Project_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customer Churn Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Business Understanding\n",
    "In the dynamic world of telecommunications, customer churn remains a pressing challenge for companies striving to achieve sustained success. Customers have alot of options and evolving preferences. Accurately predicting churn has become important to retain and satisfy them. Customer churn is essentially the loss of customers.\n",
    "\n",
    "In this project, we use Supervised Machine Learning (classification) to explore the significance of churn analytics as a strategic tool for telecommunication companies to proactively identify potential risk factors for churn, optimize retention efforts, and cultivate lasting customer relationships. By leveraging data-driven insights and advanced analytics, companies can gain a competitive edge and ensure sustainable growth in an ever-changing industry.\n",
    "\n",
    "The churn analytics predictive model is a data-driven solution designed to address the persistent challenge of customer churn in subscription-based industries. This model aims to identify customers at risk of churn, enabling businesses to take proactive measures and implement targeted retention strategies. \n",
    "\n",
    "The primary objective is to reduce customer churn rates and retain valuable customers by providing insights into customer behavior, preferences, and patterns. Utilizing machine learning models such as logistic regression and decision trees, the model evaluates the data to build accurate predictions. \n",
    "\n",
    "Its performance is assessed through metrics like accuracy, precision, recall and F1-score. Armed with the model's insights, businesses can create personalized offers, marketing campaigns, and proactive customer support initiatives, thus improving customer satisfaction and fostering loyalty. \n",
    "\n",
    "## Hypothesis\n",
    "\n",
    "**Null Hypothesis (H0)**:\n",
    "\"There is no significant relationship between customer tenure and churn rate in the telecom company.\"\n",
    "\n",
    "**Alternative Hypothesis (Ha)**:\n",
    "\"There is a significant relationship between customer tenure and churn rate in the telecom company.\"\n",
    "\n",
    "## Research Questions\n",
    "\n",
    "1. How does customer tenure relate to churn rates? Are long-tenured customers more likely to stay with the company, and do new customers exhibit higher churn behavior?\n",
    "\n",
    "2. Is there a correlation between the total charges and churn rates? Do customers with higher total charges exhibit different churn behavior compared to those with lower total charges\n",
    "\n",
    "3. What is the impact of contract type on churn rates? Do customers on long-term contracts have significantly lower churn rates compared to those on short-term contracts?\n",
    "\n",
    "4. Are there significant differences in churn behavior between customers who have device protection and those who don't?\n",
    "\n",
    "5. What is the relationship between the availability of tech support and churn rates? Are customers with access to tech support more likely to remain with the company?\n",
    "\n",
    "6. Do streaming services play a role in customer churn? Are customers with streaming services, such as StreamingTV and StreamingMovies, more likely to stay with the company?\n",
    "\n",
    "7. How does the choice of payment method impact churn rates? Are customers with specific payment methods more prone to churn than others?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Data Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries  and Packages\n",
    "Here is the section to import all the packages/libraries that will be used through this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Database connections\n",
    "import pyodbc \n",
    "from dotenv import dotenv_values\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Data handling\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import openpyxl\n",
    "import numpy as np\n",
    "\n",
    "# Vizualisation (Matplotlib, Plotly, Seaborn, etc. )\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "import plotly.colors as colors\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# EDA (pandas-profiling, etc. )\n",
    "from statsmodels.genmod.families import Binomial\n",
    "from scipy.stats import pointbiserialr\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Feature Processing (Scikit-learn processing, etc. )\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n",
    "from sklearn.metrics import mutual_info_score\n",
    "from sklearn.compose import make_column_transformer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Other packages\n",
    "import os, joblib\n",
    "from joblib import load"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "### Accessing first dataset from the database\n",
    "The first 3000 records of the dataset can be found in a remote database.\n",
    "\n",
    "-   **pyodbc**: A package for creating connection strings to your remote database\n",
    "-   **python-dotenv**: A package for creating environment variables that will help you hide sensitve configuration informantion such as database credentials and API keys\n",
    "\n",
    "The second dataset (2000 records) is already available as a .xlsx file. **This is the test dataset**\n",
    "\n",
    "The third dataset is already available as a .csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load environment variables from .env file into a dictionary\n",
    "# environment_variables = dotenv_values('.env')\n",
    "\n",
    "\n",
    "# # Get 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",
    "# # Create connection string to database\n",
    "# connection_string = f\"DRIVER={{SQL Server}};SERVER=tcp:{server};DATABASE={database};UID={username};PWD={password}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Use the connect method of the pyodbc library and pass in the connection string.\n",
    "# # This will connect to the server and might take a few seconds to be complete. \n",
    "# # Check your internet connection if it takes more time than necessary\n",
    "\n",
    "# connection = pyodbc.connect(connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get the cursor\n",
    "# # The connection cursor is used to execute statements to communicate with the MySQL database\n",
    "# cursor = connection.cursor()\n",
    "\n",
    "# # Retrieve the table names\n",
    "# table_names = cursor.tables(tableType='TABLE')\n",
    "\n",
    "# # Fetch all the table names\n",
    "# tables = table_names.fetchall()\n",
    "\n",
    "# # Print the table names\n",
    "# for table in tables:\n",
    "#     print(table.table_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # sql query to get the dataset\n",
    "\n",
    "# query = \"SELECT * FROM LP2_Telco_churn_first_3000\"\n",
    "# data=pd.read_sql(query,connection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # preview the data\n",
    "\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load Telco second 2000 data with openpyxl\n",
    "# # Preview the data\n",
    "\n",
    "# data_xls = pd.read_excel('Telco-churn-second-2000.xlsx')\n",
    "# data_xls.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is our test dataset to be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_xls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save to csv from xls\n",
    "\n",
    "# data_xls.to_csv (\"Telco-churn-second-2000.csv\", \n",
    "#                   index = None,\n",
    "#                   header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load and preview last dataset\n",
    "\n",
    "# data2 = pd.read_csv('LP2_Telco-churn-last-2000.csv')\n",
    "# data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # View columns and rows of datasets\n",
    "\n",
    "# (data.shape,data_xls.shape,data2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "<thead><tr>\n",
    "<th><strong>Feature Name</strong></th>\n",
    "<th><strong>Description</strong></th>\n",
    "<th><strong>Data Type</strong></th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr>\n",
    "<td>customerID</td>\n",
    "<td>Contains customer ID</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>gender</td>\n",
    "<td>whether the customer female or male</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>SeniorCitizen</td>\n",
    "<td>Whether the customer is a senior citizen or not (1, 0)</td>\n",
    "<td>numeric, int</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Partner</td>\n",
    "<td>Whether the customer has a partner or not (Yes, No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Dependents</td>\n",
    "<td>Whether the customer has dependents or not (Yes, No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>tenure</td>\n",
    "<td>Number of months the customer has stayed with the company</td>\n",
    "<td>numeric, int</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>PhoneService</td>\n",
    "<td>Whether the customer has a phone service or not (Yes, No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MultipleLines</td>\n",
    "<td>Whether the customer has multiple lines r not (Yes, No, No phone service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>InternetService</td>\n",
    "<td>Customer’s internet service provider (DSL, Fiber optic, No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>OnlineSecurity</td>\n",
    "<td>Whether the customer has online security or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>OnlineBackup</td>\n",
    "<td>Whether the customer has online backup or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>DeviceProtection</td>\n",
    "<td>Whether the customer has device protection or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>TechSupport</td>\n",
    "<td>Whether the customer has tech support or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>streamingTV</td>\n",
    "<td>Whether the customer has streaming TV or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>streamingMovies</td>\n",
    "<td>Whether the customer has streaming movies or not (Yes, No, No internet service)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Contract</td>\n",
    "<td>The contract term of the customer (Month-to-month, One year, Two year)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>PaperlessBilling</td>\n",
    "<td>Whether the customer has paperless billing or not (Yes, No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>PaymentMethod</td>\n",
    "<td>The customer’s payment method (Electronic check, Mailed check, Bank transfer, Credit card)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MonthlyCharges</td>\n",
    "<td>The amount charged to the customer monthly </td>\n",
    "<td> numeric , int</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>TotalCharges</td>\n",
    "<td>The total amount charged to the customer </td>\n",
    "<td>object</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Churn</td>\n",
    "<td>Whether the customer churned or not (Yes or No)</td>\n",
    "<td>categorical</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Issues with data\n",
    "-  First dataset and third dataset need to be merged, however they have inconsistent Dtypes that need to be synchronized\n",
    "-  We need to remove the Customer ID column as it is not necessary for our analysis\n",
    "-  Missing values in First 3000 dataset need to be sorted out\n",
    "-  TotalCharges Column needs to be changed to numeric Dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean First Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 = pd.read_csv('Dataset\\LP2_Telco_churn_first_3000.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Customer ID column\n",
    "\n",
    "del data[\"customerID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert bool columns to object Dtypes\n",
    "\n",
    "bool_to_obj = {'Partner': object,\n",
    "               'SeniorCitizen': object,\n",
    "               'Dependents': object, \n",
    "               'PhoneService': object,\n",
    "               'PaperlessBilling': object}\n",
    "\n",
    "data = data.astype(bool_to_obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace True and False values with Yes and No in Object columns\n",
    "\n",
    "replace_values = {True: 'Yes', False: 'No'}\n",
    "\n",
    "# Use replace() method\n",
    "data['Partner'] = data['Partner'].replace(replace_values)\n",
    "data['SeniorCitizen'] = data['SeniorCitizen'].replace(replace_values)\n",
    "data['Dependents'] = data['Dependents'].replace(replace_values)\n",
    "data['PhoneService'] = data['PhoneService'].replace(replace_values)\n",
    "data['MultipleLines'] = data['MultipleLines'].replace(replace_values)\n",
    "data['OnlineSecurity'] = data['OnlineSecurity'].replace(replace_values)\n",
    "data['OnlineBackup'] = data['OnlineBackup'].replace(replace_values)\n",
    "data['DeviceProtection'] = data['DeviceProtection'].replace(replace_values)\n",
    "data['TechSupport'] = data['TechSupport'].replace(replace_values)\n",
    "data['StreamingTV'] = data['StreamingTV'].replace(replace_values)\n",
    "data['StreamingMovies'] = data['StreamingMovies'].replace(replace_values)\n",
    "data['PaperlessBilling'] = data['PaperlessBilling'].replace(replace_values)\n",
    "data['Churn'] = data['Churn'].replace(replace_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No', 'Yes', nan], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View unique values in target column (Churn)\n",
    "\n",
    "data['Churn'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the missing values in target column (Churn)\n",
    "\n",
    "data['Churn'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop missing value in the Churn column\n",
    "\n",
    "data.dropna(subset='Churn', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view all missing values in first dataset\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</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>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>80.849998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>56.049999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "488   Female            No     Yes        Yes       0           No   \n",
       "753     Male            No      No        Yes       0          Yes   \n",
       "936   Female            No     Yes        Yes       0          Yes   \n",
       "1082    Male            No     Yes        Yes       0          Yes   \n",
       "1340  Female            No     Yes        Yes       0           No   \n",
       "\n",
       "     MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "488            NaN             DSL            Yes           No   \n",
       "753             No              No            NaN          NaN   \n",
       "936             No             DSL            Yes          Yes   \n",
       "1082           Yes              No            NaN          NaN   \n",
       "1340           NaN             DSL            Yes          Yes   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies  Contract  \\\n",
       "488               Yes         Yes         Yes              No  Two year   \n",
       "753               NaN         NaN         NaN             NaN  Two year   \n",
       "936               Yes          No         Yes             Yes  Two year   \n",
       "1082              NaN         NaN         NaN             NaN  Two year   \n",
       "1340              Yes         Yes         Yes              No  Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "488               Yes  Bank transfer (automatic)       52.549999   \n",
       "753                No               Mailed check       20.250000   \n",
       "936                No               Mailed check       80.849998   \n",
       "1082               No               Mailed check       25.750000   \n",
       "1340               No    Credit card (automatic)       56.049999   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "488            NaN    No  \n",
       "753            NaN    No  \n",
       "936            NaN    No  \n",
       "1082           NaN    No  \n",
       "1340           NaN    No  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the missing values in TotalCharges column\n",
    "\n",
    "data[np.isnan(data['TotalCharges'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values in **TotalCharges** have a **tenure** of 0, even though there are values in the **MonthlyCharges** column. We can simply drop those rows to avoid confusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where tenure = 0\n",
    "\n",
    "data.drop(labels=data[data['tenure'] == 0].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# impute missing values with the mode\n",
    "\n",
    "columns_to_replace = ['MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
    "            'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
    "            'StreamingMovies']\n",
    "\n",
    "for column in columns_to_replace:\n",
    "    data[column].fillna(data[column].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Third Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3 = pd.read_csv('Dataset\\LP2_Telco-churn-last-2000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2043 entries, 0 to 2042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        2043 non-null   object \n",
      " 1   gender            2043 non-null   object \n",
      " 2   SeniorCitizen     2043 non-null   int64  \n",
      " 3   Partner           2043 non-null   object \n",
      " 4   Dependents        2043 non-null   object \n",
      " 5   tenure            2043 non-null   int64  \n",
      " 6   PhoneService      2043 non-null   object \n",
      " 7   MultipleLines     2043 non-null   object \n",
      " 8   InternetService   2043 non-null   object \n",
      " 9   OnlineSecurity    2043 non-null   object \n",
      " 10  OnlineBackup      2043 non-null   object \n",
      " 11  DeviceProtection  2043 non-null   object \n",
      " 12  TechSupport       2043 non-null   object \n",
      " 13  StreamingTV       2043 non-null   object \n",
      " 14  StreamingMovies   2043 non-null   object \n",
      " 15  Contract          2043 non-null   object \n",
      " 16  PaperlessBilling  2043 non-null   object \n",
      " 17  PaymentMethod     2043 non-null   object \n",
      " 18  MonthlyCharges    2043 non-null   float64\n",
      " 19  TotalCharges      2043 non-null   object \n",
      " 20  Churn             2043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 335.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop Customer ID column\n",
    "\n",
    "data3.drop(columns='customerID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change Dtype of TotalCharges column from object to numeric\n",
    "\n",
    "data3['TotalCharges'] = pd.to_numeric(data3['TotalCharges'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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        3\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</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>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>73.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "218     Male              0     Yes        Yes       0          Yes   \n",
       "1670  Female              0     Yes        Yes       0          Yes   \n",
       "1754    Male              0      No        Yes       0          Yes   \n",
       "\n",
       "     MultipleLines InternetService       OnlineSecurity         OnlineBackup  \\\n",
       "218             No              No  No internet service  No internet service   \n",
       "1670           Yes             DSL                   No                  Yes   \n",
       "1754           Yes             DSL                  Yes                  Yes   \n",
       "\n",
       "         DeviceProtection          TechSupport          StreamingTV  \\\n",
       "218   No internet service  No internet service  No internet service   \n",
       "1670                  Yes                  Yes                  Yes   \n",
       "1754                   No                  Yes                   No   \n",
       "\n",
       "          StreamingMovies  Contract PaperlessBilling  \\\n",
       "218   No internet service  One year              Yes   \n",
       "1670                   No  Two year               No   \n",
       "1754                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "218                Mailed check           19.70           NaN    No  \n",
       "1670               Mailed check           73.35           NaN    No  \n",
       "1754  Bank transfer (automatic)           61.90           NaN    No  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view Missing Values in TotalCharges column\n",
    "\n",
    "data3[np.isnan(data3['TotalCharges'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where tenure = 0\n",
    "\n",
    "data3.drop(labels=data3[data3['tenure'] == 0].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeniorCitizen\n",
       "0    1696\n",
       "1     344\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3['SeniorCitizen'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 0 values with 'No' and 1 values with 'Yes'\n",
    "\n",
    "data3['SeniorCitizen'] = data3['SeniorCitizen'].replace({0: 'No', 1: 'Yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change column dtype to object\n",
    "\n",
    "data3['SeniorCitizen'] = data3['SeniorCitizen'].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge final train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5034 entries, 0 to 5033\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            5034 non-null   object \n",
      " 1   SeniorCitizen     5034 non-null   object \n",
      " 2   Partner           5034 non-null   object \n",
      " 3   Dependents        5034 non-null   object \n",
      " 4   tenure            5034 non-null   int64  \n",
      " 5   PhoneService      5034 non-null   object \n",
      " 6   MultipleLines     5034 non-null   object \n",
      " 7   InternetService   5034 non-null   object \n",
      " 8   OnlineSecurity    5034 non-null   object \n",
      " 9   OnlineBackup      5034 non-null   object \n",
      " 10  DeviceProtection  5034 non-null   object \n",
      " 11  TechSupport       5034 non-null   object \n",
      " 12  StreamingTV       5034 non-null   object \n",
      " 13  StreamingMovies   5034 non-null   object \n",
      " 14  Contract          5034 non-null   object \n",
      " 15  PaperlessBilling  5034 non-null   object \n",
      " 16  PaymentMethod     5034 non-null   object \n",
      " 17  MonthlyCharges    5034 non-null   float64\n",
      " 18  TotalCharges      5034 non-null   float64\n",
      " 19  Churn             5034 non-null   object \n",
      "dtypes: float64(2), int64(1), object(17)\n",
      "memory usage: 786.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# merge the test datasets\n",
    "\n",
    "Data_All= pd.concat([data, data3], ignore_index=True)\n",
    "Data_All.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>2554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>4215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>3481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>2247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>5034</td>\n",
       "      <td>3</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>2744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>5034</td>\n",
       "      <td>4</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>5034</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>3698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique               top  freq\n",
       "gender            5034      2              Male  2554\n",
       "SeniorCitizen     5034      2                No  4215\n",
       "Partner           5034      2                No  2583\n",
       "Dependents        5034      2                No  3481\n",
       "PhoneService      5034      2               Yes  4547\n",
       "MultipleLines     5034      3                No  2688\n",
       "InternetService   5034      3       Fiber optic  2247\n",
       "OnlineSecurity    5034      3                No  3155\n",
       "OnlineBackup      5034      3                No  2879\n",
       "DeviceProtection  5034      3                No  2867\n",
       "TechSupport       5034      3                No  3119\n",
       "StreamingTV       5034      3                No  2666\n",
       "StreamingMovies   5034      3               Yes  2629\n",
       "Contract          5034      3    Month-to-month  2744\n",
       "PaperlessBilling  5034      2               Yes  2992\n",
       "PaymentMethod     5034      4  Electronic check  1699\n",
       "Churn             5034      2                No  3698"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking all categorical values\n",
    "\n",
    "Data_All.describe(include=['object']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>tenure</th>\n",
       "      <td>5034.0</td>\n",
       "      <td>32.620580</td>\n",
       "      <td>24.511015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</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>5034.0</td>\n",
       "      <td>65.107251</td>\n",
       "      <td>30.068019</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>35.799999</td>\n",
       "      <td>70.599998</td>\n",
       "      <td>90.050003</td>\n",
       "      <td>118.650002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>5034.0</td>\n",
       "      <td>2300.954758</td>\n",
       "      <td>2268.346402</td>\n",
       "      <td>18.799999</td>\n",
       "      <td>417.662498</td>\n",
       "      <td>1401.000000</td>\n",
       "      <td>3860.599976</td>\n",
       "      <td>8670.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count         mean          std        min         25%  \\\n",
       "tenure          5034.0    32.620580    24.511015   1.000000    9.000000   \n",
       "MonthlyCharges  5034.0    65.107251    30.068019  18.400000   35.799999   \n",
       "TotalCharges    5034.0  2300.954758  2268.346402  18.799999  417.662498   \n",
       "\n",
       "                        50%          75%          max  \n",
       "tenure            29.000000    56.000000    72.000000  \n",
       "MonthlyCharges    70.599998    90.050003   118.650002  \n",
       "TotalCharges    1401.000000  3860.599976  8670.100000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking all numerical values\n",
    "\n",
    "Data_All.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Known observations: 5034\n",
      "Unique observations: 5024\n"
     ]
    }
   ],
   "source": [
    "# checking for unique columns\n",
    "\n",
    "print('Known observations: {}\\nUnique observations: {}'.format(len(Data_All.index),len(Data_All.drop_duplicates().index)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: gender - Unique Values: ['Female' 'Male']\n",
      "Column: SeniorCitizen - Unique Values: ['No' 'Yes']\n",
      "Column: Partner - Unique Values: ['Yes' 'No']\n",
      "Column: Dependents - Unique Values: ['No' 'Yes']\n",
      "Column: tenure - Unique Values: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "Column: PhoneService - Unique Values: ['No' 'Yes']\n",
      "Column: MultipleLines - Unique Values: ['No' 'Yes' 'No phone service']\n",
      "Column: InternetService - Unique Values: ['DSL' 'Fiber optic' 'No']\n",
      "Column: OnlineSecurity - Unique Values: ['No' 'Yes' 'No internet service']\n",
      "Column: OnlineBackup - Unique Values: ['Yes' 'No' 'No internet service']\n",
      "Column: DeviceProtection - Unique Values: ['No' 'Yes' 'No internet service']\n",
      "Column: TechSupport - Unique Values: ['No' 'Yes' 'No internet service']\n",
      "Column: StreamingTV - Unique Values: ['No' 'Yes' 'No internet service']\n",
      "Column: StreamingMovies - Unique Values: ['No' 'Yes' 'No internet service']\n",
      "Column: Contract - Unique Values: ['Month-to-month' 'One year' 'Two year']\n",
      "Column: PaperlessBilling - Unique Values: ['Yes' 'No']\n",
      "Column: PaymentMethod - Unique Values: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Column: MonthlyCharges - Unique Values: [ 29.85000038  56.95000076  53.84999847 ...  78.7         60.65\n",
      " 103.2       ]\n",
      "Column: TotalCharges - Unique Values: [  29.85000038 1889.5         108.15000153 ...  346.45        306.6\n",
      " 6844.5       ]\n",
      "Column: Churn - Unique Values: ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "# view unique values of each column\n",
    "\n",
    "for column in Data_All.columns:\n",
    "    print('Column: {} - Unique Values: {}'.format(column, Data_All[column].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "\n",
    "Data_All.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tenure\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyCharges\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TotalCharges\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for outliers in the numerical variables\n",
    "# Use boxplot\n",
    "\n",
    "for col in ['tenure','MonthlyCharges','TotalCharges']:\n",
    "    print(col)\n",
    "    plt.boxplot(Data_All[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **No outliers** in out Test Dataset numeric columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normality Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perfom normality test on the three numeric variables to look for signs of skewedness using the D’Agostino’s K^2 Test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=7510.47646, p=0.000\n",
      "the sample is skewed\n"
     ]
    }
   ],
   "source": [
    "# D’Agostino’s K^2 Test on monthly charges\n",
    "\n",
    "stat, p = stats.normaltest(Data_All['MonthlyCharges'])\n",
    "\n",
    "print('Statistics=%.5f, p=%.3f' % (stat, p))\n",
    "\n",
    "# significance level (5%) for the test\n",
    "alpha = 0.05\n",
    "\n",
    "if p > alpha:\n",
    "    print('the sample is normally distributed')\n",
    "else:\n",
    "    print('the sample is skewed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=53555.33858, p=0.000\n",
      "the sample is skewed\n"
     ]
    }
   ],
   "source": [
    "# D’Agostino’s K^2 Test on tenure\n",
    "\n",
    "stat, p = stats.normaltest(Data_All['tenure'])\n",
    "\n",
    "print('Statistics=%.5f, p=%.3f' % (stat, p))\n",
    "\n",
    "# significance level (5%) for the test\n",
    "alpha = 0.05\n",
    "\n",
    "if p > alpha:\n",
    "\tprint('the sample is normally distributed')\n",
    "else:\n",
    "\tprint('the sample is skewed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=569.05463, p=0.000\n",
      "the sample is skewed\n"
     ]
    }
   ],
   "source": [
    "## D’Agostino’s K^2 Test on TotalCharges\n",
    "stat, p = stats.normaltest(Data_All['TotalCharges'])\n",
    "\n",
    "print('Statistics=%.5f, p=%.3f' % (stat, p))\n",
    "\n",
    "# significance level (5%) for the test\n",
    "alpha = 0.05\n",
    "\n",
    "if p > alpha:\n",
    "\tprint('the sample is normally distributed')\n",
    "else:\n",
    "\tprint('the sample is skewed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset index and save to csv\n",
    "\n",
    "Data_All.reset_index(drop=True, inplace=True)\n",
    "Data_All.to_csv('Dataset\\Train_Data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Data Types**\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "object     17\n",
       "float64     2\n",
       "int64       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view Data Types\n",
    "\n",
    "print(\"**Data Types**\")\n",
    "Data_All.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.read_csv('Dataset\\Telco-churn-second-2000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change Dtype of TotalCharges column from object to numeric\n",
    "\n",
    "data_test['TotalCharges'] = pd.to_numeric(data_test['TotalCharges'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>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>331</th>\n",
       "      <td>7644-OMVMY</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</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>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.85</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>3213-VVOLG</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</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>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>2520-SGTTA</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</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>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "331   7644-OMVMY    Male              0     Yes        Yes       0   \n",
       "826   3213-VVOLG    Male              0     Yes        Yes       0   \n",
       "1380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
       "\n",
       "     PhoneService MultipleLines InternetService       OnlineSecurity  \\\n",
       "331           Yes            No              No  No internet service   \n",
       "826           Yes           Yes              No  No internet service   \n",
       "1380          Yes            No              No  No internet service   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "331   No internet service  No internet service  No internet service   \n",
       "826   No internet service  No internet service  No internet service   \n",
       "1380  No internet service  No internet service  No internet service   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "331   No internet service  No internet service  Two year               No   \n",
       "826   No internet service  No internet service  Two year               No   \n",
       "1380  No internet service  No internet service  Two year               No   \n",
       "\n",
       "     PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "331   Mailed check           19.85           NaN  \n",
       "826   Mailed check           25.35           NaN  \n",
       "1380  Mailed check           20.00           NaN  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view Missing Values in TotalCharges column\n",
    "\n",
    "data_test[np.isnan(data_test['TotalCharges'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where tenure = 0\n",
    "\n",
    "data_test.drop(labels=data_test[data_test['tenure'] == 0].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1997 entries, 0 to 1999\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        1997 non-null   object \n",
      " 1   gender            1997 non-null   object \n",
      " 2   SeniorCitizen     1997 non-null   int64  \n",
      " 3   Partner           1997 non-null   object \n",
      " 4   Dependents        1997 non-null   object \n",
      " 5   tenure            1997 non-null   int64  \n",
      " 6   PhoneService      1997 non-null   object \n",
      " 7   MultipleLines     1997 non-null   object \n",
      " 8   InternetService   1997 non-null   object \n",
      " 9   OnlineSecurity    1997 non-null   object \n",
      " 10  OnlineBackup      1997 non-null   object \n",
      " 11  DeviceProtection  1997 non-null   object \n",
      " 12  TechSupport       1997 non-null   object \n",
      " 13  StreamingTV       1997 non-null   object \n",
      " 14  StreamingMovies   1997 non-null   object \n",
      " 15  Contract          1997 non-null   object \n",
      " 16  PaperlessBilling  1997 non-null   object \n",
      " 17  PaymentMethod     1997 non-null   object \n",
      " 18  MonthlyCharges    1997 non-null   float64\n",
      " 19  TotalCharges      1997 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(16)\n",
      "memory usage: 327.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset index\n",
    "\n",
    "data_test.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "\n",
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dtype of SeniorCitizen column\n",
    "\n",
    "data_test['SeniorCitizen'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace 0 values with 'No' and 1 values with 'Yes'\n",
    "\n",
    "data_test['SeniorCitizen'] = data_test['SeniorCitizen'].replace({0: 'No', 1: 'Yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change dtype to object\n",
    "\n",
    "data_test['SeniorCitizen'] = data_test['SeniorCitizen'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: customerID - Unique Values: ['7613-LLQFO' '4568-TTZRT' '9513-DXHDA' ... '4816-JBHOV' '8920-NAVAY'\n",
      " '1699-TLDLZ']\n",
      "Column: gender - Unique Values: ['Male' 'Female']\n",
      "Column: SeniorCitizen - Unique Values: ['No' 'Yes']\n",
      "Column: Partner - Unique Values: ['No' 'Yes']\n",
      "Column: Dependents - Unique Values: ['No' 'Yes']\n",
      "Column: tenure - Unique Values: [12  9 27  1 24 14 32 11 38 54 29 44 59  3 18 67 22 33  5  2 72 16  8 23\n",
      " 50 17 68 25 71 46 48 61 37 65 49 64 21 10  6 51 47 52 35 45  4 60 28 39\n",
      " 30 55 58 26 43 56 36 13 70 69 41 15 19 31 34 66 40  7 53 63 42 57 62 20]\n",
      "Column: PhoneService - Unique Values: ['Yes' 'No']\n",
      "Column: MultipleLines - Unique Values: ['Yes' 'No' 'No phone service']\n",
      "Column: InternetService - Unique Values: ['Fiber optic' 'No' 'DSL']\n",
      "Column: OnlineSecurity - Unique Values: ['No' 'No internet service' 'Yes']\n",
      "Column: OnlineBackup - Unique Values: ['No' 'No internet service' 'Yes']\n",
      "Column: DeviceProtection - Unique Values: ['No' 'No internet service' 'Yes']\n",
      "Column: TechSupport - Unique Values: ['No' 'No internet service' 'Yes']\n",
      "Column: StreamingTV - Unique Values: ['Yes' 'No internet service' 'No']\n",
      "Column: StreamingMovies - Unique Values: ['No' 'No internet service' 'Yes']\n",
      "Column: Contract - Unique Values: ['Month-to-month' 'One year' 'Two year']\n",
      "Column: PaperlessBilling - Unique Values: ['Yes' 'No']\n",
      "Column: PaymentMethod - Unique Values: ['Electronic check' 'Mailed check' 'Credit card (automatic)'\n",
      " 'Bank transfer (automatic)']\n",
      "Column: MonthlyCharges - Unique Values: [ 84.45  20.4   81.7   79.5   89.15  20.3   74.95  74.4   20.    25.\n",
      "  80.45  19.75  65.65  71.    89.2   86.75  55.3   61.5   25.1   55.15\n",
      "  34.05  19.95  89.7   26.3   84.95  20.7   43.25  48.35  79.55  71.05\n",
      "  19.45 110.8   84.5   69.3   49.35  20.35 105.6   64.45 108.6   49.9\n",
      "  30.3   30.4   45.4  103.3   84.15  44.45  85.4   89.9   55.05 104.1\n",
      " 106.6   75.2   70.5   19.6   55.85  24.05  38.1  106.4   34.25 100.05\n",
      "  68.65  45.8   75.75  84.4   96.4   20.55  50.95  90.5   79.4   58.75\n",
      "  59.45 105.7   56.25  53.3   85.55  24.3   77.85  59.9   23.95  20.15\n",
      " 105.35  95.65  87.05  81.    82.45  53.5   20.5   54.4   58.6   84.8\n",
      "  61.4   79.65  94.45  79.8   54.2   74.05  49.15  19.4  113.65 106.\n",
      "  25.95  19.1  103.4  100.55  95.4   75.15 107.9   19.5   85.95  24.95\n",
      "  59.4   69.95  82.85  19.    38.85  30.6   95.    78.45  74.3   51.05\n",
      "  19.2   99.55  70.   109.1   45.3   29.85  76.45  95.1   19.8   72.8\n",
      "  18.95  76.65  99.15 101.75  75.45  64.1   25.65  75.1   95.85  72.75\n",
      "  19.85  19.05  44.95  49.55  94.85  46.25  19.35  69.6   90.7  101.4\n",
      "  20.25  48.8   74.35  68.75 100.2   20.85  95.9   45.    81.5   25.5\n",
      "  48.9   84.1   81.3   95.2   36.45  83.3   25.05  89.85  49.85  54.65\n",
      "  29.35  19.15  55.55  80.55  69.5  104.3   79.6   55.25  88.05 117.6\n",
      "  19.65  70.55  93.85  65.8   20.05  80.    35.4   80.25  50.45  20.45\n",
      "  24.7   77.3   29.75  44.9   29.8   74.65  71.95  20.75  56.3  105.25\n",
      "  94.2   19.55  53.65  29.9   19.7   43.7   49.45 106.55  20.1   39.7\n",
      "  54.5   83.8  111.6   86.65 106.75  62.1  104.5  101.8  110.6   84.9\n",
      "  93.2   24.4   85.    87.45  85.8   91.1   70.75  74.8   24.8  100.85\n",
      " 101.35  68.25 105.1   79.15  57.2   94.8  102.5   69.2   95.45 100.95\n",
      "  88.5   35.    64.    69.1   80.2   49.3   84.35 117.2  103.45  77.95\n",
      " 109.95  94.75  25.2   19.9   44.8   80.05 107.35  47.85  70.8   29.5\n",
      "  59.1   25.55  75.55  85.65  70.15  95.3   70.25  50.3   97.8   46.3\n",
      " 106.3   75.35  89.4   88.    83.15  43.8   62.05  74.15  84.05  20.9\n",
      " 105.9   99.5   44.15  53.9   85.45  85.05  44.1   90.2   50.85  59.2\n",
      "  53.45  83.2   54.9   57.5  103.9   93.8   89.25  94.15  55.6   48.7\n",
      "  19.25 104.9   54.85  19.3   79.85  75.5   73.75  96.05  68.4   20.65\n",
      "  70.6  107.6   61.55  99.25  91.7  100.7   84.3   88.95  86.8   20.95\n",
      "  50.7   53.4  101.9   59.5   87.8   41.9   83.    69.85 109.55  92.15\n",
      "  97.    58.35  50.6   89.5   70.4   69.8   94.3   95.95 101.05 107.75\n",
      "  54.6   71.3   94.7  104.15  90.55  60.8   98.8   98.15  35.35 103.15\n",
      "  81.4   61.45  95.7  104.8   70.95  97.65  35.65  85.25  88.8   55.7\n",
      "  85.2   91.15  83.85  45.9   91.4   91.5   51.3   21.1  104.75 106.15\n",
      "  85.75 100.75  78.55  77.8   83.45  73.25  90.1   29.2   46.6   85.35\n",
      "  54.   104.25  84.75  75.25  24.6   55.5   43.3  109.5   84.85 112.1\n",
      "  95.05  50.35  74.6   74.2   69.   105.2  109.2   45.15 108.65  40.65\n",
      "  55.35  90.05  68.05  96.2  102.1   23.4   92.2   43.9   80.5   89.8\n",
      "  90.45  50.75  84.6   89.65  51.7   23.3   65.4   65.1   81.2   72.9\n",
      "  74.5   60.3   75.    90.15  40.    99.45  69.05  59.7   86.25  45.65\n",
      "  70.1   40.75  70.2   84.2   66.15  45.85  49.8  103.95 100.15  99.65\n",
      "  73.7   50.05  60.25 105.75  87.3   54.25  85.3   50.    90.95  72.25\n",
      "  96.1   25.15  71.25 113.8   24.55  50.15 100.5   74.45  81.9   69.7\n",
      "  25.35  24.65  25.25  60.    24.1  109.9   35.5   87.55  88.4   50.8\n",
      "  99.    96.55  59.75 111.5   24.25  30.55 101.   100.    98.05  71.15\n",
      "  54.15  63.9   69.15  64.65 108.75  98.85  89.6   83.25  24.5   73.\n",
      "  80.4   78.5  102.    48.95  18.25  54.55  89.05  96.6   77.15  35.05\n",
      " 108.1   20.2   49.2   71.65 106.5   94.25  68.95  58.5   78.9   79.2\n",
      " 109.45  29.15  76.05  24.45  66.5   89.35  73.6   82.65  49.    80.35\n",
      "  25.45  55.8  110.9   77.75  26.2   79.05  80.85  98.4   56.35  50.4\n",
      " 109.75  91.25  54.75  81.45  49.1  100.3   65.25  94.1   73.55 104.65\n",
      "  44.55  54.45 105.    88.7   74.25  30.75 112.9   94.05  78.85  78.65\n",
      "  74.75 105.65  96.5   70.85  73.9   45.45 109.65  65.   114.1   86.95\n",
      " 105.45  25.4  102.55  24.    25.6   73.5   98.25 101.55 103.1   34.2\n",
      "  43.75 111.95 100.65  55.95 116.05  45.75  82.    65.15  88.85 106.85\n",
      "  80.15 109.25  56.1  118.6   24.15 115.5  111.3   80.6   20.8   35.2\n",
      "  78.8   89.95  49.4  115.25  81.25  93.55  86.4   66.3   94.65  82.05\n",
      "  72.1   34.7  109.4   40.25  42.9   44.    88.9   57.65 108.05 105.3\n",
      " 102.6   73.85  61.35  57.55  29.25  84.55 111.75 107.7   63.7   24.75\n",
      "  50.9   60.4   79.25 110.1   25.3   24.35  76.5   81.15  38.5   92.9\n",
      "  93.5   84.7   66.   101.5   74.9   99.75  67.8   25.7   56.15  86.7\n",
      "  50.55  54.35  45.35  59.    69.45  64.95  18.85 114.3   45.05  51.\n",
      " 110.45  84.65  60.05  44.65  93.25  20.6   34.8   60.75  51.35  64.05\n",
      "  94.6  100.25  98.9   97.7   40.3   46.2   24.9   65.7   63.35  50.1\n",
      "  74.    38.9   65.45  98.7   99.35  95.8   67.5   78.15  26.1   78.05\n",
      "  40.35  68.9   76.    82.3   29.45  59.15  44.75  90.8  106.7   67.95\n",
      "  77.4   99.7   78.95  95.55  62.85  71.55  94.95  86.1   39.3   36.25\n",
      "  23.9   98.6  103.65  99.9   39.85  60.5  103.85  24.85  89.    55.\n",
      "  76.15 117.35  45.2   89.75  49.95  67.05  87.95  75.7   62.15 101.25\n",
      " 115.15  86.55  28.6   56.4   73.3   98.65  33.6   79.9  104.05  70.05\n",
      "  23.05  59.95  78.6  116.8   43.55  65.2  102.95  90.6  108.2   92.\n",
      " 112.2   70.3   75.85  80.65  68.5  115.75  59.55  36.1   94.    61.15\n",
      " 110.2  106.35  65.9   52.5   88.75  75.3   26.    99.4   73.15  66.4\n",
      " 115.55 104.45  92.4   25.75  49.6   97.05 105.95  91.85  40.1  110.3\n",
      "  85.15  60.95  46.    58.55  86.35  69.75  65.6   82.1   79.1   90.65\n",
      " 110.    67.45  89.1   69.9   51.1   94.4   78.25  76.4   48.65  59.85\n",
      "  80.3   91.8   18.8   64.75  89.45  85.6   54.1   80.9   90.85  48.75\n",
      "  79.7  100.4   57.95  86.5   62.45  89.55  83.55  71.45  46.35  66.1\n",
      "  75.4   70.45  21.05  69.35  40.55  75.65  60.6  101.15  98.   104.7\n",
      "  93.9   86.45  98.5   78.2   88.45  69.55  83.75  98.1   53.35  69.4\n",
      "  40.15  70.35  53.85 115.6   97.95  78.3   96.8   77.35  66.05  68.15\n",
      "  92.45  45.55  93.4   88.15  79.35  79.75 105.15  79.3  105.5   92.7\n",
      "  26.25  96.95 115.8   67.75  90.35  55.75 114.6   66.8  104.85  74.1\n",
      " 118.75  85.9  101.3   21.2   24.2  102.8   99.95 115.85  35.1   99.1\n",
      "  67.25  55.1  117.8   45.25  95.35 116.6   65.05  92.5   18.75  93.6\n",
      " 104.4   70.7  108.95  26.45  86.2   51.2   75.8   36.15  61.2   99.85\n",
      "  58.4   88.3  108.9  107.4  106.65 104.35  55.45  61.3   96.85 108.25\n",
      " 105.05  66.9  110.7   38.25  54.95  79.    39.1  100.45  39.55  23.15\n",
      "  72.45  60.1   91.55  35.8  113.15  53.95  99.3   51.55  78.75  54.7\n",
      "  71.1  106.25 114.05 116.15  66.25  99.8   90.    54.05  97.25  83.05\n",
      "  41.1   74.55  40.2   78.35 109.7   33.45  39.4   76.25  46.4   59.6\n",
      " 108.5   58.95  63.05  64.4   83.9  117.45  59.05  76.55  62.5   29.4\n",
      "  94.9  111.65 106.05 113.45  92.55  49.7   30.2   85.7   74.7  107.55\n",
      "  23.85  76.1   39.2   39.15  59.8   49.75  35.75  60.15  84.   110.75\n",
      "  76.35  18.9   98.35  91.65  44.35  47.95  63.6   53.    36.85 103.75\n",
      "  56.75  59.65  45.5  106.45  30.05  44.7 ]\n",
      "Column: TotalCharges - Unique Values: [1059.55  181.8  2212.55 ...  552.95 7053.35  301.55]\n"
     ]
    }
   ],
   "source": [
    "# view unique values of each column\n",
    "\n",
    "for column in data_test.columns:\n",
    "    print('Column: {} - Unique Values: {}'.format(column, data_test[column].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save test data to csv\n",
    "\n",
    "data_test.to_csv('Dataset\\Test_Data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Null Hypothesis (H0)**:\n",
    "\"There is no significant relationship between customer tenure and churn rate in the telecom company.\"\n",
    "\n",
    "**Alternative Hypothesis (Ha)**:\n",
    "\"There is a significant relationship between customer tenure and churn rate in the telecom company.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to find the correlation between tenure and churn rates. Since churn rate is a binary outcome, we want to use a **point-biserial** correlation coefficient to assess the significance of the relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point-Biserial Correlation Coefficient: -0.3526\n",
      "P-value: 0.0000\n",
      "There is a significant correlation between customer tenure and churn rate.\n"
     ]
    }
   ],
   "source": [
    "# 'Churn' should be a binary variable (0 for no churn, 1 for churn)\n",
    "\n",
    "df_hypo = Data_All.copy()\n",
    "df_hypo['Churn'] = df_hypo['Churn'].replace({'No': 0, 'Yes': 1})\n",
    "\n",
    "# Calculate point-biserial correlation coefficient\n",
    "correlation_coefficient, p_value = stats.pointbiserialr(df_hypo['Churn'], df_hypo['tenure'])\n",
    "\n",
    "# Print the results\n",
    "print(f\"Point-Biserial Correlation Coefficient: {correlation_coefficient:.4f}\")\n",
    "print(f\"P-value: {p_value:.4f}\")\n",
    "\n",
    "# Set the significance level (5%)\n",
    "alpha = 0.05\n",
    "\n",
    "# Determine if the correlation is statistically significant\n",
    "if p_value < alpha:\n",
    "    print(\"There is a significant correlation between customer tenure and churn rate.\")\n",
    "else:\n",
    "    print(\"There is no significant correlation between customer tenure and churn rate.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The correlation coefficient of -0.3526 suggests a **moderate negative correlation**. This means that as customer tenure increases, the churn rate tends to decrease. Customers who have been with the company for a longer period are less likely to churn.\n",
    "\n",
    "- The p-value of 0.0000 suggests that there is a strong statistical evidence to **reject the null hypothesis** (no correlation) in favor of the alternative hypothesis (significant correlation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "### Univariate Analysis\n",
    "\n",
    "Here is the section to explore, analyze, visualize each variable independently of the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Distibution of Churn Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "No     3698\n",
       "Yes    1336\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data = Data_All['Churn'].value_counts()\n",
    "churn_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie chart using pyplot to visulaize churn rate\n",
    "\n",
    "plt.figure(figsize=(4, 4))\n",
    "\n",
    "plt.pie(churn_data, labels=['Not Churned','Churned'], colors=['LightBlue','LightSkyBlue'], startangle=50, shadow=True, radius=2,\n",
    "     explode=(0,0.2),autopct='%1.0f%%', pctdistance=0.75 ,  )\n",
    "Circle=plt.Circle((0,0), 1, color='white')\n",
    "c= plt.gcf()\n",
    "\n",
    "c.gca().add_artist(Circle)\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that the pie chart is drawn as a circle.\n",
    "plt.title('Churn rate among customers')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The target variable is **imbalanced**\n",
    "\n",
    "- The percentage of customers that did not churn (most majority class) can be used as a baseline to evaluate the quality of the models generated. These models should outperform the baseline capabilities to be considered for future predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate & Multivariate Analysis\n",
    "Here is the section to explore, analyze, visualize each variable in relation to the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the relationship between Churn and Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='gender',data=Data_All,hue='Churn')\n",
    "plt.title('Distribution of Gender')\n",
    "plt.xlabel('gender')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The customer gender split is about 50/50 between males and females. no significant difference between gender and churn rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. How does customer tenure relate to churn rates? Are long-tenured customers more likely to stay with the company, and do new customers exhibit higher churn behavior?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We use a probability density distribution to show distribution of churn related to tenure\n",
    "\n",
    "def kdeplot(x):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.title('Probability of churn by tenure')\n",
    "    ax0= sns.kdeplot(Data_All[Data_All['Churn'] == 'Yes'][x], color='navy', label='Churn:Yes', fill =True, cut=0)\n",
    "    ax1= sns.kdeplot(Data_All[Data_All['Churn'] == 'No'][x], color='orange', label='Churn:No', fill=True, cut=0)\n",
    "    plt.legend(['Churned', 'Not Churned'], loc='upper right')\n",
    "kdeplot('tenure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x='tenure', hue='Churn', data=Data_All, bins=14, alpha=0.8, multiple='stack')\n",
    "plt.title('Churn Count by Tenure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers with a lower tenure (recent customers) are more likely to churn.\n",
    "- Customers on a longer tenure are less likely to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Is there a correlation between the total charges and churn rates? Do customers with higher total charges exhibit different churn behavior compared to those with lower total charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation coefficient: -0.19462794572064998\n",
      "P-value: 3.6678433707849766e-44\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_temp = df_hypo\n",
    "\n",
    "# Convert \"TotalCharges\" column to numeric type and then to integers\n",
    "df_temp[\"TotalCharges\"] = pd.to_numeric(df_temp[\"TotalCharges\"], errors=\"coerce\", downcast=\"integer\")\n",
    "\n",
    "# Calculate the point-biserial correlation between TotalCharges and Churn\n",
    "corr, p_value = pointbiserialr(df_temp[\"TotalCharges\"], df_temp[\"Churn\"])\n",
    "\n",
    "# Print the correlation coefficient and p-value\n",
    "print(\"Correlation coefficient:\", corr)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "# Plot the data points and visualize the relationship between TotalCharges and Churn\n",
    "# logistic regression for binary dependent variable (Churn)\n",
    "\n",
    "sns.lmplot(data=df_temp, x=\"TotalCharges\", y=\"Churn\", logistic=True, y_jitter=.03)\n",
    "plt.xlabel(\"Total Charges\")\n",
    "plt.ylabel(\"Churn (1=Churned, 0=Not Churned)\")\n",
    "plt.title(\"Total Charges vs Churn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The analysis shows a **weak negative correlation** between total charges and churn rates. As total charges increase, the likelihood of churn slightly decreases. The relationship is statistically significant (P-value < 0.05), but the effect is not very strong. Other factors may have a more significant impact on churn behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. What is the impact of contract type on churn rates? Do customers on long-term contracts have significantly lower churn rates compared to those on short-term contracts?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation (crosstab) of the categorical column and 'Churn' column\n",
    "cross_tab = pd.crosstab(Data_All['Contract'], Data_All['Churn'])\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "\n",
    "ax = cross_tab.plot(kind=\"bar\", stacked=True, figsize=(6, 6))\n",
    "plt.title(\"Contract Duration by Churn\")\n",
    "plt.xlabel(\"Contract Duration\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel(\"Count\")\n",
    "plt.legend(title=\"Churn\", loc=\"upper right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers with short term (month-to-month) contracts have higher churn rates than customers with long term (one year+ contracts) \n",
    "- Possible reasons could include price sensitivity, competition, ineffective onboarding, and changing needs. To reduce short-term churn, focus on personalized experiences, competitive pricing, and improving onboarding processes. Building strong customer relationships and demonstrating long-term value can encourage customer loyalty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Are there significant differences in churn behavior between customers who have device protection and those who don't?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IzX736NFDy5cv17Zt26wTRq8VExMjHx8frVq1SvPnz1f58uXVrVs39evXL1+u0vrEE0/ogQce0LBhw5SZmanmzZvr9ddft5msKkmPP/64pkyZooiIiFy1W7t2ba1cuVIffvihli5dqoSEBLm7u8vPz08LFizIc0AqUaKEZsyYoSlTpigqKkre3t7WOSJAQXOwMJsHAPLNvHnz9NFHH2nr1q1ydna2dzmAXXEkAwDywerVq/XLL7/o008/Vb9+/QgYgAgZAJAvDh06pGXLlunJJ580/NQVUFRwugQAABiCr7ACAABDEDIAAIAhCBkAAMAQ9+3Ez+y7UTo6Ot7wvg0AAODGLBaLsrKy5OTkdMvr0Ny3ISMzM1P79++3dxkAABRZAQEBt/y69n0bMrKTV0BAgGH3e7jXmM1m7d+/n9cMhmOsoaAw1u5M9ut2u6vp3rchI/sUiclkYmDlEa8ZCgpjDQWFsXZnbjfdgImfAADAEIQMAABgCEIGAAAwxH07JwMAgOw7a6SlpTEn4xomk0lOTk53fYkHQgYA4L6UkZGhM2fOyGQy6cSJE1wz6Tru7u6qVKnSXd1RmJABALjvZGVl6dixYzKZTKpUqZI8PT1v+3XM+4XFYlFGRoYSExN17Ngx1ahR445fG0IGAOC+k5GRoaysLFWuXFmS5ObmxpGMa7i5ualYsWI6ceKEMjIy5OrqekftENsAAPctjl7cXH68Nry6AADAEIQMAABgCEIGAAD54MKFC3r77bcVFhamwMBAhYeH66OPPlJWVpYkyc/PT7t27bJzlQWLiZ8AANyl8+fPq1OnTipfvrxiYmJUuXJl7d+/X+PGjdPJkyc1ZswYe5doF4QMAADu0pQpU+Ts7KyFCxfKxcVFklSlShW5urqqX79+6tq1q50rtA9OlwAAcBcyMjK0YcMGdenSxRowsjVv3lwfffSRvL29JUk//PCD2rRpo4CAAHXt2lWnT5+WJO3atUt+fn42zx05cqRGjhwpSZoxY4b69eunLl26qGHDhvruu+8UFhamJUuW6Nlnn1VAQIDatWunAwcOFMAe516hCBkZGRlq3bq1zbmqkydPqnv37nr44YfVsmVLbd++3eY5O3bsUOvWrRUYGKhu3brp5MmTBV02AAD67bfflJKSooCAgBzrHBwc1KhRI+tVM1esWKHRo0dr5cqVunDhgiZPnpzrfjZv3qzWrVvr448/Vr169SRdDR+9evXS2rVr5enpqfHjx+fPTuUTu4eM9PR0DR48WIcPH7Yus1gsioqKUtmyZbVq1Sq1a9dOr7zyis6cOSNJOnPmjKKiohQREaGVK1eqTJky6tevn/Ua9AAAFJS//vpLkuTp6Xnbbfv27auQkBD5+fmpQ4cOOnToUK77KVu2rDp37qzatWtbL471zDPP6IknnpCvr6969OjBkYxrHTlyRM8++6x+++03m+XffvutTp48qTfffFMPPfSQevfurYcfflirVq2SdDUJ1q1bVz179lSNGjU0YcIEnT59Wt999509dgMAcB8rVaqUpKvfLrmdBx980Pq7p6en0tPTc91P9imXa1WtWtX6u4eHh65cuZLr9gqCXUPGd999p5CQEC1fvtxm+d69e+Xv7y93d3frsvr16ysuLs66vkGDBtZ1bm5uqlOnjnU9AAAF5cEHH5Snp6d++umnG67v27evduzYIenmV9G80SXNMzMzbR5fP99DkooVK5bXcguUXb9d8vzzz99weWJiosqXL2+zzMvLS2fPns3V+rwwm815fs79KisrS25ubtbvfANGYazBaGazWRaLRRaLRY6Ojnd1ut1kMqlly5ZasmSJIiIibO5aumXLFm3ZskWDBw+WJGuf1/5usVjk5HT14/jixYvy8PCQdHVuYtWqVW22u7bO65dd//NuZbdtNptzfFbm9rOzUH6FNTU1NcetZZ2dnZWRkZGr9Xmxf//+Oy/UAMWKFVNd/9pydCp86dRkMsnf39/eZdxUVuYVHTj4c6E7XFhYMdbuHGMtbwrjWDOZTHJwcJCjo+Md3/zrWv3791fHjh310ksvqX///qpYsaJ27dqlSZMmqVu3bqpRo4akq0csso9aZP/u4OCgmjVrytXVVXPnzlWnTp303//+Vz///LOqVq2q1NRUXblyRVlZWUpJSbH2mX231Oxl2adert3mbqSnp+vKlSt5mjdyvUIZMlxcXJScnGyz7Nq7wLm4uOQIFBkZGSpRokSe+woICJDJZLrjWo3gaDJJq16Skn6xdylFR9macmy/QHXq1LF3JUUKY+0OMNbuSKEba67lpdpR0p8WqdjdzxwoJ2npzHGa8cEyDR08UMkXLupB74oa0LOTOj/9lJT49wd18m9SYsmrv1/8XTJfkRIPyUPSuOF99e7cxfpk0cd68vHG6tKhjc6nmK13RHV0dLSZRuDg4CBnZ2frsuzTKdduczccHR1VrFgxVa9ePUcQM5vNufojvVCGjAoVKujIkSM2y5KSkqynSCpUqKCkpKQc62vXrp3nvkwmU6ELGZKu/o/4+157V1HkFMr3srBjrN0RxtodKExjzaOKVDNDykyTlD+3eK9UxkNvDX0p54qsDClLiv9y2dXHV1IlSRFPPqqIJx+1Pm7bPERtm4f8//OKuUnlakmSBgwYkKPZL7/80uZxo0aNFB8fnw97clX2UZa7+Zy0+1dYbyQwMFA//fST0tLSrMt2796twMBA6/rdu3db16WmpurgwYPW9QAAwP4KZcho2LChKlWqpFGjRunw4cOaN2+e9u3bpw4dOkiS2rdvrz179mjevHk6fPiwRo0apcqVKyskJOQ2LQMAgIJSKEOGyWTSrFmzlJiYqIiICK1du1bvv/++HnjgAUlS5cqVNWPGDK1atUodOnRQcnKy3n///Rt+BQgAANhHoZmTcf15JB8fHy1evPim2z/++ON6/PHHjS4LAADcoUJ5JAMAABR9hAwAAGAIQgYAADAEIQMAABii0Ez8BACgUDAVkxwL6OMxK/PqVT/vUYQMAACymYrJXLZ2gV3R1Ww2y5T0c66Dhp+fn1q3bq0pU6bYLI+NjdXMmTO1ZcsWI8q8Y4QMAACyOTrJZDLp1WU/6si5S4Z2Vb28h6Y9F3T1qEkejmasX79eHTp0UOPGjQ2sLn8QMgAAuM6Rc5f005m/7F3GDXl7e+vNN9/Uv//97xx3JC9smPgJAEARMnDgQCUkJGjhwoU33ebs2bN69dVX1bBhQ4WEhGj8+PE57l5eEAgZAAAUIRUqVNCAAQM0Z84cnTx5Msf6jIwMvfDCC0pNTdUnn3yi9957T1u3btXEiRMLvFZCBgAARUxkZKR8fHwUExOTY93XX3+thIQETZo0SX5+fmrcuLGio6O1dOlSXb58uUDrJGQAAFDEmEwmvfHGG9q6das2bdpks+7o0aOqWrWqSpYsaV0WHByszMxM/fbbbwVaJyEDAIAiKDg4WO3bt1dMTIxSU1Oty11cXHJsazabbX4WFEIGAABF1NChQ5WSkmIzCdTX11fHjx9XcnKydVlcXJycnJz04IMPFmh9fIUVAIDrVC/vUST6KF26tIYOHarRo0fL29tbkvTYY4+pSpUqGj58uIYMGaLz589r3Lhxat26tUqUKHHXfeYFIQMAgGxZmTKbzVcvklUAzGazTFmZd9VGhw4dtGrVKp07d07S1fkas2bN0rhx4/Tss8+qePHiatOmjQYPHpwfJecJIQMAgGzmK1cv811A9y4x5fHeJfHx8TmWOTg4aNmyZTbLqlSponnz5t11fXeLkAEAwLXMV+7pm5YVJCZ+AgAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMwXUyAAC4lqlYgV2MS3m8GFdRQ8gAACCbqZhU1u/qz4JgviIlxecqaAwePFi7d+/WF198ITc3N5t1PXr0UGpqqpYuXSoHBwejqs0zQgYAANkcna4GjFUvSUm/GNtX2ZpS+wVX+8xFyBgxYoTCw8M1Z84cDRo0yLp848aN+v777xUbG1uoAoZEyAAAIKekX6Tf99q7ChsVKlRQ//799e6776pDhw6qUqWK0tLS9Pbbb6tHjx6qWbOmvUvMgYmfAAAUEZGRkfLx8dGkSZMkSQsWLJCjo6OioqL0+++/q0+fPgoMDFRYWJhmzpwps9ksSbpy5YpGjx6tkJAQBQUFqU+fPkpISDC8Xo5kAPe7soXvr59CjdcLduTk5KTo6GhFRkZq06ZNWrhwoaZPny4XFxe98sorqlWrllavXq3ExERFR0fLwcFBUVFRWrJkib7//nt98MEHcnV11RtvvKG33npL06ZNM7ZeQ1sHUKhZssxyaL/A3mUUOZYssxwcTfYuA/epRx55RG3atNGrr76qFi1aqEmTJtq5c6fOnDmjFStWyNHRUdWqVdOIESM0atQoRUVF6dSpU3JxcZG3t7dKlSqlt99+W8nJyYbXSsgA7mMOjiZN+u8hnfwzxd6lFBlVyrhrWIta9i4D97k+ffpo7dq1ioqKkiQdPXpUycnJql+/vnWbrKwspaWl6fz58+rUqZM2bNig0NBQNWzYUE888YQiIiIMr5OQAdzntsYn6qczf9m7jCKjzgMlCBmwOxcXF5ufmZmZqlatmmbNmpVjW09PT5UuXVpbtmzR1q1btXXrVk2dOlXr16/XkiVLDP1GCiEDAIAiztfXV2fOnFGZMmXk6ekpSfrmm28UGxuriRMnas2aNXJ2dlbLli0VHh6uuLg4derUSX/88YfKli1rWF2EDAAArlcQE3zzsY/Q0FB5e3tr2LBhGjRokC5evKgxY8bo0Ucflclk0sWLFzVnzhyVLl1alStX1rp161SxYkWVLl0632q4EUIGAADZsi/zXVATos1XrvZ5l0wmk2bPnq1x48bp2Weflbu7u5566imNGDFCktSlSxedPXtWw4YN04ULF1S3bl3Nnj1bJpOxE5gJGQAAZMu+zHchv3dJ5cqVFR8fb7OsSpUqmjdv3g23d3R01LBhwzRs2LA7KvNOETIAALiW+co9fdOygsQVPwEAgCEIGQAAwBCEDAAAYAhCBgDg/mOxXPsDN2DJhxeHkAEAuO8US/9TMmcohfmdN5WScvV2A8WKFbvjNvh2CQDgvmPKTFGpE5/rnHMHSaXkXkwy8OradyhLSksr8F4tFotSUlJ07tw5lSpV6q6upUHIAADclyoe/lSSdM4nXDI527maGzA5Sxfsl3xKlSqlihUr3lUbhAwAwH3JQRZVOrxE5X+N1RVXr8J3KKNcLanTYrt0XaxYsXy5GighAwBwXzOZU2W6fMreZeTkWUZydbV3FXeFiZ8AAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiiUIeM33//Xb1791ZwcLDCwsL00UcfWdcdPHhQHTt2VGBgoNq3b68DBw7Yr1AAAJBDoQ4ZAwcOlLu7u2JjY/Xaa6/pvffe0//+9z+lpKSoV69eatCggWJjYxUUFKTevXsrJSXF3iUDAIC/FdqQceHCBcXFxalv376qWrWqnnjiCTVp0kQ7d+7Uf/7zH7m4uGj48OF66KGH9Prrr6t48eL64osv7F02AAD4W6ENGa6urnJzc1NsbKyuXLmiX3/9VXv27FHt2rW1d+9e1a9fXw4ODpIkBwcHBQcHKy4uzr5FAwAAKyd7F3AzLi4uio6O1rhx47Ro0SKZzWZFRESoY8eO2rx5s6pXr26zvZeXlw4fPpznfsxmc36VnG9MJpO9SyiyCuP7WZgx1u4cYy1vGGt3rjCOtdzWVGhDhiQdPXpUzZs3V48ePXT48GGNGzdOjRs3VmpqqpydnW22dXZ2VkZGRp772L9/f36Vmy/c3Nzk7+9v7zKKrPj4eKWmptq7jCKBsXZ3GGu5x1i7O0V5rBXakLFz506tXLlS27Ztk6urqwICApSQkKDZs2erSpUqOQJFRkaGXF1d89xPQEAACfse4ufnZ+8ScJ9grKGgFMaxZjabc/VHeqENGQcOHJCPj49NcPD399ecOXPUoEEDJSUl2WyflJSk8uXL57kfk8lEyLiH8F6ioDDWUFCK8lgrtBM/y5cvrxMnTtgcsfj1119VuXJlBQYG6scff5TFYpEkWSwW7dmzR4GBgfYqFwAAXKfQhoywsDAVK1ZMo0eP1rFjx7RlyxbNmTNHkZGReuqpp/TXX38pJiZGR44cUUxMjFJTUxUeHm7vsgEAwN8Kbcjw9PTURx99pMTERHXo0EETJkxQ37591alTJ3l4eGju3LnavXu3IiIitHfvXs2bN0/u7u72LhsAAPyt0M7JkKTq1avrww8/vOG6evXqafXq1QVcEQAAyK1CeyQDAAAUbYQMAABgCEIGAAAwRKGekwEAuIeUrWnvCoqWe+D1ImQAAAxnyTLLof0Ce5dR5FiyzHJwLLoX4yJkAAAM5+Bo0qT/HtLJP1PsXUqRUaWMu4a1qGXvMu4KIQMAUCC2xifqpzN/2buMIqPOAyWKfMhg4icAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhnCydwG4ibI17V1B0cLrBQCFDiGjELJkmeXQfoG9yyhyLFlmOTia7F0GAOBvhIxCyMHRpEn/PaSTf6bYu5Qio0oZdw1rUcveZQAArkHIKKS2xifqpzN/2buMIqPOAyUIGQBQyDDxEwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQp1yMjIyNC//vUvPfLII3r00Uc1depUWSwWSdLBgwfVsWNHBQYGqn379jpw4ICdqwUAANcq1CFj/Pjx2rFjhxYuXKgpU6bos88+0/Lly5WSkqJevXqpQYMGio2NVVBQkHr37q2UlBR7lwwAAP7mZO8CbiY5OVmrVq3Shx9+qHr16kmSevbsqb1798rJyUkuLi4aPny4HBwc9Prrr+urr77SF198oYiICDtXDgAApEJ8JGP37t3y8PBQw4YNrct69eqlCRMmaO/evapfv74cHBwkSQ4ODgoODlZcXJydqgUAANcrtEcyTp48KW9vb61Zs0Zz5szRlStXFBERob59+yoxMVHVq1e32d7Ly0uHDx/Ocz9mszm/Ss43JpPJ3iUUWYXx/SzMGGt3jrGWN4y1O1cYx1puayq0ISMlJUUnTpzQsmXLNGHCBCUmJio6Olpubm5KTU2Vs7OzzfbOzs7KyMjIcz/79+/Pr5LzhZubm/z9/e1dRpEVHx+v1NRUe5dRJDDW7g5jLfcYa3enKI+1QhsynJycdOnSJU2ZMkXe3t6SpDNnzmjp0qXy8fHJESgyMjLk6uqa534CAgJI2PcQPz8/e5eA+wRjDQWlMI41s9mcqz/SC23IKFeunFxcXKwBQ5J8fX31+++/q2HDhkpKSrLZPikpSeXLl89zPyaTiZBxD+G9REFhrKGgFOWxVmgnfgYGBio9PV3Hjh2zLvv111/l7e2twMBA/fjjj9ZrZlgsFu3Zs0eBgYH2KhcAAFyn0IaMatWqqVmzZho1apQOHTqkr7/+WvPmzVPnzp311FNP6a+//lJMTIyOHDmimJgYpaamKjw83N5lAwCAvxXakCFJkydP1oMPPqjOnTtrxIgR6tKliyIjI+Xh4aG5c+dq9+7dioiI0N69ezVv3jy5u7vbu2QAAPC3QjsnQ5I8PT01ceLEG66rV6+eVq9eXcAVAQCA3CrURzIAAEDRRcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCKe7beDgwYPavXu3LBaLHn74YdWrVy8/6gIAAEXcXR3JWLBggaKiorRnzx798MMPevnllzVz5sz8qg0AABRhuT6SkZSUpLJly9osW7p0qdatWycPDw9JV49q9OjRQ6+88kr+VgkAAIqcXIeMLl26qFmzZnr55ZetYcPHx0czZsxQaGiosrKytG7dOvn6+hpWLAAAKDpyfbpk3bp18vHxUZcuXfTWW28pKSlJkydPVnp6uiZNmqR3331Xbm5umjZtmpH1AgCAIiLXRzKcnZ31/PPPq2PHjlq5cqUiIyPVpEkTRUVFqVy5ckbWCAAAiqA8T/wsVqyYOnfurHXr1ql69ep64YUXFBMTo6SkJCPqAwAARVSuQ8a+ffsUERGhoKAgtWzZUt98842effZZrV27VjVr1tQLL7yg8ePH69y5c0bWCwAAiohch4wRI0boqaeeUmxsrPr27atBgwYpIyNDTk5O6tixo9auXatatWrpxRdfNLJeAABQROR6Tsb58+cVFBQkX19fubq6Kj09Xenp6XJ2dpYkmUwmdejQQc8884xhxQIAgKIj1yGjf//+eumll1SyZElduHBBPXv2lKenZ47tTCZTvhYIAACKpjxdJyM8PFynTp1SxYoVVb58eSPrAgAARVye7l1SpkwZlSlTxqhaAADAPYS7sAIAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDFJmQ0atXL40cOdL6+ODBg+rYsaMCAwPVvn17HThwwI7VAQCA6xWJkLFhwwZt27bN+jglJUW9evVSgwYNFBsbq6CgIPXu3VspKSl2rBIAAFyr0IeM5ORkTZw4UQEBAdZl//nPf+Ti4qLhw4froYce0uuvv67ixYvriy++sGOlAADgWoU+ZLzzzjtq166dqlevbl22d+9e1a9fXw4ODpIkBwcHBQcHKy4uzk5VAgCA6znZu4Bb2blzp3744QetW7dOb7zxhnV5YmKiTeiQJC8vLx0+fDjPfZjN5rstM9+ZTCZ7l1BkFcb3szBjrN05xlreMNbuXGEca7mtqdCGjPT0dI0dO1bR0dFydXW1WZeamipnZ2ebZc7OzsrIyMhzP/v377+rOvObm5ub/P397V1GkRUfH6/U1FR7l1EkMNbuDmMt9xhrd6coj7VCGzJmzpypunXrqkmTJjnWubi45AgUGRkZOcJIbgQEBJCw7yF+fn72LgH3CcYaCkphHGtmszlXf6QX2pCxYcMGJSUlKSgoSJKsoeK///2vWrduraSkJJvtk5KSVL58+Tz3YzKZCBn3EN5LFBTGGgpKUR5rhTZkfPLJJ8rMzLQ+njx5siRp6NCh+v777zV//nxZLBY5ODjIYrFoz5496tOnj73KBQAA1ym0IcPb29vmcfHixSVJPj4+8vLy0pQpUxQTE6PnnntOy5YtU2pqqsLDw+1RKgAAuIFC/xXWG/Hw8NDcuXO1e/duRUREaO/evZo3b57c3d3tXRoAAPhboT2Scb23337b5nG9evW0evVqO1UDAABup0geyQAAAIUfIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYo1CEjISFBAwYMUMOGDdWkSRNNmDBB6enpkqSTJ0+qe/fuevjhh9WyZUtt377dztUCAIBrFdqQYbFYNGDAAKWmpmrJkiV699139eWXX+q9996TxWJRVFSUypYtq1WrVqldu3Z65ZVXdObMGXuXDQAA/uZk7wJu5tdff1VcXJy++eYblS1bVpI0YMAAvfPOO2ratKlOnjypZcuWyd3dXQ899JB27typVatWqX///nauHAAASIX4SEa5cuW0YMECa8DIdunSJe3du1f+/v5yd3e3Lq9fv77i4uIKuEoAAHAzhfZIRokSJdSkSRPr46ysLC1evFiNGjVSYmKiypcvb7O9l5eXzp49m+d+zGbzXdea30wmk71LKLIK4/tZmDHW7hxjLW8Ya3euMI613NZUaEPG9SZNmqSDBw9q5cqV+uijj+Ts7Gyz3tnZWRkZGXlud//+/flVYr5wc3OTv7+/vcsosuLj45WammrvMooExtrdYazlHmPt7hTlsVYkQsakSZP08ccf691331XNmjXl4uKi5ORkm20yMjLk6uqa57YDAgJI2PcQPz8/e5eA+wRjDQWlMI41s9mcqz/SC33IGDdunJYuXapJkyapRYsWkqQKFSroyJEjNtslJSXlOIWSGyaTiZBxD+G9REFhrKGgFOWxVmgnfkrSzJkztWzZMk2dOlWtWrWyLg8MDNRPP/2ktLQ067Ldu3crMDDQHmUCAIAbKLQh4+jRo5o1a5Zefvll1a9fX4mJidb/GjZsqEqVKmnUqFE6fPiw5s2bp3379qlDhw72LhsAAPyt0J4u2bx5s8xms2bPnq3Zs2fbrIuPj9esWbP0+uuvKyIiQj4+Pnr//ff1wAMP2KlaAABwvUIbMnr16qVevXrddL2Pj48WL15cgBUBAIC8KLSnSwAAQNFGyAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCEIGQAAwBCEDAAAYAhCBgAAMAQhAwAAGIKQAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDIAAIAhCBkAAMAQhAwAAGAIQgYAADAEIQMAABiCkAEAAAxByAAAAIYgZAAAAEMQMgAAgCGKdMhIT0/Xa6+9pgYNGig0NFQffPCBvUsCAAB/c7J3AXdj4sSJOnDggD7++GOdOXNGI0aM0AMPPKCnnnrK3qUBAHDfK7IhIyUlRStWrND8+fNVp04d1alTR4cPH9aSJUsIGQAAFAJF9nTJoUOHlJmZqaCgIOuy+vXra+/evcrKyrJjZQAAQCrCRzISExNVunRpOTs7W5eVLVtW6enpSk5OVpkyZW75fIvFIknKyMiQyWQytNa8MplMql2xuFwKV1mFWrVyxWU2m2U2m+1dSpHCWMs7xtqdYazlXWEea9k1ZX+W3kyRDRmpqak2AUOS9XFGRsZtn599tOPgwYP5X1w+6PyQpIfc7V1GEWJRXFycvYsokhhrecVYu1OMtbwq/GPtdmcOimzIcHFxyREmsh+7urre9vlOTk4KCAiQo6OjHBwcDKkRAIB7kcViUVZWlpycbh0jimzIqFChgs6fP6/MzEzrTiYmJsrV1VUlSpS47fMdHR1zHAkBAAD5p8hO/Kxdu7acnJxsDiXt3r3benQCAADYV5H9NHZzc9PTTz+tN954Q/v27dOmTZv0wQcfqFu3bvYuDQAASHKw3G5qaCGWmpqqN954Qxs3bpSHh4defPFFde/e3d5lAQAAFfGQAQAACq8ie7oEAAAUboQMAABgCEIGAAAwBCEDNvz8/DRkyJAcy2NjYxUWFmaHinAjRrxPM2bMUGRkZK62zcjI0GeffXZH/eSHP/74Q59//rnd+s8WFham2NhYe5eBGxg8eLAef/xxpaam5ljXo0cPPffcc7e9JDbuHiEDOaxfv147d+60dxm4jfx+n3r27KkZM2bkatsNGzZozpw5+dZ3Xk2ePFnbtm2zW//ZVq5cqZYtW9q7DNzAiBEjdPHixRzjdOPGjfr+++/15ptvcrXnAkDIQA7e3t568803c3UPGNhPfr9PxYsXV6lSpXK1rb3/ArR3/9nKlCmTq9sYoOBVqFBB/fv314cffqiTJ09KktLS0vT222+rR48eqlmzpp0rvD8QMpDDwIEDlZCQoIULF950m7Nnz+rVV19Vw4YNFRISovHjxxNKClh+v0/Xni6JjY1VZGSkpk+frpCQEDVo0EATJkyQxWLRrl27NGrUKJ0+fVp+fn46deqULBaL3n//fYWGhqpBgwbq06ePzpw5Y23bz89P06ZNU0hIiPr06XPL9rMtW7ZMYWFhCgoKUmRkpOLj4611rl69WqtXr77pqaFFixapefPmCggIUEREhH744Qfrul9++UWRkZGqV6+eWrRooSVLlti8Bv369VOXLl3UsGFDzZw5M0cfy5cv1z//+U9JtqdLMjMzNXXqVIWGhqp+/foaMGCAzp8/L+nq6aXx48crJCREISEhGjp0qJKTk2/6viF/REZGysfHR5MmTZIkLViwQI6OjoqKitLvv/+uPn36KDAwUGFhYZo5c6b1zqJXrlzR6NGjFRISoqCgIPXp00cJCQn23JUii5CBHCpUqKABAwZozpw51r8ArpWRkaEXXnhBqamp+uSTT/Tee+9p69atmjhxoh2qvX8Z/T79+OOPOnbsmJYuXaoxY8Zo0aJF2rFjh4KCgvTaa6+pYsWK2r59uypVqqTFixdr3bp1mjJlipYvXy4vLy/17NlTV65csbb35ZdfaunSpRo6dOgt25ekLVu2aObMmRozZoxWr16t+vXrq1u3brpw4YJ69uyp8PBwhYeHa+XKlTnqPnjwoCZOnKixY8fq888/V4MGDTRw4EBlZWUpLS1NL7/8surXr6+1a9dqxIgRmjVrltasWWN9/ubNm9W6dWt9/PHHat++vRISEnTgwAHr+o0bNyo8PDxHv9OmTdPq1av11ltvafny5frjjz80duxYSdLUqVN14MABzZ8/X4sWLdKlS5f06quv5up9wJ1zcnJSdHS0Nm7cqE2bNmnhwoUaO3asXFxc9Morr8jLy0urV6/WhAkTtG7dOuuplSVLluj777/XBx98oJUrV+ry5ct666237Lw3RZQFuEbNmjUt3377rSUzM9PSpk0bS+/evS0Wi8WyatUqS/PmzS0Wi8WyadMmS2BgoCU5Odn6vG3btln8/f0tly5dskvd9xsj3qfp06dbunbtam2nVq1alosXL1rXP/3005bZs2fn6MdisViaNm1q2bx5s/VxZmampVGjRtZlNWvWtHz66afW9bdrv3PnzpZFixbZ1PfMM89Yl40YMcIyYsSIG742GzdutNStW9cSHx9vsVgslsuXL1t27NhhuXLliuWzzz6zPPPMMzbbL1q0yLps+vTplkcffdRmfc+ePS1TpkyxWCwWS3JysqVOnTqWQ4cOWSwWi6V58+aWVatWWbKysiwNGza0rFq1yvq8w4cPW6ZPn25JSUmxeY7FYrFcuHDBUqtWLZtlMM7QoUMt/v7+lkGDBlksFotlx44dlkaNGlnMZrN1m82bN1saNmxosVgslnHjxlnatGljOX/+vMVisVhOnTplOXDgQIHXfS8osndhhbFMJpPeeOMNPf/889q0aZPNuqNHj6pq1aoqWbKkdVlwcLAyMzP122+/qXbt2gVd7n3LyPfJy8tLHh4e1sceHh7KzMzMsd3ly5d19uxZDRo0yObmhGlpaTp+/Lj1sbe3d67bP3r0qCZNmqSpU6da16enp9u0dzOhoaGqWbOm2rRpI39/f/3jH/9Qx44d5eTkpF9//VWHDh1SUFCQdXuz2SyTyXTTOlu1aqV58+Zp8ODB2rx5s3x8fOTn52ezzfnz55WcnKw6depYl1WvXl39+/fXL7/8oitXrui5556zeU5WVpaOHz+eoy3kvz59+mjt2rWKioqSdHV8JScnq379+tZtso90nT9/Xp06ddKGDRsUGhqqhg0b6oknnlBERIS9yi/SCBm4qeDgYLVv314xMTF66aWXrMtdXFxybJt9LjP7JwqOUe+Ts7NzjmWWG0y4zG5r2rRp8vX1tVl3bcC5vp5btW82m/Xaa6+pcePGNuuvDSU34+bmphUrVui7777Tl19+qdjYWC1dulSxsbHKzMxU48aNFR0dfdPnX1/nk08+qbFjx+rw4cM3PVXi5HTzf0qzX59PP/1U7u7uNuu8vLxuuz+4e9nvafbPzMxMVatWTbNmzcqxraenp0qXLq0tW7Zo69at2rp1q6ZOnar169dryZIlfCMlj5iTgVsaOnSoUlJSbCYX+vr66vjx4zYT1+Li4uTk5KQHH3zQDlWioN+na/+hLVGihLy8vJSYmCgfHx/5+PioUqVKmjRpko4dO3ZH7fv6+urs2bPW9nx8fDRnzhzFxcXl6P96P/74o+bOnatGjRpp1KhR+uKLL5Senq7du3fL19dXx44dU+XKla3txsXF6ZNPPrlpe56enmrSpIk+//xz7dixQ61atcqxTYkSJVS6dGkdOnTIuuznn39W06ZNVaVKFZlMJiUnJ1v79PDw0IQJE/THH3/c0euDu+Pr66szZ86oTJky1vfk1KlTmj59uhwcHLRmzRp9+eWXCg8P1zvvvKMFCxZo9+7dvF93gJCBWypdurSGDh2q06dPW5c99thjqlKlioYPH674+Hh9++23GjdunFq3bq0SJUrYsdr7V0G/T25ubrpw4YKOHz+uzMxMde/eXe+99562bNmi48ePa/To0dqzZ4+qVat2R+336NFDH3/8sdasWaPffvtNkyZN0ueff66HHnrI2v/p06dvOOPf1dVV77//vlasWKFTp05pw4YNSklJkZ+fn9q2bau0tDRFR0fr6NGj2rZtm2JiYm57RKFVq1b68MMPVa1atRxHa7JFRkZq2rRp+vbbb3X48GHFxMTo4YcfloeHhzp27Kg33nhDu3bt0pEjRzR8+HCdOHFClStXvqPXB3cnNDRU3t7eGjZsmOLj4/XDDz9ozJgxcnNzk8lk0sWLFxUTE6OdO3fq5MmTWrdunSpWrKjSpUvbu/Qih9MluK0OHTpo1apVOnfunKSr8wBmzZqlcePG6dlnn1Xx4sXVpk0bDR482M6V3t8K8n1q1KiRfHx81KZNG3366ad68cUXdfnyZUVHR+vSpUuqW7euFi5caHO6JC9atmyppKQkTZ8+XUlJSapevbpmz56tqlWrSpLatWunqKgotW3bVt9++63NkY3atWsrJiZGs2bN0ptvvqkHHnhAkyZNsgaU+fPn66233tLTTz+tUqVKqUuXLurdu/ct62nevLksFsstL7zVq1cvXbx4UQMHDlRmZqaaNWumMWPGSJJGjhypd955RwMGDNCVK1f0yCOPaN68eTZzQVBwTCaTZs+ebf1/w93dXU899ZRGjBghSerSpYvOnj2rYcOG6cKFC6pbt65mz57N+3UHuNU7AAAwBKdLAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABDEDKAIi4sLEx+fn7y8/NTrVq1FBQUpOeee05ff/11vrS/a9cuQ+4UGhsba607u/bg4GANGDBAR48eveN2//jjD33++ef5UuPnn39uvV/FjBkzFBkZmS/tAvcLQgZwD3jttde0fft2bdu2TcuXL1dwcLB69+6tHTt23HXbQUFB2r59ez5UmVPFihW1fft2bd++XV999ZWWLVum8+fPq2/fvsrKyrqjNidPnqxt27bddW2nT5/WwIEDlZqaKknq2bOnZsyYcdftAvcTQgZwD/D09FS5cuVUoUIF1axZU8OHD1erVq00YcKEu27b2dlZ5cqVy4cqczKZTCpXrpzKlSun8uXLq2bNmhoyZIhOnDih+Pj4O2ozv+6UcH07xYsXV6lSpfKlbeB+QcgA7lGdOnXSL7/8ohMnTkiS/vrrLw0bNkzBwcEKDQ3VuHHjlJaWJkl69tlnNX36dJvnP/fcc5o1a1aO0yUnTpzQiy++qKCgIDVr1kyLFi2yrvvll18UGRmpevXqqUWLFlqyZEme686+CVWxYsW0a9cuhYWFaezYsapfv77mzZsn6eqplvDwcNWrV08RERH6/vvvJV09pbF69WqtXr1aYWFht91vSdq3b586d+6swMBAtWjRQhs2bJAk/eMf/7D+jI2NzXG65Mcff1Tnzp318MMPKywsTEuXLrWuGzlypCZMmKCBAwcqMDBQjz/+uNasWZPn1wIo6ggZwD0q+66jR44ckSS9/vrrunjxopYuXapZs2Zp//79evPNNyVdvevp//73P+tzExISFBcXp1atWtm0mZ6erp49e6p48eL67LPPFB0drXfffVdffvml0tLS9PLLL6t+/fpau3atRowYoVmzZuXpwzUhIUHTpk2zuaX66dOnlZGRodjYWLVu3VqxsbEaN26cevfurTVr1ujRRx9Vr169lJCQoJ49eyo8PFzh4eFauXLlbff7jz/+UM+ePVW7dm2tXr1avXv31ogRI3To0CGtWLFCkrRixYocd189evSoXnjhBT3yyCOKjY1V//799c4779i8hkuWLFGdOnW0fv16/fOf/9TYsWN18eLFXL8WwL2AW70D9yhPT09J0uXLl/Xbb79p06ZN+u6776zLx40bp6efflqjRo1SeHi43nnnHR0/flxVq1bVxo0b5e/vLx8fH509e9ba5vbt2/Xnn3/qrbfekoeHh2rUqKHRo0fL0dFR69atk5eXlwYOHChJqlq1qk6fPq1Fixbp6aefvmGNZ86cUVBQkCTJbDYrPT1dtWvX1tSpU21uq/3SSy/Jx8dHkvTJJ58oMjLS2ubQoUP1/fffa/HixRoyZIhcXV0lSWXKlLntfm/YsEElS5a07kO1atV04cIFpaWlqWzZstZ2stvM9tlnn8nf31+DBw+WJFWrVk1Hjx7VggUL9OSTT0qS/Pz89PLLL0uSXn31VS1atEiHDx9WcHBwHt9JoOgiZAD3qEuXLkmSPDw8dPToUWVlZalp06Y222RlZenEiROqW7euGjRooI0bN6pXr17auHFjjr/eJenYsWPy9fWVh4eHdVn79u0lSe+8844OHTpkDQ3S1eBwbVi4Xvny5fXJJ59IkhwdHVWyZEmVKFEix3aVK1e2/n706FFFRUXZrH/44Ydv+I2U2+33sWPH5O/vL0fH/z+o26NHD0nSqVOnblr30aNHVa9ePZtlQUFBWrZsmfVx1apVrb9nv16ZmZk3bRO4FxEygHtU9sTJGjVqKD4+Xp6enlq1alWO7SpUqCDp6imTlStXqn379tqzZ4/efvvtHNs6Od38n4zMzEw1btxY0dHRua7RycnJeoTiVlxcXG74ezaz2XzDb6OYzeZb7vet9ie39WTLysqS2Wy2Pi5WrFiObfJrUipQVDAnA7hHrVq1SnXq1FGVKlXk6+urixcvysHBQT4+PvLx8VFaWpomTpyojIwMSVKLFi0UHx+vFStWKCAgQN7e3jnarFq1qk6cOGH9Wqd09QjG+PHj5evrq2PHjqly5crWPuLi4qxHKvKLr6+v9u7da7Ns79691jkcDg4ONtvear+rVq2q+Ph4mw//gQMHasGCBTbt5KaGH3/80VoDgKsIGcA94OLFi0pMTNS5c+cUHx+vmJgY/ec//9HIkSMlXZ0E2qRJEw0dOlT79u3TTz/9pFGjRiklJcV6eqJMmTIKCQnR3LlzFR4efsN+QkNDVbZsWUVHR+vo0aPavHmzli1bptDQULVt21ZpaWnWddu2bVNMTIy8vLzydV+7d++uxYsXa82aNTp27JgmT56sQ4cOqUOHDpIkNzc3nT59WgkJCbfd7zZt2ig5OVkTJ07U8ePHFRsbq82bN+uxxx6Tm5ubJOnQoUO6fPmyTQ3PP/+8fv75Z02dOlXHjh3T6tWr9emnn6pLly75uq9AUUfIAO4Bb731lkJDQ9W0aVP16NFDx44d00cffaSGDRtat5k4caIqV66s7t27q0ePHvL19dXUqVNt2mnVqpXS0tJuGjKcnJw0a9YsnTt3Ts8884xiYmI0fPhwNWvWTB4eHpo/f76OHz+up59+WqNHj1aXLl3Uu3fvfN3Xli1batCgQZo+fbratm2r7777Th988IH12zTt2rXTsWPH1LZtW1ksllvud4kSJTR37lz98MMPat26tebPn68pU6aodu3aKlOmjNq2bauBAwdav2mS7YEHHtDcuXP19ddfq02bNpo9e7ZGjhxpnZ8C4CoHCycJAQCAATiSAQAADEHIAAAAhiBkAAAAQxAyAACAIQgZAADAEIQMAABgCEIGAAAwBCEDAAAYgpABAAAMQcgAAACGIGQAAABD/B8MbxfZ6vcxNQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation (crosstab) of the categorical column and 'Churn' column\n",
    "cross_tab = pd.crosstab(Data_All['DeviceProtection'], Data_All['Churn'])\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "percentages = cross_tab.div(cross_tab.sum(1), axis=0) * 100\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "ax = percentages.plot(kind=\"bar\", stacked=True, figsize=(6, 6))\n",
    "plt.title(\"Device Protection by Churn\")\n",
    "plt.xlabel(\"Device Protection\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel(\"%\")\n",
    "plt.legend(title=\"Churn\", loc=\"upper right\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers without device protection are slightly more likely to churn, although not significantly enough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. What is the relationship between the availability of tech support and churn rates? Are customers with access to tech support more likely to remain with the company?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation (crosstab) of the categorical column and 'Churn' column\n",
    "cross_tab = pd.crosstab(Data_All['TechSupport'], Data_All['Churn'])\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "# sns.set(style=\"whitegrid\")\n",
    "ax = cross_tab.plot(kind=\"bar\", stacked=True, figsize=(6, 6))\n",
    "plt.title(\"TechSupport by Churn\")\n",
    "plt.xlabel(\"TechSupport\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend(title=\"Churn\", loc=\"upper right\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers with no tech support were more likely to churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. Do streaming services play a role in customer churn? Are customers with streaming services, such as StreamingTV and StreamingMovies, more likely to stay with the company?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation (crosstab) of the two categorical columns and 'Churn' column\n",
    "cross_tab1 = pd.crosstab(Data_All['StreamingTV'], Data_All['Churn'])\n",
    "cross_tab2 = pd.crosstab(Data_All['StreamingMovies'], Data_All['Churn'])\n",
    "\n",
    "# Set up the subplots\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Plot the first stacked bar chart\n",
    "cross_tab1.plot(kind=\"bar\", stacked=True, ax=axes[0])\n",
    "axes[0].set_title(\"Streaming TV by Churn\", fontsize=16)\n",
    "axes[0].set_xlabel(\"Streaming TV\", fontsize=15)\n",
    "axes[0].set_ylabel(\"Count\", fontsize=15)\n",
    "axes[0].legend(title=\"Churn\", loc=\"upper right\", fontsize=14)\n",
    "\n",
    "axes[0].set_xticklabels(axes[0].get_xticklabels(), rotation=45, ha='right', fontsize=13)\n",
    "# Plot the second stacked bar chart\n",
    "cross_tab2.plot(kind=\"bar\", stacked=True, ax=axes[1])\n",
    "axes[1].set_title(\"Streaming Movies by Churn\", fontsize=16)\n",
    "axes[1].set_xlabel(\"Streaming Movies\",fontsize=15)\n",
    "axes[1].set_ylabel(\"Count\", fontsize=15)\n",
    "axes[1].legend(title=\"Churn\", loc=\"upper right\", fontsize=14)\n",
    "\n",
    "axes[1].set_xticklabels(axes[1].get_xticklabels(), rotation=45, ha='right', fontsize=13)\n",
    "# Adjust layout and display\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- No significant relationship between streaming services and churn rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7. How does the choice of payment method impact churn rates? Are customers with specific payment methods more prone to churn than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation (crosstab) of the categorical column and 'Churn' column\n",
    "cross_tab = pd.crosstab(Data_All['PaymentMethod'], Data_All['Churn'])\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = cross_tab.plot(kind=\"bar\", stacked=True, figsize=(10, 6))\n",
    "plt.title(\"Payment Method by Churn\")\n",
    "plt.xlabel(\"Payment Method\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylabel(\"count\")\n",
    "plt.legend(title=\"Churn\", loc=\"upper right\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Customers who pay by electronic check were more likely to churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at correlation between numeric values and target variable (Churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.239642</td>\n",
       "      <td>0.826188</td>\n",
       "      <td>-0.352634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>0.239642</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.646813</td>\n",
       "      <td>0.201995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.826188</td>\n",
       "      <td>0.646813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.194628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>-0.352634</td>\n",
       "      <td>0.201995</td>\n",
       "      <td>-0.194628</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tenure  MonthlyCharges  TotalCharges     Churn\n",
       "tenure          1.000000        0.239642      0.826188 -0.352634\n",
       "MonthlyCharges  0.239642        1.000000      0.646813  0.201995\n",
       "TotalCharges    0.826188        0.646813      1.000000 -0.194628\n",
       "Churn          -0.352634        0.201995     -0.194628  1.000000"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_columns = df_hypo.select_dtypes(include=['int64', 'float64'])\n",
    "numeric_columns.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.heatmap(numeric_columns.corr(),annot=True,cmap='coolwarm')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **tenure vs. Churn**: The negative correlation coefficient (-0.352634) indicates a moderate negative linear relationship between the two variables. As 'tenure' (the duration a customer has been with the company) increases, the 'Churn' rate (the likelihood of a customer churning) tends to decrease.\n",
    "\n",
    "2. **MonthlyCharges vs. Churn**: The positive correlation coefficient (0.201995) indicates a weak positive linear relationship between the two variables. As 'MonthlyCharges' increase, the 'Churn' rate tends to slightly increase. However, the correlation is not as strong as with 'tenure'.\n",
    "\n",
    "3. **TotalCharges vs. Churn**: The negative correlation (-0.194628) suggests a weak negative linear relationship between the two variables. As 'TotalCharges' (the total amount charged to the customer) increases, the 'Churn' rate tends to slightly decrease. However, similar to 'MonthlyCharges', the correlation is not as strong as with 'tenure'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Processing & Engineering\n",
    "Here is the section to **clean**, **process** the dataset and **create new features**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5034 entries, 0 to 5033\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            5034 non-null   object \n",
      " 1   SeniorCitizen     5034 non-null   object \n",
      " 2   Partner           5034 non-null   object \n",
      " 3   Dependents        5034 non-null   object \n",
      " 4   tenure            5034 non-null   int64  \n",
      " 5   PhoneService      5034 non-null   object \n",
      " 6   MultipleLines     5034 non-null   object \n",
      " 7   InternetService   5034 non-null   object \n",
      " 8   OnlineSecurity    5034 non-null   object \n",
      " 9   OnlineBackup      5034 non-null   object \n",
      " 10  DeviceProtection  5034 non-null   object \n",
      " 11  TechSupport       5034 non-null   object \n",
      " 12  StreamingTV       5034 non-null   object \n",
      " 13  StreamingMovies   5034 non-null   object \n",
      " 14  Contract          5034 non-null   object \n",
      " 15  PaperlessBilling  5034 non-null   object \n",
      " 16  PaymentMethod     5034 non-null   object \n",
      " 17  MonthlyCharges    5034 non-null   float64\n",
      " 18  TotalCharges      5034 non-null   float64\n",
      " 19  Churn             5034 non-null   object \n",
      "dtypes: float64(2), int64(1), object(17)\n",
      "memory usage: 786.7+ KB\n"
     ]
    }
   ],
   "source": [
    "Data_All.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutual Information (MI) scores\n",
    "\n",
    "We calculate the mutual scores between categorical features and the target variable (Churn) in the test dataset. Mutual Information is a measure of the dependence between two variables, which quantifies how much knowing the value of one variable reduces uncertainty about the other variable.\n",
    "\n",
    "This helps identify which categorical features are more informative in relation to the target variable, making them potentially valuable for predicting or understanding the 'Churn' behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contract - 0.10284790516675524\n",
      "InternetService - 0.05944428811167132\n",
      "PaymentMethod - 0.04163984913943765\n",
      "TechSupport - 0.030007109249305178\n",
      "OnlineSecurity - 0.029670157336360276\n",
      "PaperlessBilling - 0.019508874345666505\n",
      "OnlineBackup - 0.015826778687180174\n",
      "Dependents - 0.015615368107435551\n",
      "DeviceProtection - 0.013717699147021103\n",
      "StreamingMovies - 0.013349078621584257\n",
      "SeniorCitizen - 0.010389723457655958\n",
      "Partner - 0.010018147440621322\n",
      "StreamingTV - 0.009953736504141789\n",
      "MultipleLines - 0.0008281912846177519\n",
      "PhoneService - 0.00010045042468448395\n",
      "gender - 3.2190163546386152e-06\n"
     ]
    }
   ],
   "source": [
    "# Lets find out feature dependency on the target variable using mutual information score\n",
    "\n",
    "x_cat = Data_All.select_dtypes(include=object).drop('Churn', axis=1)\n",
    "y_cat = Data_All['Churn']\n",
    "\n",
    "mi_scores = []\n",
    "\n",
    "#  loop to calculate the Mutual Information Score for each categorical feature \n",
    "#  with respect to the 'Churn' target variable\n",
    "\n",
    "for column in x_cat.columns:\n",
    "    mi_score = mutual_info_score(x_cat[column], y_cat)\n",
    "    mi_scores.append((column, mi_score))\n",
    "\n",
    "# sort features by their importance dependency on the target variable, \n",
    "# with the most important ones at the top.\n",
    "\n",
    "mi_scores.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "for feature, score in mi_scores:\n",
    "    print(f\"{feature} - {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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       "layout": {
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
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        "title": {
         "text": "Categorical Features Relationship with Target (Churn)"
        },
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         "domain": [
          0,
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         "title": {
          "text": "Mutual Information Score"
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   ],
   "source": [
    "features = [score[0] for score in mi_scores]\n",
    "scores = [score[1] for score in mi_scores]\n",
    "\n",
    "# Create a bar plot using Plotly\n",
    "fig = px.bar(x=scores, y=features, orientation='h', color=scores,\n",
    "             labels={'x': 'Mutual Information Score', 'y': 'Features'},\n",
    "             title='Categorical Features Relationship with Target (Churn)',\n",
    "             template='plotly_dark',\n",
    "             color_continuous_scale=px.colors.sequential.Plasma)\n",
    "\n",
    "fig.show()"
   ]
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   "source": [
    "- The gender, Phoneservice and Multiplelines have a very low mutual score (close to 0). This suggests that they have little to no predictive power or relationship with the target variable. \n",
    "- Contract and Internet Service have the highest mutual scores. Different contract lengths and the type of internet service a customer has may be associated with different churn rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Screenshots\\Mutual_information_plot.png'\n",
    "\n",
    "# save the figure as a PNG image\n",
    "\n",
    "pio.write_image(fig, file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns\n",
    "\n",
    "Data_All.drop(columns=['gender','PhoneService', 'MultipleLines'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_encode = Data_All.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X-y Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_encode.drop(columns=['Churn'])\n",
    "y = df_encode['Churn']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the target variable (Churn) to have 0 or 1 instead of No or Yes\n",
    "\n",
    "labelEncoder = LabelEncoder()\n",
    "\n",
    "y = labelEncoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5034, 16), (5034,))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One hot encoding for categorical columns\n",
    "\n",
    "categorical_columns = ['SeniorCitizen', 'Partner', 'Dependents',\n",
    "                       'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', \n",
    "                       'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', \n",
    "                       'PaperlessBilling', 'PaymentMethod']\n",
    "\n",
    "# Create column transformer instance\n",
    "\n",
    "transformer = make_column_transformer(\n",
    "    (OneHotEncoder(sparse=False), categorical_columns)\n",
    ")\n",
    "\n",
    "transformed_data = transformer.fit_transform(X[categorical_columns])\n",
    "\n",
    "# Transformating back to a dataframe\n",
    "transformed_df = pd.DataFrame(transformed_data, columns=transformer.get_feature_names_out())\n",
    "\n",
    "# One-hot encoding removed an index. Let's put it back\n",
    "transformed_df.index = X.index\n",
    "\n",
    "# Joining tables\n",
    "encoded_df = pd.concat([X, transformed_df], axis=1)\n",
    "\n",
    "# Dropping old categorical columns\n",
    "encoded_df.drop(categorical_columns, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>onehotencoder__SeniorCitizen_No</th>\n",
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       "      <th>onehotencoder__InternetService_DSL</th>\n",
       "      <th>onehotencoder__InternetService_Fiber optic</th>\n",
       "      <th>onehotencoder__InternetService_No</th>\n",
       "      <th>onehotencoder__OnlineSecurity_No</th>\n",
       "      <th>onehotencoder__OnlineSecurity_No internet service</th>\n",
       "      <th>onehotencoder__OnlineSecurity_Yes</th>\n",
       "      <th>onehotencoder__OnlineBackup_No</th>\n",
       "      <th>onehotencoder__OnlineBackup_No internet service</th>\n",
       "      <th>onehotencoder__OnlineBackup_Yes</th>\n",
       "      <th>onehotencoder__DeviceProtection_No</th>\n",
       "      <th>onehotencoder__DeviceProtection_No internet service</th>\n",
       "      <th>onehotencoder__DeviceProtection_Yes</th>\n",
       "      <th>onehotencoder__TechSupport_No</th>\n",
       "      <th>onehotencoder__TechSupport_No internet service</th>\n",
       "      <th>onehotencoder__TechSupport_Yes</th>\n",
       "      <th>onehotencoder__StreamingTV_No</th>\n",
       "      <th>onehotencoder__StreamingTV_No internet service</th>\n",
       "      <th>onehotencoder__StreamingTV_Yes</th>\n",
       "      <th>onehotencoder__StreamingMovies_No</th>\n",
       "      <th>onehotencoder__StreamingMovies_No internet service</th>\n",
       "      <th>onehotencoder__StreamingMovies_Yes</th>\n",
       "      <th>onehotencoder__Contract_Month-to-month</th>\n",
       "      <th>onehotencoder__Contract_One year</th>\n",
       "      <th>onehotencoder__Contract_Two year</th>\n",
       "      <th>onehotencoder__PaperlessBilling_No</th>\n",
       "      <th>onehotencoder__PaperlessBilling_Yes</th>\n",
       "      <th>onehotencoder__PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>onehotencoder__PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>onehotencoder__PaymentMethod_Electronic check</th>\n",
       "      <th>onehotencoder__PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>29.850000</td>\n",
       "      <td>29.850000</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>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>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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>56.950001</td>\n",
       "      <td>1889.500000</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>53.849998</td>\n",
       "      <td>108.150002</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>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>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>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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>42.299999</td>\n",
       "      <td>1840.750000</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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>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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>70.699997</td>\n",
       "      <td>151.649994</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  MonthlyCharges  TotalCharges  onehotencoder__SeniorCitizen_No  \\\n",
       "0       1       29.850000     29.850000                              1.0   \n",
       "1      34       56.950001   1889.500000                              1.0   \n",
       "2       2       53.849998    108.150002                              1.0   \n",
       "3      45       42.299999   1840.750000                              1.0   \n",
       "4       2       70.699997    151.649994                              1.0   \n",
       "\n",
       "   onehotencoder__SeniorCitizen_Yes  onehotencoder__Partner_No  \\\n",
       "0                               0.0                        0.0   \n",
       "1                               0.0                        1.0   \n",
       "2                               0.0                        1.0   \n",
       "3                               0.0                        1.0   \n",
       "4                               0.0                        1.0   \n",
       "\n",
       "   onehotencoder__Partner_Yes  onehotencoder__Dependents_No  \\\n",
       "0                         1.0                           1.0   \n",
       "1                         0.0                           1.0   \n",
       "2                         0.0                           1.0   \n",
       "3                         0.0                           1.0   \n",
       "4                         0.0                           1.0   \n",
       "\n",
       "   onehotencoder__Dependents_Yes  onehotencoder__InternetService_DSL  \\\n",
       "0                            0.0                                 1.0   \n",
       "1                            0.0                                 1.0   \n",
       "2                            0.0                                 1.0   \n",
       "3                            0.0                                 1.0   \n",
       "4                            0.0                                 0.0   \n",
       "\n",
       "   onehotencoder__InternetService_Fiber optic  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         1.0   \n",
       "\n",
       "   onehotencoder__InternetService_No  onehotencoder__OnlineSecurity_No  \\\n",
       "0                                0.0                               1.0   \n",
       "1                                0.0                               0.0   \n",
       "2                                0.0                               0.0   \n",
       "3                                0.0                               0.0   \n",
       "4                                0.0                               1.0   \n",
       "\n",
       "   onehotencoder__OnlineSecurity_No internet service  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   onehotencoder__OnlineSecurity_Yes  onehotencoder__OnlineBackup_No  \\\n",
       "0                                0.0                             0.0   \n",
       "1                                1.0                             1.0   \n",
       "2                                1.0                             0.0   \n",
       "3                                1.0                             1.0   \n",
       "4                                0.0                             1.0   \n",
       "\n",
       "   onehotencoder__OnlineBackup_No internet service  \\\n",
       "0                                              0.0   \n",
       "1                                              0.0   \n",
       "2                                              0.0   \n",
       "3                                              0.0   \n",
       "4                                              0.0   \n",
       "\n",
       "   onehotencoder__OnlineBackup_Yes  onehotencoder__DeviceProtection_No  \\\n",
       "0                              1.0                                 1.0   \n",
       "1                              0.0                                 0.0   \n",
       "2                              1.0                                 1.0   \n",
       "3                              0.0                                 0.0   \n",
       "4                              0.0                                 1.0   \n",
       "\n",
       "   onehotencoder__DeviceProtection_No internet service  \\\n",
       "0                                                0.0     \n",
       "1                                                0.0     \n",
       "2                                                0.0     \n",
       "3                                                0.0     \n",
       "4                                                0.0     \n",
       "\n",
       "   onehotencoder__DeviceProtection_Yes  onehotencoder__TechSupport_No  \\\n",
       "0                                  0.0                            1.0   \n",
       "1                                  1.0                            1.0   \n",
       "2                                  0.0                            1.0   \n",
       "3                                  1.0                            0.0   \n",
       "4                                  0.0                            1.0   \n",
       "\n",
       "   onehotencoder__TechSupport_No internet service  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   onehotencoder__TechSupport_Yes  onehotencoder__StreamingTV_No  \\\n",
       "0                             0.0                            1.0   \n",
       "1                             0.0                            1.0   \n",
       "2                             0.0                            1.0   \n",
       "3                             1.0                            1.0   \n",
       "4                             0.0                            1.0   \n",
       "\n",
       "   onehotencoder__StreamingTV_No internet service  \\\n",
       "0                                             0.0   \n",
       "1                                             0.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   onehotencoder__StreamingTV_Yes  onehotencoder__StreamingMovies_No  \\\n",
       "0                             0.0                                1.0   \n",
       "1                             0.0                                1.0   \n",
       "2                             0.0                                1.0   \n",
       "3                             0.0                                1.0   \n",
       "4                             0.0                                1.0   \n",
       "\n",
       "   onehotencoder__StreamingMovies_No internet service  \\\n",
       "0                                                0.0    \n",
       "1                                                0.0    \n",
       "2                                                0.0    \n",
       "3                                                0.0    \n",
       "4                                                0.0    \n",
       "\n",
       "   onehotencoder__StreamingMovies_Yes  onehotencoder__Contract_Month-to-month  \\\n",
       "0                                 0.0                                     1.0   \n",
       "1                                 0.0                                     0.0   \n",
       "2                                 0.0                                     1.0   \n",
       "3                                 0.0                                     0.0   \n",
       "4                                 0.0                                     1.0   \n",
       "\n",
       "   onehotencoder__Contract_One year  onehotencoder__Contract_Two year  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               1.0                               0.0   \n",
       "2                               0.0                               0.0   \n",
       "3                               1.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   onehotencoder__PaperlessBilling_No  onehotencoder__PaperlessBilling_Yes  \\\n",
       "0                                 0.0                                  1.0   \n",
       "1                                 1.0                                  0.0   \n",
       "2                                 0.0                                  1.0   \n",
       "3                                 1.0                                  0.0   \n",
       "4                                 0.0                                  1.0   \n",
       "\n",
       "   onehotencoder__PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                                0.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "3                                                1.0        \n",
       "4                                                0.0        \n",
       "\n",
       "   onehotencoder__PaymentMethod_Credit card (automatic)  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   onehotencoder__PaymentMethod_Electronic check  \\\n",
       "0                                            1.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            1.0   \n",
       "\n",
       "   onehotencoder__PaymentMethod_Mailed check  \n",
       "0                                        0.0  \n",
       "1                                        1.0  \n",
       "2                                        1.0  \n",
       "3                                        0.0  \n",
       "4                                        0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df.columns = encoded_df.columns.str.replace('onehotencoder__', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5034 entries, 0 to 5033\n",
      "Data columns (total 39 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   tenure                                   5034 non-null   int64  \n",
      " 1   MonthlyCharges                           5034 non-null   float64\n",
      " 2   TotalCharges                             5034 non-null   float64\n",
      " 3   SeniorCitizen_No                         5034 non-null   float64\n",
      " 4   SeniorCitizen_Yes                        5034 non-null   float64\n",
      " 5   Partner_No                               5034 non-null   float64\n",
      " 6   Partner_Yes                              5034 non-null   float64\n",
      " 7   Dependents_No                            5034 non-null   float64\n",
      " 8   Dependents_Yes                           5034 non-null   float64\n",
      " 9   InternetService_DSL                      5034 non-null   float64\n",
      " 10  InternetService_Fiber optic              5034 non-null   float64\n",
      " 11  InternetService_No                       5034 non-null   float64\n",
      " 12  OnlineSecurity_No                        5034 non-null   float64\n",
      " 13  OnlineSecurity_No internet service       5034 non-null   float64\n",
      " 14  OnlineSecurity_Yes                       5034 non-null   float64\n",
      " 15  OnlineBackup_No                          5034 non-null   float64\n",
      " 16  OnlineBackup_No internet service         5034 non-null   float64\n",
      " 17  OnlineBackup_Yes                         5034 non-null   float64\n",
      " 18  DeviceProtection_No                      5034 non-null   float64\n",
      " 19  DeviceProtection_No internet service     5034 non-null   float64\n",
      " 20  DeviceProtection_Yes                     5034 non-null   float64\n",
      " 21  TechSupport_No                           5034 non-null   float64\n",
      " 22  TechSupport_No internet service          5034 non-null   float64\n",
      " 23  TechSupport_Yes                          5034 non-null   float64\n",
      " 24  StreamingTV_No                           5034 non-null   float64\n",
      " 25  StreamingTV_No internet service          5034 non-null   float64\n",
      " 26  StreamingTV_Yes                          5034 non-null   float64\n",
      " 27  StreamingMovies_No                       5034 non-null   float64\n",
      " 28  StreamingMovies_No internet service      5034 non-null   float64\n",
      " 29  StreamingMovies_Yes                      5034 non-null   float64\n",
      " 30  Contract_Month-to-month                  5034 non-null   float64\n",
      " 31  Contract_One year                        5034 non-null   float64\n",
      " 32  Contract_Two year                        5034 non-null   float64\n",
      " 33  PaperlessBilling_No                      5034 non-null   float64\n",
      " 34  PaperlessBilling_Yes                     5034 non-null   float64\n",
      " 35  PaymentMethod_Bank transfer (automatic)  5034 non-null   float64\n",
      " 36  PaymentMethod_Credit card (automatic)    5034 non-null   float64\n",
      " 37  PaymentMethod_Electronic check           5034 non-null   float64\n",
      " 38  PaymentMethod_Mailed check               5034 non-null   float64\n",
      "dtypes: float64(38), int64(1)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "encoded_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardization for numeric values\n",
    "\n",
    "cols = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "encoded_scaled = scaler.fit_transform(encoded_df[cols])\n",
    "\n",
    "# Create new DataFrames with the scaled values\n",
    "X_scaled_df = pd.DataFrame(encoded_scaled, columns=cols, index=X.index)\n",
    "\n",
    "# Drop the original unscaled columns\n",
    "encoded_df.drop(cols, axis=1, inplace=True)\n",
    "\n",
    "# Concatenate the scaled columns with the original DataFrames\n",
    "encoded_df = pd.concat([encoded_df, X_scaled_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen_No</th>\n",
       "      <th>SeniorCitizen_Yes</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_No</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>OnlineSecurity_No</th>\n",
       "      <th>OnlineSecurity_No internet service</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_No</th>\n",
       "      <th>OnlineBackup_No internet service</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_No</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_No</th>\n",
       "      <th>TechSupport_No internet service</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No</th>\n",
       "      <th>StreamingTV_No internet service</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_No</th>\n",
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       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>-1.290184</td>\n",
       "      <td>-1.172700</td>\n",
       "      <td>-1.001315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>-0.271320</td>\n",
       "      <td>-0.181408</td>\n",
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       "      <td>-0.374430</td>\n",
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       "      <td>-0.758597</td>\n",
       "      <td>-0.202901</td>\n",
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       "    <tr>\n",
       "      <th>4</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.249382</td>\n",
       "      <td>0.186022</td>\n",
       "      <td>-0.947615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen_No  SeniorCitizen_Yes  Partner_No  Partner_Yes  \\\n",
       "0               1.0                0.0         0.0          1.0   \n",
       "1               1.0                0.0         1.0          0.0   \n",
       "2               1.0                0.0         1.0          0.0   \n",
       "3               1.0                0.0         1.0          0.0   \n",
       "4               1.0                0.0         1.0          0.0   \n",
       "\n",
       "   Dependents_No  Dependents_Yes  InternetService_DSL  \\\n",
       "0            1.0             0.0                  1.0   \n",
       "1            1.0             0.0                  1.0   \n",
       "2            1.0             0.0                  1.0   \n",
       "3            1.0             0.0                  1.0   \n",
       "4            1.0             0.0                  0.0   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  OnlineSecurity_No  \\\n",
       "0                          0.0                 0.0                1.0   \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                          1.0                 0.0                1.0   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  OnlineBackup_No  \\\n",
       "0                                 0.0                 0.0              0.0   \n",
       "1                                 0.0                 1.0              1.0   \n",
       "2                                 0.0                 1.0              0.0   \n",
       "3                                 0.0                 1.0              1.0   \n",
       "4                                 0.0                 0.0              1.0   \n",
       "\n",
       "   OnlineBackup_No internet service  OnlineBackup_Yes  DeviceProtection_No  \\\n",
       "0                               0.0               1.0                  1.0   \n",
       "1                               0.0               0.0                  0.0   \n",
       "2                               0.0               1.0                  1.0   \n",
       "3                               0.0               0.0                  0.0   \n",
       "4                               0.0               0.0                  1.0   \n",
       "\n",
       "   DeviceProtection_No internet service  DeviceProtection_Yes  TechSupport_No  \\\n",
       "0                                   0.0                   0.0             1.0   \n",
       "1                                   0.0                   1.0             1.0   \n",
       "2                                   0.0                   0.0             1.0   \n",
       "3                                   0.0                   1.0             0.0   \n",
       "4                                   0.0                   0.0             1.0   \n",
       "\n",
       "   TechSupport_No internet service  TechSupport_Yes  StreamingTV_No  \\\n",
       "0                              0.0              0.0             1.0   \n",
       "1                              0.0              0.0             1.0   \n",
       "2                              0.0              0.0             1.0   \n",
       "3                              0.0              1.0             1.0   \n",
       "4                              0.0              0.0             1.0   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  StreamingMovies_No  \\\n",
       "0                              0.0              0.0                 1.0   \n",
       "1                              0.0              0.0                 1.0   \n",
       "2                              0.0              0.0                 1.0   \n",
       "3                              0.0              0.0                 1.0   \n",
       "4                              0.0              0.0                 1.0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  0.0                  0.0   \n",
       "2                                  0.0                  0.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  0.0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                      1.0                0.0                0.0   \n",
       "1                      0.0                1.0                0.0   \n",
       "2                      1.0                0.0                0.0   \n",
       "3                      0.0                1.0                0.0   \n",
       "4                      1.0                0.0                0.0   \n",
       "\n",
       "   PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
       "0                  0.0                   1.0   \n",
       "1                  1.0                   0.0   \n",
       "2                  0.0                   1.0   \n",
       "3                  1.0                   0.0   \n",
       "4                  0.0                   1.0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      1.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                    0.0                             1.0   \n",
       "1                                    0.0                             0.0   \n",
       "2                                    0.0                             0.0   \n",
       "3                                    0.0                             0.0   \n",
       "4                                    0.0                             1.0   \n",
       "\n",
       "   PaymentMethod_Mailed check    tenure  MonthlyCharges  TotalCharges  \n",
       "0                         0.0 -1.290184       -1.172700     -1.001315  \n",
       "1                         1.0  0.056283       -0.271320     -0.181408  \n",
       "2                         1.0 -1.249382       -0.374430     -0.966794  \n",
       "3                         0.0  0.505106       -0.758597     -0.202901  \n",
       "4                         0.0 -1.249382        0.186022     -0.947615  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train set Balancing (SMOTE Algorithm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SMOTE (Synthetic Minority Over-sampling Technique) is a method used to address class imbalance in a binary classification problem. \n",
    "\n",
    "Earlier we realised that our target vaiable has a class imbalance. One class (the minority class) has significantly fewer instances than the other class (the majority class). This imbalance can negatively impact the performance of machine learning models, as they might become biased toward the majority class.\n",
    "\n",
    "SMOTE will aim to balance the class distribution by generating synthetic samples until the minority class has the same number of instances as the majority class. By creating synthetic samples, SMOTE helps the model better capture the patterns in the minority class and prevents it from favoring the majority class due to the imbalance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply SMOTE to the training data (oversampling)\n",
    "\n",
    "smote = SMOTE(random_state=42, k_neighbors=5, sampling_strategy='auto')\n",
    "\n",
    "X_resampled, y_resampled = smote.fit_resample(encoded_df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(6, 6))\n",
    "\n",
    "# Plot the first bar graph for the original distribution\n",
    "unique, counts = np.unique(y, return_counts=True)\n",
    "axes[0].bar(unique, counts)\n",
    "axes[0].set_xticks([0, 1])\n",
    "axes[0].set_xticklabels(['Not Churned', 'Churned'])\n",
    "axes[0].set_ylabel('Number of Instances')\n",
    "axes[0].set_title('Original Distribution')\n",
    "\n",
    "# Plot the second bar graph for a separate y value\n",
    "unique2, counts2 = np.unique(y_resampled, return_counts=True) \n",
    "axes[1].bar(unique2, counts2)\n",
    "axes[1].set_xticks([0, 1])  # Adjust as needed\n",
    "axes[1].set_xticklabels(['Not Churned', 'Churned'])  # Adjust labels\n",
    "axes[1].set_ylabel('Number of Instances')\n",
    "axes[1].set_title('Balanced Distribution')\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our train dataset is now balaced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42, stratify=y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5916, 39), (5916, 1))"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check shape after resampling\n",
    "\n",
    "pd.DataFrame(X_train).shape, pd.DataFrame(y_train).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2958\n",
       "1    2958\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view class distribution\n",
    "\n",
    "pd.value_counts(pd.Series(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Modeling \n",
    "Here is the section to **build**, **train**, **evaluate** and **compare** the models to each others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LogisticRegression(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "LR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 2. K-nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Train the model\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "rfm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 4. Support Vector Machines Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=42)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 5. Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=42)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "gb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 6. XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(random_state=42)\n",
    "\n",
    "# Train the model\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Model Evaluation\n",
    "We create a pandas dataframe that will allow us to compare our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold Cross-Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "k-fold cross-validation  estimates the performance on our models across multiple subsets of the data (k-folds), providing a comprehensive evaluation of their generalization ability. The model is trained and evaluated k times, with each fold serving as the validation set once. This process helps estimate the model's performance across different subsets of the data.\n",
    "\n",
    "It helps estimate how well a model will perform on new, unseen data and provides insights into its stability and consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Mean Accuracy</th>\n",
       "      <th>Std Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.846180</td>\n",
       "      <td>0.007953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.841954</td>\n",
       "      <td>0.009768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.839925</td>\n",
       "      <td>0.014080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.806117</td>\n",
       "      <td>0.009330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K nearest neighbors</td>\n",
       "      <td>0.787693</td>\n",
       "      <td>0.009645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.776028</td>\n",
       "      <td>0.013290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Mean Accuracy  Std Deviation\n",
       "2        Random Forest       0.846180       0.007953\n",
       "5              XGBoost       0.841954       0.009768\n",
       "4    Gradient Boosting       0.839925       0.014080\n",
       "3                  SVC       0.806117       0.009330\n",
       "1  K nearest neighbors       0.787693       0.009645\n",
       "0  Logistic Regression       0.776028       0.013290"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe with the K-fold Cross-Validation results\n",
    "\n",
    "models = [\n",
    "    ('Logistic Regression', LR),\n",
    "    ('K nearest neighbors', knn),\n",
    "    ('Random Forest', rfm),\n",
    "    ('SVC', svm),\n",
    "    ('Gradient Boosting', gb),\n",
    "    ('XGBoost', xgb)\n",
    "]\n",
    "\n",
    "# number of k-folds\n",
    "k = 5\n",
    "\n",
    "results = []\n",
    "\n",
    "for name, model in models:\n",
    "    kf = KFold(n_splits=k, shuffle=True, random_state=42)  # Create a KFold object\n",
    "    scores = cross_val_score(model, X_train, y_train, cv=kf, scoring='accuracy')\n",
    "\n",
    "    # Append results to the list\n",
    "    results.append((name, scores.mean(), scores.std()))\n",
    "\n",
    "results_df = pd.DataFrame(results, columns=['Model', 'Mean Accuracy', 'Std Deviation'])\n",
    "\n",
    "results_df.sort_values(by='Mean Accuracy', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of our k-fold cross-validation is the mean accuracy and std deviation.\n",
    "\n",
    "1. **Average Accuracy** is the mean across all k folds during the cross-validation process. Higher mean accuracy values indicate better predictive performance.\n",
    "2. **Standard Deviation** measures the variability or spread of accuracy values across the k folds. A lower standard deviation suggests that the model's performance is consistent across different subsets of the data (folds), while a higher standard deviation indicates that the model's performance varies more widely. Smaller standard deviations are generally desirable because they indicate a more stable model.\n",
    "\n",
    "The **Random Forest Classifier** model has the highest mean accuracy (0.846180) **85%** among the evaluated models. This means that, on average, the model correctly predicted the target variable for about 85% of the data points in each fold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.852027</td>\n",
       "      <td>0.848728</td>\n",
       "      <td>0.856757</td>\n",
       "      <td>0.852724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.851351</td>\n",
       "      <td>0.845745</td>\n",
       "      <td>0.859459</td>\n",
       "      <td>0.852547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.841892</td>\n",
       "      <td>0.824359</td>\n",
       "      <td>0.868919</td>\n",
       "      <td>0.846053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.798649</td>\n",
       "      <td>0.779040</td>\n",
       "      <td>0.833784</td>\n",
       "      <td>0.805483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k-NN</td>\n",
       "      <td>0.791216</td>\n",
       "      <td>0.734494</td>\n",
       "      <td>0.912162</td>\n",
       "      <td>0.813743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.777703</td>\n",
       "      <td>0.754647</td>\n",
       "      <td>0.822973</td>\n",
       "      <td>0.787330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Precision    Recall  F1-Score\n",
       "5              XGBoost  0.852027   0.848728  0.856757  0.852724\n",
       "2        Random Forest  0.851351   0.845745  0.859459  0.852547\n",
       "4    Gradient Boosting  0.841892   0.824359  0.868919  0.846053\n",
       "3                  SVM  0.798649   0.779040  0.833784  0.805483\n",
       "1                 k-NN  0.791216   0.734494  0.912162  0.813743\n",
       "0  Logistic Regression  0.777703   0.754647  0.822973  0.787330"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_names = ['Logistic Regression', 'k-NN', 'Random Forest', 'SVM', 'Gradient Boosting', 'XGBoost']\n",
    "models = [LR, knn, rfm, svm, gb, xgb]\n",
    "model_names_list = []\n",
    "accuracy_scores = []\n",
    "precision_scores = []\n",
    "recall_scores = []\n",
    "f1_scores = []\n",
    "\n",
    "# Loop through each model to calculate metrics and store information\n",
    "for name, model in zip(model_names, models):\n",
    "    # Make predictions on the test data\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate accuracy, precision, recall, and F1-score\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred)\n",
    "    recall = recall_score(y_test, y_pred)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # Store model name and metrics\n",
    "    model_names_list.append(name)\n",
    "    accuracy_scores.append(accuracy)\n",
    "    precision_scores.append(precision)\n",
    "    recall_scores.append(recall)\n",
    "    f1_scores.append(f1)\n",
    "\n",
    "# Create a DataFrame with the calculated metrics\n",
    "metrics_df = pd.DataFrame({\n",
    "    'Model': model_names_list,\n",
    "    'Accuracy': accuracy_scores,\n",
    "    'Precision': precision_scores,\n",
    "    'Recall': recall_scores,\n",
    "    'F1-Score': f1_scores\n",
    "})\n",
    "\n",
    "# Display the DataFrame\n",
    "metrics_df.sort_values(by='Accuracy', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The XGBoost is our highest performing model with an accuracy of 0.852027 / **85%**.\n",
    "\n",
    "- Our top 3 models (Random Forest, XGBoost and Gradient Boosting) are all tree based models, specifically ensemble learning techniques that combine multiple individual trees to improve overall performance and robustness. They reduce overfitting by averaging or boosting the individual trees' predictions. They offer a combination of powerful features that make them robust, accurate, and versatile for classification tasks across a wide range of domains and data characteristics."
   ]
  },
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         "categoryarray": [
          "Logistic Regression",
          "k-NN",
          "SVM",
          "Gradient Boosting",
          "Random Forest",
          "XGBoost"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Model Name"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Accuracy Score"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the DataFrame by the 'Accuracy' column in ascending order\n",
    "metrics_df_sorted = metrics_df.sort_values(by='Accuracy')\n",
    "\n",
    "# Create the plot\n",
    "fig = px.bar(metrics_df_sorted, x='Model', y='Accuracy', color='Model',\n",
    "             title='Model Performance Metrics (Accuracy)',\n",
    "             template='plotly_dark',\n",
    "             labels={'Model': 'Model Name', 'Accuracy': 'Accuracy Score'}\n",
    "            )\n",
    "\n",
    "# Show the plot\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the performance metrics from our classification report that help us see how our models are performing.\n",
    "\n",
    "1. **Accuracy**: Accuracy measures the proportion of correctly predicted instances out of the total instances in the dataset. It is an important metric for classification tasks. Our XGBoost model has the highest accuracy (0.852). This means that around 85% of the predictions made by the were correctly classified. Since our data is balanced, we want to use the accuracy score as our main evaluation metric.\n",
    "\n",
    "With balanced data, there is less risk of the model being biased towards one class due to class imbalance. As a result, accuracy effectively captures the model's ability to correctly classify instances from both classes equally. When the classes are balanced, a high accuracy score indicates that the model is performing well across both classes and is making accurate predictions overall.\n",
    "\n",
    "2. **Precision**: Precision quantifies how many of the positive predictions made by the model were actually correct. It's the ratio of true positives (correctly predicted positives) to the total number of instances predicted as positive. A higher precision indicates fewer false positives. In the XGBoost model, the precision is approximately 0.8487, meaning that about 85% of the positive predictions made by the model were accurate.\n",
    "\n",
    "3. **Recall**: Recall, also known as *sensitivity* or true positive rate, measures the proportion of actual positive instances that were correctly predicted by the model. It's the ratio of true positives to the total number of actual positives (true positives and false negatives). A higher recall indicates fewer false negatives. In the XGBoost model, the recall is approximately 0.8568, indicating that about 86% of the actual positive instances were correctly identified by the model.\n",
    "\n",
    "4. **F1-Score**: The F1-Score is the harmonic mean of precision and recall. It combines both precision and recall into a single metric. The F1-Score gives us a balanced measure of a model's performance, considering both false positives and false negatives. In the XGBoost model, the F1-Score is approximately 0.8527 which takes into account both precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'Screenshots\\Model_Performance_Accuracy.png'\n",
    "\n",
    "# save the figure as a PNG image\n",
    "\n",
    "pio.write_image(fig, file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning \n",
    "\n",
    "We will Fine-tune our top 3 models using a ` RandomizedSearchCV`  (that is in sklearn.model_selection\n",
    ") to find the best hyperparameters and achieve the maximum performance of each of the Top 3 models, then compare them again to select the best one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Tuning Model 1 (XGBoost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:logistic',\n",
       " 'base_score': None,\n",
       " 'booster': None,\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': None,\n",
       " 'device': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': None,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'multi_strategy': None,\n",
       " 'n_estimators': None,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'random_state': 42,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': None,\n",
       " 'tree_method': None,\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check current model parameters\n",
    "\n",
    "current_params = xgb.get_params()\n",
    "current_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.6,\n",
       " 'reg_lambda': 0.1,\n",
       " 'reg_alpha': 0.1,\n",
       " 'n_estimators': 100,\n",
       " 'min_child_weight': 1,\n",
       " 'max_depth': 7,\n",
       " 'learning_rate': 0.1,\n",
       " 'gamma': None}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGBoost model tuning\n",
    "\n",
    "param_dist = {\n",
    "    'n_estimators': [None, 50, 100, 500, 1000],\n",
    "    'max_depth': [None, 3, 5, 7, 10],\n",
    "    'min_child_weight': [None, 1, 2, 4, 6],\n",
    "    'subsample': [None, 0.1, 0.2, 0.4, 0.6],\n",
    "    'gamma': [None, 0, 0.1, 0.2, 0.3],\n",
    "    'learning_rate': [None, 0.1, 0.2, 0.3],\n",
    "    'reg_alpha': [None, 0, 0.1, 0.5, 1],\n",
    "    'reg_lambda': [None, 0, 0.1, 0.5, 1],\n",
    "}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "random_search_xgb = RandomizedSearchCV(estimator=xgb, param_distributions=param_dist, scoring='accuracy',\n",
    "                                     cv=5, n_jobs=-1, random_state=42, n_iter=150, verbose = 1)\n",
    "\n",
    "# fit best estimator on train data\n",
    "random_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# best parameters\n",
    "best_params = random_search_xgb.best_params_\n",
    "\n",
    "# mean accuracy score of the best estimator\n",
    "best_score = random_search_xgb.best_score_\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8536168353476047"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean accuracy score of the best estimator\n",
    "\n",
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit tuned model on train data\n",
    "tuned_xgb_model = random_search_xgb.best_estimator_\n",
    "tuned_xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# make the predictions\n",
    "random_search_xgb_pred = tuned_xgb_model.predict(X_test)\n",
    "original_xgb_model = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the tuned model and the original model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Total Precision</th>\n",
       "      <th>Total Recall</th>\n",
       "      <th>Total F1-Score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Original XGBoost</th>\n",
       "      <td>0.852059</td>\n",
       "      <td>0.852027</td>\n",
       "      <td>0.852024</td>\n",
       "      <td>0.852027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned XGBoost</th>\n",
       "      <td>0.849938</td>\n",
       "      <td>0.849324</td>\n",
       "      <td>0.849258</td>\n",
       "      <td>0.849324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Total Precision  Total Recall  Total F1-Score  Accuracy\n",
       "Original XGBoost         0.852059      0.852027        0.852024  0.852027\n",
       "Tuned XGBoost            0.849938      0.849324        0.849258  0.849324"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the classification report\n",
    "\n",
    "report = classification_report(y_test, original_xgb_model, output_dict=True)\n",
    "report_2 = classification_report(y_test, random_search_xgb_pred, output_dict=True)\n",
    "\n",
    "# Extract precision, recall, f1-score, and accuracy metrics for both classes\n",
    "\n",
    "precision = ((report['1']['precision'] + report['0']['precision'])) / 2\n",
    "recall = ((report['1']['recall'] + report['0']['recall'])) / 2\n",
    "f1 = ((report['1']['f1-score'] + report['0']['f1-score'])) / 2\n",
    "\n",
    "precision2 = ((report_2['1']['precision'] + report_2['0']['precision'])) / 2\n",
    "recall2 = ((report_2['1']['recall'] + report_2['0']['recall'])) / 2\n",
    "f12 = ((report_2['1']['f1-score'] + report_2['0']['f1-score'])) / 2\n",
    "\n",
    "# Create dictionaries for saving metrics\n",
    "metrics_original_XGB = {\n",
    "    'Total Precision': precision,\n",
    "    'Total Recall': recall,\n",
    "    'Total F1-Score': f1,\n",
    "    'Accuracy': report['accuracy']\n",
    "}\n",
    "\n",
    "metrics_Tuned_XGB = {\n",
    "    'Total Precision': precision2,\n",
    "    'Total Recall': recall2,\n",
    "    'Total F1-Score': f12,\n",
    "    'Accuracy': report_2['accuracy']\n",
    "}\n",
    "\n",
    "# Create DataFrames from the metrics dictionaries\n",
    "metrics_df_original = pd.DataFrame(metrics_original_XGB, index=['Original XGBoost'])\n",
    "metrics_df_xgb = pd.DataFrame(metrics_Tuned_XGB, index=['Tuned XGBoost'])\n",
    "\n",
    "# Concatenate the DataFrames vertically to combine the metrics\n",
    "combined_metrics_df = pd.concat([metrics_df_original, metrics_df_xgb])\n",
    "\n",
    "combined_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[613, 127],\n",
       "       [ 96, 644]], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct the confusion matrix for tuned model\n",
    "confusion_matrix_xgb = confusion_matrix(y_test, random_search_xgb_pred)\n",
    "\n",
    "confusion_matrix_xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tr4mfTFPVajWs7Qf279WCubMUevKEnJydVKmyl3r2G6jnixSVJPV6u5N+Ovi/h47746Hf0r12pC+7CBQzZsyQdG8vxYMsFguB4ingVbaQvp7SQVsPntYbH6xUofwuCgzw08rn31C9Xv/3/0VWZ0eFDPNXDc/n9e3+0ERjFMqXS19P6aDTF66rS9A65ciWRWPe9tPGyW+pZre5io2Lz+iPBTxRLl+6qAG9AnTzZmSi9l9/PqQBvQJUp249jRr3oe7cvq3P5s1W9y7ttXTll8rj5qb3hn6gWzcTB/s/z4dp7Mihav5a64z8GEgndhEojh8/busSYGPjujfQLycvqfWw5bp/3VHkrbv6uG9DFSuYR2cvhcunUlF90q+RCj/jmuwYnV+pqtw5s6nl0OW6duO2JOlKeJS+ndpRvlVL6Lv/hSb7PgD/Lj4+Xpu/+lLTp3yc5FlLkrR0YYiKlyiloI8+lYPDvZX0ilW81KJxfW3asE5tO3RWiZKlE70nLi5On04ar9Jl3dV/0JAM+RxIX3axhwJPt7yu2fVSlWKas+6A/vl31Zc7j6tM66k6eylckrRq/BsK++uGXnx7XrLjzF13QPX7fGYNE5IUExMnScrm7Jhu9QNPulMnf9ek8YFq1PRVjRw7Mcnx8hUq6Y22b1nDhCQ988yzypkrl/48H5bsmOvWrNTvx47q/WEj5eTknG61I+PYxQyFn5+fLBbLQ4+nxX0oYL8qlCogR0cHXQ2P0mcftFBTn7KyyKIvdx7TwGlfK+LmXUnSy30X6bc//nroOFcjonQ14t5+i6zOjqpcuqA+HdBYoeev6bv//ZEhnwV4EhUsWEgrv9ysZwsU1KED+5Mc79TtnSRtPx38nyJv3FCJkqWSHIuKuqX5s6erUdNmKl+hUrrUjIxnF4GiRYsWiQJFbGyszpw5o507d6pv3742rAwZ4Zk8OSRJcwY30zf7Tun14StV+vm8CgzwU4lCbqrfZ6Ek/WuYeND+kHdUtmh+Rd2J0RsfrNSd6Nj0KB14KrjmziPX3CnvH379uiaOHaX8zzyrxs38kxz/6ssvFHnjhjp0CUi7ImFzdhEo+vTpk2z78uXL9eOPP6pjx44ZXBEyklOWe8sRh05cVM9JX0mSth06rYibd7R4VEvVr1ZS3x94vBmG/lM2y8FiUY+WNbR2Yhu9NmQ5eyiADHD1yhUN6BWgv69e0dRZIcqZM2eSPmtXfq7adeupaLHiGV8g0o1d76GoU6eOdu7caesykM5uRt1b0ti852Si9m/3n5IkVSlT8LHH/OHgaX1/4A+98cEKnb5wXQPbvmheKIB/FXryhN7u+Kb++uuSPpk+R54Vky5nnDrxu86dPaP/NG5qgwqRnuw6UHzzzTfJpls8WU6dvyZJyuqUeOOkk+O917dTuFzxkldxNfR+cCd5gn774y8VyueSBpUCeJiD/9un7l3fkpSgWSFLVKlK1WT77d65XdmyZZdP7boZWyDSnV0seSS3KfPWrVuKiIh46HIInhzHz17VmYvX1dqvgmat/b8b3zT1KStJ2v3ruRSN0/Y/FdX0RXd5tJmmm7ejJUm5sjvL2/N57TmS/E5zAOZ+P35Mg/r1UqHChTVl5jw988yzD+372+Ff5F7OQ1kfuIEhMj+7CBQPbsqU7t2Ou0qVKvL29rZRVchIw2Z9p6WjW2nJqJZa8NUheRR/RqO71dMX247ql5OXUjTGp8v3qKWvp9ZMaKNPl+9RVidHDWzro1w5siros+2PHgBAqkwIHKHY2Bh1695Lly9e1OWLF63H8ri5We+WKUmhp06qRk2WIJ9EdhEomIXAF9uPqdWw5RrW8SWtnfCmrkfe1vwvD2p0yA8pHuP3s1fVoO9CBb7tp5Dh/sri6KCdv5xV94/W6/jZq+lYPfD0+vN8mE4cPyZJGj5oQJLjTZo11wdjxltfX7v2t1xck785HTI3S0Jytz2zgX379unIkSO6c+dOkjux9e7d23j87HUDjccAkD7Obxpm6xIA/It8OR89/2AXMxRz587VJ598IhcXF7m4JN48Z7FY0iRQAACA9GMXgWLp0qXq16+fevToYetSAABAKtjFZaPh4eFq1qyZrcsAAACpZBeB4oUXXtBPP/1k6zIAAEAq2WzJY926ddavK1asqNGjR+vkyZMqVqyYHB0T3+DI398/Y4sDAACPxWZXeZQrVy5F/SwWi44dO2Z8Pq7yAOwXV3kA9s2ur/I4fvy4JCkqKkrZsmWTg8P/rb6cOnVKzz//vLJxJzUAADIFm+6h2Lhxo+rXr6+jR48map8wYYLq1q2rLVu22KgyAADwOGwWKPbt26dBgwapXr16KlCgQKJjw4YNk5+fn/r3769Dhw7ZqEIAAJBSNttD0bVrV5UqVUrDhj187XTo0KG6evWq5s2bZ3w+9lAA9os9FIB9S8keCpvNUBw9elStWrX61z5t27ZNshwCAADsj80Cxd27dx+56TJPnjy6fft2BlUEAABSy2aBokSJEo+8mdWhQ4dUuHDhDKoIAACkls0CxauvvqqpU6fq8uXLyR6/fPmypk6dqkaNGmVwZQAA4HHZ7D4U7du31zfffKNXXnlFLVu2lJeXl1xdXRUeHq5Dhw7piy++UPHixdW1a1dblQgAAFLIZld5SFJ0dLSmTJmiNWvWKCIiwtqeP39+tWzZUj169Eizm1txlQdgv7jKA7BvKbnKw6aB4r7Y2FiFhYUpIiJCefPmVZEiRWSxWNL0HAQKwH4RKAD7Zte33v6nLFmyqESJErYuAwAApJJdPL4cAABkbgQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADCWJaUd161b91gD+/v7P2YpAAAgs0pxoBgyZEii1xaLRZKUkJCQpE0iUAAA8DRJcaD4/vvvrV8fO3ZMgwYNUs+ePdW4cWM9++yzun79urZu3arg4GBNmDAhXYoFAAD2KcWBonDhwtav+/Tpo549e+rtt9+2thUoUEBvvvmmoqOjNWnSJNWtWzdtKwUAAHYrVZsyQ0NDVb58+WSPlSxZUufPnzcqCgAAZC6pChTFixfXhg0bkj22YsUKlS1b1qgoAACQuaR4yeOfevXqpX79+unMmTOqV6+e3NzcdPXqVX377bc6deqU5s2bl9Z1AgAAO2ZJ+OdlGo9h69atmjFjho4ePaqEhAQ5ODjIy8tLAwYMULVq1dK6TmPZ6wbaugQAD3F+0zBblwDgX+TL+ej5h1TNUEiSn5+f/Pz8dPfuXUVERChPnjxydnZO7XAAACATS3WgkO5tzty9e7euXLmi9u3bKywsTOXKlVOuXLnSqj4AAJAJpCpQxMfHa+TIkVqzZo0SEhJksVjUqFEjzZw5U+fOndPSpUtVsGDBtK4VAADYqVRd5TFz5kxt2LBBQUFB2r17t/VumYMGDVJ8fLw+/fTTNC0SAADYt1QFijVr1qhv375q2bKl8uTJY2338PBQ3759tXv37rSqDwAAZAKpChRXr16Vh4dHsscKFCigGzduGBUFAAAyl1QFimLFimn79u3JHtu/f7+KFStmVBQAAMhcUrUps2PHjho5cqRiYmJUr149WSwWnT17Vvv27dOCBQuSPJkUAAA82VJ9Y6s5c+Zo1qxZunv3rnVTppOTk7p166Z+/fqlaZFpgRtbAfaLG1sB9i0lN7ZKdaCQpJs3b+qnn35SeHi4XF1dVbly5USbNO0JgQKwXwQKwL6lJFCkag/F0KFDFRYWply5cqlOnTpq1qyZ6tatqzx58uiPP/5Q9+7dUzMsAADIpFK8h+LChQvWr9etW6cGDRrI0dExSb8dO3boxx9/TJvqAABAppDiQDFmzBjt2LHD+rp3797J9ktISJCPj495ZQAAINNIcaAIDAzUjz/+qISEBA0bNkw9evRQ0aJFE/VxcHCQq6urvL2907xQAABgv1IcKAoUKKAWLVpIkiwWi3x9feXq6mpd9rhz545iYmLk4uKSPpUCAAC7lapNma+88oqmTJmi119/3dp26NAh1apVSx9++KHi4+PTrEAAAGD/UhUogoODtX79er3yyivWtvLly+u9997TypUrNX/+/DQrEAAA2L9U3Slzw4YNGjx4sNq0aWNty5Mnjzp16qQsWbJo8eLFCggISLMiAQCAfUvVDMX169dVpEiRZI+VLFlSly5dMioKAABkLqkKFCVLltQ333yT7LGtW7fycDAAAJ4yqVry6NChg4YMGaLw8HA1aNBA+fLl07Vr1/TDDz9o8+bNmjBhQlrXCQAA7FiqAoW/v79u3bqlmTNn6ttvv7W2u7m5acSIEfL390+r+gAAQCZg9HCwhIQEnT592vpwsJIlS8rBIVWrKOmOh4MB9ouHgwH2Ld2fNpqZ3Im1dQUAHsatevK38gdgH27/NP2RfVK85OHh4aEVK1aoUqVKKleunCwWy0P7WiwWHT16NKVDAwCATC7FgaJXr14qUKCA9et/CxQAAODpwpIHAJtjyQOwb2m65HHhwoXHOvlzzz33WP0BAEDmleJA4efn91jLHMeOHUtVQQAAIPNJcaAYP368NVBERETo448/Vq1atdS4cWM988wzCg8P19atW7Vt2zYNGTIk3QoGAAD2J1V7KHr16iU3NzcFBQUlOTZu3DidPHlSCxcuTIv60gx7KAD7xR4KwL6lZA9Fqu5CtXv3bjVu3DjZY76+vvrpp59SMywAAMikUhUo3Nzc9OuvvyZ7bO/evdbLSwEAwNMhVc/yaN26tWbMmKE7d+7I19dXbm5uunr1qr7++mt9/vnnGjaM2+gCAPA0SVWg6NGjhyIjIxUSEqK5c+dKuvdcj2zZsqlfv35q165dmhYJAADsm9GNrSIjI/Xzzz8rIiJCbm5u8vLyUo4cOdKyvjTDpkzAfrEpE7BvaXpjq+TkzJlTzzzzjBISElS5cmVFR0fbbaAAAADpJ9WB4ssvv9TkyZN15coVWSwWrVq1SsHBwXJyctLkyZPl7OyclnUCAAA7lqqrPDZt2qTBgwerZs2a+uSTTxQfHy9Jevnll7V9+3bNnDkzTYsEAAD2LVUzFLNnz1abNm00evRoxcXFWdtbtmypa9euaeXKlerfv39a1QgAAOxcqmYoTp8+rZdffjnZY5UrV9bly5eNigIAAJlLqgJFvnz5FBoamuyx0NBQ5cuXz6goAACQuaQqUDRp0kTTpk3T119/rejoaEmSxWLRkSNHNHPmTDVq1ChNiwQAAPYtVfehiI6OVs+ePbVr1y45ODgoPj5eOXPmVFRUlKpVq6Z58+YpW7Zs6VFvqnEfCsB+cR8KwL6l230onJ2dNX/+fO3evVt79+5VeHi4XFxcVKNGDdWtW9f6mHMAAPB0SFWg6Nq1q7p16yYfHx/5+PikdU0AACCTSdUeikOHDjELAQAArFIVKOrUqaP169crJiYmresBAACZUKqWPLJmzar169dr8+bNKlWqVJLnd1gsFi1atChNCgQAAPYvVYHi0qVL8vLysr5+8EIRgweYAgCATOixA8Wvv/6qtm3bqmjRovL09EyPmgAAQCaT4kBx48YNvfPOO/r555+tbV5eXpo8ebIKFSqUHrUBAIBMIsWbMqdMmaKjR4+qT58+mjNnjgYPHqw//vhDI0eOTM/6AABAJpDiGYoffvhB7777rjp27ChJeumll1SgQAG99957ioqKSrIxEwAAPD1SPENx5cqVJHsmvL29FRcXp4sXL6Z5YQAAIPNIcaCIjY2Vs7NzorbcuXNLku7evZu2VQEAgEwlVTe2ehCXiQIA8HRLk0DBbbgBAHi6PdZ9KEaPHq1cuXJZX9+fmRgxYoRy5sxpbedOmQAAPF1SHCiqV68uKenyRnLtLIEAAPB0SXGgWLJkSXrWAQAAMrE02UMBAACebgQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwBiBAgAAGCNQAAAAYwQKAABgjEABAACMESgAAIAxAgUAADBGoAAAAMYIFAAAwFgWWxdw359//qlffvlF0dHRSY75+/tnfEEAACDF7CJQrFy5UmPGjFFcXFySYxaLhUABAICds4tAMXv2bLVp00YDBgxQrly5bF0OAAB4THaxh+LKlSvq3LkzYQIAgEzKLgKFh4eHTp06ZesyAABAKtnFkke3bt0UGBiosLAwlSxZUs7OzomOV69e3UaVAQCAlLAkJCQk2LqIcuXKPfSYxWLRsWPHjM9xJ9Z4CADpxK16b1uXAOBf3P5p+iP72MUMxZYtW+TgYBerLwAAIBXsIlAMGDBAQUFB/zpTAQAA7JddTAuEhYUpR44cti4DAACkkl3MUHTr1k3Dhw9X165dVbRoUWXLli3R8eeee85GlQEAgJSwi02Znp6e1rtkWiwWa3tCQgKbMoGnAJsyAfuWaTZlfvbZZ7YuAQAAGLCLQFGjRg1blwAAAAzYRaAYOnTovx6fMGFCBlUCAABSwy4Cxfnz5xO9jouL07lz53Tz5k01bdrURlUBAICUsotAsWTJkiRtCQkJGjt2rHLmzGmDigAAwOOwi/tQJMdisahTp05avXq1rUsBAACPYLeBQpLOnj2r6OhoW5cBAAAewS6WPJLblHnr1i3t3r1b9evXt0FFAADgcdhFoHhwU6YkOTs7q1OnTurcubMNKgIAAI/DLgJFcpsyAQBA5mEXgUKSbt++rVOnTunu3bt68G7g1atXt1FVAAAgJewiUGzfvl39+/fXnTt3koSJtHqWBzKX+Ph4LVn0mVatWK7Lly+p8PPP64027fRmu/bWPjdu3FDwlE/0/XdbFBUVpTJly6p33/7yrlnLhpUDT6YaFYsrsM+rqlahmG5G3dWWH49p2Kdf6Mr1m0n69nrTVx+/30ruTUbq3MVryY7n5VFE2xe9p55j/6ulG/ald/nIAHYRKCZNmiQfHx/16tVLrq6uti4HdmDyRxO1dMkitX6jjfzqv6zzYec0I3iq/vzzvN57f4ji4uLUq/vbunjxggYMHKS8+fLpv0sXq3ePAC1bvkpl3cvZ+iMATwwvjyL6em5fbd33u954d54KPZNbgX1e1cpPA1Sv0yeJ+pYu+qwC+7z6r+M5O2XRvMC35OTkmJ5lI4PZRaA4e/asZsyYoWLFitm6FNiB69ev6fP/LtVrLVvrg5FjrO0FChZS/z491bJVax05clhHfzui5avWqkxZd0lSteo11KrFq9rz424CBZCGxvXz1y+/n1frAXOts8iRt+7o40GtVOy5fDp74W9JkoODRfMC2+taxC3lyO780PFG9Wyq3LmyZ0jtyDh2cR+K4sWL68qVK7YuA3bi7JkziouLU13feonaq9fwVnx8vHbv2qnvvv1GL1Srbg0TkpQ1a1Zt2PSNOnbumtElA0+svLlz6qVqZTRn5c5ES9Jfbv1FZRqPsIYJSRrQob6ezeuiSQu+feh4NSuXUI82ddV/4sp0rRsZz2aB4sKFC9Z/2rRpoxEjRmjXrl0KCwtLdOzChQu2KhE2ksfNTZJ04WLi//bnw87d+/f58/r9+HGVKl1aSxcvVOOX/fRCZU+9+fprOnTwQIbXCzzJKpR5To6ODrp6/aY+G9dRf+36WFd2T9b8sW8lmmXwKFlQw99ponfGLFPUneRvSJg9m5PmjnlLHy34VodP/JlRHwEZxGZLHn5+frJYLNbXCQkJ6tatW5I2NmU+fYoXLyGvqi9o1vRgFShQUDW8a+r8+TCNHTVCzs7Oun07StevX9OWb76Wi2tuvfve+8qWPbsWzJ+r7m930dLPV7LkAaSRZ9xySZLmjG6nb3Yf1evvzrXukyhROL/qd/lUjo4Omj+2gxau26NdB0+p+HP5kh0rqG9z3Yy6q0kLvlXhZ/Nk4KdARrBZoFi8eLGtTo1MYPKn0zR2zEi926+3JMnF1VUD3h2kWTODlS1bdsXExCgyMlLLlq9WgYIFJUlVX3hBTRu9rAXz52nipMm2LB94Yjg53fsxcehYmHoG/leStG3/CUVE3tbiiZ1Vv2Y5eVcqoTwu2fXB1C8fOk6dF8qoy2s+qvPWJMXFxWdI7chYNgsUNWrUeOix6OhoOTs/fEMPnnz58ufXlOCZunHjhq789ZeKFC0qBwcHBQWOUu7cuZUjZ06VLFnKGiYkKWfOXKpSxUvHjx+1YeXAk+Vm1B1J0uYdRxK1f7v73vdZlXLP6/2u/5F/n1m6GxMrR0cHOTjcm2m+/3X2rE6aO6adJi/comN/XJKjo4McHe+tuDs4WOTo6EDIeALY9CqP0NBQTZgwQSNHjlTRokWt7YMHD9aNGzc0atSoRO14emzetFGlSpVSWfdy1kuJfztyWPHx8fIoX15FixZL9sFxsbGxypo1W0aXCzyxTp27t2E+q3PiHxf3L/l8t9PLyurspM1z+iZ579ENo7XjwEkFzd6k4oXza/g7TTT8nSaJ+swZ3V5zRrdXdq/e6fQJkFFsFijOnTundu3aKW/evLp7926iY3Xr1tX8+fPVpk0brVq1SoULF7ZRlbCVeXNmqUyZsvrw4/+7xn3p4oVycXFRtereOn7smObOnqk/QkNVslQpSVJ4+HX9/NMhNWvub6OqgSfP8T8u6cyfV9W6YVXNWr7d2t60bkVJUqt+c3Q3JjbRexrXqaAPujdRy36zdfLsX7p4JUI+7T5K1KdgfletmdpdQbM3afPOxLMfyJxsFihmzJghT09PzZo1K8nyhr+/vxo2bKguXbpo5syZGjdunI2qhK20bfeWggJHqXSZMqpcxUtfb96kTRu/0vCRo+Xi4qJ27Tvoyy/WqnfPAPXpO0DZs2fX3DmzJItFHTtx2SiQloZNWaelH3bRkomdteCLH+VRsqBG92qmL777SXt++SNJ//KlCkmSjpy8YL1T5qGj5xL1KVooryTp7IW/kxxD5mSzQLFv3z598sknD90rkT17dvXu3VsjRozI4MpgD1q9/obu3r2jz5ct1fx5c1S8eAlN/GiyGjd9RZLkmju3Fi39XFM+maTxQYGKiYmRV9WqWrjkvypYqJCNqweeLF9897Na9Z+rYQGNtHZqd12/EaX5q3dp9IyvbF0a7Igl4cGHZ2SQypUra9OmTf+6nHH+/Hm98sor+vnnn43Pdyf20X0A2IZbddbPAXt2+6fpj+xjsxtbFSxYUGfOnPnXPmfOnFH+/PkzpiAAAJBqNgsU9evX16xZsxQbm/zUQWxsrObMmaMXX3wxgysDAACPy2aBolu3bjp37pzat2+vrVu36vr164qPj9e1a9f03XffqV27dgoNDdU777xjqxIBAEAK2WxTZt68ebVo0SINGjRIPXv2THLLbS8vLy1evJhLRgEAyARsemOrEiVKaPXq1frtt9905MgRRUREyM3NTVWrVlWp/39vAQAAYP9sGiju8/T0lKenp63LAAAAqWSzPRQAAODJQaAAAADGCBQAAMAYgQIAABizi02ZknTkyBGFhIToxIkTypIli0qXLq2OHTuqUqVKti4NAAA8gl3MUOzfv19t2rTR2bNn5ePjo+rVq+v06dNq27atDh48aOvyAADAI9js4WD/9Oabb6ps2bIaM2ZMovYxY8bo1KlTWrJkifE5eDgYYL94OBhg3+z64WD/dPToUXXo0CFJe/v27XXkyBEbVAQAAB6HXQQKNzc3Xb9+PUn7tWvX5OzsbIOKAADA47CLQFGvXj2NHTtWoaGh1rZTp04pKChIfn5+NqwMAACkhF3soYiIiFDnzp117Ngxubi4SJIiIyNVrlw5LViwQG5ubsbnYA8FYL/YQwHYt5TsobCLy0Zz586t1atXa+fOnTp58qQSEhLk7u6u2rVry8HBLiZRAADAv7CLGYqMwAwFYL+YoQDsm13PUCR3VUdyLBaLFi1alM7VAAAAEzYLFIULF/7X4wcOHFBYWJhcXV0zqCIAAJBaNgsUEyZMSLb95s2bmjhxosLCwuTj46Nx48ZlcGUAAOBx2cWmzPt+/PFHffDBB4qMjNTYsWPVunVrW5cEAABSwC4CRVRUlCZOnKiVK1fKx8dHQUFBKlSokK3LAgAAKWTzQLFnzx4NHz5cERERCgwM1Ouvv27rkgAAwGOyWaCIiorSRx99pBUrVqhWrVoaN24csxIAAGRSNgsUzZo104ULF1SkSBFVrVpVa9aseWjf3r25Rh0AAHtms0CRkJCgQoUKKTY2VmvXrn1oP4vFQqAAAMDO2SxQbN261VanBgAAaYwHZQAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGMECgAAYIxAAQAAjBEoAACAMQIFAAAwRqAAAADGCBQAAMAYgQIAABgjUAAAAGOWhISEBFsXAQAAMjdmKAAAgDECBQAAMEagAAAAxggUAADAGIECAAAYI1AAAABjBAoAAGCMQAEAAIwRKAAAgDECBQAAMEagAAAAxggUAADAGIECAAAYI1AgQ8XGxmrRokV67bXX5OXlpZo1a6pLly7au3evtY+7u7vWrl1rwyqBzM/Pz09+fn66efNmkmNDhgzRW2+9ZXyOa9eu6aOPPlLDhg1VqVIl1a1bV++//77Onj1r7bNv3z65u7vr/PnzxueDfSNQIMPcvXtXHTp00MKFC/XWW2/piy++0MKFC1WqVCl17txZGzZssHWJwBPlzz//1EcffZQuY58+fVrNmzfXzz//rOHDh2vjxo2aPHmyrl69qtdff10nT55Ml/PCfmWxdQF4ekydOlW///67vvrqKxUqVMjaPnz4cN28eVNBQUHy8/OzYYXAk6VIkSJasWKFGjVqpBdffDFNxx40aJAKFSqkhQsXytnZ2Xq+2bNnq3nz5po4caJCQkLS9Jywb8xQIEPExMRozZo1eu211xKFifv69++vefPmKVu2bJLu/fbTqVMnVaxYUXXq1NGcOXOsfYODg5MEjwfb3N3dNW3aNNWrV0+1a9fWmTNn5Ofnp5CQEPXp00deXl7y9vZWUFCQYmNj0+lTA7b16quvqlatWtbQ/jDh4eEaM2aM6tatq0qVKqlNmzbat2/fQ/sfOXJEhw8fVkBAgDVM3Ofs7KwpU6ZoxIgRidq3b9+uV155RRUqVFDTpk21bds267G33npLQ4YMSdT/n2379u1T+fLlNXfuXHl7e+u1115TWFiY3N3d9c0336h169aqUKGC/Pz8tGLFipT+8SCNESiQIcLCwhQeHq6qVasme7xAgQKqVKmSHB0dJUlLly6Vv7+/Nm3apDfffFOffPKJ9uzZ81jn/O9//6tp06Zp+vTpKl68uKR7syTVq1fX+vXr9f7772vp0qX66quvjD4bYK8sFovGjRuniIgIffjhh8n2iYuLU5cuXXTgwAFNmjRJa9euVdmyZdW1a1f9+uuvyb7nyJEjkvTQ72d3d3fr99x9ixcv1ogRI7RhwwYVL15c/fv3161bt1L8WeLi4rR9+3atWLFC48aNk8VikSRNmDBB3bt31+bNm+Xr66vRo0crLCwsxeMi7RAokCEiIiIkSblz505R/7Zt28rf319FihRRz5495eLiYv1LLKWaN2+uihUrqkqVKta22rVrq0OHDipSpIhatmypcuXK6dChQ481LpCZFC5cWIMHD9bKlSu1a9euJMd37dql3377TZMnT1aNGjVUunRpjRkzRmXKlHnoksX972dXV9cU1zFs2DB5e3urRIkS6tWrl27fvq3Q0NDH+ixdunRR8eLF5eHhYW3r1KmT6tevryJFimjAgAGKj4/XL7/88ljjIm0QKJAh8ubNK+ne1GpKPPjbjaurq+7evftY5yxWrFiStlKlSiV67eLiopiYmMcaF8hs3njjDfn4+OiDDz5IsvRx4sQJubi4qGzZstY2i8WiatWq6cSJE8mO97jfz5JUokQJ69f3g8idO3dS/H4p6d8LUuLvaRcXF0nie9pGCBTIEEWKFFH+/PkfOhsQGhqqLl26WHeG31/6+KeEhISHjp/cPoj7+zH+6cH13keNCzwpgoKCFBkZqQkTJiRqf9j//wkJCcqSJfl9+15eXpL00O/ndevWqX///ol+CXBwSPrj5nG/p7NmzZqkje9p+0GgQIZwcHBQq1attHbtWl28eDHJ8fnz5+vw4cMqXLjwI8dycnJKsvb6z+veAST13HPPaciQIVq9erUOHDhgbXd3d1dkZGSi2YiEhAQdPHhQpUuXTnas0qVLy8vLS/PmzUsyG3D79m3NmzdPERERyQaA5Dg5OSWaOYmPj2cfRCZEoECG6d69u4oXL662bdtq3bp1OnfunH799VcNHTpU69at09ixY5UjR45HjlOlShWFh4crJCRE58+f1/Lly7Vjx44M+ARA5ta6dWvVrl070Q/r2rVry8PDQwMHDtT+/fsVGhqqwMBAnThxQh07dnzoWGPHjlVYWJg6deqknTt3KiwsTLt371bnzp31999/a+TIkSmuq0qVKtq9e7d27Nihs2fPauzYsbpx44bRZ0XG4z4UyDDZs2fX0qVLtWDBAs2bN08XLlxQtmzZVL58eS1ZskTVqlVL0Tg1a9ZUnz59tGDBAk2bNk0vvfSS+vbtq8WLF6fzJwAyv6CgIDVr1sz62tHRUQsWLNCHH36o3r17Kzo6WhUqVNDChQsTbWh+UJkyZbRq1SrNnTtXo0aN0tWrV5UvXz7VrFlTkyZNUpEiRVJcU5cuXXTu3Dn169dPzs7OatWqlZo2bcrSRSZjSeC/GAAAMMSSBwAAMEagAAAAxggUAADAGIECAAAYI1AAAABjBAoAAGCMQAHAprhyHXgyECgApNrAgQPl7u6uBQsWPPZ7L126pICAAP35559pXldwcLDc3d3TfFwAD0egAJAqkZGR+u6771S2bFmtWLHisWcafvzxR23fvj2dqgOQ0QgUAFLlq6++kiQNHz5cZ86c0d69e21cEQBbIlAASJU1a9aoVq1aqlmzpooVK6bly5cn6bNu3Tq1aNFClStXlq+vryZPnqzo6GitXbtWQ4cOlSTVr19fQ4YMkXTvyZfBwcGJxkhu+WLVqlV67bXXVKVKFVWqVEnNmzfX5s2b0+mTAkgJAgWAx3by5EkdPnxY/v7+kiR/f399//33unr1qrXPsmXLNHjwYHl6emr69OkKCAjQkiVLFBQUJF9fX/Xo0UOSNH36dPXs2TPF5162bJlGjhypBg0aaM6cOfr444/l7Oys9957T5cuXUrTzwkg5XjaKIDHtmbNGuXJk0d+fn6SpBYtWig4OFirV69W9+7dFR8frxkzZqhBgwYKCgqyvu/27dvauHGjXFxcVLRoUUmSh4eHnn/++RSfOywsTF27dk0UQgoXLqzXXntNBw8eVNOmTdPoUwJ4HAQKAI8lJiZG69evV4MGDXTnzh3duXNHOXPm1AsvvKCVK1cqICBAp0+f1t9//62XX3450Xu7du2qrl27Gp3//vLIjRs39Mcff+js2bPat2+fJCk6OtpobACpR6AA8Fi2bdumv//+W6tXr9bq1auTHN+5c6dy5colScqXL1+an//cuXMaOXKk9uzZIycnJ5UsWVLlypWTxD0tAFsiUAB4LGvWrFGRIkU0bty4RO0JCQnq3bu3li9frnfffVeSdO3atUR9rl+/rqNHj8rLy+uh48fFxSV6HRUVZf06Pj5eAQEBcnJy0urVq+Xh4aEsWbLo1KlT+vLLL00/GgADbMoEkGJXrlzRzp071bRpU3l7eyf6p2bNmmrUqJG2b98uV1dXubm56Ycffkj0/i+//FIBAQGKiYmRg0PSv35y5cqly5cvJ2o7dOiQ9evr16/r9OnTatWqlSpWrKgsWe79TrRjxw5J9wIHANtghgJAiq1bt06xsbEP3fjo7++vVatWaeXKlerTp48CAwOVL18++fn56fTp05o2bZratWun3Llzy9XVVZK0ZcsWvfTSSypVqpR8fX21ceNGVa5cWcWKFdPatWt19uxZ6/j58uVT4cKFtWzZMhUsWFCurq7auXOnFi9eLOnepk8AtmFJYNERQAo1btxYjo6O1ptaPSghIUENGjRQTEyMfvjhB61fv14hISE6c+aMChYsqJYtW+rtt99WlixZdOvWLfXu3Vv/+9//9OKLL2ru3Lm6evWqxo4dqx07dihLlixq0qSJKlSooA8++EC///67JOn48eMaN26cjhw5ImdnZ5UuXVrdu3fX+PHjVbZsWU2dOlXBwcGaPn269T0A0h+BAgAAGGMPBQAAMEagAAAAxggUAADAGIECAAAYI1AAAABjBAoAAGCMQAEAAIwRKAAAgDECBQAAMEagAAAAxggUAADAGIECAAAY+3+e5+k83qKB4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(confusion_matrix_xgb, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['Churn', 'No Churn'],\n",
    "            yticklabels=['Churn', 'No Churn'])\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 613 instances were correctly classified as True Positivess (TP).\n",
    "- 127 instances were classified as False Positives (FP).\n",
    "- 96 instances were classified as False Negatives (FN).\n",
    "- 644 instances were correctly classified as True Negatives (TN)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Tuning Model 2 (Random Forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check current model parameters\n",
    "\n",
    "current_params = rfm.get_params()\n",
    "current_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 200,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': None,\n",
       " 'criterion': 'entropy',\n",
       " 'class_weight': 'balanced'}"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest tuning\n",
    "\n",
    "# Define the parameter distributions for hyperparameter tuning\n",
    "param_grid = {\n",
    "  'n_estimators': [20, 50, 100, 200, 300],\n",
    "  'max_depth': [None, 10, 15, 20, 25],\n",
    "  'min_samples_split': [2, 3, 4, 5, 6],\n",
    "  'min_samples_leaf': [1, 2, 3, 4, 5],\n",
    "  'class_weight': ['balanced', None],\n",
    "  'max_features': ['auto', 'sqrt', 'log2'],\n",
    "  'criterion': ['gini', 'entropy']\n",
    "}\n",
    "\n",
    "# Initialize RandomizedSearchCV with the RandomForestClassifier model and parameter distributions\n",
    "random_search_rf = RandomizedSearchCV(estimator=rfm, param_distributions=param_grid, \n",
    "                                      scoring='accuracy', n_iter=150, random_state=42,\n",
    "                                      cv=5, n_jobs=-1, verbose = 1)\n",
    "\n",
    "# fit best estimator on train data\n",
    "random_search_rf.fit(X_train, y_train)\n",
    "\n",
    "# best parameters\n",
    "best_params = random_search_rf.best_params_\n",
    "\n",
    "# mean accuracy score of the best estimator\n",
    "best_score = random_search_rf.best_score_\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8492226588380435"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean accuracy score of the best estimator\n",
    "\n",
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit tuned model on train data\n",
    "\n",
    "tuned_rf_model = random_search_rf.best_estimator_\n",
    "tuned_rf_model.fit(X_train, y_train)\n",
    "\n",
    "# make the predictions\n",
    "random_search_rf_pred = tuned_rf_model.predict(X_test)\n",
    "original_rf_model = rfm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Total Precision</th>\n",
       "      <th>Total Recall</th>\n",
       "      <th>Total F1-Score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Original Random Forest</th>\n",
       "      <td>0.851444</td>\n",
       "      <td>0.851351</td>\n",
       "      <td>0.851342</td>\n",
       "      <td>0.851351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <td>0.860246</td>\n",
       "      <td>0.860135</td>\n",
       "      <td>0.860124</td>\n",
       "      <td>0.860135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Total Precision  Total Recall  Total F1-Score  \\\n",
       "Original Random Forest         0.851444      0.851351        0.851342   \n",
       "Tuned Random Forest            0.860246      0.860135        0.860124   \n",
       "\n",
       "                        Accuracy  \n",
       "Original Random Forest  0.851351  \n",
       "Tuned Random Forest     0.860135  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the classification report\n",
    "\n",
    "report = classification_report(y_test, original_rf_model, output_dict=True)\n",
    "report_2 = classification_report(y_test, random_search_rf_pred, output_dict=True)\n",
    "\n",
    "# Extract precision, recall, f1-score, and accuracy metrics for both classes\n",
    "\n",
    "precision = ((report['1']['precision'] + report['0']['precision'])) / 2\n",
    "recall = ((report['1']['recall'] + report['0']['recall'])) / 2\n",
    "f1 = ((report['1']['f1-score'] + report['0']['f1-score'])) / 2\n",
    "\n",
    "precision2 = ((report_2['1']['precision'] + report_2['0']['precision'])) / 2\n",
    "recall2 = ((report_2['1']['recall'] + report_2['0']['recall'])) / 2\n",
    "f12 = ((report_2['1']['f1-score'] + report_2['0']['f1-score'])) / 2\n",
    "\n",
    "# Create dictionaries for metrics\n",
    "metrics_original_Random_Forest = {\n",
    "    'Total Precision': precision,\n",
    "    'Total Recall': recall,\n",
    "    'Total F1-Score': f1,\n",
    "    'Accuracy': report['accuracy']\n",
    "}\n",
    "\n",
    "metrics_Tuned_Random_Forest = {\n",
    "    'Total Precision': precision2,\n",
    "    'Total Recall': recall2,\n",
    "    'Total F1-Score': f12,\n",
    "    'Accuracy': report_2['accuracy']\n",
    "}\n",
    "\n",
    "# Create DataFrames from the metrics dictionaries\n",
    "metrics_df_original = pd.DataFrame(metrics_original_Random_Forest, index=['Original Random Forest'])\n",
    "metrics_df_tuned = pd.DataFrame(metrics_Tuned_Random_Forest, index=['Tuned Random Forest'])\n",
    "\n",
    "# Concatenate the DataFrames vertically to combine the metrics\n",
    "combined_metrics_df = pd.concat([metrics_df_original, metrics_df_tuned])\n",
    "\n",
    "combined_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[630, 110],\n",
       "       [ 97, 643]], dtype=int64)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct the confusion matrix for tuned model\n",
    "confusion_matrix_rf = confusion_matrix(y_test, random_search_rf_pred)\n",
    "\n",
    "confusion_matrix_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(confusion_matrix_rf, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['Churn', 'No Churn'],\n",
    "            yticklabels=['Churn', 'No Churn'])\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 630 instances were correctly classified as True Positivess (TP).\n",
    "- 110 instances were classified as False Positives (FP).\n",
    "- 97 instances were classified as False Negatives (FN).\n",
    "- 643 instances were correctly classified as True Negatives (TN)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Tuning Model 3 (Gradient Boosting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ccp_alpha': 0.0,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'init': None,\n",
       " 'learning_rate': 0.1,\n",
       " 'loss': 'log_loss',\n",
       " 'max_depth': 3,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': 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_iter_no_change': None,\n",
       " 'random_state': 42,\n",
       " 'subsample': 1.0,\n",
       " 'tol': 0.0001,\n",
       " 'validation_fraction': 0.1,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check current model parameters\n",
    "\n",
    "current_params = gb.get_params()\n",
    "current_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.8,\n",
       " 'n_estimators': 200,\n",
       " 'min_samples_split': 8,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_impurity_decrease': 0.2,\n",
       " 'max_features': None,\n",
       " 'max_depth': 5,\n",
       " 'learning_rate': 0.1,\n",
       " 'criterion': 'friedman_mse',\n",
       " 'ccp_alpha': 0.0}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gradient boost tuning\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [None, 50, 100, 200, 300],\n",
    "    'learning_rate': [None, 0.1, 0.2, 0.3, 0.4],\n",
    "    'max_depth': [None, 3, 5, 7, 10],\n",
    "    'subsample': [0.8, 0.9, 1.0, 1.1, 1.2],\n",
    "    'min_samples_split': [2, 3, 5, 8, 10],\n",
    "    'min_samples_leaf': [1, 2, 3, 4, 8],\n",
    "    'max_features': [None, 'sqrt', 'log2'],\n",
    "    'criterion': ['friedman_mse', 'mse', 'mae'],\n",
    "    'min_impurity_decrease': [0.0, 0.1, 0.2],\n",
    "    'ccp_alpha': [0.0, 0.1, 0.2]\n",
    "}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "random_search_gb = RandomizedSearchCV(estimator=gb, param_distributions=param_grid, scoring='accuracy',\n",
    "                                      cv=5,n_jobs=-1, n_iter=150, random_state=42, verbose = 2)\n",
    "\n",
    "# fit best estimator on train data\n",
    "random_search_gb.fit(X_train, y_train)\n",
    "\n",
    "# best parameters\n",
    "best_params = random_search_gb.best_params_\n",
    "\n",
    "# mean accuracy score of the best estimator\n",
    "best_score = random_search_gb.best_score_\n",
    "\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8509125619702542"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean accuracy score of the best estimator\n",
    "\n",
    "best_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit tuned model on train data\n",
    "tuned_gb_model = random_search_gb.best_estimator_\n",
    "tuned_gb_model.fit(X_train, y_train)\n",
    "\n",
    "# make the predictions\n",
    "random_search_gb_pred = tuned_gb_model.predict(X_test)\n",
    "original_gb_model = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Total Precision</th>\n",
       "      <th>Total Recall</th>\n",
       "      <th>Total F1-Score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Original Gradient Boosting</th>\n",
       "      <td>0.842894</td>\n",
       "      <td>0.841892</td>\n",
       "      <td>0.841776</td>\n",
       "      <td>0.841892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Gradient Boosting</th>\n",
       "      <td>0.849149</td>\n",
       "      <td>0.848649</td>\n",
       "      <td>0.848594</td>\n",
       "      <td>0.848649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Total Precision  Total Recall  Total F1-Score  \\\n",
       "Original Gradient Boosting         0.842894      0.841892        0.841776   \n",
       "Tuned Gradient Boosting            0.849149      0.848649        0.848594   \n",
       "\n",
       "                            Accuracy  \n",
       "Original Gradient Boosting  0.841892  \n",
       "Tuned Gradient Boosting     0.848649  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the classification report\n",
    "\n",
    "report = classification_report(y_test, original_gb_model, output_dict=True)\n",
    "report_2 = classification_report(y_test, random_search_gb_pred, output_dict=True)\n",
    "\n",
    "\n",
    "# Extract precision, recall, f1-score, and accuracy metrics for both classes\n",
    "\n",
    "precision = ((report['1']['precision'] + report['0']['precision'])) / 2\n",
    "recall = ((report['1']['recall'] + report['0']['recall'])) / 2\n",
    "f1 = ((report['1']['f1-score'] + report['0']['f1-score'])) / 2\n",
    "\n",
    "precision2 = ((report_2['1']['precision'] + report_2['0']['precision'])) / 2\n",
    "recall2 = ((report_2['1']['recall'] + report_2['0']['recall'])) / 2\n",
    "f12 = ((report_2['1']['f1-score'] + report_2['0']['f1-score'])) / 2\n",
    "\n",
    "# Create dictionaries for metrics\n",
    "metrics_original_GB = {\n",
    "    'Total Precision': precision,\n",
    "    'Total Recall': recall,\n",
    "    'Total F1-Score': f1,\n",
    "    'Accuracy': report['accuracy']\n",
    "}\n",
    "\n",
    "metrics_Tuned_GB = {\n",
    "    'Total Precision': precision2,\n",
    "    'Total Recall': recall2,\n",
    "    'Total F1-Score': f12,\n",
    "    'Accuracy': report_2['accuracy']\n",
    "}\n",
    "\n",
    "# Create DataFrames from the metrics dictionaries\n",
    "metrics_df_original = pd.DataFrame(metrics_original_GB, index=['Original Gradient Boosting'])\n",
    "metrics_df_gb = pd.DataFrame(metrics_Tuned_GB, index=['Tuned Gradient Boosting'])\n",
    "\n",
    "# Concatenate the DataFrames vertically to combine the metrics\n",
    "combined_metrics_df = pd.concat([metrics_df_original, metrics_df_gb])\n",
    "\n",
    "combined_metrics_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[614, 126],\n",
       "       [ 98, 642]], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construct the confusion matrix for tuned model\n",
    "confusion_matrix_gb = confusion_matrix(y_test, random_search_gb_pred)\n",
    "\n",
    "confusion_matrix_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(confusion_matrix_gb, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['Churn', 'No Churn'],\n",
    "            yticklabels=['Churn', 'No Churn'])\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 614 instances were correctly classified as True Positivess (TP).\n",
    "- 126 instances were classified as False Positives (FP).\n",
    "- 98 instances were classified as False Negatives (FN).\n",
    "- 642 instances were correctly classified as True Negatives (TN)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- After tuning all our models, the Random Forest Model has the highest accuracy of 0.860135(**86%**). We will save this model and use it on unseen data to make predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export key components\n",
    "Here is the section to **export** the important ML objects that will be use to develop an app: *Encoder, Scaler, ColumnTransformer, Model, Pipeline, etc*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "destination = \"Toolkit\"\n",
    "\n",
    "#create a directory if it doesn't exist\n",
    "if not os.path.exists(destination):\n",
    "    os.makedirs(destination)\n",
    "\n",
    "#Create a dictionary  to store the object and their filename\n",
    "models ={\n",
    "    \"Random Forest Classifier\":tuned_rf_model,\n",
    "}\n",
    "\n",
    "#Loop through the model and save using joblib.dump()\n",
    "for name,model in models.items():\n",
    "    file_path =os.path.join(destination,f\"{name}.joblib\")\n",
    "    joblib.dump(model,file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing of test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1997 entries, 0 to 1996\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        1997 non-null   object \n",
      " 1   gender            1997 non-null   object \n",
      " 2   SeniorCitizen     1997 non-null   object \n",
      " 3   Partner           1997 non-null   object \n",
      " 4   Dependents        1997 non-null   object \n",
      " 5   tenure            1997 non-null   int64  \n",
      " 6   PhoneService      1997 non-null   object \n",
      " 7   MultipleLines     1997 non-null   object \n",
      " 8   InternetService   1997 non-null   object \n",
      " 9   OnlineSecurity    1997 non-null   object \n",
      " 10  OnlineBackup      1997 non-null   object \n",
      " 11  DeviceProtection  1997 non-null   object \n",
      " 12  TechSupport       1997 non-null   object \n",
      " 13  StreamingTV       1997 non-null   object \n",
      " 14  StreamingMovies   1997 non-null   object \n",
      " 15  Contract          1997 non-null   object \n",
      " 16  PaperlessBilling  1997 non-null   object \n",
      " 17  PaymentMethod     1997 non-null   object \n",
      " 18  MonthlyCharges    1997 non-null   float64\n",
      " 19  TotalCharges      1997 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(17)\n",
      "memory usage: 312.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test = pd.read_csv('Dataset\\Test_Data.csv')\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the CustomerID column before dropping it from the test data\n",
    "customerID = test['customerID']\n",
    "\n",
    "# drop columns from test set\n",
    "\n",
    "test.drop(columns=['gender','PhoneService', 'MultipleLines', 'customerID'],axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>onehotencoder__SeniorCitizen_No</th>\n",
       "      <th>onehotencoder__SeniorCitizen_Yes</th>\n",
       "      <th>onehotencoder__Partner_No</th>\n",
       "      <th>onehotencoder__Partner_Yes</th>\n",
       "      <th>onehotencoder__Dependents_No</th>\n",
       "      <th>onehotencoder__Dependents_Yes</th>\n",
       "      <th>onehotencoder__InternetService_DSL</th>\n",
       "      <th>onehotencoder__InternetService_Fiber optic</th>\n",
       "      <th>onehotencoder__InternetService_No</th>\n",
       "      <th>onehotencoder__OnlineSecurity_No</th>\n",
       "      <th>onehotencoder__OnlineSecurity_No internet service</th>\n",
       "      <th>onehotencoder__OnlineSecurity_Yes</th>\n",
       "      <th>onehotencoder__OnlineBackup_No</th>\n",
       "      <th>onehotencoder__OnlineBackup_No internet service</th>\n",
       "      <th>onehotencoder__OnlineBackup_Yes</th>\n",
       "      <th>onehotencoder__DeviceProtection_No</th>\n",
       "      <th>onehotencoder__DeviceProtection_No internet service</th>\n",
       "      <th>onehotencoder__DeviceProtection_Yes</th>\n",
       "      <th>onehotencoder__TechSupport_No</th>\n",
       "      <th>onehotencoder__TechSupport_No internet service</th>\n",
       "      <th>onehotencoder__TechSupport_Yes</th>\n",
       "      <th>onehotencoder__StreamingTV_No</th>\n",
       "      <th>onehotencoder__StreamingTV_No internet service</th>\n",
       "      <th>onehotencoder__StreamingTV_Yes</th>\n",
       "      <th>onehotencoder__StreamingMovies_No</th>\n",
       "      <th>onehotencoder__StreamingMovies_No internet service</th>\n",
       "      <th>onehotencoder__StreamingMovies_Yes</th>\n",
       "      <th>onehotencoder__Contract_Month-to-month</th>\n",
       "      <th>onehotencoder__Contract_One year</th>\n",
       "      <th>onehotencoder__Contract_Two year</th>\n",
       "      <th>onehotencoder__PaperlessBilling_No</th>\n",
       "      <th>onehotencoder__PaperlessBilling_Yes</th>\n",
       "      <th>onehotencoder__PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>onehotencoder__PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>onehotencoder__PaymentMethod_Electronic check</th>\n",
       "      <th>onehotencoder__PaymentMethod_Mailed check</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>84.45</td>\n",
       "      <td>1059.55</td>\n",
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       "      <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>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>20.40</td>\n",
       "      <td>181.80</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>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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>81.70</td>\n",
       "      <td>2212.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <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",
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       "      <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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>79.50</td>\n",
       "      <td>2180.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>89.15</td>\n",
       "      <td>89.15</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>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>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>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>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenure  MonthlyCharges  TotalCharges  onehotencoder__SeniorCitizen_No  \\\n",
       "0      12           84.45       1059.55                              1.0   \n",
       "1       9           20.40        181.80                              1.0   \n",
       "2      27           81.70       2212.55                              1.0   \n",
       "3      27           79.50       2180.55                              1.0   \n",
       "4       1           89.15         89.15                              1.0   \n",
       "\n",
       "   onehotencoder__SeniorCitizen_Yes  onehotencoder__Partner_No  \\\n",
       "0                               0.0                        1.0   \n",
       "1                               0.0                        1.0   \n",
       "2                               0.0                        1.0   \n",
       "3                               0.0                        1.0   \n",
       "4                               0.0                        0.0   \n",
       "\n",
       "   onehotencoder__Partner_Yes  onehotencoder__Dependents_No  \\\n",
       "0                         0.0                           1.0   \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",
       "   onehotencoder__Dependents_Yes  onehotencoder__InternetService_DSL  \\\n",
       "0                            0.0                                 0.0   \n",
       "1                            0.0                                 0.0   \n",
       "2                            0.0                                 1.0   \n",
       "3                            1.0                                 0.0   \n",
       "4                            1.0                                 0.0   \n",
       "\n",
       "   onehotencoder__InternetService_Fiber optic  \\\n",
       "0                                         1.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         1.0   \n",
       "4                                         1.0   \n",
       "\n",
       "   onehotencoder__InternetService_No  onehotencoder__OnlineSecurity_No  \\\n",
       "0                                0.0                               1.0   \n",
       "1                                1.0                               0.0   \n",
       "2                                0.0                               0.0   \n",
       "3                                0.0                               1.0   \n",
       "4                                0.0                               1.0   \n",
       "\n",
       "   onehotencoder__OnlineSecurity_No internet service  \\\n",
       "0                                                0.0   \n",
       "1                                                1.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "\n",
       "   onehotencoder__OnlineSecurity_Yes  onehotencoder__OnlineBackup_No  \\\n",
       "0                                0.0                             1.0   \n",
       "1                                0.0                             0.0   \n",
       "2                                1.0                             1.0   \n",
       "3                                0.0                             1.0   \n",
       "4                                0.0                             1.0   \n",
       "\n",
       "   onehotencoder__OnlineBackup_No internet service  \\\n",
       "0                                              0.0   \n",
       "1                                              1.0   \n",
       "2                                              0.0   \n",
       "3                                              0.0   \n",
       "4                                              0.0   \n",
       "\n",
       "   onehotencoder__OnlineBackup_Yes  onehotencoder__DeviceProtection_No  \\\n",
       "0                              0.0                                 1.0   \n",
       "1                              0.0                                 0.0   \n",
       "2                              0.0                                 0.0   \n",
       "3                              0.0                                 1.0   \n",
       "4                              0.0                                 1.0   \n",
       "\n",
       "   onehotencoder__DeviceProtection_No internet service  \\\n",
       "0                                                0.0     \n",
       "1                                                1.0     \n",
       "2                                                0.0     \n",
       "3                                                0.0     \n",
       "4                                                0.0     \n",
       "\n",
       "   onehotencoder__DeviceProtection_Yes  onehotencoder__TechSupport_No  \\\n",
       "0                                  0.0                            1.0   \n",
       "1                                  0.0                            0.0   \n",
       "2                                  1.0                            0.0   \n",
       "3                                  0.0                            0.0   \n",
       "4                                  0.0                            1.0   \n",
       "\n",
       "   onehotencoder__TechSupport_No internet service  \\\n",
       "0                                             0.0   \n",
       "1                                             1.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   onehotencoder__TechSupport_Yes  onehotencoder__StreamingTV_No  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             0.0                            0.0   \n",
       "2                             1.0                            0.0   \n",
       "3                             1.0                            1.0   \n",
       "4                             0.0                            0.0   \n",
       "\n",
       "   onehotencoder__StreamingTV_No internet service  \\\n",
       "0                                             0.0   \n",
       "1                                             1.0   \n",
       "2                                             0.0   \n",
       "3                                             0.0   \n",
       "4                                             0.0   \n",
       "\n",
       "   onehotencoder__StreamingTV_Yes  onehotencoder__StreamingMovies_No  \\\n",
       "0                             1.0                                1.0   \n",
       "1                             0.0                                0.0   \n",
       "2                             1.0                                0.0   \n",
       "3                             0.0                                1.0   \n",
       "4                             1.0                                0.0   \n",
       "\n",
       "   onehotencoder__StreamingMovies_No internet service  \\\n",
       "0                                                0.0    \n",
       "1                                                1.0    \n",
       "2                                                0.0    \n",
       "3                                                0.0    \n",
       "4                                                0.0    \n",
       "\n",
       "   onehotencoder__StreamingMovies_Yes  onehotencoder__Contract_Month-to-month  \\\n",
       "0                                 0.0                                     1.0   \n",
       "1                                 0.0                                     1.0   \n",
       "2                                 1.0                                     0.0   \n",
       "3                                 0.0                                     1.0   \n",
       "4                                 1.0                                     1.0   \n",
       "\n",
       "   onehotencoder__Contract_One year  onehotencoder__Contract_Two year  \\\n",
       "0                               0.0                               0.0   \n",
       "1                               0.0                               0.0   \n",
       "2                               1.0                               0.0   \n",
       "3                               0.0                               0.0   \n",
       "4                               0.0                               0.0   \n",
       "\n",
       "   onehotencoder__PaperlessBilling_No  onehotencoder__PaperlessBilling_Yes  \\\n",
       "0                                 0.0                                  1.0   \n",
       "1                                 1.0                                  0.0   \n",
       "2                                 1.0                                  0.0   \n",
       "3                                 0.0                                  1.0   \n",
       "4                                 1.0                                  0.0   \n",
       "\n",
       "   onehotencoder__PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                                0.0        \n",
       "1                                                0.0        \n",
       "2                                                0.0        \n",
       "3                                                0.0        \n",
       "4                                                0.0        \n",
       "\n",
       "   onehotencoder__PaymentMethod_Credit card (automatic)  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   onehotencoder__PaymentMethod_Electronic check  \\\n",
       "0                                            1.0   \n",
       "1                                            0.0   \n",
       "2                                            1.0   \n",
       "3                                            1.0   \n",
       "4                                            0.0   \n",
       "\n",
       "   onehotencoder__PaymentMethod_Mailed check  \n",
       "0                                        0.0  \n",
       "1                                        1.0  \n",
       "2                                        0.0  \n",
       "3                                        0.0  \n",
       "4                                        1.0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding categorical values with One-Hot ecndoder\n",
    "\n",
    "cat_cols = ['SeniorCitizen', 'Partner', 'Dependents',\n",
    "            'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', \n",
    "            'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', \n",
    "            'PaperlessBilling', 'PaymentMethod']\n",
    "\n",
    "transformer = make_column_transformer(\n",
    "    (OneHotEncoder(sparse=False), cat_cols)\n",
    ")\n",
    "\n",
    "transformed_data = transformer.fit_transform(test[cat_cols])\n",
    "\n",
    "# Transformating back to a dataframe\n",
    "transformed_df = pd.DataFrame(transformed_data, columns=transformer.get_feature_names_out())\n",
    "\n",
    "# One-hot encoding removed an index. Let's put it back\n",
    "transformed_df.index = test.index\n",
    "\n",
    "# Joining tables\n",
    "test = pd.concat([test, transformed_df], axis=1)\n",
    "\n",
    "# Dropping old categorical columns\n",
    "test.drop(cat_cols, axis=1, inplace=True)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.columns =test.columns.str.replace('onehotencoder__', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen_No</th>\n",
       "      <th>SeniorCitizen_Yes</th>\n",
       "      <th>Partner_No</th>\n",
       "      <th>Partner_Yes</th>\n",
       "      <th>Dependents_No</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
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       "      <th>OnlineSecurity_No</th>\n",
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       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_No</th>\n",
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       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_No</th>\n",
       "      <th>DeviceProtection_No internet service</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_No</th>\n",
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       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_No</th>\n",
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       "      <th>StreamingMovies_No</th>\n",
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       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>PaperlessBilling_No</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
       "      <th>PaymentMethod_Electronic check</th>\n",
       "      <th>PaymentMethod_Mailed check</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
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       "  </thead>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeniorCitizen_No  SeniorCitizen_Yes  Partner_No  Partner_Yes  \\\n",
       "0               1.0                0.0         1.0          0.0   \n",
       "1               1.0                0.0         1.0          0.0   \n",
       "2               1.0                0.0         1.0          0.0   \n",
       "3               1.0                0.0         1.0          0.0   \n",
       "4               1.0                0.0         0.0          1.0   \n",
       "\n",
       "   Dependents_No  Dependents_Yes  InternetService_DSL  \\\n",
       "0            1.0             0.0                  0.0   \n",
       "1            1.0             0.0                  0.0   \n",
       "2            1.0             0.0                  1.0   \n",
       "3            0.0             1.0                  0.0   \n",
       "4            0.0             1.0                  0.0   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  OnlineSecurity_No  \\\n",
       "0                          1.0                 0.0                1.0   \n",
       "1                          0.0                 1.0                0.0   \n",
       "2                          0.0                 0.0                0.0   \n",
       "3                          1.0                 0.0                1.0   \n",
       "4                          1.0                 0.0                1.0   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  OnlineBackup_No  \\\n",
       "0                                 0.0                 0.0              1.0   \n",
       "1                                 1.0                 0.0              0.0   \n",
       "2                                 0.0                 1.0              1.0   \n",
       "3                                 0.0                 0.0              1.0   \n",
       "4                                 0.0                 0.0              1.0   \n",
       "\n",
       "   OnlineBackup_No internet service  OnlineBackup_Yes  DeviceProtection_No  \\\n",
       "0                               0.0               0.0                  1.0   \n",
       "1                               1.0               0.0                  0.0   \n",
       "2                               0.0               0.0                  0.0   \n",
       "3                               0.0               0.0                  1.0   \n",
       "4                               0.0               0.0                  1.0   \n",
       "\n",
       "   DeviceProtection_No internet service  DeviceProtection_Yes  TechSupport_No  \\\n",
       "0                                   0.0                   0.0             1.0   \n",
       "1                                   1.0                   0.0             0.0   \n",
       "2                                   0.0                   1.0             0.0   \n",
       "3                                   0.0                   0.0             0.0   \n",
       "4                                   0.0                   0.0             1.0   \n",
       "\n",
       "   TechSupport_No internet service  TechSupport_Yes  StreamingTV_No  \\\n",
       "0                              0.0              0.0             0.0   \n",
       "1                              1.0              0.0             0.0   \n",
       "2                              0.0              1.0             0.0   \n",
       "3                              0.0              1.0             1.0   \n",
       "4                              0.0              0.0             0.0   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  StreamingMovies_No  \\\n",
       "0                              0.0              1.0                 1.0   \n",
       "1                              1.0              0.0                 0.0   \n",
       "2                              0.0              1.0                 0.0   \n",
       "3                              0.0              0.0                 1.0   \n",
       "4                              0.0              1.0                 0.0   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                  0.0                  0.0   \n",
       "1                                  1.0                  0.0   \n",
       "2                                  0.0                  1.0   \n",
       "3                                  0.0                  0.0   \n",
       "4                                  0.0                  1.0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                      1.0                0.0                0.0   \n",
       "1                      1.0                0.0                0.0   \n",
       "2                      0.0                1.0                0.0   \n",
       "3                      1.0                0.0                0.0   \n",
       "4                      1.0                0.0                0.0   \n",
       "\n",
       "   PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
       "0                  0.0                   1.0   \n",
       "1                  1.0                   0.0   \n",
       "2                  1.0                   0.0   \n",
       "3                  0.0                   1.0   \n",
       "4                  1.0                   0.0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                    0.0                             1.0   \n",
       "1                                    0.0                             0.0   \n",
       "2                                    0.0                             1.0   \n",
       "3                                    0.0                             1.0   \n",
       "4                                    0.0                             0.0   \n",
       "\n",
       "   PaymentMethod_Mailed check    tenure  MonthlyCharges  TotalCharges  \n",
       "0                         0.0 -0.808517        0.679346     -0.520729  \n",
       "1                         1.0 -0.930399       -1.448044     -0.909246  \n",
       "2                         0.0 -0.199108        0.588006     -0.010377  \n",
       "3                         0.0 -0.199108        0.514934     -0.024542  \n",
       "4                         1.0 -1.255417        0.835454     -0.950256  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling numerical columns\n",
    "\n",
    "cols = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "test_scaled = scaler.fit_transform(test[cols])\n",
    "\n",
    "# Create new DataFrames with the scaled values\n",
    "test_scaled_df = pd.DataFrame(test_scaled, columns=cols, index=test.index)\n",
    "\n",
    "# Drop the original unscaled columns\n",
    "test.drop(cols, axis=1, inplace=True)\n",
    "\n",
    "# Concatenate the scaled columns with the original DataFrames\n",
    "test = pd.concat([test, test_scaled_df], axis=1)\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted_Label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7613-LLQFO</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4568-TTZRT</th>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3801-HMYNL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Predicted_Label\n",
       "customer_id                 \n",
       "7613-LLQFO                 1\n",
       "4568-TTZRT                 0\n",
       "9513-DXHDA                 0\n",
       "2640-PMGFL                 0\n",
       "3801-HMYNL                 1"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the model\n",
    "loaded_model = load('Toolkit\\Random Forest Classifier.joblib')\n",
    "\n",
    "# Predict the labels on the preprocessed test data using the trained model\n",
    "predictions = loaded_model.predict(test)\n",
    "\n",
    "# Create a DataFrame to store the predictions\n",
    "predictions_df = pd.DataFrame(predictions, columns=['Predicted_Label'])\n",
    "\n",
    "predictions_df['customer_id'] = customerID.reset_index(drop=True)\n",
    "\n",
    "predictions_df.set_index('customer_id', inplace=True)\n",
    "\n",
    "predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_df.to_csv('Dataset\\predictions.csv', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Predicted_Label\n",
       "No                 1427\n",
       "Yes                 570\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map the numerical predictions to 'no' and 'yes'\n",
    "predictions_df['Predicted_Label'] = predictions_df['Predicted_Label'].map({0: 'No', 1: 'Yes'})\n",
    "\n",
    "# predictions_df['customerID'] = customerID\n",
    "\n",
    "predictions_df.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions yielded 1427 instances classified as Not Churn and 570 instances classified as Churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_counts = predictions_df['Predicted_Label'].value_counts()\n",
    "\n",
    "# Convert the counts to numeric values\n",
    "count_values = label_counts.values\n",
    " \n",
    "# Create a pie chart using pyplot to visulaize churn rate\n",
    "\n",
    "plt.figure(figsize=(4, 4))\n",
    "\n",
    "plt.pie(count_values, labels=label_counts.index, colors=['LightBlue','LightSkyBlue'], startangle=50, shadow=True, radius=2,\n",
    "     explode=(0,0.2),autopct='%1.0f%%', pctdistance=0.75 ,  )\n",
    "Circle=plt.Circle((0,0), 1, color='white')\n",
    "c= plt.gcf()\n",
    "\n",
    "c.gca().add_artist(Circle)\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that the pie chart is drawn as a circle.\n",
    "plt.title('Churn rate among customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Number of months the customer has stayed with the company (tenure) and the contract term of the customer (contract) are the most important features that have strong correlation with churn of the customer\n",
    "\n",
    "- Vodafone should  enhance Early Customer Experience because in the first 5-10 months,  customer tenure shows a higher churn rate, suggesting that customer experience in the initial stages is vital. Focusing on improving onboarding processes, service quality, and addressing customer concerns during this crucial period with tech support can enhance customer satisfaction and loyalty.\n",
    "\n",
    "- Vodafone should Promote Long-Term Contracts, since the analysis indicates that customers with month-to-month contracts have a significantly higher churn rate compared to those with one-year or two-year contracts. Encouraging customers to opt for longer-term contracts through incentives, benefits, and increased tech support can potentially reduce churn rates and foster customer commitment.\n",
    "\n",
    "- Hyperparameter tuning does not always drasitically improve model performance\n",
    "\n",
    "- With 80/20 train/eval split, the random forest model achieved an accuracy of ~86% after hyperparameter tuning\n",
    "\n",
    "- Ensemble methods perform well on classification tasks, compared to using single classifiers"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
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}