Tenure')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Distribution of Tenure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows that there are relatively high frequencies of customers at the beginning and end of their tenure periods, with lower frequencies in the middle.\n",
    "The middle curve, may indicate a potential period where customers are more likely to churn or transition to different services."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of monthly charges across different contract types.\n",
    "sns.boxplot(x='Contract', y='MonthlyCharges', data=train)\n",
    "plt.xlabel('Contract')\n",
    "plt.ylabel('Monthly Charges')\n",
    "plt.title('Monthly Charges by Contract Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The median monthly charge for a customer with a month-to-month contract, is 75, while the one for a one-year contract is 70, and for a two year contract is approximately 65. A two year contract has a wider distribution of charges followed by a one year contract and then a month to month contract."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate and Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. What is the overall churn rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall churn rate: 26.49%\n"
     ]
    }
   ],
   "source": [
    "# Count the number of churned customers\n",
    "churned_customers = train[train['Churn'] == 'Yes'].shape[0]\n",
    "\n",
    "# Count the total number of customers\n",
    "total_customers = train.shape[0]\n",
    "\n",
    "# Calculate the churn rate\n",
    "churn_rate = (churned_customers / total_customers) * 100\n",
    "\n",
    "# Print the churn rate\n",
    "print(\"Overall churn rate: {:.2f}%\".format(churn_rate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall churn rate in the dataset is 26.49%. This means that approximately 26.49% of the customers in the dataset have churned, while the remaining 73.51% have not churned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Is there a relationship between the tenure of a customer and their likelihood of churn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping the data by tenure\n",
    "tenure_groups = pd.cut(train['tenure'], bins=[0, 12, 24, 36, 48, 60, 72])\n",
    "churn_by_tenure = train.groupby(tenure_groups)['Churn'].value_counts(normalize=True).unstack()\n",
    "#Calculating the churn rate for each tenure group\n",
    "churn_rate_by_tenure = churn_by_tenure['Yes']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RfqoS8DUCM7PhokwiuD3/rQVsVG04ZmY22Nomgvw7xWZmNkyVebL4GtKTxCuJiDdWEpGZmQ2qMk1DxxaG1wXeBSyrJhwzMxtsZZqGZjdNuk7SDRXFY2Zmg6xM09BmhdG1gF1IP2NpZmbDQJmmodmkawQiNQn9HXh/lUGZmdngKdM0tO1gBGJmZkOj7A/TvAYYz8o/THNuRTGZmdkgKnON4DzgRcAc4Nk8OQAnAjOzYaDMGUE3MCEiVnmWoB1Jk4BvACOA70XEqU3zPwkcRbr2sBR4X0Tc2dftmJlZ/5VJBHOBLYF7+rLi3HPpWcA+wGJgpqRpEXFrodifge6IeELSh4DTgIP7sh0bXsZPnT7UIZSy8NT9hjoEs47pMRFIupzUBLQRcGt+dqD4wzT7t1n3RGBBRNyR13chcADwXCKIiGsK5f8EHNHXCpiZ2cD0dkbwlQGue2vSz1w2LAZ27aX8+4H/azVD0tHA0QDjxo0bYFhmZlbUWyJYAmwREdcVJ0ragz42E7Uj6QjStYjXt5ofEWcDZwN0d3f3+VqFmZn1rLcfpvk68EiL6Q/nee0sAcYWxsfkaSuR9CbgBGD/iHiqeb6ZmVWrtzOCLSLiluaJEXGLpPEl1j0T2F7StqQEcAhwWLGApJ2B7wCTIuK+0lGbrUF8AdxWd72dEYzqZd567VYcEcuAKcCVwHzg4oiYJ+lkSY0LzacDGwKXSJqTfwXNzMwGUW9nBLMkfSAivlucKOkoUv9DbUXEDGBG07QTC8Nv6kOsZmZWgd4SwceBn0s6nBU7/m5gJPCOiuMyM7NB0mMiiIh7gddIegPwijx5ekT8elAiMzOzQVGm99FrgGvalTMzszVTbxeLzcysBpwIzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqrm031GZmRf4N5uHHZwRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc5UmAkmTJN0maYGkqS3mv07SjZKWSTqwyljMzKy1yhKBpBHAWcC+wATgUEkTmordBUwGflxVHGZm1rsqf6pyIrAgIu4AkHQhcABwa6NARCzM85ZXGIeZmfWiyqahrYFFhfHFeVqfSTpa0ixJs5YuXdqR4MzMLFkjLhZHxNkR0R0R3aNHjx7qcMzMhpUqE8ESYGxhfEyeZmZmq5EqE8FMYHtJ20oaCRwCTKtwe2Zm1g+VJYKIWAZMAa4E5gMXR8Q8SSdL2h9A0qslLQYOAr4jaV5V8ZiZWWtV3jVERMwAZjRNO7EwPJPUZGRmZkNkjbhYbGZm1XEiMDOrOScCM7OacyIwM6s5JwIzs5pzIjAzqzknAjOzmnMiMDOruUofKDMzWxOMnzp9qEMoZeGp+1WyXp8RmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNVZoIJE2SdJukBZKmtpi/jqSL8vzrJY2vMh4zM1tVZYlA0gjgLGBfYAJwqKQJTcXeD/wzIrYDvgZ8uap4zMystSrPCCYCCyLijoh4GrgQOKCpzAHAOXn4UmBvSaowJjMza6KIqGbF0oHApIg4Ko+/B9g1IqYUyszNZRbn8dtzmfub1nU0cHQefQlwWyVB98/mwP1tS61Zhludhlt9YPjVabjVB1a/Om0TEaNbzega7Ej6IyLOBs4e6jhakTQrIrqHOo5OGm51Gm71geFXp+FWH1iz6lRl09ASYGxhfEye1rKMpC5gE+CBCmMyM7MmVSaCmcD2kraVNBI4BJjWVGYacGQePhD4dVTVVmVmZi1V1jQUEcskTQGuBEYAP4iIeZJOBmZFxDTg+8B5khYAD5KSxZpmtWyyGqDhVqfhVh8YfnUabvWBNahOlV0sNjOzNYOfLDYzqzknAjOzmnMiMDOrOScCQNJ6kn6Tu8VA0pGS/pb/jiyx/EGS5klaLqm7MH0fSbMl3ZL/v7Ew7xpJjxXLd7gOv5T0kKQrmspdkPt/mivpB5LWbrPenST9MdfvZkkHtyjzTUmPFcY/IekuSWd2qk79iaOX9U6UNCf/3STpHYV5oyRdKukvkuZL2j1PP13SPyQd26H6bCPpxhzDPEkfLJQbKelsSX/Ncbyr5PrH5c/UsYVpn8jrnyvpJ5LWzdMvkPRgfvCz35o/d3naxpIWF99/SYfm78HN+bO5eYl171V4fX5TmN6yD7NO1KnF92icpKvyZ+FW5f7QlO6GvD7HcJHSnZG9rffwwmduTt5X7CRpfUnT8/s8T9KphWU68j0qJSJq/wd8BDgmD28G3JH/b5qHN22z/MtITzxfC3QXpu8MbJWHXwEsaVpupfKdqkMe3xt4G3BFU7m3Asp/PwE+1Ga9Lwa2z8NbAfcAowrzu4HzgMealpsMnNnB96VfcfSw3vWBrjz8AuC+wvg5wFF5eGTTNk4Cju1QfUYC6+ThDYGFhc/K54FT8vBawOYl138pcEkjRmBr4O/Aenn8YmByofyPgAM7+bnL074B/Ljx/pPuTryvUQ/gNOCkNusdBdwKjMvjz8//RwC3Ay/Mr+FNwIRO1anF9+haYJ/C+7R+4bU8JA9/u933qGkbrwRuL3wW31D4TPwO2LdQdsDfozJ/PiNIDgcuy8NvAa6OiAcj4p/A1cCk3haOiPkRsUq3FxHx54i4O4/OA9aTtE4H4y4q1oGI+BXwaIuYZkQG3EB60K9HEfHXiPhbHr6b9IUeDc91LHg68OlOVaLJc3XqZBwR8URELMuj6wKR17MJ8DrSbc1ExNMR8VCnKsPK9Xk6Ip7K09dh5bPz9wH/ncstj6YuV1qR9HbSTn9e06wu0ueui7TTuZvOWulzJ2kXYAvgqmJ4+W8DSQI2LhHHYcDPIuIugIi4L08v04fZQDxXH6VOMrsi4uocw2MR8USuwxtJiRfSwcPb+7CNQ0lxNz6L1+Thp4EbafOdrELtE0E+pXthRCzMk7YGFhWKLM7TBupdwI2FL3/HtKhDmWXWBt4D/LIPy0wkHbXcnidNAaZFxD3loy29rR7r1Ik4JO0qaR5wC/DBnBi2BZYCP5T0Z0nfk7TBAKvS2N4q9ZE0VtLNpM/blyPibkmj8uwv5KajSyRt0WbdGwLHk84knhMRS4CvAHeRzqAejoirVl1DZ+okaS3gDGCl5rOIeAb4EOm1vpvUG/H326z+xcCmkq5ValZ9b55e1fez1Xv0YuAhST/Ln4fT80HH84CHCgcTfY3hYNLZePP2R5HO4n/Vzyr0W+0TAaljqIeq3ICkl5O62P6PijbRnzp8C/htRPyuTGFJLyA1vfx7RCyXtBVwEPA/fdxuWS3r1Kk4IuL6iHg58GrgP3PbeRfwKuB/I2Jn4HFgld/R6KdV6hMRiyJiB2A74Mi8w+8iHRH+ISJeBfyRtDPvzUnA1yJipesjkjYlHS1vS2pO20DSEQOvynOa6/RhYEbkTiQLcaxNSgQ75zhuBv6zzbq7gF2A/Uhn6f8l6cWdCbtHzfXpAvYkJbZXk5qjJg9kA5J2BZ6IiLlN07tIyeGbEXHHQLbRH2tEp3MV+xepeaBhCbBXYXwMqZ2wXySNAX4OvDcibm9Xvp+a69Aups+RmlVKJSZJGwPTgRMi4k958s6kHdiCdKbM+pIWRPptiU5YpU5VxBER85UuML+CdGS3OCKuz7MvpXOJoMf3KJ8JzCXtdH4KPAH8LM++hPS7Hb3ZFThQ0mmktvXlkp4E7gX+HhFLAST9DHgNcP7AqvKc5jrtDuwp6cOk9vSR+bX9KUDj8y/pYtq/rouBByLiceBxSb8FdszT2/Vh1l/N9VkMzGnsmCX9AtgN+AEwSlJXPivoSwyH0OJsgPQU8t8i4uv9C31gan9GkK8DjMhHhJC6xHizpE3zEdWb8zQknZubJUrJp3rTgakRcV1nI1+hRR16i+ko0hHWoRGxvDB9oqRzW5QfSUpk50ZEo02UiJgeEVtGxPiIGE86yulUElilTv2JQ9I7JP13izptm4/AkLQN8FJgYUT8A1gk6SW56N6kC5ZV1GeMpPXy8KbAHsBt+drN5aw4GHkuhp7qExF7Fur/deBLEXEmqUlot3xnivK65neiPq3qFBGHR8S4HMexpPdqKmknOUFSowvkfRpxSJqi1BVNs8uAPSR1SVqflOzmU64Ps47UJ29rVCHuNwK35vfoGlL/aJD6S2tcV2j5HuV5awHvJl8fKEw/hdTh5sc7UY/+qH0iyK4ifRGJiAeBL5A+BDOBk/M0gB1ocZErv/mLSUdE0yVdmWdNIR2tnqgVt409v+o65Jh+Rzqa3FvpVr635FnfJl3M+2OO58Q8fRzpiKjZu0kXUCcX6rBTRXVoVqxTf+J4EfBIi+l7ADdJmkNKLh8uXJD9KHBBbrvfCfjSgGqwsmJ9XgZcL+km4DfAVyLiljzveOCkHMN7gE+1qU9L+czmUtIFyFtI3/dO93+z0ueuhzjuJl2/+G2L1/WltOhxOCLmk65f3Uy6qeF7ETE3H4E3+jCbD1wcEc0XyAeiuC94lpTQfiXpFtIF7+/mcscDn1TqJ+15rLjm0dt79DpgUbHpJ7cYnEC6btK4nfioDtannKj4tqQ14Y/ULnxemzIbA5d0eLvX0rnbR9vWoc3ypwM7dLBukxn47aMDrdP5wOgO1ukkBnb76OpWnx8x8NtHB1qnK4CRq0udVsP3aMDfozJ/PiMAIuJG4BoVHoppUeaRiDioU9uUdA3p4tMznVhfmTq0Wf64iLi5E7FI+gTpYmDpo9ceYhponY6I3D4+UJJOB44gXUDul9WsPhcArweeHMh6OlCnf4t02+SAdaJOq9l71JHvUalt5axjZmY15TMCM7OacyIwM6s5P0dgw4Kk57HiicwtgWdJTwkDTOxUO3QfY+oCTiY98Na4tnBJRHxxsGMx640TgQ0LEfEA6bZEJJ1E6nyu3RO5/SJpRKRbC9s5hZSUXhkRT0raiBW3ghbXJ9L1uuXN88wGg5uGbNiStItSl8KzJV2p1D0FSv3XfFnSDUpdPe+Zp0/Wyl0nXyFprzz8mKQz8n3/u0s6Ii8/R9J3mu8yyQ9BfQD4aEQ8CRARj0bESXn+eKWulM8F5gJjlfqymavUXfPBudxeKnQlLulMSZPz8EJJp+XyN0jq2AN9Vi9OBDZcidT/0IERsQupW4Bik0xXREwkPc35uRLr2wC4PiJ2JD0AdTDw2ojYidQMdXhT+e2AuyJilR5gC7YHvhWpz6Nu0hnNjsCbgNMbiauNhyPilcCZpKeKzfrMTUM2XK1D6j/o6tTywghSD5wNjb58ZgPjS6zvWXKfOaSuGnYBZuZ1r0fqFrtHkv4dOIb0FOpr8uQ7Y0WfSXsAP8lNTvcq/RDLq2l/D/lPCv+/VqIeZqtwIrDhSsC8iNi9h/mN7sCfZcX3YBkrnyUX+256snBdQMA5EdFbD5oLgHGSNspNQj8kdW89l5SUoNzDab3FBPm3FFoMm5XmpiEbrp4CRmvFT02urdQdeG8WAjtJWkvSWNKPoLTyK1Jvn8/P695MqfO650TEE6T+Z87Uio7mRpB+R6GV3wEHK/2M5WhSvzQ3AHeSOmxbR6kTw72blju48P+Pbepn1pLPCGy4Wk7qHfKbSr881kVqQ++tg7LrSL/ydSupQ7MbWxWKiFslfRa4Kvco+QzpJw7vbCp6AqkDw7mSHiV16ncOqePCrZrK/pzUaeFNpCP7T0fqDbXRbfPcHNufm5bbNHfk9hTpl6/M+sxdTJitoSQtJHVa2PanLM1646YhM7Oa8xmBmVnN+YzAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5v4fzbNqN52kzlYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the relationship\n",
    "x = churn_rate_by_tenure.index.astype(str)\n",
    "y = churn_rate_by_tenure.values\n",
    "\n",
    "plt.bar(x, y)\n",
    "plt.xlabel('Tenure Group')\n",
    "plt.ylabel('Churn Rate')\n",
    "plt.title('Churn Rate by Tenure Group')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Churn rate is decreasing as tenure increases, this suggests that customers with longer tenures are less likely to churn. Therefore, as customers stay with the company for a longer period of time, their likelihood of churn decreases.\n",
    "This decreasing trend can be seen as a positive sign for the company, as it indicates that customer loyalty tends to increase with longer tenures. This implies that customers who have been with the company for a while are more satisfied with the services or products provided, and are less likely to switch to a competitor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Does the type of internet service influence customer churn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o+xbgLaOrTpKkds36SXaSJGnmGfCSJDXIgJckqUEGvCRJDTLgJUlqkAEvSVKDDHhJkhpkwEuS1CADXpKkBhnwkiQ1yICXJKlBBrwkSQ0y4CVJapABL0lSgwx4SZIaZMBLktQgA16SpAYZ8JIkNciAlySpQQa8JEkNMuAlSWqQAS9JUoMMeEmSGmTAS5LUIANekqQGDRXwSfZLckg/fbsku462LEmStCWmDfgkLwROAz7QN+0N/OsIa5IkSVtomC34lwAPB64BqKrvA3cZZVGSJGnLDBPw11fVDeMzSXYEapjBkyxKsjrJmiTHTrL8mCQXJ7kwyVeT7Dew7MYkF/SP5cM8nyRJ6uw4xDpnJflb4HZJHgf8DfC56TolmQOcADwOWAesTLK8qi4eWO07wMKq+m2SFwPHA0f0y66tqgOGfymSJGncMFvwxwLrgYuAvwZWVNXrhuh3ELCmqtb2ewCWAYsHV6iqM6vqt/3sOXTH9yVJ0hYaJuBfWlUfrKrDq+ppVfXBJC8fot9ewGUD8+v6to15PvDFgfmdk4wlOSfJUybrkOTofp2x9evXD1GSJEnbh2EC/qhJ2p43k0UkeTawEPjHgeb9qmoh8EzgnUnuObFfVZ1YVQurauHcuXNnsiRJkrZpGz0Gn+QZdOE6f8JJbrsCVw4x9uXAPgPze/dtE5/nEOB1wKOr6vrx9qq6vP93bZKvAwcClwzxvJIkbfemOsnum8BPgD2Btw+0/wq4cIixVwILksynC/Yj6b4w3CzJgXS/r19UVT8faL8T8Nuquj7JnnQ/0zt+iOeUJElMEfBV9SPgR8DDNmfgqtqQZAlwBjAHOKmqViVZCoxV1XK6XfJ3AE5NAnBpVR0G3Bf4QJKb6A4jvHXC2feSJGkK0/5MLslDgffQhe5OdGH9m6rabbq+VbUCWDGh7biB6UM20u+bwAOmG1+SJE1umJPs3gs8A/g+cDvgBXS/b5ckSVupoW42U1VrgDlVdWNVfRhYNNqyJEnSlhjmSna/TbITcEGS4+lOvPM2s5IkbcWGCern9OstAX5D99O3p46yKEmStGWmDfiq+lFVXVdV11TVG4E30f3kTZIkbaU2GvBJ9klyYpLPJ3lBkl2SvB1YjbeLlSRpqzbVMfiPAWcBp9OdVDcGXAA8sKp+OvrSJEnS5poq4Peoqr/rp89IcjjwrKq6afRlSZKkLTHlWfT9JWPTz14B3DH9Jeeqapjr0UuSpFkwVcDfETiP3wc8wPn9vwXcY1RFSZKkLTPVtejn3Yp1SJKkGeQFayRJapABL0lSgwx4SZIaNMy16EkyB7jr4PpVdemoipIkSVtmmPvBvxR4A/AzYPw38AU8cIR1SZKkLTDMFvzLgT+uqitGXYwkSZoZwxyDvwy4etSFSJKkmTPMFvxa4OtJvgBcP95YVe8YWVWSJGmLDBPwl/aPnfqHJEnayk13Lfo5wL2r6lm3Uj2SJGkGTHkMvqpuBPZL4pa7JEnbkGGPwZ+dZDnwm/FGj8FLkrT1GibgL+kfOwC7jrYcSZI0E6YN+Kp6461RiCRJmjnDXMnuTLor191CVT12JBVJkqQtNswu+lcOTO8M/CWwYTTlSJKkmTDMLvrzJjSdneTcEdUjSZJmwLSXqk2yx8BjzySPB+44zOBJFiVZnWRNkmMnWX5MkouTXJjkq0n2G1h2VJLv94+jNulVSZK0nRtmF/15dMfgQ7dr/gfA86fr1F8k5wTgccA6YGWS5VV18cBq3wEWVtVvk7wYOB44IskedHewW9g/93l936uGf2mSJG2/htlFP38zxz4IWFNVawGSLAMWAzcHfFWdObD+OcCz++nHA1+pqiv7vl8BFgGf2sxaJEnargyzBU+SPwXmDa5fVR+bpttedHeiG7cOOHiK9Z8PfHGKvntNUtfRwNEA++677zTlSJK0/RjmZ3IfB+4JXADc2DcXMF3ADy3Js+l2xz96U/pV1YnAiQALFy78g5/ySZK0vRpmC34hsH9VbWqAXg7sMzC/d992C0kOAV4HPLqqrh/o+5gJfb++ic8vSdJ2a9qz6IH/Au62GWOvBBYkmd/frOZIYPngCkkOBD4AHFZVPx9YdAZwaJI7JbkTcGjfJkmShrDRLfgkn6PbFb8rcHH/2/fxLWyq6rCpBq6qDUmW0AXzHOCkqlqVZCkwVlXLgX8E7gCcmgTg0qo6rKquTPImui8JAEvHT7iTJEnTm2oX/T9t6eBVtQJYMaHtuIHpQ6boexJw0pbWIEnS9miqgL8cuGtVnT3YmOQRwE9GWpUkSdoiUx2DfydwzSTtV/fLJEnSVmqqgL9rVV00sbFvmzeyiiRJ0habKuB3n2LZ7Wa4DkmSNIOmCvixJC+c2JjkBXTXp5ckSVupqU6yewXw2STP4veBvhDYCfhfI65LkiRtgY0GfFX9DPjTJH8G3L9v/kJVfe1WqUySJG22Ye4mdyZw5nTrSZKkrccwl6qVJEnbmKFuF9u6P3nVjN0YT1uZ8/7xubNdgiTNCrfgJUlqkAEvSVKDDHhJkhpkwEuS1CADXpKkBhnwkiQ1yICXJKlBBrwkSQ0y4CVJapABL0lSgwx4SZIaZMBLktQgA16SpAYZ8JIkNciAlySpQQa8JEkNMuAlSWrQSAM+yaIkq5OsSXLsJMsfleT8JBuSPG3CshuTXNA/lo+yTkmSWrPjqAZOMgc4AXgcsA5YmWR5VV08sNqlwPOAV04yxLVVdcCo6pMkqWUjC3jgIGBNVa0FSLIMWAzcHPBV9cN+2U0jrEOSpO3OKHfR7wVcNjC/rm8b1s5JxpKck+QpM1qZJEmNG+UW/Jbar6ouT3IP4GtJLqqqSwZXSHI0cDTAvvvuOxs1SpK0VRrlFvzlwD4D83v3bUOpqsv7f9cCXwcOnGSdE6tqYVUtnDt37pZVK0lSQ0YZ8CuBBUnmJ9kJOBIY6mz4JHdKctt+ek/g4Qwcu5ckSVMbWcBX1QZgCXAG8D3glKpalWRpksMAkjwkyTrgcOADSVb13e8LjCX5LnAm8NYJZ99LkqQpjPQYfFWtAFZMaDtuYHol3a77if2+CTxglLVJktQyr2QnSVKDDHhJkhpkwEuS1CADXpKkBhnwkiQ1yICXJKlBBrwkSQ0y4CVJapABL0lSgwx4SZIaZMBLktQgA16SpAYZ8JIkNciAlySpQQa8JEkNMuAlSWqQAS9JUoMMeEmSGmTAS5LUIANekqQGGfCSJDXIgJckqUEGvCRJDTLgJUlqkAEvSVKDDHhJkhpkwEuS1CADXpKkBo004JMsSrI6yZokx06y/FFJzk+yIcnTJiw7Ksn3+8dRo6xTkqTWjCzgk8wBTgCeAOwPPCPJ/hNWuxR4HnDyhL57AG8ADgYOAt6Q5E6jqlWSpNaMcgv+IGBNVa2tqhuAZcDiwRWq6odVdSFw04S+jwe+UlVXVtVVwFeARSOsVZKkpowy4PcCLhuYX9e3zVjfJEcnGUsytn79+s0uVJKk1mzTJ9lV1YlVtbCqFs6dO3e2y5EkaasxyoC/HNhnYH7vvm3UfSVJ2u6NMuBXAguSzE+yE3AksHzIvmcAhya5U39y3aF9myRJGsLIAr6qNgBL6IL5e8ApVbUqydIkhwEkeUiSdcDhwAeSrOr7Xgm8ie5Lwkpgad8mSZKGsOMoB6+qFcCKCW3HDUyvpNv9Plnfk4CTRlmfJEmt2qZPspMkSZMz4CVJapABL0lSgwx4SZIaZMBLktQgA16SpAYZ8JIkNciAlySpQQa8JEkNMuAlSWqQAS9JUoMMeEmSGmTAS5LUIANekqQGGfCSJDXIgJckqUE7znYBkqTpPfw9D5/tEjQiZ7/07JGM6xa8JEkNMuAlSWqQAS9JUoMMeEmSGmTAS5LUIANekqQGGfCSJDXIgJckqUEGvCRJDTLgJUlqkAEvSVKDRhrwSRYlWZ1kTZJjJ1l+2ySf7pd/O8m8vn1ekmuTXNA/3j/KOiVJas3IbjaTZA5wAvA4YB2wMsnyqrp4YLXnA1dV1b2SHAm8DTiiX3ZJVR0wqvokSWrZKLfgDwLWVNXaqroBWAYsnrDOYuCj/fRpwJ8nyQhrkiRpuzDKgN8LuGxgfl3fNuk6VbUBuBq4c79sfpLvJDkrySMne4IkRycZSzK2fv36ma1ekqRt2NZ6kt1PgH2r6kDgGODkJLtNXKmqTqyqhVW1cO7cubd6kZIkba1GGfCXA/sMzO/dt026TpIdgTsCV1TV9VV1BUBVnQdcAtx7hLVKktSUUQb8SmBBkvlJdgKOBJZPWGc5cFQ//TTga1VVSeb2J+mR5B7AAmDtCGuVJKkpIzuLvqo2JFkCnAHMAU6qqlVJlgJjVbUc+BDw8SRrgCvpvgQAPApYmuR3wE3Ai6rqylHVKklSa0YW8ABVtQJYMaHtuIHp64DDJ+l3OnD6KGuTJKllW+tJdpIkaQsY8JIkNciAlySpQQa8JEkNMuAlSWqQAS9JUoMMeEmSGmTAS5LUIANekqQGGfCSJDXIgJckqUEGvCRJDTLgJUlqkAEvSVKDDHhJkhpkwEuS1CADXpKkBhnwkiQ1yICXJKlBBrwkSQ0y4CVJapABL0lSgwx4SZIaZMBLktQgA16SpAYZ8JIkNciAlySpQSMN+CSLkqxOsibJsZMsv22ST/fLv51k3sCy1/btq5M8fpR1SpLUmpEFfJI5wAnAE4D9gWck2X/Cas8HrqqqewH/DLyt77s/cCRwP2AR8L5+PEmSNIRRbsEfBKypqrVVdQOwDFg8YZ3FwEf76dOAP0+Svn1ZVV1fVT8A1vTjSZKkIYwy4PcCLhuYX9e3TbpOVW0ArgbuPGRfSZK0ETvOdgFbIsnRwNH97K+TrJ7NerYhewK/mO0ibg35p6Nmu4TtxXbzmeINme0Ktgfbz+cJyMu26DO138YWjDLgLwf2GZjfu2+bbJ11SXYE7ghcMWRfqupE4MQZrHm7kGSsqhbOdh1qh58pzSQ/TzNjlLvoVwILksxPshPdSXPLJ6yzHBjfxHoa8LWqqr79yP4s+/nAAuDcEdYqSVJTRrYFX1UbkiwBzgDmACdV1aokS4GxqloOfAj4eJI1wJV0XwLo1zsFuBjYALykqm4cVa2SJLUm3QaztidJju4Pb0gzws+UZpKfp5lhwEuS1CAvVStJUoMM+MYkuTHJBUlWJflukv+bZId+2e2TfDLJRUn+K8k3ktyhX/br2a1cozLwmRh/zEvyzX7ZY5J8fhZrOyDJEwfmD5vsstZqV5JK8vaB+Vcm+btZLKkZ2/Tv4DWpa6vqAIAkdwFOBnYD3gC8HPhZVT2gX/7HwO9mqU7dem7+TAz405kYOMmcLTwB9gBgIbACoD/5duKvbdS264GnJvmHqtpufvt+a3ALvmFV9XO6CwEt6S8B/EcMXE+gqlZX1fWzVZ9mz4Q9Nrsl+UJ/Y6f3D+zxOTTJt5Kcn+TUgb09P0zytiTnA4dPGHdekq8luTDJV5Ps27d/pB97LMn/JHly//PZpcAR/Z6FI5I8L8l7+z53TfLZfk/Ud5PMyJcSbXU20F3P5P9MXLCxz5OGY8A3rqrW0v1M8S7AScBr+v9pvznJgtmtTreS2w3snv/sJMsPAl5Kd1Ooe9JtTe0JvB44pKoeDIwBxwz0uaKqHlxVyyaM9R7go1X1QOCTwLsHls3rn+tJwPvp/v9zHPDpqjqgqj49Yax3A2dV1YOABwOrNvWFa5txAvCsJHec0D7V50nTcBf9dqSqLkhyD+BQ4BBgZZKHVdX3Zrk0jdZku+gHndt/ESTJp4BHANfRBf7Z3c4fdgK+NdBnYhiPexjw1H7648DxA8tOqaqbgO8nWQvcZ5q6Hws8F6A/DHD1NOtrG1VV1yT5GPAy4NqBRVN9njQNA75xfaDfCPwcoKp+DXwG+EySm4AnAgb89m3ib2ULCPCVqnrGRvr8ZoaeRxr3TuB84MOzXEcz3EXfsCRz6XaFvreqKsnDk9ypX7YT3Rbaj2azRm0VDuovKb0DcATwDeAc4OFJ7gWQZJck9x5irG/SX5ESeBbwnwPLDk+yQ5J7AvcAVgO/AnbdyFhfBV7cP/+cSXbfqiFVdSVwCvD8geapPk+ahgHfnvHjrauAfwe+DLyxX3ZP4KwkFwHfoTuuenq/7PZJ1g08jvmDkdWqlcB76fbk/AD4bFWtB54HfCrJhXS756fbpQ7dsfz/3fd5Dt0vN8ZdSndPiS8CL6qq64Azgf3HT7KbMNbLgT/rP6/n0X0hVdveTncnuXFTfZ40Da9kJ2nkknwE+HxVnTbbtUjbC7fgJUlqkFvwkiQ1yC14SZIaZMBLktQgA16SpAYZ8NI2Ypg7/iV5RZLb3wq13OIucBOWbfSuhTPwvCuS7D4TY0mtM+CltrwC2KSATzJnM57nALqrIE7m5rsWVtX96S5cMvRdC6eqp6qeWFW/3IQ6pe2WAS9tY/p7uH89yWlJ/rvfWk6SlwF3B85Mcma/7lB3hOvn39ivd1GS+/Tr7ZLkpCTnJvlOksWT3QVuQokbvWthkmf3Y12Q5APjYZ7k10nenuS7wGuTnDrh9X5+oO49++nn9ncZ+26Sj/dtc5OcnmRl/3j4zP8FpG1EVfnw4WMbeAC/7v99DN2NV/am+5L+LeAR/bIfAnv203sC/wHs0s+/BjhuYL1XD4z9Q+Cl/fTfAP+vn/574Nn99O7A/wC70F3l7r0bqfMAunsffAt4M7Cgb78v8DngNv38+4Dn9tMFPL2f3pHuqnfjdf/LQA0/7F/X/fpaxl/rHv2/Jw+8F/sC35vtv5sPH7P18GYz0rbp3KpaB5DkArpbsX5jwjoPZdPuCPeZ/t/z+P0dvA4FDkvyyn5+Z7rg3KjayF0LgT8H/qSfB7gd/U2Q6G6IdHrff0OSLwF/keQ0utvLvnrC0zwWOLWqftH3ubJvP4Tu0rfj6+2W5A7V3WRJ2q4Y8NK26fqB6RuZ/L/lTb0j3PiYg+MF+MuqWn2LgZODpyquJr9r4Q109/Z+7SRdrqvulrDjlgFLgCuBsar61VTPN2AH4KHVXede2q55DF5qy+Dd2Tb3jnCDzgBemn6TOMmBkzzPLUxx18KvAk9Lcpd+2R5J9tvI854FPBh4IV3YT/Q1unMH7jw+Vt/+ZboblIzXcsBwL1NqjwEvteVE4EtJzqzNvyPcoDcBtwEu7O9Q+Ka+faq7wE1618Kquhh4PfDlvp6v0J2Q9wf6rfnPA0/o/524fBXwlv55vgu8o1/0MmBhf/LdxcCLNvH1Ss3wWvSSJDXILXhJkhpkwEuS1CADXpKkBhnwkiQ1yICXJKlBBrwkSQ0y4CVJapABL0lSg/4/lT2wzlcl1mQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data by InternetService and calculate churn rate\n",
    "churn_by_internet_service = train.groupby('InternetService')['Churn'].value_counts(normalize=True).unstack()\n",
    "churn_by_internet_service = churn_by_internet_service.loc[:, 'Yes']\n",
    "\n",
    "# Plotting the churn rates for each internet service category\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(x=churn_by_internet_service.index, y=churn_by_internet_service.values)\n",
    "plt.xlabel('Internet Service')\n",
    "plt.ylabel('Churn Rate')\n",
    "plt.title('Churn Rate by Internet Service')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graph above, it can be observed that customers who had fiber optic internet service had the highest churn rate, followed by those who had DSL, and the lowest churn rate was among those who did not have internet services.\n",
    "This could be due to the Service Quality of the Internet service. Fiber optic internet service is typically associated with higher speeds and better performance compared to DSL. However, if the quality of the fiber optic service provided by the telecom company is not up to the customers' expectations or if there are frequent disruptions or outages, it could lead to customer dissatisfaction and ultimately churn.\n",
    "In addition, the high churn rate by the people using fiber optic could be due to the pricig. Fiber optic internet service is often priced higher than DSL, which may attract customers initially due to the perceived benefits of faster speeds. However, if customers feel that the higher cost does not justify the value they are receiving or if they find alternative providers offering similar services at a lower price, they may choose to switch and churn.\n",
    "FOr the low churn rate by customers with no internet service could be due to the customers having access to alternative internet sources like public Wi-Fi or using mobile data. Since they are not relying on the telecom company for internet services, they may have lower churn rates.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Is there a correlation between the customer's payment method and churn rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(x='PaymentMethod', hue='Churn', data=train)\n",
    "plt.xlabel('Payment Method')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Churn Rates by Payment Method')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Based on the graph of the churn rates by payment method, it appears that there is a correlation between the customer's payment method and churn rate.\n",
    "\n",
    "Among the customers who churned, the highest number of churned customers used the electronic check as their payment method, followed by those who used the mailed check. This indicates that customers who use electronic check may have a higher likelihood of churning compared to other payment methods.\n",
    "\n",
    "On the other hand, customers who used bank transfer (automatic) or credit card (automatic) as their payment method had relatively lower churn rates compared to those using electronic check or mailed check."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Does the presence of paperless billing affect customer churn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>PaperlessBilling</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.8383</td>\n",
       "      <td>0.664553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>0.1617</td>\n",
       "      <td>0.335447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PaperlessBilling      No       Yes\n",
       "Churn                             \n",
       "No                0.8383  0.664553\n",
       "Yes               0.1617  0.335447"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_by_paperless = train.groupby('PaperlessBilling')['Churn'].value_counts(normalize=True).unstack()\n",
    "churn_by_paperless.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The presence of paperless billing affects the customer churn. This is because the number of customers who have paperless billing (Yes) have a higher churn rate (33.54%) compared to those who do not have paperless billing (No) with a churn rate of 16.17%. This suggests that customers who opt for paperless billing are more likely to churn compared to those who prefer traditional billing methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null Hypothesis: There is no significant relationship between the customer's internet service provider and their likelihood of churn\n",
    "\n",
    "Alterntive Hypothesis : There is a significant relationship between the Customer's internet service provider and their likelihood of churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is no significant relationship between the customer's internet service provider and their likelihood of churn.\n"
     ]
    }
   ],
   "source": [
    "# Create the contingency table\n",
    "contingency_table = [[0.8383, 0.664553], [0.1617, 0.335447]]\n",
    "\n",
    "# Perform the chi-square test\n",
    "chi2, p_value, _, _ = stats.chi2_contingency(contingency_table)\n",
    "\n",
    "# Compare the p-value with the significance level\n",
    "if p_value < 0.05:\n",
    "    print(\"There is a significant relationship between the customer's internet service provider and their likelihood of churn.\")\n",
    "else:\n",
    "    print(\"There is no significant relationship between the customer's internet service provider and their likelihood of churn.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not to reject the null hypothesis. This means that the customer's internet service provider is not a significant factor in determining their likelihood of churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We create a total charges column that  is based on the number of months of stay and the the monthly charges.\n",
    "train['TotalCharges'] = train['tenure'] * train['MonthlyCharges']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding the target column\n",
    "# Perform label encoding on 'Churn' column\n",
    "label_encoder = LabelEncoder()\n",
    "train['Churn'] = label_encoder.fit_transform(train['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5043 entries, 0 to 2042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            5043 non-null   object \n",
      " 1   SeniorCitizen     5043 non-null   int64  \n",
      " 2   Partner           5043 non-null   object \n",
      " 3   Dependents        5043 non-null   object \n",
      " 4   tenure            5043 non-null   float64\n",
      " 5   PhoneService      5043 non-null   object \n",
      " 6   MultipleLines     5043 non-null   object \n",
      " 7   InternetService   5043 non-null   object \n",
      " 8   OnlineSecurity    5043 non-null   object \n",
      " 9   OnlineBackup      5043 non-null   object \n",
      " 10  DeviceProtection  5043 non-null   object \n",
      " 11  TechSupport       5043 non-null   object \n",
      " 12  StreamingTV       5043 non-null   object \n",
      " 13  StreamingMovies   5043 non-null   object \n",
      " 14  Contract          5043 non-null   object \n",
      " 15  PaperlessBilling  5043 non-null   object \n",
      " 16  PaymentMethod     5043 non-null   object \n",
      " 17  MonthlyCharges    5043 non-null   float64\n",
      " 18  TotalCharges      5043 non-null   float64\n",
      " 19  Churn             5043 non-null   int32  \n",
      "dtypes: float64(3), int32(1), int64(1), object(15)\n",
      "memory usage: 807.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5043, 5), (5043, 15))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dividing numerical and categorical columns\n",
    "numerical_columns = train.select_dtypes(include=['int64', 'float64','int32']).columns\n",
    "categorical_columns = train.select_dtypes(include=['object']).columns\n",
    "\n",
    "# Subset of the numerical columns\n",
    "train_num = train[numerical_columns]\n",
    "\n",
    "# A subset of the categorical columns\n",
    "train_cat = train[categorical_columns]\n",
    "# Convert boolean columns to strings\n",
    "train_cat = train_cat.astype(str)\n",
    "#THe shape of each\n",
    "train_num.shape,train_cat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numerical columns in the train dataset are: Index(['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges', 'Churn'], dtype='object')\n",
      "\n",
      "The categorical columns in the train dataset are: Index(['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n",
      "       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
      "       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n",
      "       'PaperlessBilling', 'PaymentMethod'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#Numerical columns\n",
    "print(f\"The numerical columns in the train dataset are:\",train_num.columns)\n",
    "\n",
    "#Categorical columns\n",
    "print(f\"\\nThe categorical columns in the train dataset are:\",train_cat.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_None</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <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",
       "      <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>2</th>\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",
       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <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>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",
       "    </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>2038</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\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>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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_Male  Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0             0.0          1.0             0.0               0.0   \n",
       "1             1.0          0.0             0.0               1.0   \n",
       "2             1.0          0.0             0.0               1.0   \n",
       "3             1.0          0.0             0.0               0.0   \n",
       "4             0.0          0.0             0.0               1.0   \n",
       "...           ...          ...             ...               ...   \n",
       "2038          1.0          1.0             1.0               1.0   \n",
       "2039          0.0          1.0             1.0               1.0   \n",
       "2040          0.0          1.0             1.0               0.0   \n",
       "2041          1.0          1.0             0.0               1.0   \n",
       "2042          1.0          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_Yes  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                   0.0                          0.0                 0.0   \n",
       "1                   0.0                          0.0                 0.0   \n",
       "2                   0.0                          0.0                 0.0   \n",
       "3                   0.0                          0.0                 0.0   \n",
       "4                   0.0                          1.0                 0.0   \n",
       "...                 ...                          ...                 ...   \n",
       "2038                1.0                          0.0                 0.0   \n",
       "2039                1.0                          1.0                 0.0   \n",
       "2040                0.0                          0.0                 0.0   \n",
       "2041                1.0                          1.0                 0.0   \n",
       "2042                0.0                          1.0                 0.0   \n",
       "\n",
       "      OnlineSecurity_Yes  OnlineBackup_None  OnlineBackup_Yes  \\\n",
       "0                    0.0                0.0               1.0   \n",
       "1                    1.0                0.0               0.0   \n",
       "2                    1.0                0.0               1.0   \n",
       "3                    1.0                0.0               0.0   \n",
       "4                    0.0                0.0               0.0   \n",
       "...                  ...                ...               ...   \n",
       "2038                 1.0                0.0               0.0   \n",
       "2039                 0.0                0.0               1.0   \n",
       "2040                 1.0                0.0               0.0   \n",
       "2041                 0.0                0.0               0.0   \n",
       "2042                 1.0                0.0               0.0   \n",
       "\n",
       "      DeviceProtection_Yes  TechSupport_Yes  StreamingTV_Yes  \\\n",
       "0                      0.0              0.0              0.0   \n",
       "1                      1.0              0.0              0.0   \n",
       "2                      0.0              0.0              0.0   \n",
       "3                      1.0              1.0              0.0   \n",
       "4                      0.0              0.0              0.0   \n",
       "...                    ...              ...              ...   \n",
       "2038                   1.0              1.0              1.0   \n",
       "2039                   1.0              0.0              1.0   \n",
       "2040                   0.0              0.0              0.0   \n",
       "2041                   0.0              0.0              0.0   \n",
       "2042                   1.0              1.0              1.0   \n",
       "\n",
       "      StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
       "0                     0.0                0.0                0.0   \n",
       "1                     0.0                1.0                0.0   \n",
       "2                     0.0                0.0                0.0   \n",
       "3                     0.0                1.0                0.0   \n",
       "4                     0.0                0.0                0.0   \n",
       "...                   ...                ...                ...   \n",
       "2038                  1.0                1.0                0.0   \n",
       "2039                  1.0                1.0                0.0   \n",
       "2040                  0.0                0.0                0.0   \n",
       "2041                  0.0                0.0                0.0   \n",
       "2042                  1.0                0.0                1.0   \n",
       "\n",
       "      PaperlessBilling_Yes  PaymentMethod_Credit card (automatic)  \\\n",
       "0                      1.0                                    0.0   \n",
       "1                      0.0                                    0.0   \n",
       "2                      1.0                                    0.0   \n",
       "3                      0.0                                    0.0   \n",
       "4                      1.0                                    0.0   \n",
       "...                    ...                                    ...   \n",
       "2038                   1.0                                    0.0   \n",
       "2039                   1.0                                    1.0   \n",
       "2040                   1.0                                    0.0   \n",
       "2041                   1.0                                    0.0   \n",
       "2042                   1.0                                    0.0   \n",
       "\n",
       "      PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
       "0                                1.0                         0.0  \n",
       "1                                0.0                         1.0  \n",
       "2                                0.0                         1.0  \n",
       "3                                0.0                         0.0  \n",
       "4                                1.0                         0.0  \n",
       "...                              ...                         ...  \n",
       "2038                             0.0                         1.0  \n",
       "2039                             0.0                         0.0  \n",
       "2040                             1.0                         0.0  \n",
       "2041                             0.0                         1.0  \n",
       "2042                             0.0                         0.0  \n",
       "\n",
       "[5043 rows x 20 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder(sparse=False,drop='first',handle_unknown='error').set_output(transform='pandas')\n",
    "encoder.fit(train_cat)\n",
    "encoded_data = encoder.transform(train_cat)\n",
    "#encoded_df = pd.DataFrame(encoded_data, columns=encoder.get_feature_names_out(),index=train_cat.index)\n",
    "#encoded_df\n",
    "encoded_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_None</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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",
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       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
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       "      <td>0.0</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",
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       "      <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>2038</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\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>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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender_Male  Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
       "0             0.0          1.0             0.0               0.0   \n",
       "1             1.0          0.0             0.0               1.0   \n",
       "2             1.0          0.0             0.0               1.0   \n",
       "3             1.0          0.0             0.0               0.0   \n",
       "4             0.0          0.0             0.0               1.0   \n",
       "...           ...          ...             ...               ...   \n",
       "2038          1.0          1.0             1.0               1.0   \n",
       "2039          0.0          1.0             1.0               1.0   \n",
       "2040          0.0          1.0             1.0               0.0   \n",
       "2041          1.0          1.0             0.0               1.0   \n",
       "2042          1.0          0.0             0.0               1.0   \n",
       "\n",
       "      MultipleLines_Yes  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                   0.0                          0.0                 0.0   \n",
       "1                   0.0                          0.0                 0.0   \n",
       "2                   0.0                          0.0                 0.0   \n",
       "3                   0.0                          0.0                 0.0   \n",
       "4                   0.0                          1.0                 0.0   \n",
       "...                 ...                          ...                 ...   \n",
       "2038                1.0                          0.0                 0.0   \n",
       "2039                1.0                          1.0                 0.0   \n",
       "2040                0.0                          0.0                 0.0   \n",
       "2041                1.0                          1.0                 0.0   \n",
       "2042                0.0                          1.0                 0.0   \n",
       "\n",
       "      OnlineSecurity_Yes  OnlineBackup_None  OnlineBackup_Yes  \\\n",
       "0                    0.0                0.0               1.0   \n",
       "1                    1.0                0.0               0.0   \n",
       "2                    1.0                0.0               1.0   \n",
       "3                    1.0                0.0               0.0   \n",
       "4                    0.0                0.0               0.0   \n",
       "...                  ...                ...               ...   \n",
       "2038                 1.0                0.0               0.0   \n",
       "2039                 0.0                0.0               1.0   \n",
       "2040                 1.0                0.0               0.0   \n",
       "2041                 0.0                0.0               0.0   \n",
       "2042                 1.0                0.0               0.0   \n",
       "\n",
       "      DeviceProtection_Yes  TechSupport_Yes  StreamingTV_Yes  \\\n",
       "0                      0.0              0.0              0.0   \n",
       "1                      1.0              0.0              0.0   \n",
       "2                      0.0              0.0              0.0   \n",
       "3                      1.0              1.0              0.0   \n",
       "4                      0.0              0.0              0.0   \n",
       "...                    ...              ...              ...   \n",
       "2038                   1.0              1.0              1.0   \n",
       "2039                   1.0              0.0              1.0   \n",
       "2040                   0.0              0.0              0.0   \n",
       "2041                   0.0              0.0              0.0   \n",
       "2042                   1.0              1.0              1.0   \n",
       "\n",
       "      StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
       "0                     0.0                0.0                0.0   \n",
       "1                     0.0                1.0                0.0   \n",
       "2                     0.0                0.0                0.0   \n",
       "3                     0.0                1.0                0.0   \n",
       "4                     0.0                0.0                0.0   \n",
       "...                   ...                ...                ...   \n",
       "2038                  1.0                1.0                0.0   \n",
       "2039                  1.0                1.0                0.0   \n",
       "2040                  0.0                0.0                0.0   \n",
       "2041                  0.0                0.0                0.0   \n",
       "2042                  1.0                0.0                1.0   \n",
       "\n",
       "      PaperlessBilling_Yes  PaymentMethod_Credit card (automatic)  \\\n",
       "0                      1.0                                    0.0   \n",
       "1                      0.0                                    0.0   \n",
       "2                      1.0                                    0.0   \n",
       "3                      0.0                                    0.0   \n",
       "4                      1.0                                    0.0   \n",
       "...                    ...                                    ...   \n",
       "2038                   1.0                                    0.0   \n",
       "2039                   1.0                                    1.0   \n",
       "2040                   1.0                                    0.0   \n",
       "2041                   1.0                                    0.0   \n",
       "2042                   1.0                                    0.0   \n",
       "\n",
       "      PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
       "0                                1.0                         0.0  \n",
       "1                                0.0                         1.0  \n",
       "2                                0.0                         1.0  \n",
       "3                                0.0                         0.0  \n",
       "4                                1.0                         0.0  \n",
       "...                              ...                         ...  \n",
       "2038                             0.0                         1.0  \n",
       "2039                             0.0                         0.0  \n",
       "2040                             1.0                         0.0  \n",
       "2041                             0.0                         1.0  \n",
       "2042                             0.0                         0.0  \n",
       "\n",
       "[5043 rows x 20 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 20)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender_Male', 'Partner_Yes', 'Dependents_Yes', 'PhoneService_Yes',\n",
       "       'MultipleLines_Yes', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'OnlineSecurity_Yes', 'OnlineBackup_None',\n",
       "       'OnlineBackup_Yes', 'DeviceProtection_Yes', 'TechSupport_Yes',\n",
       "       'StreamingTV_Yes', 'StreamingMovies_Yes', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaperlessBilling_Yes',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>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>...</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>56.950001</td>\n",
       "      <td>1936.300026</td>\n",
       "      <td>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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.849998</td>\n",
       "      <td>107.699997</td>\n",
       "      <td>1</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>...</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.299999</td>\n",
       "      <td>1903.499966</td>\n",
       "      <td>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>...</td>\n",
       "      <td>1.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.699997</td>\n",
       "      <td>141.399994</td>\n",
       "      <td>1</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>...</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <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>2038</th>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>84.800000</td>\n",
       "      <td>2035.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2039</th>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>103.200000</td>\n",
       "      <td>7430.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>325.600000</td>\n",
       "      <td>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>...</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.400000</td>\n",
       "      <td>297.600000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>105.650000</td>\n",
       "      <td>6972.900000</td>\n",
       "      <td>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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5043 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  tenure  MonthlyCharges  TotalCharges  Churn  gender_Male  \\\n",
       "0                 0     1.0       29.850000     29.850000      0          0.0   \n",
       "1                 0    34.0       56.950001   1936.300026      0          1.0   \n",
       "2                 0     2.0       53.849998    107.699997      1          1.0   \n",
       "3                 0    45.0       42.299999   1903.499966      0          1.0   \n",
       "4                 0     2.0       70.699997    141.399994      1          0.0   \n",
       "...             ...     ...             ...           ...    ...          ...   \n",
       "2038              0    24.0       84.800000   2035.200000      0          1.0   \n",
       "2039              0    72.0      103.200000   7430.400000      0          0.0   \n",
       "2040              0    11.0       29.600000    325.600000      0          0.0   \n",
       "2041              1     4.0       74.400000    297.600000      1          1.0   \n",
       "2042              0    66.0      105.650000   6972.900000      0          1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  MultipleLines_Yes  ...  \\\n",
       "0             1.0             0.0               0.0                0.0  ...   \n",
       "1             0.0             0.0               1.0                0.0  ...   \n",
       "2             0.0             0.0               1.0                0.0  ...   \n",
       "3             0.0             0.0               0.0                0.0  ...   \n",
       "4             0.0             0.0               1.0                0.0  ...   \n",
       "...           ...             ...               ...                ...  ...   \n",
       "2038          1.0             1.0               1.0                1.0  ...   \n",
       "2039          1.0             1.0               1.0                1.0  ...   \n",
       "2040          1.0             1.0               0.0                0.0  ...   \n",
       "2041          1.0             0.0               1.0                1.0  ...   \n",
       "2042          0.0             0.0               1.0                0.0  ...   \n",
       "\n",
       "      DeviceProtection_Yes  TechSupport_Yes  StreamingTV_Yes  \\\n",
       "0                      0.0              0.0              0.0   \n",
       "1                      1.0              0.0              0.0   \n",
       "2                      0.0              0.0              0.0   \n",
       "3                      1.0              1.0              0.0   \n",
       "4                      0.0              0.0              0.0   \n",
       "...                    ...              ...              ...   \n",
       "2038                   1.0              1.0              1.0   \n",
       "2039                   1.0              0.0              1.0   \n",
       "2040                   0.0              0.0              0.0   \n",
       "2041                   0.0              0.0              0.0   \n",
       "2042                   1.0              1.0              1.0   \n",
       "\n",
       "      StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
       "0                     0.0                0.0                0.0   \n",
       "1                     0.0                1.0                0.0   \n",
       "2                     0.0                0.0                0.0   \n",
       "3                     0.0                1.0                0.0   \n",
       "4                     0.0                0.0                0.0   \n",
       "...                   ...                ...                ...   \n",
       "2038                  1.0                1.0                0.0   \n",
       "2039                  1.0                1.0                0.0   \n",
       "2040                  0.0                0.0                0.0   \n",
       "2041                  0.0                0.0                0.0   \n",
       "2042                  1.0                0.0                1.0   \n",
       "\n",
       "      PaperlessBilling_Yes  PaymentMethod_Credit card (automatic)  \\\n",
       "0                      1.0                                    0.0   \n",
       "1                      0.0                                    0.0   \n",
       "2                      1.0                                    0.0   \n",
       "3                      0.0                                    0.0   \n",
       "4                      1.0                                    0.0   \n",
       "...                    ...                                    ...   \n",
       "2038                   1.0                                    0.0   \n",
       "2039                   1.0                                    1.0   \n",
       "2040                   1.0                                    0.0   \n",
       "2041                   1.0                                    0.0   \n",
       "2042                   1.0                                    0.0   \n",
       "\n",
       "      PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
       "0                                1.0                         0.0  \n",
       "1                                0.0                         1.0  \n",
       "2                                0.0                         1.0  \n",
       "3                                0.0                         0.0  \n",
       "4                                1.0                         0.0  \n",
       "...                              ...                         ...  \n",
       "2038                             0.0                         1.0  \n",
       "2039                             0.0                         0.0  \n",
       "2040                             1.0                         0.0  \n",
       "2041                             0.0                         1.0  \n",
       "2042                             0.0                         0.0  \n",
       "\n",
       "[5043 rows x 25 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_train = pd.concat([train_num, encoded_data], axis=1)\n",
    "encoded_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Imbalanced Data using SMOTE\n",
    "SMOTE (Synthetic Minority Over-sampling Technique) is a popular technique used to address class imbalance in machine learning. In our dataset, the number of instances in the minority class (Churned) is much smaller than the number of instances in the majority class (those who did not churn). This can lead to poor performance of our machine learning models, as they tend to be biased towards the majority class.\n",
    "\n",
    "The basic idea behind SMOTE is to create new minority class samples by randomly selecting one or more of the k-nearest neighbors of each minority class sample, and using them to create new synthetic samples (Nababan et al., 2023)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into features (X) and target variable (y)\n",
    "X = encoded_train.drop('Churn', axis=1)\n",
    "y = encoded_train['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3707\n",
       "1    3707\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Cloning the class SMOTE and specifying the sampling strategy\n",
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X_resampled, y_resampled= smote.fit_resample(X, y)\n",
    "\n",
    "#\n",
    "y_resampled.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7414, 24)\n",
      "(7414,)\n"
     ]
    }
   ],
   "source": [
    "print(X_resampled.shape)\n",
    "print(y_resampled.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "# Split the data into training and evaluation sets\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42,stratify=y_resampled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5931, 24), (1483, 24), (5931,), (1483,))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_eval.shape,y_train.shape,y_eval.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Cloning the standard scaler\n",
    "#scaler = StandardScaler().set_output(transform='pandas')\n",
    "\n",
    "# Columns to be scaled\n",
    "#columns_to_scale = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "\n",
    "# Fit and transform the selected columns in the training set\n",
    "#X_train_scaled = X_train.copy()\n",
    "#X_train_scaled[columns_to_scale] = scaler.fit_transform(X_train_scaled[columns_to_scale])\n",
    "#X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "\n",
    "# Transform the selected columns in the validation set\n",
    "#X_eval_scaled = X_val.copy()\n",
    "#X_eval_scaled[columns_to_scale] = scaler.transform(X_eval_scaled[columns_to_scale])\n",
    "#X_eval_scaled = pd.DataFrame(X_eval_scaled, columns=X_val.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cloning the standardscaler class\n",
    "scaler =StandardScaler().set_output(transform=\"pandas\")\n",
    "# Columns to be scaled\n",
    "columns_to_scale = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "#Fitting and transforming the selected columns in the training data set\n",
    "X_train_scaled = X_train.copy()\n",
    "X_train_scaled[columns_to_scale]=scaler.fit_transform(X_train[columns_to_scale])\n",
    "\n",
    "#Transforming the selected columns  in the evaluation dataset\n",
    "X_eval_scaled=X_eval.copy() #TO create a copy\n",
    "X_eval_scaled[columns_to_scale] = scaler.transform(X_eval[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.124111</td>\n",
       "      <td>-0.674633</td>\n",
       "      <td>-0.921214</td>\n",
       "      <td>0.710243</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3918</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen    tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "969               0 -1.082286        0.971058     -0.855408     0.000000   \n",
       "7255              0  0.450656       -0.586215     -0.033545     0.000000   \n",
       "6738              0  0.812993        1.463788      1.450463     1.000000   \n",
       "5734              0 -1.124111       -0.674633     -0.921214     0.710243   \n",
       "3918              0  1.301768       -1.698966     -0.414794     1.000000   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  MultipleLines_Yes  \\\n",
       "969           0.0        0.000000          1.000000                1.0   \n",
       "7255          0.0        0.000000          0.034912                0.0   \n",
       "6738          1.0        0.104654          1.000000                1.0   \n",
       "5734          0.0        0.710243          1.000000                0.0   \n",
       "3918          1.0        1.000000          1.000000                0.0   \n",
       "\n",
       "      InternetService_Fiber optic  ...  DeviceProtection_Yes  TechSupport_Yes  \\\n",
       "969                           1.0  ...              0.000000         0.000000   \n",
       "7255                          0.0  ...              1.000000         0.000000   \n",
       "6738                          1.0  ...              1.000000         0.895346   \n",
       "5734                          0.0  ...              0.710243         0.000000   \n",
       "3918                          0.0  ...              0.000000         0.000000   \n",
       "\n",
       "      StreamingTV_Yes  StreamingMovies_Yes  Contract_One year  \\\n",
       "969          1.000000                  1.0           0.000000   \n",
       "7255         0.965088                  1.0           0.000000   \n",
       "6738         1.000000                  1.0           0.895346   \n",
       "5734         0.000000                  0.0           0.000000   \n",
       "3918         0.000000                  0.0           0.000000   \n",
       "\n",
       "      Contract_Two year  PaperlessBilling_Yes  \\\n",
       "969                 0.0              1.000000   \n",
       "7255                0.0              1.000000   \n",
       "6738                0.0              1.000000   \n",
       "5734                0.0              0.289757   \n",
       "3918                1.0              0.000000   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "969                                0.000000                        1.000000   \n",
       "7255                               0.000000                        0.965088   \n",
       "6738                               0.895346                        0.000000   \n",
       "5734                               0.289757                        0.710243   \n",
       "3918                               0.000000                        0.000000   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "969                          0.0  \n",
       "7255                         0.0  \n",
       "6738                         0.0  \n",
       "5734                         0.0  \n",
       "3918                         1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking on the scaled training set\n",
    "X_train_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>gender_Male</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
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       "      <th>StreamingTV_Yes</th>\n",
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       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
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       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
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       "  </thead>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.018098</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "      SeniorCitizen    tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
       "888               0  0.381607        0.803527      0.607515          1.0   \n",
       "3404              0  0.758036        0.958842      1.078929          0.0   \n",
       "6490              0 -0.472688        0.660198     -0.280540          0.0   \n",
       "1643              0  0.381607        0.773860      0.593058          1.0   \n",
       "954               0 -0.245776        0.939646      0.018098          1.0   \n",
       "\n",
       "      Partner_Yes  Dependents_Yes  PhoneService_Yes  MultipleLines_Yes  \\\n",
       "888           1.0        1.000000               1.0           0.000000   \n",
       "3404          0.0        0.000000               1.0           0.000000   \n",
       "6490          1.0        0.425228               1.0           0.574772   \n",
       "1643          1.0        0.000000               1.0           0.000000   \n",
       "954           0.0        0.000000               1.0           1.000000   \n",
       "\n",
       "      InternetService_Fiber optic  ...  DeviceProtection_Yes  TechSupport_Yes  \\\n",
       "888                           1.0  ...                   0.0              0.0   \n",
       "3404                          1.0  ...                   1.0              0.0   \n",
       "6490                          1.0  ...                   1.0              0.0   \n",
       "1643                          1.0  ...                   1.0              1.0   \n",
       "954                           1.0  ...                   0.0              0.0   \n",
       "\n",
       "      StreamingTV_Yes  StreamingMovies_Yes  Contract_One year  \\\n",
       "888          1.000000                  1.0                1.0   \n",
       "3404         1.000000                  0.0                1.0   \n",
       "6490         0.425228                  0.0                0.0   \n",
       "1643         0.000000                  1.0                0.0   \n",
       "954          1.000000                  1.0                0.0   \n",
       "\n",
       "      Contract_Two year  PaperlessBilling_Yes  \\\n",
       "888                 0.0                   0.0   \n",
       "3404                0.0                   1.0   \n",
       "6490                0.0                   1.0   \n",
       "1643                0.0                   1.0   \n",
       "954                 0.0                   1.0   \n",
       "\n",
       "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "888                                1.000000                             0.0   \n",
       "3404                               1.000000                             0.0   \n",
       "6490                               0.425228                             0.0   \n",
       "1643                               0.000000                             1.0   \n",
       "954                                0.000000                             1.0   \n",
       "\n",
       "      PaymentMethod_Mailed check  \n",
       "888                          0.0  \n",
       "3404                         0.0  \n",
       "6490                         0.0  \n",
       "1643                         0.0  \n",
       "954                          0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check on the scaled evaluation on the predictor set\n",
    "X_eval_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a list of models that will be evaluated\n",
    "models = [\n",
    "    ('Logistic Regression', LogisticRegression(random_state=42)),\n",
    "    ('Random Forest', RandomForestClassifier(random_state=42)),\n",
    "    ('Gradient Boosting', GradientBoostingClassifier(random_state=42)),\n",
    "    ('Support Vector Machine', SVC(random_state=42)),\n",
    "    ('Gaussian Naive Bayes', GaussianNB()),\n",
    "    ('K-Nearest Neighbors', KNeighborsClassifier()),\n",
    "    ('Decision Tree', DecisionTreeClassifier(random_state=42)),\n",
    "    ('XGBoost', XGBClassifier(random_state=42)),\n",
    "    ('LightGBM', LGBMClassifier(random_state=42)),\n",
    "]\n",
    "\n",
    "# Initializing an empty dataframe that will store the evaluation scores\n",
    "results_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'AUC-ROC'])\n",
    "\n",
    "# Iterate over the models and train, evaluate, and store the results\n",
    "for model_name, model in models:\n",
    "    # Fitting the models to the training dataset\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "\n",
    "    # Make predictions using the evaluation dataset\n",
    "    y_pred = model.predict(X_eval_scaled)\n",
    "\n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(y_eval, y_pred)\n",
    "    precision = precision_score(y_eval, y_pred)\n",
    "    recall = recall_score(y_eval, y_pred)\n",
    "    f1 = f1_score(y_eval, y_pred)\n",
    "    auc_roc = roc_auc_score(y_eval, y_pred)\n",
    "\n",
    "    # Store the results in the dataframe\n",
    "    results_df = results_df.append({\n",
    "        'Model': model_name,\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1 Score': f1,\n",
    "        'AUC-ROC': auc_roc\n",
    "    }, ignore_index=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>AUC-ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.768038</td>\n",
       "      <td>0.739445</td>\n",
       "      <td>0.827260</td>\n",
       "      <td>0.780892</td>\n",
       "      <td>0.768078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.858395</td>\n",
       "      <td>0.869263</td>\n",
       "      <td>0.843455</td>\n",
       "      <td>0.856164</td>\n",
       "      <td>0.858385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.857047</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.857046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.821308</td>\n",
       "      <td>0.812336</td>\n",
       "      <td>0.835358</td>\n",
       "      <td>0.823686</td>\n",
       "      <td>0.821318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.755226</td>\n",
       "      <td>0.719767</td>\n",
       "      <td>0.835358</td>\n",
       "      <td>0.773267</td>\n",
       "      <td>0.755280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K-Nearest Neighbors</td>\n",
       "      <td>0.776129</td>\n",
       "      <td>0.719656</td>\n",
       "      <td>0.904184</td>\n",
       "      <td>0.801435</td>\n",
       "      <td>0.776216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.819960</td>\n",
       "      <td>0.806202</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.823762</td>\n",
       "      <td>0.819974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.852326</td>\n",
       "      <td>0.857534</td>\n",
       "      <td>0.844804</td>\n",
       "      <td>0.851122</td>\n",
       "      <td>0.852321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.855024</td>\n",
       "      <td>0.863260</td>\n",
       "      <td>0.843455</td>\n",
       "      <td>0.853242</td>\n",
       "      <td>0.855016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall  F1 Score   AUC-ROC\n",
       "0     Logistic Regression  0.768038   0.739445  0.827260  0.780892  0.768078\n",
       "1           Random Forest  0.858395   0.869263  0.843455  0.856164  0.858385\n",
       "2       Gradient Boosting  0.857047   0.856950  0.856950  0.856950  0.857046\n",
       "3  Support Vector Machine  0.821308   0.812336  0.835358  0.823686  0.821318\n",
       "4    Gaussian Naive Bayes  0.755226   0.719767  0.835358  0.773267  0.755280\n",
       "5     K-Nearest Neighbors  0.776129   0.719656  0.904184  0.801435  0.776216\n",
       "6           Decision Tree  0.819960   0.806202  0.842105  0.823762  0.819974\n",
       "7                 XGBoost  0.852326   0.857534  0.844804  0.851122  0.852321\n",
       "8                LightGBM  0.855024   0.863260  0.843455  0.853242  0.855016"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation scores dataframe\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "F1 score is measures a model's accuracy through combines the precision and recall scores of the model.It combines the precision and recall of the model. Based on the F1 score, the three best perfoming models are: LightGBM with a score of 0.853242, Random forest with 0.856164, and Gradient boosting with a score of 0.857046. The Gradient boosting model has the highest F1 score among the evaluated models, followed closely by Randome forest and Light GBM.\n",
    "The Three models have a relatively high f1 score which shows a good balance between precision and recall. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "Hyperparameter tuning aims to find the optimal combination of hyperparameters that will result in the best performance of the model on the given dataset (Bartz-Beielstein & Zaefferer, 2023). There are various approaches to perform hyperparameter tuning. For this project, grid search was used where you get to define a grid of the possible hyperparameters values and evaluate the model's performance for each combination. This ensures that all the possible combinations have been explored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning for the Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the parameter grid for Random Forest m\n",
    "param_grid_rf = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5,10],\n",
    "    'min_samples_leaf': [1, 2,4]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
       "             scoring=&#x27;f1&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "             param_grid={'max_depth': [None, 10, 20],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'n_estimators': [100, 200, 300]},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining a random forest model\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "#Perfoming a grid search using cross validation\n",
    "grid_search_rf = GridSearchCV(rf, param_grid_rf, scoring='f1', cv=5)\n",
    "grid_search_rf.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the best parameters\n",
    "best_params_rf = grid_search_rf.best_params_\n",
    "best_score_rf = grid_search_rf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters for the Random Forest model are:\n",
      "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "#Displaying the best parameters\n",
    "print(f\"The best hyperparameters for the Random Forest model are:\\n{best_params_rf}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning for The LightGBM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the parameter grid for LightGBM\n",
    "param_grid_lgbm = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [5, 10, 15],\n",
    "    'learning_rate': [0.1, 0.01, 0.001]\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a LightGBM classifier\n",
    "lgbm = LGBMClassifier(random_state=42)\n",
    "\n",
    "# Performing Grid Search Cross Validation\n",
    "grid_search_lgbm = GridSearchCV(lgbm, param_grid_lgbm, scoring='f1', cv=5)\n",
    "grid_search_lgbm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the best parameters and best F1 score\n",
    "best_params_lgbm = grid_search_lgbm.best_params_\n",
    "best_score_lgbm = grid_search_lgbm.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Printing the best hyperparameters\n",
    "print(f\"The best hyperparameters for the light gbm model are:\\n{best_params_lgbm}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning on the Catboost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameter grid for Grid Search\n",
    "param_grid = {\n",
    "    'learning_rate': [0.1, 0.01, 0.001],\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [3, 5, 7]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Gradient Boosting model\n",
    "gb_model = GradientBoostingClassifier(random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing Grid Search with cross-validation\n",
    "grid_search = GridSearchCV(gb_model, param_grid, cv=5, scoring='f1')\n",
    "grid_search.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best hyperparameters and the best model\n",
    "best_params = grid_search.best_params_\n",
    "best_gb = grid_search.best_estimator_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"The best hyperparameters for the Gradient boosting classifier are: \\n{best_params}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  ### Perfomance Comparison after Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize an empty dataframe to store the evaluation scores\n",
    "results_tuned_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score', 'AUC-ROC'])\n",
    "\n",
    "# Define the models with the best hyperparameters\n",
    "rf_model_tuned = RandomForestClassifier(n_estimators=300, max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=42)\n",
    "lgbm_model_tuned = LGBMClassifier(learning_rate=0.1, n_estimators=200, max_depth=5, random_state=42)\n",
    "gb_model_tuned = GradientBoostingClassifier(random_state=42,learning_rate=0.1,max_depth=3,n_estimators=100)\n",
    "\n",
    "# Fit the tuned models to the training data\n",
    "rf_model_tuned.fit(X_train_scaled, y_train)\n",
    "lgbm_model_tuned.fit(X_train_scaled, y_train)\n",
    "gb_model_tuned.fit(X_train_scaled,y_train)\n",
    "\n",
    "\n",
    "# Make predictions on the evaluation data\n",
    "y_pred_rf = rf_model_tuned.predict(X_eval_scaled)\n",
    "y_pred_lgbm = lgbm_model_tuned.predict(X_eval_scaled)\n",
    "y_pred_gb =gb_model_tuned.predict(X_eval_scaled)\n",
    "\n",
    "# Calculate the evaluation scores\n",
    "accuracy_rf = accuracy_score(y_eval, y_pred_rf)\n",
    "precision_rf = precision_score(y_eval, y_pred_rf)\n",
    "recall_rf = recall_score(y_eval, y_pred_rf)\n",
    "f1_rf = f1_score(y_eval, y_pred_rf)\n",
    "auc_roc_rf = roc_auc_score(y_eval, y_pred_rf)\n",
    "\n",
    "accuracy_lgbm = accuracy_score(y_eval, y_pred_lgbm)\n",
    "precision_lgbm = precision_score(y_eval, y_pred_lgbm)\n",
    "recall_lgbm = recall_score(y_eval, y_pred_lgbm)\n",
    "f1_lgbm = f1_score(y_eval, y_pred_lgbm)\n",
    "auc_roc_lgbm = roc_auc_score(y_eval, y_pred_lgbm)\n",
    "\n",
    "accuracy_gb = accuracy_score(y_eval, y_pred_gb)\n",
    "precision_gb = precision_score(y_eval, y_pred_gb)\n",
    "recall_gb = recall_score(y_eval, y_pred_gb)\n",
    "f1_gb = f1_score(y_eval, y_pred_gb)\n",
    "auc_roc_gb = roc_auc_score(y_eval, y_pred_gb)\n",
    "\n",
    "# Adding the results to the dataframe\n",
    "results_tuned_df = results_tuned_df.append({'Model': 'Random Forest (Tuned)',\n",
    "                                'Accuracy': accuracy_rf,\n",
    "                                'Precision': precision_rf,\n",
    "                                'Recall': recall_rf,\n",
    "                                'F1 Score': f1_rf,\n",
    "                                'AUC-ROC': auc_roc_rf}, ignore_index=True)\n",
    "\n",
    "results_tuned_df = results_tuned_df.append({'Model': 'LightGBM (Tuned)',\n",
    "                                'Accuracy': accuracy_lgbm,\n",
    "                                'Precision': precision_lgbm,\n",
    "                                'Recall': recall_lgbm,\n",
    "                                'F1 Score': f1_lgbm,\n",
    "                                'AUC-ROC': auc_roc_lgbm}, ignore_index=True)\n",
    "\n",
    "results_tuned_df = results_tuned_df.append({'Model': 'Gradient Boosting (Tuned)',\n",
    "                                'Accuracy': accuracy_gb,\n",
    "                                'Precision': precision_gb,\n",
    "                                'Recall': recall_gb,\n",
    "                                'F1 Score': f1_gb,\n",
    "                                'AUC-ROC': auc_roc_gb}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>AUC-ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest (Tuned)</td>\n",
       "      <td>0.857047</td>\n",
       "      <td>0.844850</td>\n",
       "      <td>0.874494</td>\n",
       "      <td>0.859416</td>\n",
       "      <td>0.857058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LightGBM (Tuned)</td>\n",
       "      <td>0.855024</td>\n",
       "      <td>0.862259</td>\n",
       "      <td>0.844804</td>\n",
       "      <td>0.853442</td>\n",
       "      <td>0.855017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting (Tuned)</td>\n",
       "      <td>0.857047</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.857046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Accuracy  Precision    Recall  F1 Score  \\\n",
       "0      Random Forest (Tuned)  0.857047   0.844850  0.874494  0.859416   \n",
       "1           LightGBM (Tuned)  0.855024   0.862259  0.844804  0.853442   \n",
       "2  Gradient Boosting (Tuned)  0.857047   0.856950  0.856950  0.856950   \n",
       "\n",
       "    AUC-ROC  \n",
       "0  0.857058  \n",
       "1  0.855017  \n",
       "2  0.857046  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation scores for the tuned models\n",
    "results_tuned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>AUC-ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.768038</td>\n",
       "      <td>0.739445</td>\n",
       "      <td>0.827260</td>\n",
       "      <td>0.780892</td>\n",
       "      <td>0.768078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.858395</td>\n",
       "      <td>0.869263</td>\n",
       "      <td>0.843455</td>\n",
       "      <td>0.856164</td>\n",
       "      <td>0.858385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.857047</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.856950</td>\n",
       "      <td>0.857046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.821308</td>\n",
       "      <td>0.812336</td>\n",
       "      <td>0.835358</td>\n",
       "      <td>0.823686</td>\n",
       "      <td>0.821318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>0.755226</td>\n",
       "      <td>0.719767</td>\n",
       "      <td>0.835358</td>\n",
       "      <td>0.773267</td>\n",
       "      <td>0.755280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K-Nearest Neighbors</td>\n",
       "      <td>0.776129</td>\n",
       "      <td>0.719656</td>\n",
       "      <td>0.904184</td>\n",
       "      <td>0.801435</td>\n",
       "      <td>0.776216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.819960</td>\n",
       "      <td>0.806202</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.823762</td>\n",
       "      <td>0.819974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.852326</td>\n",
       "      <td>0.857534</td>\n",
       "      <td>0.844804</td>\n",
       "      <td>0.851122</td>\n",
       "      <td>0.852321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.855024</td>\n",
       "      <td>0.863260</td>\n",
       "      <td>0.843455</td>\n",
       "      <td>0.853242</td>\n",
       "      <td>0.855016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy  Precision    Recall  F1 Score   AUC-ROC\n",
       "0     Logistic Regression  0.768038   0.739445  0.827260  0.780892  0.768078\n",
       "1           Random Forest  0.858395   0.869263  0.843455  0.856164  0.858385\n",
       "2       Gradient Boosting  0.857047   0.856950  0.856950  0.856950  0.857046\n",
       "3  Support Vector Machine  0.821308   0.812336  0.835358  0.823686  0.821318\n",
       "4    Gaussian Naive Bayes  0.755226   0.719767  0.835358  0.773267  0.755280\n",
       "5     K-Nearest Neighbors  0.776129   0.719656  0.904184  0.801435  0.776216\n",
       "6           Decision Tree  0.819960   0.806202  0.842105  0.823762  0.819974\n",
       "7                 XGBoost  0.852326   0.857534  0.844804  0.851122  0.852321\n",
       "8                LightGBM  0.855024   0.863260  0.843455  0.853242  0.855016"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation scores for the untuned models\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "Feature importance is a technique used to identify the most influential features or variables in predicting the target variable (Ghoualmi & Benkechkache, 2022). It helps us understand which features have the most significant impact on the model's predictions and provides insights into the underlying relationships between the features and the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PaymentMethod_Electronic check</td>\n",
       "      <td>0.113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tenure</td>\n",
       "      <td>0.105634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Contract_Two year</td>\n",
       "      <td>0.090773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>0.082428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>InternetService_Fiber optic</td>\n",
       "      <td>0.081134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>0.067895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PaperlessBilling_Yes</td>\n",
       "      <td>0.058998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Contract_One year</td>\n",
       "      <td>0.056744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TechSupport_Yes</td>\n",
       "      <td>0.056642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OnlineSecurity_Yes</td>\n",
       "      <td>0.048952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dependents_Yes</td>\n",
       "      <td>0.032129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Partner_Yes</td>\n",
       "      <td>0.031081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>InternetService_No</td>\n",
       "      <td>0.027922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OnlineBackup_Yes</td>\n",
       "      <td>0.026397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PaymentMethod_Credit card (automatic)</td>\n",
       "      <td>0.017362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gender_Male</td>\n",
       "      <td>0.016082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>DeviceProtection_Yes</td>\n",
       "      <td>0.014802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>PaymentMethod_Mailed check</td>\n",
       "      <td>0.013289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>StreamingMovies_Yes</td>\n",
       "      <td>0.013126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OnlineBackup_None</td>\n",
       "      <td>0.012077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>StreamingTV_Yes</td>\n",
       "      <td>0.010446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MultipleLines_Yes</td>\n",
       "      <td>0.010109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PhoneService_Yes</td>\n",
       "      <td>0.007611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SeniorCitizen</td>\n",
       "      <td>0.004563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Feature  Importance\n",
       "22         PaymentMethod_Electronic check    0.113803\n",
       "1                                  tenure    0.105634\n",
       "19                      Contract_Two year    0.090773\n",
       "3                            TotalCharges    0.082428\n",
       "9             InternetService_Fiber optic    0.081134\n",
       "2                          MonthlyCharges    0.067895\n",
       "20                   PaperlessBilling_Yes    0.058998\n",
       "18                      Contract_One year    0.056744\n",
       "15                        TechSupport_Yes    0.056642\n",
       "11                     OnlineSecurity_Yes    0.048952\n",
       "6                          Dependents_Yes    0.032129\n",
       "5                             Partner_Yes    0.031081\n",
       "10                     InternetService_No    0.027922\n",
       "13                       OnlineBackup_Yes    0.026397\n",
       "21  PaymentMethod_Credit card (automatic)    0.017362\n",
       "4                             gender_Male    0.016082\n",
       "14                   DeviceProtection_Yes    0.014802\n",
       "23             PaymentMethod_Mailed check    0.013289\n",
       "17                    StreamingMovies_Yes    0.013126\n",
       "12                      OnlineBackup_None    0.012077\n",
       "16                        StreamingTV_Yes    0.010446\n",
       "8                       MultipleLines_Yes    0.010109\n",
       "7                        PhoneService_Yes    0.007611\n",
       "0                           SeniorCitizen    0.004563"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get feature importances from the Random Forest model\n",
    "feature_importances = rf_model_tuned.feature_importances_\n",
    "\n",
    "# Create a dataframe to store the feature importances\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': X_train.columns,\n",
    "    'Importance': feature_importances\n",
    "})\n",
    "\n",
    "# Sort the dataframe by feature importance in descending order\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Print the feature importance dataframe\n",
    "feature_importance_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the feature importances\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Random Forest Feature Importance')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest model without Senior Citizen Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining our tuned model \n",
    "rf_model_tuned_Sn = RandomForestClassifier(n_estimators=300, max_depth=10, min_samples_split=2, min_samples_leaf=1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the Senior Citizen column in both the training and evaluation datasets\n",
    "X_train_scaled_Sn = X_train_scaled.drop('SeniorCitizen',axis=1)\n",
    "X_eval_scaled_Sn = X_eval_scaled.drop('SeniorCitizen',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting the model in the train dataset\n",
    "rf_model_tuned_Sn.fit(X_train_scaled_Sn,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting on the evaluation dataser\n",
    "y_pred_Sn= rf_model_tuned_Sn.predict(X_eval_scaled_Sn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating the metrics\n",
    "f1_rf = f1_score(y_pred_Sn, y_pred_rf)\n",
    "accuracy_rf = accuracy_score(y_pred_Sn,y_pred_rf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The f1 score for the random forest model with Senior Citizen column is: 0.991486574983628\n",
      "\n",
      " The accuracy score for the random forest model without Senior Citizen column is: 0.9912339851652057\n"
     ]
    }
   ],
   "source": [
    "#Displaying the result\n",
    "print(f'The f1 score for the random forest model with Senior Citizen column is: {f1_rf}')\n",
    "print(f'\\n The accuracy score for the random forest model without Senior Citizen column is: {accuracy_rf}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "final_model = 'Churn_Model.pkl'\n",
    "\n",
    "pickle.dump(rf_model_tuned_Sn,open('final_model','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the Model\n",
    "final_model = pickle.load(open('final_model','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expoting the Key Components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Requirements file\n",
    "!pip freeze > requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Object to Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "components = {\n",
    "    'label_encoder': label_encoder,\n",
    "    'encoder': encoder,\n",
    "    'scaler': scaler,\n",
    "    'imbalance': smote,\n",
    "    'grid_search_rfc':grid_search_rf,\n",
    "    'model': rf_model_tuned_Sn\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('ML_Model.pkl', 'wb') as file:\n",
    "    pickle.dump(components, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading After Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open ('ML_Model.pkl', 'rb') as file:\n",
    "    loaded_object = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking on the Loaded objects\n",
    "loaded_object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "In conclusion, customer churn is a significant challenge for the telecommunications industry, with a considerable proportion of customers discontinuing their services. The following insights can be drawn from the analysis regarding Vodafone telecommunication Company:\n",
    "\n",
    "Tenure: The churn rate decreases as tenure increases, indicating that customers with longer tenures are less likely to churn. This suggests that customer loyalty tends to increase over time, indicating higher satisfaction and a stronger relationship with the company.\n",
    "\n",
    "Payment Method: There is a correlation between the customer's payment method and churn rate. Customers using electronic check or mailed check as their payment method had higher churn rates, while those using bank transfer or credit card with automatic payment had lower churn rates. This suggests that customers using more automated and convenient payment methods may have higher satisfaction and loyalty.\n",
    "\n",
    "Paperless Billing: Customers who opt for paperless billing have a higher churn rate compared to those who prefer traditional billing methods. This implies that customers who embrace digital and paperless processes may have different expectations or experiences that influence their decision to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recommendations\n",
    "Focus on customer retention strategies for new customers: Since customers with longer tenures are less likely to churn, it is crucial to implement strategies to enhance customer satisfaction and engagement during the early stages of their relationship with the company. Provide personalized onboarding experiences, exceptional customer support, and incentives to promote loyalty.\n",
    "\n",
    "\n",
    "Offer incentives for automated payment methods: Encourage customers to use bank transfer or credit card with automatic payment by offering incentives such as discounts or rewards. This can promote customer convenience and increase loyalty.\n",
    "\n",
    "\n",
    "Enhance paperless billing experience: Identify and address pain points or concerns related to paperless billing. Improve the user interface, provide clear communication about billing details, and offer additional benefits or rewards for customers who choose paperless billing.\n",
    "\n",
    "\n",
    "Conduct customer satisfaction surveys: Regularly collect feedback from customers to understand their needs, preferences, and satisfaction levels. This can help identify areas for improvement and enable the company to take proactive measures to address customer concerns.\n",
    "\n",
    "\n",
    "Implement targeted marketing campaigns. The company can Focus on educating customers about the benefits of the company's services, addressing common pain points, and highlighting the advantages of long-term relationships with the company.\n",
    "\n",
    "\n",
    "Continuously monitoring and updating the churn prediction model. Customer churn patterns may evolve over time, so it's important to regularly monitor the performance of the predictive model and update them with new data. This will ensure that the model remain accurate and effective in predicting churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "Bartz-Beielstein, T., & Zaefferer, M. (2023). Hyperparameter tuning approaches. Hyperparameter Tuning for Machine and Deep Learning with R, 71-119. https://doi.org/10.1007/978-981-19-5170-1_4\n",
    "\n",
    "Crisp dm - Google search. (n.d.). Google. https://www.google.com/search?sxsrf=APwXEdc766Sm1fRZINun8IIilYR4FPOFFg:1687712418116&q=crisp+dm&tbm=isch&sa=X&ved=2ahUKEwiHt6jZ8t7_AhVpSPEDHVL-BVYQ0pQJegQIDhAB&biw=1366&bih=592&dpr=1#imgrc=6YKkW8XCZfns4M\n",
    "\n",
    "Ghoualmi, L., & Benkechkache, M. E. (2022). Feature selection based on machine learning algorithms: A weighted score feature importance approach for facial authentication. 2022 3rd International Informatics and Software Engineering Conference (IISEC). https://doi.org/10.1109/iisec56263.2022.9998240\n",
    "\n",
    "Nababan, A. A., Sutarman, Zarlis, M., & Nababan, E. B. (2023). Improving the accuracy of k-nearest neighbor (k-NN) using synthetic minority Oversampling technique (SMOTE) and gain ratio (GR) for imbalanced class data. AIP Conference Proceedings. https://doi.org/10.1063/5.0128413\n",
    "\n",
    "Vodafone. (2023). Who we are. Vodafone.com. https://www.vodafone.com/about-vodafone/who-we-are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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