Project 2 - Ensemble Techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f2b35b3",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# <center><span style=\"font-family: Arial; font-weight:bold;font-size:2.4em;color:#FF8C00;\">Project - Ensemble Techniques</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a365ce35",
   "metadata": {},
   "source": [
    "### Library packages version:\n",
    "\n",
    "    numpy - 1.25.1\n",
    "    pandas - 2.0.3\n",
    "    scikit-learn - 1.2.2\n",
    "    scipy - 1.11.1\n",
    "    seaborn - 0.12.2\n",
    "    matplotlib - 3.7.2\n",
    "    matplotlib-inline - 0.1.6\n",
    "    statsmodels - 0.14.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f18966d",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#8B008B;\">Project & Data Information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24005a9f",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:#666666\"> `DOMAIN`: Telecommunications.\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:#666666\"> `CONTEXT`: A telecom company wants to use their historical customer data and leverage machine learning to predict behaviour in an attempt\n",
    "to retain customers. The end goal is to develop focused customer retention programs. \n",
    "\n",
    "        \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:#666666\"> `DATA DESCRIPTION`: The dataset is relevant for understanding customer churn behavior and predicting customer churn. Analyzing customer churn is important for businesses to identify factors that contribute to customer attrition and develop strategies to retain customers.\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:purple\"> Data Dictionary for dataset 'TelcomCustomer-Churn_1':\n",
    "    \n",
    "1. `customerID`: Unique identifier for each customer.\n",
    "2. `gender`: Gender of the customer (e.g., Male or Female).\n",
    "3. `SeniorCitizen`: Indicates if the customer is a senior citizen (1) or not (0).\n",
    "4. `Partner`: Indicates if the customer has a partner (e.g., Yes or No).\n",
    "5. `Dependents`: Indicates if the customer has dependents (e.g., Yes or No).\n",
    "6. `tenure`: Number of months the customer has been with the company.\n",
    "7. `PhoneService`: Indicates if the customer has phone service (e.g., Yes or No).\n",
    "8. `MultipleLines`: Indicates if the customer has multiple lines (e.g., Yes, No, or No phone service).\n",
    "9. `InternetService`: Type of internet service subscribed by the customer (e.g., DSL, Fiber optic, or No).\n",
    "10. `OnlineSecurity`: Indicates if the customer has online security service (e.g., Yes, No, or No internet service).\n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:purple\"> Data Dictionary for dataset 'TelcomCustomer-Churn_2':\n",
    "    \n",
    "1. `customerID`: Unique identifier for each customer.\n",
    "2. `OnlineBackup`: Indicates if the customer has online backup service (e.g., Yes, No, or No internet service).\n",
    "3. `DeviceProtection`: Indicates if the customer has device protection service (e.g., Yes, No, or No internet service).\n",
    "4. `TechSupport`: Indicates if the customer has technical support service (e.g., Yes, No, or No internet service).\n",
    "5. `StreamingTV`: Indicates if the customer has streaming TV service (e.g., Yes, No, or No internet service).\n",
    "6. `StreamingMovies`: Indicates if the customer has streaming movie service (e.g., Yes, No, or No internet service).\n",
    "7. `Contract`: Type of contract the customer has (e.g., Month-to-month, One year, Two year).\n",
    "8. `PaperlessBilling`: Indicates if the customer has opted for paperless billing (e.g., Yes or No).\n",
    "9. `PaymentMethod`: Payment method used by the customer (e.g., Electronic check, Credit card, Bank transfer, Mailed check).\n",
    "10. `MonthlyCharges`: The amount charged to the customer per month (Numeric).\n",
    "11. `TotalCharges`: The total amount charged to the customer (Numeric).\n",
    "12. `Churn`: It is the target variable that indicates whether a customer has churned (e.g., Yes or No).\n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.1em;color:#666666\"> `PROJECT OBJECTIVE`: The objective, as a data scientist hired by the telecom company, is to build a model that will help to identify the potential customers who have a higher probability to churn. This will help the company to understand the pain points and patterns of customer churn and will increase the focus on strategising customer retention.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1523420",
   "metadata": {},
   "source": [
    "### Importing all necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64ecec33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9373330",
   "metadata": {},
   "source": [
    "### <span style=\"font-family: Arial; font-weight:bold;font-size:2.1em;color:#008080;\">1. Data Understanding & Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a709f2",
   "metadata": {},
   "source": [
    "### 1 a. Read ‘TelcomCustomer-Churn_1.csv’ as a DataFrame and assign it to a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86bcd608",
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = pd.read_csv('TelcomCustomer-Churn_1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff783256",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 1 b. Read ‘TelcomCustomer-Churn_2.csv’ as a DataFrame and assign it to a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0b1ce1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "d2 = pd.read_csv('TelcomCustomer-Churn_2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2f36c8",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 1 c. Merge both the DataFrames on key ‘customerID’ to form a single DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "32df8747",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(d1, d2, on='customerID')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adc8404b",
   "metadata": {},
   "source": [
    "### 1 d. Verify if all the columns are incorporated in the merged DataFrame by using simple comparisonOperator in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c8888f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All columns are incorporated in the merged DataFrame: True\n"
     ]
    }
   ],
   "source": [
    "# Get the column names from both original DataFrames\n",
    "columns_d1 = d1.columns\n",
    "columns_d2 = d2.columns\n",
    "\n",
    "# Get the column names from the merged DataFrame\n",
    "columns_merged = df.columns\n",
    "\n",
    "# Check if the columns from both original DataFrames are incorporated in the merged DataFrame\n",
    "all_cols_incorporated = set(columns_d1).union(set(columns_d2)) == set(columns_merged)\n",
    "\n",
    "# Print the result\n",
    "print(\"All columns are incorporated in the merged DataFrame:\", all_cols_incorporated)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b51fdea5",
   "metadata": {},
   "source": [
    "### <span style=\"font-family: Arial; font-weight:bold;font-size:2.1em;color:#008080;\"> 2. Data Cleaning & Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15831b9a",
   "metadata": {},
   "source": [
    "### 2 a. Impute missing/unexpected values in the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66175393",
   "metadata": {},
   "source": [
    "Let's first take a glance at the Dataset and its shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4ccb62c",
   "metadata": {},
   "outputs": [
    {
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       "</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",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>1964-SVLEA</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20</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>415.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3396</th>\n",
       "      <td>5578-NKCXI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>99.25</td>\n",
       "      <td>5846.65</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>6576-FBXOJ</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>17</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>54.60</td>\n",
       "      <td>934.8</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6597</th>\n",
       "      <td>9210-IAHGH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>70.75</td>\n",
       "      <td>450.8</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>2408-TZMJL</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>59</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</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>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>110.15</td>\n",
       "      <td>6448.05</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>8430-TWCBX</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>49</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>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>20.70</td>\n",
       "      <td>1032.05</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>7714-YXSMB</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>100.50</td>\n",
       "      <td>2599.95</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>4430-YHXGG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</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>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.45</td>\n",
       "      <td>84.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>3351-NQLDI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>67</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>94.35</td>\n",
       "      <td>6341.45</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>0362-RAOQO</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>40</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>Credit card (automatic)</td>\n",
       "      <td>19.65</td>\n",
       "      <td>830.25</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "1933  1964-SVLEA    Male              0      No         No      20   \n",
       "3396  5578-NKCXI  Female              0     Yes        Yes      58   \n",
       "6493  6576-FBXOJ    Male              0     Yes         No      17   \n",
       "6597  9210-IAHGH  Female              0      No         No       7   \n",
       "1268  2408-TZMJL    Male              0     Yes         No      59   \n",
       "4362  8430-TWCBX  Female              0     Yes         No      49   \n",
       "3838  7714-YXSMB  Female              0      No         No      26   \n",
       "5952  4430-YHXGG  Female              0      No        Yes       4   \n",
       "2614  3351-NQLDI  Female              0     Yes        Yes      67   \n",
       "2247  0362-RAOQO  Female              0      No         No      40   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
       "1933          Yes                No              No  No internet service   \n",
       "3396          Yes                No     Fiber optic                  Yes   \n",
       "6493           No  No phone service             DSL                   No   \n",
       "6597          Yes                No             DSL                   No   \n",
       "1268          Yes               Yes     Fiber optic                  Yes   \n",
       "4362          Yes                No              No  No internet service   \n",
       "3838          Yes               Yes     Fiber optic                   No   \n",
       "5952          Yes               Yes              No  No internet service   \n",
       "2614          Yes               Yes     Fiber optic                  Yes   \n",
       "2247          Yes                No              No  No internet service   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "1933  No internet service  No internet service  No internet service   \n",
       "3396                  Yes                   No                   No   \n",
       "6493                   No                  Yes                  Yes   \n",
       "6597                   No                   No                  Yes   \n",
       "1268                  Yes                  Yes                   No   \n",
       "4362  No internet service  No internet service  No internet service   \n",
       "3838                   No                  Yes                   No   \n",
       "5952  No internet service  No internet service  No internet service   \n",
       "2614                   No                   No                  Yes   \n",
       "2247  No internet service  No internet service  No internet service   \n",
       "\n",
       "              StreamingTV      StreamingMovies        Contract  \\\n",
       "1933  No internet service  No internet service        One year   \n",
       "3396                  Yes                  Yes        One year   \n",
       "6493                  Yes                  Yes        One year   \n",
       "6597                  Yes                  Yes  Month-to-month   \n",
       "1268                  Yes                  Yes        One year   \n",
       "4362  No internet service  No internet service        One year   \n",
       "3838                  Yes                  Yes  Month-to-month   \n",
       "5952  No internet service  No internet service  Month-to-month   \n",
       "2614                   No                  Yes        One year   \n",
       "2247  No internet service  No internet service        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "1933              Yes               Mailed check           19.70        415.9   \n",
       "3396               No    Credit card (automatic)           99.25      5846.65   \n",
       "6493               No           Electronic check           54.60        934.8   \n",
       "6597              Yes    Credit card (automatic)           70.75        450.8   \n",
       "1268              Yes           Electronic check          110.15      6448.05   \n",
       "4362               No    Credit card (automatic)           20.70      1032.05   \n",
       "3838              Yes  Bank transfer (automatic)          100.50      2599.95   \n",
       "5952              Yes               Mailed check           25.45         84.2   \n",
       "2614              Yes    Credit card (automatic)           94.35      6341.45   \n",
       "2247               No    Credit card (automatic)           19.65       830.25   \n",
       "\n",
       "     Churn  \n",
       "1933    No  \n",
       "3396    No  \n",
       "6493    No  \n",
       "6597   Yes  \n",
       "1268   Yes  \n",
       "4362    No  \n",
       "3838    No  \n",
       "5952    No  \n",
       "2614   Yes  \n",
       "2247    No  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22f11def",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of Rows in Dataset: 7043\n",
      "Total number of Columns in Dataset: 21\n"
     ]
    }
   ],
   "source": [
    "print('Total number of Rows in Dataset:',df.shape[0])\n",
    "print('Total number of Columns in Dataset:',df.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c811dfdc",
   "metadata": {},
   "source": [
    "Now let's look at the overview of the Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e96016e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d228e12e",
   "metadata": {},
   "source": [
    "### Observations -\n",
    "\n",
    "* `Categorical Features`: The majority of the features in the dataset are categorical, represented as objects. These features include 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', and 'Churn'. These features likely have discrete categories describing different attributes of the customers or their services.\n",
    "* `Numerical Features`: There are three numerical features in the dataset: 'SeniorCitizen', 'tenure', and 'MonthlyCharges'. 'SeniorCitizen' is a binary variable, 'tenure' represents the number of months, and 'MonthlyCharges' represents the charges as a continuous numeric value.\n",
    "* `TotalCharges DataType`: The 'TotalCharges' column, which might be expected to contain numeric values, is represented as an object data type. This suggests that there might be some unexpected values or missing values represented as strings in this column.\n",
    "* `Missing Values`: The 'Non-Null Count' information shows that there are no missing values in any of the columns (all columns have 7043 non-null values). However, it's important to consider the possibility that missing values might be encoded as unexpected values in certain columns (e.g., 'TotalCharges')."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179919ea",
   "metadata": {},
   "source": [
    "Let's now look at the number of unique values in column 'TotalCharges'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39b12f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6531"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotalCharges'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6332d5a",
   "metadata": {},
   "source": [
    "As we can see that there are total 6531 uniques values in this columns and so manually looking for the unexpected values individually would not be feasible. Therefore what we can do is to attempt converting the column to a numeric data type using `pd.to_numeric()` and then check for any non-numeric values that could not be converted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e768ec9c",
   "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",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4472-LVYGI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>3115-CZMZD</td>\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>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>20.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>5709-LVOEQ</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>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>80.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>4367-NUYAO</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>25.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>1371-DWPAZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</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>56.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</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",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>2923-ARZLG</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>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>6670</th>\n",
       "      <td>4075-WKNIU</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>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>6754</th>\n",
       "      <td>2775-SEFEE</td>\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": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "488   4472-LVYGI  Female              0     Yes        Yes       0   \n",
       "753   3115-CZMZD    Male              0      No        Yes       0   \n",
       "936   5709-LVOEQ  Female              0     Yes        Yes       0   \n",
       "1082  4367-NUYAO    Male              0     Yes        Yes       0   \n",
       "1340  1371-DWPAZ  Female              0     Yes        Yes       0   \n",
       "3331  7644-OMVMY    Male              0     Yes        Yes       0   \n",
       "3826  3213-VVOLG    Male              0     Yes        Yes       0   \n",
       "4380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
       "5218  2923-ARZLG    Male              0     Yes        Yes       0   \n",
       "6670  4075-WKNIU  Female              0     Yes        Yes       0   \n",
       "6754  2775-SEFEE    Male              0      No        Yes       0   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
       "488            No  No phone service             DSL                  Yes   \n",
       "753           Yes                No              No  No internet service   \n",
       "936           Yes                No             DSL                  Yes   \n",
       "1082          Yes               Yes              No  No internet service   \n",
       "1340           No  No phone service             DSL                  Yes   \n",
       "3331          Yes                No              No  No internet service   \n",
       "3826          Yes               Yes              No  No internet service   \n",
       "4380          Yes                No              No  No internet service   \n",
       "5218          Yes                No              No  No internet service   \n",
       "6670          Yes               Yes             DSL                   No   \n",
       "6754          Yes               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55           NaN    No  \n",
       "753                Mailed check           20.25           NaN    No  \n",
       "936                Mailed check           80.85           NaN    No  \n",
       "1082               Mailed check           25.75           NaN    No  \n",
       "1340    Credit card (automatic)           56.05           NaN    No  \n",
       "3331               Mailed check           19.85           NaN    No  \n",
       "3826               Mailed check           25.35           NaN    No  \n",
       "4380               Mailed check           20.00           NaN    No  \n",
       "5218               Mailed check           19.70           NaN    No  \n",
       "6670               Mailed check           73.35           NaN    No  \n",
       "6754  Bank transfer (automatic)           61.90           NaN    No  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'TotalCharges' to numeric and handle non-numeric values as NaN\n",
    "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n",
    "\n",
    "# Identify rows with non-numeric values in 'TotalCharges' column\n",
    "non_numeric_rows = df[df['TotalCharges'].isnull()]\n",
    "\n",
    "# Display the rows with non-numeric values\n",
    "non_numeric_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba5e71d5",
   "metadata": {},
   "source": [
    "So these are the rows where the column 'TotalCharges' have unexpected values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0c3900",
   "metadata": {},
   "source": [
    "Let's see the percentage of these missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ce41dce",
   "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>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>11</td>\n",
       "      <td>0.156183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Count  Percentage\n",
       "TotalCharges     11    0.156183"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Count':df.isnull().sum()[df.isnull().sum()>0],'Percentage':(df.isnull().sum()[df.isnull().sum()>0]/df.shape[0])*100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1b357e0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "No     5174\n",
       "Yes    1869\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for the Distribution of Target variable\n",
    "df['Churn'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9b60c8",
   "metadata": {},
   "source": [
    "We observe that the distribution of target variable is highly imbalanced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d03c472",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1em;color:#228B22;\"> We can observe that the target column(Churn) datapoints in all the missing rows for column 'TotalCharges' are the same (i.e., 'No' for Churn), therefore using the median value of the 'TotalCharges' column for the class 'No' is a reasonable and better option for imputation. By imputing the missing values with the median value of the 'TotalCharges' column for the 'No' class, we can maintain the consistency of the data and avoid introducing any bias in the data distribution and also ensures that the imputed values are relevant and meaningful in the context of the data.\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1em;color:#228B22;\">`NOTE`: Both imputing the missing values with the mean or median would be reasonable choices but we will go with `Median Imputation` method as it is more robust in the presence of outliers and in a highly imbalanced target column, imputing missing values with the median might be a safer option to avoid affecting the minority class (Churn = 'Yes')."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9610db49",
   "metadata": {},
   "source": [
    "Let's now look at the median value for the column 'TargetCharges' with respect to different class labels of Target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0ad7ec7",
   "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>TotalCharges</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>1683.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>703.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TotalCharges\n",
       "Churn              \n",
       "No          1683.60\n",
       "Yes          703.55"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Churn'])[['TotalCharges']].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ac24d5",
   "metadata": {},
   "source": [
    "Let's now impute the missing values with the median value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc08906d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the median value for each group (Churn = 'Yes' and Churn = 'No')\n",
    "median_by_churn = df.groupby(['Churn'])[['TotalCharges']].transform('median')\n",
    "\n",
    "# Impute missing values with the respective median values based on 'Churn' group\n",
    "df['TotalCharges'].fillna(median_by_churn['TotalCharges'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2485dbe",
   "metadata": {},
   "source": [
    "Let's verify the imputation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8234b1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TotalCharges'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af107225",
   "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",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>4472-LVYGI</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>3115-CZMZD</td>\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>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>20.25</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>5709-LVOEQ</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>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>80.85</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>4367-NUYAO</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>25.75</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>1371-DWPAZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</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>56.05</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</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>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</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>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</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>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>2923-ARZLG</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>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>4075-WKNIU</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>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>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>2775-SEFEE</td>\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>1683.6</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "488   4472-LVYGI  Female              0     Yes        Yes       0   \n",
       "753   3115-CZMZD    Male              0      No        Yes       0   \n",
       "936   5709-LVOEQ  Female              0     Yes        Yes       0   \n",
       "1082  4367-NUYAO    Male              0     Yes        Yes       0   \n",
       "1340  1371-DWPAZ  Female              0     Yes        Yes       0   \n",
       "3331  7644-OMVMY    Male              0     Yes        Yes       0   \n",
       "3826  3213-VVOLG    Male              0     Yes        Yes       0   \n",
       "4380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
       "5218  2923-ARZLG    Male              0     Yes        Yes       0   \n",
       "6670  4075-WKNIU  Female              0     Yes        Yes       0   \n",
       "6754  2775-SEFEE    Male              0      No        Yes       0   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService       OnlineSecurity  \\\n",
       "488            No  No phone service             DSL                  Yes   \n",
       "753           Yes                No              No  No internet service   \n",
       "936           Yes                No             DSL                  Yes   \n",
       "1082          Yes               Yes              No  No internet service   \n",
       "1340           No  No phone service             DSL                  Yes   \n",
       "3331          Yes                No              No  No internet service   \n",
       "3826          Yes               Yes              No  No internet service   \n",
       "4380          Yes                No              No  No internet service   \n",
       "5218          Yes                No              No  No internet service   \n",
       "6670          Yes               Yes             DSL                   No   \n",
       "6754          Yes               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55        1683.6    No  \n",
       "753                Mailed check           20.25        1683.6    No  \n",
       "936                Mailed check           80.85        1683.6    No  \n",
       "1082               Mailed check           25.75        1683.6    No  \n",
       "1340    Credit card (automatic)           56.05        1683.6    No  \n",
       "3331               Mailed check           19.85        1683.6    No  \n",
       "3826               Mailed check           25.35        1683.6    No  \n",
       "4380               Mailed check           20.00        1683.6    No  \n",
       "5218               Mailed check           19.70        1683.6    No  \n",
       "6670               Mailed check           73.35        1683.6    No  \n",
       "6754  Bank transfer (automatic)           61.90        1683.6    No  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind = [488, 753, 936, 1082, 1340, 3331, 3826, 4380, 5218, 6670, 6754]\n",
    "df.iloc[ind]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c574ebed",
   "metadata": {},
   "source": [
    "#### Therefore the missing values have been imputed successfully."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "370de8ab",
   "metadata": {},
   "source": [
    "### 2 b. Make sure all the variables with continuous values are of ‘Float’ type"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46566c75",
   "metadata": {},
   "source": [
    "First lets convert the columns with an 'object' datatype into 'categorical' variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3657a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in df.columns: # Loop through all columns in the dataframe\n",
    "    if df[feature].dtype == 'object': # Only apply for columns with categorical strings\n",
    "        df[feature] = pd.Categorical(df[feature])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e3ef2a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          category\n",
       "gender              category\n",
       "SeniorCitizen          int64\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure                 int64\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float64\n",
       "TotalCharges         float64\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccbc9958",
   "metadata": {},
   "source": [
    "Columns with an 'object' datatype have converted into 'categorical' variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2bc3d8e",
   "metadata": {},
   "source": [
    "We can see that columns `tenure`, `MonthlyCharges` and `TotalCharges` are continuous in nature out of which columns 'MonthlyCharges' and 'TotalCharges' are already of datatype 'float64' except 'tenure' which is of datatype 'int64'. So let's convert column 'tenure' to datatype 'float'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09f5da91",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tenure'] = df['tenure'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5f08431b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          category\n",
       "gender              category\n",
       "SeniorCitizen          int64\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure               float64\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float64\n",
       "TotalCharges         float64\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76712227",
   "metadata": {},
   "source": [
    "#### Therefore all the variables with continuous values are converted to ‘Float’ type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f9e5f3",
   "metadata": {},
   "source": [
    "### 2 c. Create a function that will accept a DataFrame as input and return pie-charts for all the appropriate Categorical features. Clearly show percentage distribution in the pie-chart."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bade182",
   "metadata": {},
   "source": [
    "Let's first drop 'customerID' column from the dataset as it typically represents a unique identifier for each record in the dataset, which does not provide any meaningful information for model prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6fe8075d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('customerID', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c617644",
   "metadata": {},
   "source": [
    "Let's now create a function that will accept a DataFrame as input and return pie-charts for all the appropriate Categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe160a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_categorical_pie_charts(dataframe, ncols=3, colors=None):\n",
    "    # Identify categorical columns\n",
    "    cat_cols = dataframe.select_dtypes(include='category').columns\n",
    "\n",
    "    # Calculate the number of rows needed for subplots\n",
    "    nrows = (len(cat_cols) + ncols - 1) // ncols\n",
    "\n",
    "    # Create subplots with exact number of rows and columns needed\n",
    "    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(14, 5 * nrows), constrained_layout=True)\n",
    "\n",
    "    # Create pie chart for each categorical column\n",
    "    for idx, column in enumerate(cat_cols):\n",
    "        cat_counts = dataframe[column].value_counts()\n",
    "        total_count = len(dataframe)\n",
    "        percentages = cat_counts / total_count * 100\n",
    "\n",
    "        # Plot the pie chart in the appropriate subplot\n",
    "        ax = axes[idx // ncols, idx % ncols]\n",
    "        ax.pie(percentages, labels=cat_counts.index, autopct='%1.1f%%', startangle=140, colors=colors)\n",
    "        ax.set_title(f\"Percentage Distribution of '{column}'\")\n",
    "        ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "    # If there are empty subplots, remove them\n",
    "    if len(cat_cols) < nrows * ncols:\n",
    "        for i in range(len(cat_cols), nrows * ncols):\n",
    "            fig.delaxes(axes.flatten()[i])\n",
    "\n",
    "    # Adjust layout and display the plot\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "06764df8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x3000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "custom_colors = ['#F9C0CB', '#B0E57C', 'beige', '#FFB347']\n",
    "\n",
    "plot_categorical_pie_charts(df, ncols=3, colors=custom_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f53f7a4c",
   "metadata": {},
   "source": [
    "### 2 d. Share insights for Q2.c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab18390c",
   "metadata": {},
   "source": [
    "### Observation -\n",
    "\n",
    "* `Gender`: The number of male customers is slightly higher than female customers, but the difference is not significant.\n",
    "* `Partner and Dependents`: The number of customers without partners is slightly higher than those with partners. However, there are more customers without dependents compared to customers with dependents.\n",
    "* `PhoneService`: The vast majority of customers have phone service, while a small number of customers do not.\n",
    "* `MultipleLines`: Most customers either have a single phone line or multiple lines. A small number of customers have no phone service at all.\n",
    "* `InternetService`: Fiber optic and DSL are the two main types of internet services provided to customers. A significant number of customers have fiber optic internet, followed by DSL, while a smaller group of customers do not have internet service.\n",
    "* `OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies`: For each of these features, a large number of customers do not subscribe to the service. Many customers have 'No internet service,' indicating that they may have not subscribed to internet service at all.\n",
    "* `Contract`: Most customers have month-to-month contracts, followed by two-year contracts and one-year contracts.\n",
    "* `PaperlessBilling`: There are more customers who prefer paperless billing compared to those who don't.\n",
    "* `PaymentMethod`: The electronic check is the most common payment method, followed by mailed check, bank transfer (automatic), and credit card (automatic).\n",
    "* `Churn`: The dataset is imbalanced with a higher number of customers labeled as 'No' (not churned) compared to 'Yes' (churned)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aaf98dc",
   "metadata": {},
   "source": [
    "### 2 e. Encode all the appropriate Categorical features with the best suitable approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37d924f",
   "metadata": {},
   "source": [
    "Let's first look at the number of unique values of all the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "97e94d50",
   "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>Categorical_column</th>\n",
       "      <th>Unique_value_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gender</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Partner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PhoneService</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultipleLines</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>StreamingTV</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>StreamingMovies</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Contract</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PaperlessBilling</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Churn</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Categorical_column  Unique_value_count\n",
       "0              gender                   2\n",
       "1             Partner                   2\n",
       "2          Dependents                   2\n",
       "3        PhoneService                   2\n",
       "4       MultipleLines                   3\n",
       "5     InternetService                   3\n",
       "6      OnlineSecurity                   3\n",
       "7        OnlineBackup                   3\n",
       "8    DeviceProtection                   3\n",
       "9         TechSupport                   3\n",
       "10        StreamingTV                   3\n",
       "11    StreamingMovies                   3\n",
       "12           Contract                   3\n",
       "13   PaperlessBilling                   2\n",
       "14      PaymentMethod                   4\n",
       "15              Churn                   2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list to store column names and corresponding unique value counts\n",
    "column_names = list(df.select_dtypes(include='category').columns)\n",
    "unique_val_counts = []\n",
    "\n",
    "# Iterate through each categorical column and calculate the unique value count\n",
    "for column in column_names:\n",
    "    unique_vals = df[column].nunique()\n",
    "    unique_val_counts.append(unique_vals)\n",
    "\n",
    "# Create a DataFrame to display the results\n",
    "cat_info_df = pd.DataFrame({'Categorical_column': column_names, 'Unique_value_count': unique_val_counts})\n",
    "\n",
    "cat_info_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c9d54bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "replaceStruct = {\n",
    "                \"Contract\":{\"Month-to-month\": 0, \"One year\": 1 ,\"Two year\": 2},\n",
    "                \"gender\":{\"Female\":0, \"Male\":1},\n",
    "                \"Partner\":{\"No\":0, \"Yes\":1},\n",
    "                \"Dependents\":{\"No\":0, \"Yes\":1},\n",
    "                \"PhoneService\":{\"No\":0, \"Yes\":1},\n",
    "                \"PaperlessBilling\":{\"No\":0, \"Yes\":1},\n",
    "                \"Churn\":{\"No\":0, \"Yes\":1}\n",
    "}# Encoding these columns using Label-encoding\n",
    "\n",
    "oneHotCols=[\n",
    "    'MultipleLines',\n",
    "    'InternetService',\n",
    "    'OnlineSecurity',\n",
    "    'OnlineBackup',\n",
    "    'DeviceProtection',\n",
    "    'TechSupport',\n",
    "    'StreamingTV',\n",
    "    'StreamingMovies',\n",
    "    'PaymentMethod'\n",
    "]\n",
    "# Encoding these columns using one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4e87ac01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(replaceStruct)\n",
    "df = pd.get_dummies(df, columns=oneHotCols, drop_first=True)\n",
    "# Argument drop_first = True to handle the issue of Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c863cf01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                   category\n",
       "SeniorCitizen                               int64\n",
       "Partner                                  category\n",
       "Dependents                               category\n",
       "tenure                                    float64\n",
       "PhoneService                             category\n",
       "Contract                                 category\n",
       "PaperlessBilling                         category\n",
       "MonthlyCharges                            float64\n",
       "TotalCharges                              float64\n",
       "Churn                                    category\n",
       "MultipleLines_No phone service               bool\n",
       "MultipleLines_Yes                            bool\n",
       "InternetService_Fiber optic                  bool\n",
       "InternetService_No                           bool\n",
       "OnlineSecurity_No internet service           bool\n",
       "OnlineSecurity_Yes                           bool\n",
       "OnlineBackup_No internet service             bool\n",
       "OnlineBackup_Yes                             bool\n",
       "DeviceProtection_No internet service         bool\n",
       "DeviceProtection_Yes                         bool\n",
       "TechSupport_No internet service              bool\n",
       "TechSupport_Yes                              bool\n",
       "StreamingTV_No internet service              bool\n",
       "StreamingTV_Yes                              bool\n",
       "StreamingMovies_No internet service          bool\n",
       "StreamingMovies_Yes                          bool\n",
       "PaymentMethod_Credit card (automatic)        bool\n",
       "PaymentMethod_Electronic check               bool\n",
       "PaymentMethod_Mailed check                   bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f7411625",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>MultipleLines_No phone service</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
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       "      <td>45.0</td>\n",
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       "      <td>42.30</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  SeniorCitizen Partner Dependents  tenure PhoneService Contract  \\\n",
       "0      0              0       1          0     1.0            0        0   \n",
       "1      1              0       0          0    34.0            1        1   \n",
       "2      1              0       0          0     2.0            1        0   \n",
       "3      1              0       0          0    45.0            0        1   \n",
       "4      0              0       0          0     2.0            1        0   \n",
       "\n",
       "  PaperlessBilling  MonthlyCharges  TotalCharges Churn  \\\n",
       "0                1           29.85         29.85     0   \n",
       "1                0           56.95       1889.50     0   \n",
       "2                1           53.85        108.15     1   \n",
       "3                0           42.30       1840.75     0   \n",
       "4                1           70.70        151.65     1   \n",
       "\n",
       "   MultipleLines_No phone service  MultipleLines_Yes  \\\n",
       "0                            True              False   \n",
       "1                           False              False   \n",
       "2                           False              False   \n",
       "3                            True              False   \n",
       "4                           False              False   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  \\\n",
       "0                        False               False   \n",
       "1                        False               False   \n",
       "2                        False               False   \n",
       "3                        False               False   \n",
       "4                         True               False   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  \\\n",
       "0                               False               False   \n",
       "1                               False                True   \n",
       "2                               False                True   \n",
       "3                               False                True   \n",
       "4                               False               False   \n",
       "\n",
       "   OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "0                             False              True   \n",
       "1                             False             False   \n",
       "2                             False              True   \n",
       "3                             False             False   \n",
       "4                             False             False   \n",
       "\n",
       "   DeviceProtection_No internet service  DeviceProtection_Yes  \\\n",
       "0                                 False                 False   \n",
       "1                                 False                  True   \n",
       "2                                 False                 False   \n",
       "3                                 False                  True   \n",
       "4                                 False                 False   \n",
       "\n",
       "   TechSupport_No internet service  TechSupport_Yes  \\\n",
       "0                            False            False   \n",
       "1                            False            False   \n",
       "2                            False            False   \n",
       "3                            False             True   \n",
       "4                            False            False   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0                            False            False   \n",
       "1                            False            False   \n",
       "2                            False            False   \n",
       "3                            False            False   \n",
       "4                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99b8f96",
   "metadata": {},
   "source": [
    "As we can see that all the Categorical features have been encoded with suitable approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cf715e",
   "metadata": {},
   "source": [
    "### 2 f. Split the data into 80% train and 20% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e6266fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender  SeniorCitizen Partner Dependents  tenure PhoneService Contract  \\\n",
       "0      0              0       1          0     1.0            0        0   \n",
       "1      1              0       0          0    34.0            1        1   \n",
       "2      1              0       0          0     2.0            1        0   \n",
       "3      1              0       0          0    45.0            0        1   \n",
       "4      0              0       0          0     2.0            1        0   \n",
       "\n",
       "  PaperlessBilling  MonthlyCharges  TotalCharges  \\\n",
       "0                1           29.85         29.85   \n",
       "1                0           56.95       1889.50   \n",
       "2                1           53.85        108.15   \n",
       "3                0           42.30       1840.75   \n",
       "4                1           70.70        151.65   \n",
       "\n",
       "   MultipleLines_No phone service  MultipleLines_Yes  \\\n",
       "0                            True              False   \n",
       "1                           False              False   \n",
       "2                           False              False   \n",
       "3                            True              False   \n",
       "4                           False              False   \n",
       "\n",
       "   InternetService_Fiber optic  InternetService_No  \\\n",
       "0                        False               False   \n",
       "1                        False               False   \n",
       "2                        False               False   \n",
       "3                        False               False   \n",
       "4                         True               False   \n",
       "\n",
       "   OnlineSecurity_No internet service  OnlineSecurity_Yes  \\\n",
       "0                               False               False   \n",
       "1                               False                True   \n",
       "2                               False                True   \n",
       "3                               False                True   \n",
       "4                               False               False   \n",
       "\n",
       "   OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
       "0                             False              True   \n",
       "1                             False             False   \n",
       "2                             False              True   \n",
       "3                             False             False   \n",
       "4                             False             False   \n",
       "\n",
       "   DeviceProtection_No internet service  DeviceProtection_Yes  \\\n",
       "0                                 False                 False   \n",
       "1                                 False                  True   \n",
       "2                                 False                 False   \n",
       "3                                 False                  True   \n",
       "4                                 False                 False   \n",
       "\n",
       "   TechSupport_No internet service  TechSupport_Yes  \\\n",
       "0                            False            False   \n",
       "1                            False            False   \n",
       "2                            False            False   \n",
       "3                            False             True   \n",
       "4                            False            False   \n",
       "\n",
       "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
       "0                            False            False   \n",
       "1                            False            False   \n",
       "2                            False            False   \n",
       "3                            False            False   \n",
       "4                            False            False   \n",
       "\n",
       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
       "0                                False                False   \n",
       "1                                False                False   \n",
       "2                                False                False   \n",
       "3                                False                False   \n",
       "4                                False                False   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                  False                            True   \n",
       "1                                  False                           False   \n",
       "2                                  False                           False   \n",
       "3                                  False                           False   \n",
       "4                                  False                            True   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                        True  \n",
       "3                       False  \n",
       "4                       False  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting data into x and y\n",
    "X = df.drop(labels=\"Churn\", axis = 1)\n",
    "y = df[\"Churn\"]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eca93cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split X and y into training and test set in 80:20 ratio\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e472cd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5634, 29)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ab0983",
   "metadata": {},
   "source": [
    "##### We have split the data into train and test set in ratio 80:20."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563a16ed",
   "metadata": {},
   "source": [
    "### 2 g. Normalize/Standardize the data with the best suitable approach"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983fce7f",
   "metadata": {},
   "source": [
    "Let's first look at the basic statistical description of numerical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d34c69c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>7043.0</td>\n",
       "      <td>0.162147</td>\n",
       "      <td>0.368612</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>7043.0</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.000</td>\n",
       "      <td>29.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>72.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>7043.0</td>\n",
       "      <td>64.761692</td>\n",
       "      <td>30.090047</td>\n",
       "      <td>18.25</td>\n",
       "      <td>35.500</td>\n",
       "      <td>70.35</td>\n",
       "      <td>89.85</td>\n",
       "      <td>118.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>7043.0</td>\n",
       "      <td>2282.363808</td>\n",
       "      <td>2265.124074</td>\n",
       "      <td>18.80</td>\n",
       "      <td>402.225</td>\n",
       "      <td>1400.55</td>\n",
       "      <td>3786.60</td>\n",
       "      <td>8684.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count         mean          std    min      25%      50%  \\\n",
       "SeniorCitizen   7043.0     0.162147     0.368612   0.00    0.000     0.00   \n",
       "tenure          7043.0    32.371149    24.559481   0.00    9.000    29.00   \n",
       "MonthlyCharges  7043.0    64.761692    30.090047  18.25   35.500    70.35   \n",
       "TotalCharges    7043.0  2282.363808  2265.124074  18.80  402.225  1400.55   \n",
       "\n",
       "                    75%      max  \n",
       "SeniorCitizen      0.00     1.00  \n",
       "tenure            55.00    72.00  \n",
       "MonthlyCharges    89.85   118.75  \n",
       "TotalCharges    3786.60  8684.80  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03053a4a",
   "metadata": {},
   "source": [
    "Let's look at the boxplot for the columns 'tenure', 'MonthlyCharges' and 'TotalCharges'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "50dbc6ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "num_cols = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "plt.figure(figsize=(15, 12))\n",
    "\n",
    "for i, variable in enumerate(num_cols):\n",
    "    plt.subplot(4, 4, i + 1)\n",
    "    plt.boxplot(df[variable], whis=1.5)\n",
    "    plt.tight_layout()\n",
    "    plt.title(variable)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c553dc5d",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1em;color:saddlebrown;\"> From the above table & visualization the statistics for the features 'tenure', 'MonthlyCharges' and 'TotalCharges' seem within a reasonable range and there are no outliers present in the columns. Therefore we can use `MinMaxScaler` method to scale the features as it preserves the relative relationships between data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b1506e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Min-Max scaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Fit and transform the Min-Max scaler on X_train\n",
    "X_train[num_cols] = scaler.fit_transform(X_train[num_cols])\n",
    "\n",
    "# Fit and transform the Min-Max scaler on X_test\n",
    "X_test[num_cols] = scaler.transform(X_test[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "69dbe65c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>5634.0</td>\n",
       "      <td>0.161874</td>\n",
       "      <td>0.368368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>5634.0</td>\n",
       "      <td>0.448305</td>\n",
       "      <td>0.341045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.402778</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>5634.0</td>\n",
       "      <td>0.463438</td>\n",
       "      <td>0.299851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170817</td>\n",
       "      <td>0.519173</td>\n",
       "      <td>0.713147</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>5634.0</td>\n",
       "      <td>0.260906</td>\n",
       "      <td>0.261354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043154</td>\n",
       "      <td>0.159047</td>\n",
       "      <td>0.434883</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      mean       std  min       25%       50%       75%  \\\n",
       "SeniorCitizen   5634.0  0.161874  0.368368  0.0  0.000000  0.000000  0.000000   \n",
       "tenure          5634.0  0.448305  0.341045  0.0  0.125000  0.402778  0.763889   \n",
       "MonthlyCharges  5634.0  0.463438  0.299851  0.0  0.170817  0.519173  0.713147   \n",
       "TotalCharges    5634.0  0.260906  0.261354  0.0  0.043154  0.159047  0.434883   \n",
       "\n",
       "                max  \n",
       "SeniorCitizen   1.0  \n",
       "tenure          1.0  \n",
       "MonthlyCharges  1.0  \n",
       "TotalCharges    1.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9ed33a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1409.0</td>\n",
       "      <td>0.454775</td>\n",
       "      <td>0.341413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.402778</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>1409.0</td>\n",
       "      <td>0.462570</td>\n",
       "      <td>0.299211</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>0.185259</td>\n",
       "      <td>0.516932</td>\n",
       "      <td>0.713147</td>\n",
       "      <td>1.000996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>1409.0</td>\n",
       "      <td>0.262377</td>\n",
       "      <td>0.261575</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.047063</td>\n",
       "      <td>0.161926</td>\n",
       "      <td>0.434480</td>\n",
       "      <td>0.998575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      mean       std       min       25%       50%  \\\n",
       "SeniorCitizen   1409.0  0.163236  0.369712  0.000000  0.000000  0.000000   \n",
       "tenure          1409.0  0.454775  0.341413  0.000000  0.125000  0.402778   \n",
       "MonthlyCharges  1409.0  0.462570  0.299211  0.004482  0.185259  0.516932   \n",
       "TotalCharges    1409.0  0.262377  0.261575  0.000046  0.047063  0.161926   \n",
       "\n",
       "                     75%       max  \n",
       "SeniorCitizen   0.000000  1.000000  \n",
       "tenure          0.777778  1.000000  \n",
       "MonthlyCharges  0.713147  1.000996  \n",
       "TotalCharges    0.434480  0.998575  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f347b1",
   "metadata": {},
   "source": [
    "##### Therefore we have Normalize/Standardize the data with the best suitable approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4fab79",
   "metadata": {},
   "source": [
    "### <span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#008080;\">3. Model building and Performance improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a361274",
   "metadata": {},
   "source": [
    "Before we build the model let's first creat a function that evaluates the performance metrics for a given classifier model and prints the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0543fc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # Train the model on the scaled training data\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on both training and test data\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # Evaluate the model's performance on the training data\n",
    "    print(\"• Performance on Training Data: \\n\")\n",
    "    print(\"  Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "    print(\"  Precision:\", precision_score(y_train, y_train_pred))\n",
    "    print(\"  Recall:\", recall_score(y_train, y_train_pred))\n",
    "    print(\"  F1 Score:\", f1_score(y_train, y_train_pred))\n",
    "    print(\"  Confusion Matrix:\\n\", confusion_matrix(y_train, y_train_pred),'\\n')\n",
    "\n",
    "    # Evaluate the model's performance on the test data\n",
    "    print(\"\\n• Performance on Test Data: \\n\")\n",
    "    print(\"  Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
    "    print(\"  Precision:\", precision_score(y_test, y_test_pred))\n",
    "    print(\"  Recall:\", recall_score(y_test, y_test_pred))\n",
    "    print(\"  F1 Score:\", f1_score(y_test, y_test_pred),'\\n')\n",
    "\n",
    "    # Print confusion matrix for Test data\n",
    "    print(\"  Confusion Matrix:\")\n",
    "    cm1=metrics.confusion_matrix(y_test, y_test_pred, labels=[0, 1])\n",
    "    df_cm1 = pd.DataFrame(cm1, index = [i for i in [\"No(0)\",\"Yes(1)\"]],\n",
    "                  columns = [i for i in [\"No(0)\",\"Yes(1)\"]])\n",
    "    plt.figure(figsize = (5,4))\n",
    "    sns.heatmap(df_cm1, annot=True ,fmt='g', cmap='Pastel1')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actuals')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21de1dd",
   "metadata": {},
   "source": [
    "### 3 a. Train a model using Decision tree and check the performance of the model on train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cd6b7370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.9980475683351083\n",
      "  Precision: 0.9993386243386243\n",
      "  Recall: 0.9934253780407627\n",
      "  F1 Score: 0.9963732278272337\n",
      "  Confusion Matrix:\n",
      " [[4112    1]\n",
      " [  10 1511]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.7430801987224982\n",
      "  Precision: 0.48372093023255813\n",
      "  Recall: 0.5977011494252874\n",
      "  F1 Score: 0.5347043701799485 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the Decision Tree classifier\n",
    "decision_tree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Evaluate the performance of the Decision Tree model\n",
    "evaluate_model(decision_tree, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e297c82a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3.b Use grid search and improve the performance of the Decision tree model , check the performance of the model on train and test data , provide the differences observed in performance in Q3.a and Q3.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e7f91d2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'criterion': 'gini', 'max_depth': 7, 'min_samples_leaf': 4, 'min_samples_split': 10, 'splitter': 'random'}\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Decision Tree classifier\n",
    "decision_tree = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Define the hyperparameter grid to search over\n",
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'splitter': ['best', 'random'],\n",
    "    'max_depth': list(range(2, 15)),\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': list(range(1, 5))\n",
    "}\n",
    "\n",
    "# Initialize GridSearchCV with the Decision Tree classifier and the hyperparameter grid\n",
    "grid_search = GridSearchCV(decision_tree, param_grid, cv=10, scoring='accuracy')\n",
    "\n",
    "# Perform grid search on the training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and best model\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d43a1c3",
   "metadata": {},
   "source": [
    "##### We have got the best parameter combination so lets now check the performance of the model on train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a9f73212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.8136315228966986\n",
      "  Precision: 0.6928746928746928\n",
      "  Recall: 0.5562130177514792\n",
      "  F1 Score: 0.6170678336980306\n",
      "  Confusion Matrix:\n",
      " [[3738  375]\n",
      " [ 675  846]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.794889992902768\n",
      "  Precision: 0.5896656534954408\n",
      "  Recall: 0.5574712643678161\n",
      "  F1 Score: 0.5731166912850814 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_decision_tree = DecisionTreeClassifier(\n",
    "    criterion='gini',\n",
    "    max_depth=7,\n",
    "    min_samples_leaf=4,\n",
    "    min_samples_split=10,\n",
    "    splitter='random',\n",
    "    random_state=1\n",
    ")\n",
    "\n",
    "# Evaluate the performance of the Decision Tree model with best parameter combination\n",
    "evaluate_model(best_decision_tree, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba8367e",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> Differences Observed in Performance:\n",
    "\n",
    "* `Overfitting`: The default Decision Tree model showed signs of overfitting, with significantly higher accuracy, precision, and recall on the training data compared to the test data. In contrast, the model with hyper-parameter tuning (Grid Search) improved generalization to some extent by reducing the overfitting.\n",
    "* `Model Complexity`: The default model's high accuracy on the training data might be due to its high complexity, which allowed it to memorize the training data. However, this complexity did not translate well to the test data, resulting in lower performance on unseen data. The model with grid search used more appropriate hyper parameters, which reduced the complexity and improved generalization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53da0cb1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 c. Train a model using Random forest and check the performance of the model on train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f0cc553a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.9980475683351083\n",
      "  Precision: 0.997364953886693\n",
      "  Recall: 0.9953977646285339\n",
      "  F1 Score: 0.9963803882856204\n",
      "  Confusion Matrix:\n",
      " [[4109    4]\n",
      " [   7 1514]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.7991483321504613\n",
      "  Precision: 0.6031746031746031\n",
      "  Recall: 0.5459770114942529\n",
      "  F1 Score: 0.5731523378582203 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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WxqeffkpGRgaLFi1i3bp1bNiwgYsXL1KlShWaNm3KnDlz6Ny5My5/cQw/ISGBkSNHWrXl5eWVdvm3FU9PTzw9PcnJySEtLY2hQ4ded2xRUZHl769+/fq4u7tz+PBhmjRpAkBBQQHHjx+natWqZVG6lBNXwi791O/MG/Qk/sU4PbH7t2M4OzkR4K1TGVIy7H7RSs2aNRk1ahSjRo26ofU9PDyuejO6nrRyWVpaGmazmVq1anHkyBGmTZtG7dq1efjhh7l48SLvvPMObdu2pUqVKmRnZ/PJJ59w8uRJoqKiAPD29iY2NpbZs2djMBgwGAy89957AJYxIgDnTSYyTv3f0/ePnj7Nrt+O4VexIoG+PoyY/z67fjvGm/3jKCwyczLn8n+jfhU9cXd1ZdvhdH5JP8I9de7Aq4IHPx/OYPLnKTzYrCl+FSvaa7eknLF74P3ZlfNGTk5Odq7k9pebm8uMGTM4ceIEvr6+tG/fnvj4eFxdXSksLOTw4cOkpKSQnZ2Nn58fERERzJkzhzvvvNOyjWHDhuHi4sK4ceMwmUw0aNCAWbNm6XYRsbLjyFGemDnH8jnp8y8B6HL3XcRHR/Htjl0AxE61Phc/b9CT3FPnTtxdXVn+08/MXPk1eQUFVKscQN+29xJ3X5uy2wkp926Z9+G9++67TJkyhX379gFQr149xowZQ58+fWzelmZ4Ulb0LE0pK3qW5s27JWZ4r732GmPHjmXw4MG0bt0auPw4q6effppTp04xYsQIO1coIiK3u1si8KZPn86sWbPo27evpe3hhx+mQYMGjB8/XoEnIiI3ze4vgAU4fvw4rVq1uqq9VatWf3mfnoiISHHdEoFXp04dPvnkk6vaP/74Y+rWrWuHikREpCTUrl0bJyenq5b4+Hjg8nN/4+PjqVy5suXK8KysLKttZGRkEBMTQ8WKFQkKCmLMmDEU3MADCW6JQ5ovvfQS//znP1m7dq3lHN7333/P6tWrrxmEIiJye9i8eTOFhYWWz7/++iv3338/jzzyCHD5ofVffvklixYtws/Pj8GDB9O9e3e+//574PJzgWNiYjAYDGzYsIHjx4/Tt29f3NzcmDhxok213DJXaW7dupXXXnuN3bt3AxAeHs6oUaNo2rSpzdvSVZpSVnSVppSV8nKV5vDhw0lJSWHfvn3k5OQQGBjIwoUL6dGjB3D5ofXh4eGkpaXRsmVLli9fzoMPPsixY8csr5BLTk7m2Wef5eTJk1c9IP+v3BIzPIBmzZrxwQcf2LsMERH5GyaT6apnFl/rISB/lpeXx/vvv8/IkSNxcnJi69at5OfnWz3Ion79+tSsWdMSeGlpaURGRlrCDiA6OpqBAweyY8cOmyZFdj2H5+zsjIuLy18uf34TuoiI2FdiYiJ+fn5WS2Ji4t+ut3TpUrKzs3n88ccByMzMxN3d3fKGliuCg4Mtz+vNzMy0Crsr/Vf6bGHXNFmyZMl1+9LS0pg2bRpFRUVlWJGIiPydaz3D+O9mdwBz586lc+fOhISElFZpf8mugdelS5er2vbs2cNzzz3HF198Qe/evS2vuBERkVtDcQ5f/ll6ejpff/01n332maXNYDCQl5dHdna21SwvKysLg8FgGfPDDz9YbevKVZxXxhTXLXFbAsCxY8d48skniYyMpKCggG3btrFgwQJq1apl79JEROQmzZs3j6CgIGJiYixtzZo1w83NjdWrV1va9uzZQ0ZGBkajEQCj0cj27ds5ceKEZUxqaiq+vr5ERETYVIPdT5CdPXuWiRMnMn36dJo0acLq1atp00YPjBURKS+KioqYN28ecXFxVtdl+Pn50b9/f0aOHElAQAC+vr4MGTIEo9FIy5YtAejYsSMRERH06dOHpKQkMjMzeeGFF4iPj7d5lmnXwEtKSmLy5MkYDAY+/PDDax7iFBGR29vXX39NRkYG/fr1u6rv9ddfx9nZmdjYWEwmE9HR0cycOdPS7+LiQkpKCgMHDsRoNOLl5UVcXNwNne6y6314zs7OeHp6EhUV9Zcvef3jMd/i0H14UlZ0H56UlfJyH5492XWG17dvX733TkREyoRdA2/+/Pn2/HoREXEgt8xVmiIiIqVJgSciIg5BgSciIg5BgSciIg7B7jeei4hI2QsJ87N3CWVOMzwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwREXEICjwRESlVv/32G//617+oXLkynp6eREZGsmXLFku/2Wxm3LhxVK1aFU9PT6Kioti3b5/VNk6fPk3v3r3x9fXF39+f/v37k5uba1MdCjwRESk1Z86coXXr1ri5ubF8+XJ27tzJ1KlTqVSpkmVMUlIS06ZNIzk5mU2bNuHl5UV0dDSXLl2yjOnduzc7duwgNTWVlJQU1q5dy1NPPWVTLU5ms9lcYnt2izh37py9SxAH4bn2a3uXIA7CNaZbCW9x602s26zYI5977jm+//571q1bd81+s9lMSEgIo0aNYvTo0QCcPXuW4OBg5s+fT69evdi1axcRERFs3ryZ5s2bA7BixQoeeOABjh49SkhISLFq0QxPRERsYjKZyMnJsVpMJtM1xy5btozmzZvzyCOPEBQURNOmTZkzZ46l/9ChQ2RmZhIVFWVp8/Pzo0WLFqSlpQGQlpaGv7+/JewAoqKicHZ2ZtOmTcWuW4EnIiI2SUxMxM/Pz2pJTEy85tiDBw8ya9Ys6taty8qVKxk4cCBDhw5lwYIFAGRmZgIQHBxstV5wcLClLzMzk6CgIKt+V1dXAgICLGOKw7XYI0VERICEhARGjhxp1ebh4XHNsUVFRTRv3pyJEycC0LRpU3799VeSk5OJi4sr9Vr/SDM8ERGxiYeHB76+vlbL9QKvatWqREREWLWFh4eTkZEBgMFgACArK8tqTFZWlqXPYDBw4sQJq/6CggJOnz5tGVMcJRJ42dnZJbEZEREpZ1q3bs2ePXus2vbu3UutWrUACA0NxWAwsHr1akt/Tk4OmzZtwmg0AmA0GsnOzmbr1v+70Oabb76hqKiIFi1aFLsWmwNv8uTJfPzxx5bPPXv2pHLlylSrVo2ff/7Z1s2JiEg5NmLECDZu3MjEiRPZv38/CxcuZPbs2cTHxwPg5OTE8OHDeeWVV1i2bBnbt2+nb9++hISE0LVrV+DyjLBTp048+eST/PDDD3z//fcMHjyYXr16FfsKTbiBwEtOTqZGjRoApKamkpqayvLly+ncuTNjxoyxdXMiIlKO3X333SxZsoQPP/yQhg0bMmHCBN544w169+5tGfPMM88wZMgQnnrqKe6++25yc3NZsWIFFSpUsIz54IMPqF+/Ph06dOCBBx7g3nvvZfbs2TbVYvN9eJ6enuzdu5caNWowbNgwLl26xFtvvcXevXtp0aIFZ86csamA0qD78KSs6D48KSu36314txKbZ3iVKlXiyJEjwOUb/67cO2E2myksLCzZ6kREREqIzbcldO/enccee4y6devy+++/07lzZwB++ukn6tSpU+IFioiIlASbA+/111+ndu3aHDlyhKSkJLy9vQE4fvw4gwYNKvECRURESoKepSlyE3QOT8pKSZ/DM+/98IbXdar3aAlWUnaKNcNbtmxZsTf48MMP33AxIiIipaVYgXflXoi/4+TkpAtXRETkllSswCsqKirtOkREREqVnqUpIiIO4YbelnD+/HnWrFlDRkYGeXl5Vn1Dhw4tkcJERERKks2B99NPP/HAAw9w4cIFzp8/T0BAAKdOnaJixYoEBQUp8ERE5JZk8yHNESNG8NBDD3HmzBk8PT3ZuHEj6enpNGvWjP/+97+lUaOIiMhNsznwtm3bxqhRo3B2dsbFxQWTyUSNGjVISkri+eefL40aRUREbprNgefm5oaz8+XVgoKCLC/x8/PzszxjU0RE5FZj8zm8pk2bsnnzZurWrcs//vEPxo0bx6lTp3jvvfdo2LBhadQoIiJy02ye4U2cOJGqVasC8Oqrr1KpUiUGDhzIyZMnbX43kYiISFmxeYbXvHlzy5+DgoJYsWJFiRYkIiJSGnTjuYiIOASbZ3ihoaE4OTldt//gwYM3VZCIiEhpsDnwhg8fbvU5Pz+fn376iRUrVjBmzJiSqktERKRE2Rx4w4YNu2b7m2++yZYtW266IBERkdJQYi+APXjwIE2aNCEnJ6ckNndTCr5cYu8SRERKlF4Ae/NK7KKVxYsXExAQUFKbExERKVE3dOP5Hy9aMZvNZGZmcvLkSWbOnFmixYmIiJQUmwOvS5cuVoHn7OxMYGAg9913H/Xr1y/R4kREREqKzYE3fvz4UihDRESkdNl8Ds/FxYUTJ05c1f7777/j4uJSIkWJiIiUNJsD73oXdZpMJtzd3W+6IBERkdJQ7EOa06ZNA8DJyYm3334bb29vS19hYSFr167VOTwREbEyfvx4XnrpJau2sLAwdu/eDcClS5cYNWoUH330ESaTiejoaGbOnElwcLBlfEZGBgMHDuTbb7/F29ubuLg4EhMTcXW17axcsUe//vrrwOUZXnJystXhS3d3d2rXrk1ycrJNXy4iIuVfgwYN+Prrry2f/xhUI0aM4Msvv2TRokX4+fkxePBgunfvzvfffw9cnlDFxMRgMBjYsGEDx48fp2/fvri5uTFx4kSb6ih24B06dAiAdu3a8dlnn1GpUiWbvkhERByTq6srBoPhqvazZ88yd+5cFi5cSPv27QGYN28e4eHhbNy4kZYtW7Jq1Sp27tzJ119/TXBwME2aNGHChAk8++yzjB8/3qZTaTafw/v2228VdiIiDsxkMpGTk2O1mEym647ft28fISEh3HHHHfTu3ZuMjAwAtm7dSn5+PlFRUZax9evXp2bNmqSlpQGQlpZGZGSk1SHO6OhocnJy2LFjh0112xx4sbGxTJ48+ar2pKQkHnnkEVs3JyIit5nExET8/PyslsTExGuObdGiBfPnz2fFihXMmjWLQ4cO0aZNG86dO0dmZibu7u74+/tbrRMcHExmZiYAmZmZVmF3pf9Kny1svg9v7dq117wXr3PnzkydOtXWzYmIyG0mISGBkSNHWrV5eHhcc2znzp0tf27UqBEtWrSgVq1afPLJJ3h6epZqnX9m8wwvNzf3msdM3dzcbokHR4uISOny8PDA19fXarle4P2Zv78/9erVY//+/RgMBvLy8sjOzrYak5WVZTnnZzAYyMrKuqr/Sp8tbA68yMhIPv7446vaP/roIyIiImzdnIiIOJDc3FwOHDhA1apVadasGW5ubqxevdrSv2fPHjIyMjAajQAYjUa2b99u9cCT1NRUfH19bc4cmw9pjh07lu7du3PgwAHLVTWrV69m4cKFLF682NbNiYhIOTZ69GgeeughatWqxbFjx3jxxRdxcXHh0Ucfxc/Pj/79+zNy5EgCAgLw9fVlyJAhGI1GWrZsCUDHjh2JiIigT58+JCUlkZmZyQsvvEB8fHyxZ5VX2Bx4Dz30EEuXLmXixIksXrwYT09PGjduzDfffKPXA4mIiJWjR4/y6KOP8vvvvxMYGMi9997Lxo0bCQwMBC7f4+3s7ExsbKzVjedXuLi4kJKSwsCBAzEajXh5eREXF8fLL79scy03/QLYnJwcPvzwQ+bOncvWrVspLCy8mc2VCL0AVkTKm5J+AeyhLb/d8LqhzauVYCVl54ZfALt27Vri4uIICQlh6tSptG/fno0bN5ZkbSIiIiXGpkOamZmZzJ8/n7lz55KTk0PPnj0xmUwsXbpUF6yIiMgtrdgzvIceeoiwsDB++eUX3njjDY4dO8b06dNLszYREZESU+wZ3vLlyxk6dCgDBw6kbt26pVmTiIhIiSv2DG/9+vWcO3eOZs2a0aJFC2bMmMGpU6dKszYREZESU+zAa9myJXPmzOH48eMMGDCAjz76iJCQEIqKikhNTeXcuXOlWaeIiMhNsfkqTS8vL/r168f69evZvn07o0aNYtKkSQQFBfHwww+XRo0iIiI37YZvS4DLb61NSkri6NGjfPjhhyVVk4iISIm7qcC7wsXFha5du7Js2bKS2JyIiEiJK5HAExERudUp8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8EREpExMmjQJJycnhg8fbmm7dOkS8fHxVK5cGW9vb2JjY8nKyrJaLyMjg5iYGCpWrEhQUBBjxoyhoKDA5u9X4ImISKnbvHkzb731Fo0aNbJqHzFiBF988QWLFi1izZo1HDt2jO7du1v6CwsLiYmJIS8vjw0bNrBgwQLmz5/PuHHjbK5BgSciIqUqNzeX3r17M2fOHCpVqmRpP3v2LHPnzuW1116jffv2NGvWjHnz5rFhwwY2btwIwKpVq9i5cyfvv/8+TZo0oXPnzkyYMIE333yTvLw8m+pQ4ImIiE1MJhM5OTlWi8lkuu74+Ph4YmJiiIqKsmrfunUr+fn5Vu3169enZs2apKWlAZCWlkZkZCTBwcGWMdHR0eTk5LBjxw6b6lbgiYiITRITE/Hz87NaEhMTrzn2o48+4scff7xmf2ZmJu7u7vj7+1u1BwcHk5mZaRnzx7C70n+lzxauNo0WERGHl5CQwMiRI63aPDw8rhp35MgRhg0bRmpqKhUqVCir8q5LgSci4oBO106/4XVDPapdM+D+bOvWrZw4cYK77rrL0lZYWMjatWuZMWMGK1euJC8vj+zsbKtZXlZWFgaDAQCDwcAPP/xgtd0rV3FeGVNcOqQpIiKlokOHDmzfvp1t27ZZlubNm9O7d2/Ln93c3Fi9erVlnT179pCRkYHRaATAaDSyfft2Tpw4YRmTmpqKr68vERERNtWjGZ6IiJQKHx8fGjZsaNXm5eVF5cqVLe39+/dn5MiRBAQE4Ovry5AhQzAajbRs2RKAjh07EhERQZ8+fUhKSiIzM5MXXniB+Pj4Ys0y/0iBJyIidvP666/j7OxMbGwsJpOJ6OhoZs6cael3cXEhJSWFgQMHYjQa8fLyIi4ujpdfftnm73Iym83mkiz+VlDw5RJ7lyAiUqJcY7qV6Pa2ntpww+s2q9KqBCspOzqHJyIiDkGBJyIiDsGu5/CKiopYs2YN69atIz09nQsXLhAYGEjTpk2JioqiRo0a9ixPRETKEbvM8C5evMgrr7xCjRo1eOCBB1i+fDnZ2dm4uLiwf/9+XnzxRUJDQ3nggQcsz1MTERG5GXaZ4dWrVw+j0cicOXO4//77cXNzu2pMeno6CxcupFevXvznP//hySeftEOlIiJSXtjlKs1du3YRHh5erLH5+flkZGRw5513Fnv7ukpTRMobXaV58+xySLO4YQfg5uZmU9iJiIhcyy17leb58+dZu3atvcsQEZFy4pYNvP3799OuXTt7lyEiIuXELRt4IiIiJclu9+EFBAT8ZX9hYWEZVSIiIo7AboFnMpkYOHAgkZGR1+xPT0/npZdeKuOqRESkvLJb4DVp0oQaNWoQFxd3zf6ff/5ZgSciIiXGbufwYmJiyM7Ovm5/QEAAffv2LbuCRESkXNPrgUREbgO68fzm6QWw5diWAwd559u17Dz6GydzzjHtiT50iGxg6X/+w0/4fPOPVuu0DqvH7AH9LJ8PnzjJf7/4ip8Op5NfUEi9EANDOnWkRV09DED+z9/91k6dO8drKcvZsGcf5y5eotkdofyn+8PUCqxiGWPKzydp2Zcs/+kX8goKaB1Wl7E9ulLFx8ceuyTlkF0OadryQOgLFy6wY8eOUqym/LqYl09YSFVe6N7lumPurV+P78b/x7JM6dPLqn/Q3AUUFhXxzsAnWTRyCGEhVYmfO5+TOedKu3y5jfzVb81sNjP0nfc4+vtppvfry+JRQwmp5E//5Le5YMqzjJv8eQrf7djFa3GPsSD+KU7mnGPYvPfLcjeknLNL4PXp04fo6GgWLVrE+fPnrzlm586dPP/889x5551s3bq1jCssH9qEhzHsgWiiGjW87hh3V1cCfX0si1/Fipa+M7nnST95in93uI+wkKrUCqzCyJjOXMzLZ39mZlnsgtwm/uq3ln7yFD+nZzCuRzcia9YgNCiQcT26YsrP56uftgFw7uIlPt20hWe6PEjLunVoUKM6r/TqwbbD6fx8OKOM90bKK7sc0ty5cyezZs3ihRde4LHHHqNevXqEhIRQoUIFzpw5w+7du8nNzaVbt26sWrXqurcuyM3bvP8gbcZNwNfTkxZ172Ro5474e3kB4O9VkdCgQD7f/CPh1arh7urCJ2mbqOztTUT1anauXG4XeQWX76l1d/2/f26cnZ1xd3Xlx0OH6dHyHnYcPUpBYSHGenUsY+4IDqJqJX+2pafTuHbNMq9byh+7BJ6bmxtDhw5l6NChbNmyhfXr15Oens7Fixdp3LgxI0aMoF27dn97c7rcnHvrhxEV2ZDqAQEc+f133vhqJQNmz2PhsEG4ODvj5OTE20//m6HvvMs9z7+Is5MTAd5evPXUE1YzQZG/EhocSNVK/rzx5QpefKQbnu7uvLtmPZnZZy2Hxk/l5OLm4oKvp6fVupW9vTmVk2uPsqUcsvtFK82bN6d58+Y3vL7JZMJkMlm1ueTn43GNd+yJtQeaNrb8uV6IgXohBjq9OoXN+w/Ssl4dzGYzr3y6lABvb94dPIAKbm4s3riZ+LkL+HjEYAJ9fe1Yvdwu3Fxc+N/j/2Lsx5/S6oWXcXF2pmXdOrSpH4aZcneRuNzC7P4szYsXL3LhwgXL5/T0dN544w1WrlxZrPUTExPx8/OzWiZ/8mlplVuu1ahcmUpeXmSc+h2ATfsOsGbnbv7b91HuCq1NRPVqjOvRFQ83N5b+6epOkb/SoEZ1Phs9jI2vjue78c8ze0A/si9coHrly0dxqvh6k19YSM7Fi1br/Z6bSxVfb3uULOWQ3QOvS5cuvPvuuwBkZ2dzzz33MHXqVLp27cqsWbP+dv2EhATOnj1rtTzbM7a0yy6XMrPPkn3hAlV8L18GfjH/8hV0Tk5OVuOcnZwoh7dvShnw8axAgLc36SdPsePIUdo3jACgQfXquLq4sHHvfsvYQydOcvxMNk1q1bJXuVLO2P2Q5o8//sjrr78OwOLFizEYDPz00098+umnjBs3joEDB/7l+h4eHnh4eFi1FehwJgDnTSbLbA3g6OnT7PrtGH4VK+JX0ZNZK1dzf6OGVPH15sip00xNWU7NKpW5t349AJrUqoVvRU+eX/gJAzt2sBzSPHr6DG3Dw+y1W3IL+qvfWkglf1Zu+4VK3l5UreTPvuOZJC75gvYNI2gddvm35uNZgdgWzUla9iV+FSviXcGDiUuW0aR2TV2wIiXG7oF34cIFfP7/jaWrVq2ie/fuODs707JlS9LT0+1c3e1tx5GjPDFzjuVz0udfAtDl7rsYF9uNPceP8/mWreRcvESQrw+twuoxpPP9lqvpKnl78dZT/fjfVyvpN+ttCgoLqWMIZka/vtSvFmKXfZJb01/91iY+2pOTOedIWvYlp87lEujrw8PN7+Lp+9tbbePZLg/i5OTE8Pnvk19YQOuwerwQ27Usd0PKObs/WqxRo0b8+9//plu3bjRs2JAVK1ZgNBrZunUrMTExZN7A/V56tJiIlDd6tNjNs/s5vHHjxjF69Ghq167NPffcg9FoBC7P9po2bWrn6kREpLywe+D16NGDjIwMtmzZYnVlZocOHSzn9kRE5PY0a9YsGjVqhK+vL76+vhiNRpYvX27pv3TpEvHx8VSuXBlvb29iY2PJysqy2kZGRgYxMTFUrFiRoKAgxowZQ0FBgc212D3wAAwGAz4+PqSmpnLx/1+WfPfdd1O/fn07VyYiIjejevXqTJo0ia1bt7Jlyxbat29Ply5dLM9IHjFiBF988QWLFi1izZo1HDt2jO7du1vWLywsJCYmhry8PDZs2MCCBQuYP38+48aNs7kWu5/D+/333+nZsyfffvstTk5O7Nu3jzvuuIN+/fpRqVIlpk6davM2dQ5PRMqbkj6Hl/Lj0Rte98G7qt/UdwcEBDBlyhR69OhBYGAgCxcupEePHgDs3r2b8PBw0tLSaNmyJcuXL+fBBx/k2LFjBAcHA5CcnMyzzz7LyZMncXd3L/b32n2GN2LECNzc3MjIyKDiHx5X9c9//pMVK1bYsTIREbkWk8lETk6O1fLnJ15dS2FhIR999BHnz5+3XJyYn59PVFSUZUz9+vWpWbMmaWlpAKSlpREZGWkJO4Do6GhycnJsfpOO3QNv1apVTJ48merVrf8fQ926dXVbgojILehaT7hKTEy87vjt27fj7e2Nh4cHTz/9NEuWLCEiIoLMzEzc3d3x9/e3Gh8cHGy5Qj8zM9Mq7K70X+mzhd3vwzt//rzVzO6K06dPX3VDuYiI2F9CQgIjR460avurf6/DwsLYtm0bZ8+eZfHixcTFxbFmzZrSLvMqdpvhHTt2DIA2bdpYHi0Glx9jVVRURFJSEu3atbNXeSIich0eHh6Wqy6vLH8VeO7u7tSpU4dmzZqRmJhI48aN+d///ofBYCAvL4/s7Gyr8VlZWRgMBuDyRY1/vmrzyucrY4rLboHXoEEDFi5cyJQpU5g9ezadO3cmLy+PZ555hoYNG7J27VomT55sr/JERKSUFBUVYTKZaNasGW5ubqxevdrSt2fPHjIyMiz3ZBuNRrZv386JEycsY1JTU/H19SUiIsKm77XbIc1XX32VAQMG0KlTJ3bu3ElycjI+Pj7k5ubSvXt34uPjqVq1qr3KExGREpCQkEDnzp2pWbMm586dY+HChXz33XesXLkSPz8/+vfvz8iRIwkICMDX15chQ4ZgNBpp2bIlAB07diQiIoI+ffqQlJREZmYmL7zwAvHx8Taf9rJb4A0aNIjOnTvTv39/GjRowOzZs/nPf/5jr3JERKQUnDhxgr59+3L8+HH8/Pxo1KgRK1eu5P777wfg9ddfx9nZmdjYWEwmE9HR0cycOdOyvouLCykpKQwcOBCj0YiXlxdxcXG8/PLLNtdi9/vwAGbMmMGIESMIDw/H1dU6g3/80fb3ruk+PBEpb8rTfXj2YverNNPT0/nss8+oVKkSXbp0uSrwRERESoJd02XOnDmMGjWKqKgoduzYQWBgoD3LERGRcsxugdepUyd++OEHZsyYQd++fe1VhoiIOAi7BV5hYSG//PLLVU9YERERKQ12C7zU1FR7fbWIiDgguz9LU0REpCwo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCEo8ERExCG42rsAEREpe52Ob76JtauXWB1lSTM8EREpNYmJidx99934+PgQFBRE165d2bNnj9WYS5cuER8fT+XKlfH29iY2NpasrCyrMRkZGcTExFCxYkWCgoIYM2YMBQUFNtWiwBMRkVKzZs0a4uPj2bhxI6mpqeTn59OxY0fOnz9vGTNixAi++OILFi1axJo1azh27Bjdu3e39BcWFhITE0NeXh4bNmxgwYIFzJ8/n3HjxtlUi5PZbDaX2J7dIgq+XGLvEkRESpRrTLcS3d7N/Dt5M7WcPHmSoKAg1qxZQ9u2bTl79iyBgYEsXLiQHj16ALB7927Cw8NJS0ujZcuWLF++nAcffJBjx44RHBwMQHJyMs8++ywnT57E3d29WN+tGZ6IiNjEZDKRk5NjtZhMpmKte/bsWQACAgIA2Lp1K/n5+URFRVnG1K9fn5o1a5KWlgZAWloakZGRlrADiI6OJicnhx07dhS7bgWeiIjYJDExET8/P6slMTHxb9crKipi+PDhtG7dmoYNGwKQmZmJu7s7/v7+VmODg4PJzMy0jPlj2F3pv9JXXLpKU0REbJKQkMDIkSOt2jw8PP52vfj4eH799VfWr19fWqX9JQWeiIjYxMPDo1gB90eDBw8mJSWFtWvXUr36/93WYDAYyMvLIzs722qWl5WVhcFgsIz54YcfrLZ35SrOK2OKQ4c0RUSk1JjNZgYPHsySJUv45ptvCA0Ntepv1qwZbm5urF692tK2Z88eMjIyMBqNABiNRrZv386JEycsY1JTU/H19SUiIqLYtWiGJyIipSY+Pp6FCxfy+eef4+PjYznn5ufnh6enJ35+fvTv35+RI0cSEBCAr68vQ4YMwWg00rJlSwA6duxIREQEffr0ISkpiczMTF544QXi4+Ntmmkq8EREpNTMmjULgPvuu8+qfd68eTz++OMAvP766zg7OxMbG4vJZCI6OpqZM2daxrq4uJCSksLAgQMxGo14eXkRFxfHyy+/bFMtug9PROQ2UF7uw7MnncMTERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGHoMATERGH4GQ2m832LkLsz2QykZiYSEJCAh4eHvYuR8ox/dbEXhR4AkBOTg5+fn6cPXsWX19fe5cj5Zh+a2IvOqQpIiIOQYEnIiIOQYEnIiIOQYEnAHh4ePDiiy/qIgIpdfqtib3oohUREXEImuGJiIhDUOCJiIhDUOCJiIhDUOCJlT179mAwGDh37lyx13nuuecYMmRIKVYl5VleXh516tRhw4YNxV5n586dVK9enfPnz5diZVLeKPDKgccffxwnJycmTZpk1b506VKcnJxs2lZCQgJDhgzBx8fH0vbLL7/Qpk0bKlSoQI0aNUhKSrJaZ/To0SxYsICDBw/e+E7ILctsNhMVFUV0dPRVfTNnzsTf35+jR4/e8PaTk5MJDQ2lVatWlrZXX32VVq1aUbFiRfz9/a9aJyIigpYtW/Laa6/d8PeK41HglRMVKlRg8uTJnDlz5oa3kZGRQUpKCo8//rilLScnh44dO1KrVi22bt3KlClTGD9+PLNnz7aMqVKlCtHR0cyaNetmdkFuUU5OTsybN49Nmzbx1ltvWdoPHTrEM888w/Tp06levfoNbdtsNjNjxgz69+9v1Z6Xl8cjjzzCwIEDr7vuE088waxZsygoKLih7xYHZJbbXlxcnPnBBx80169f3zxmzBhL+5IlS8x//J948eLF5oiICLO7u7u5Vq1a5v/+979W25kyZYq5efPmVm0zZ840V6pUyWwymSxtzz77rDksLMxq3IIFC8zVq1cvyd2SW8z8+fPN3t7e5oMHD5qLiorM7dq1M3fr1s28fft2c6dOncxeXl7moKAg87/+9S/zyZMnLestWrTI3LBhQ3OFChXMAQEB5g4dOphzc3PNZrPZvHnzZrOzs7M5Jyfnmt85b948s5+f3zX7TCaT2cPDw/z111+X+L5K+aQZXjnh4uLCxIkTmT59+jUPL23dupWePXvSq1cvtm/fzvjx4xk7dizz58+3jFm3bh3Nmze3Wi8tLY22bdvi7u5uaYuOjmbPnj1Ws8l77rmHo0ePcvjw4RLfN7k1xMXF0aFDB/r168eMGTP49ddfeeutt2jfvj1NmzZly5YtrFixgqysLHr27AnA8ePHefTRR+nXrx+7du3iu+++o3v37pj//+2/69ato169elaH0IvL3d2dJk2asG7duhLdTym/XO1dgJScbt260aRJE1588UXmzp1r1ffaa6/RoUMHxo4dC0C9evXYuXMnU6ZMsRzCTE9PvyrwMjMzCQ0NtWoLDg629FWqVAmAkJAQyzZq165d0rsmt4jZs2fToEED1q5dy6effspbb71F06ZNmThxomXMO++8Q40aNdi7dy+5ubkUFBTQvXt3atWqBUBkZKRlbHp6uuW3cyNCQkJIT0+/8R0Sh6IZXjkzefJkFixYwK5du6zad+3aRevWra3aWrduzb59+ygsLATg4sWLVKhQ4Ya+19PTE4ALFy7c0PpyewgKCmLAgAGEh4fTtWtXfv75Z7799lu8vb0tS/369QE4cOAAjRs3pkOHDkRGRvLII48wZ84cqyMDN/Obg8u/O/3mpLgUeOVM27ZtiY6OJiEhweZ1q1SpctVFLwaDgaysLKu2K58NBoOl7fTp0wAEBgba/L1ye3F1dcXV9fLBodzcXB566CG2bdtmtezbt4+2bdvi4uJCamoqy5cvJyIigunTpxMWFsahQ4eAa//mbHH69Gn95qTYFHjl0KRJk/jiiy9IS0uztIWHh/P9999bjfv++++pV68eLi4uADRt2pSdO3dajTEajaxdu5b8/HxLW2pqKmFhYZbDmQC//vorbm5uNGjQoDR2SW5Rd911Fzt27KB27drUqVPHavHy8gIuX+XZunVrXnrpJX766Sfc3d1ZsmQJcPk3t3v3bss5PVv9+uuvNG3atMT2R8o3BV45FBkZSe/evZk2bZqlbdSoUaxevZoJEyawd+9eFixYwIwZMxg9erRlTHR0NGlpaZZDnACPPfYY7u7u9O/fnx07dvDxxx/zv//9j5EjR1p957p162jTpo3l0KY4hvj4eE6fPs2jjz7K5s2bOXDgACtXruSJJ56gsLCQTZs2MXHiRLZs2UJGRgafffYZJ0+eJDw8HIB27dqRm5vLjh07rLabkZHBtm3byMjIoLCw0DJzzM3NtYw5fPgwv/32G1FRUWW6z3Ibs/dlonLz4uLizF26dLFqO3TokNnd3f2atyW4ubmZa9asaZ4yZYrVOvn5+eaQkBDzihUrrNp//vln87333mv28PAwV6tWzTxp0qSraggLCzN/+OGHJbdTcst68cUXzY0bN7Z83rt3r7lbt25mf39/s6enp7l+/frm4cOHm4uKisw7d+40R0dHmwMDA80eHh7mevXqmadPn261vZ49e5qfe+45q7a4uDgzcNXy7bffWsZMnDjRHB0dXZq7KuWMXg8kVt58802WLVvGypUri73O8uXLGTVqFL/88ovl3I5Icf3yyy/cf//9HDhwAG9v72Ktk5eXR926dVm4cOFVF2OJXI8OaYqVAQMG0LZtW5uepXn+/HnmzZunsJMb0qhRIyZPnmy5kKU4MjIyeP755xV2YhPN8ERExCFohiciIg5BgSciIg5BgSciIg5BgSciIg5BgSciIg5BgSdSTI8//jhdu3a1fL7vvvsYPnx4mdfx3Xff4eTkRHZ2dpl/t8jtTIEnt73HH38cJycnnJyccHd3p06dOrz88sul/ibszz77jAkTJhRrrEJKxP50p7CUC506dWLevHmYTCa++uor4uPjcXNzu+qtEXl5eVYvs70ZAQEBJbIdESkbmuFJueDh4YHBYKBWrVoMHDiQqKgoli1bZjkM+eqrrxISEkJYWBgAR44coWfPnvj7+xMQEECXLl2s3tZeWFjIyJEj8ff3p3LlyjzzzDNXPdH/z4c0TSYTzz77LDVq1MDDw4M6deowd+5cDh8+TLt27QCoVKkSTk5OlpfuFhUVkZiYSGhoKJ6enjRu3JjFixdbfc9XX31FvXr18PT0pF27dnqrvMgNUuBJueTp6UleXh4Aq1evZs+ePaSmppKSkkJ+fj7R0dH4+Piwbt06vv/+e7y9venUqZNlnalTpzJ//nzeeecd1q9fz+nTpy2vtLmevn378uGHHzJt2jR27drFW2+9hbe3NzVq1ODTTz8FYM+ePRw/fpz//e9/ACQmJvLuu++SnJzMjh07GDFiBP/6179Ys2YNcDmYu3fvbnnn3L///W+ee+650vprEynf7ProapES8Me3RRQVFZlTU1PNHh4e5tGjR5vj4uLMwcHBZpPJZBn/3nvvmcPCwsxFRUWWNpPJZPb09DSvXLnSbDabzVWrVjUnJSVZ+vPz883Vq1e3eivFP/7xD/OwYcPMZrPZvGfPHjNgTk1NvWaN3377rRkwnzlzxtJ26dIlc8WKFc0bNmywGtu/f3/zo48+ajabzeaEhARzRESEVf+zzz571bZE5O/pHJ6UCykpKXh7e5Ofn09RURGPPfYY48ePJz4+nsjISKvzdj///DP79+/Hx8fHahuXLl3iwIEDnD17luPHj9OiRQtLn6urK82bN7/ui0q3bduGi4sL//jHP4pd8/79+7lw4QL333+/VXteXp7lpaa7du2yqgMuv5RXRGynwJNyoV27dsyaNQt3d3dCQkKs3txw5c3bV+Tm5tKsWTM++OCDq7YTGBh4Q99/Iy++vfIy0y+//JJq1apZ9Xl4eNxQHSJyfQo8KRe8vLyoU6dOscbeddddfPzxxwQFBeHr63vNMVWrVmXTpk20bdsWgIKCArZu3cpdd911zfGRkZEUFRWxZs2aa76B+8oM849vk4+IiMDDw4OMjIzrzgzDw8NZtmyZVdvGjRv/fidF5Cq6aEUcTu/evalSpQpdunRh3bp1HDp0iO+++46hQ4dy9OhRAIYNG8akSZNYunQpu3fvZtCgQX95D13t2rWJi4ujX79+LF261LLNTz75BIBatWrh5ORESkoKJ0+eJDc3Fx8fH0aPHs2IESNYsGABBw4c4Mcff2T69OksWLAAgKeffpp9+/YxZswY9uzZw8KFC5k/f35p/xWJlEsKPHE4FStWZO3atdSsWZPu3bsTHh5O//79uXTpkmXGN2rUKPr06UNcXBxGoxEfHx+6dev2l9udNWsWPXr0YNCgQdSvX58nn3yS8+fPA1CtWjVeeuklnnvuOYKDgxk8eDAAEyZMYOzYsSQmJhIeHk6nTp348ssvCQ0NBaBmzZp8+umnLF26lMaNG5OcnMzEiRNL8W9HpPzSC2BFRMQhaIYnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIOQYEnIiIO4f8BUHJd8ADTHSwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the Random Forest classifier\n",
    "random_forest = RandomForestClassifier(random_state=1)\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(random_forest, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ade81f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 d. Use grid search and improve the performance of the Random tree model , check the performance of the model on train and test data , provide the differences observed in performance in Q3.c and Q3.d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "41f1573b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'bootstrap': True, 'max_depth': 10, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# Initialize the Random Forest classifier\n",
    "random_forest = RandomForestClassifier(random_state=1)\n",
    "\n",
    "# Define the hyperparameter grid to search over\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300], # Number of trees in the forest\n",
    "    'max_depth': [10, 20, 30], # Maximum depth of the trees\n",
    "    'min_samples_split': [2, 5, 10], # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': list(range(1, 5)), # Minimum number of samples required to be at a leaf node\n",
    "    'bootstrap': [True, False], # Whether bootstrap samples are used when building trees\n",
    "}\n",
    "\n",
    "# Initialize GridSearchCV with the Random Forest classifier and the hyperparameter grid\n",
    "grid_search = GridSearchCV(random_forest, param_grid, cv=10, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Perform grid search on the training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and best model\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b21aa4",
   "metadata": {},
   "source": [
    "##### These are the best parameter combination so lets now check the performance of the model on train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f34bb426",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.8597799077032304\n",
      "  Precision: 0.7959514170040486\n",
      "  Recall: 0.646285338593031\n",
      "  F1 Score: 0.7133526850507983\n",
      "  Confusion Matrix:\n",
      " [[3861  252]\n",
      " [ 538  983]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.815471965933286\n",
      "  Precision: 0.6419354838709678\n",
      "  Recall: 0.5718390804597702\n",
      "  F1 Score: 0.6048632218844985 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the Random Forest classifier\n",
    "best_random_forest = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    max_depth=10,\n",
    "    min_samples_leaf=2,\n",
    "    min_samples_split=5,\n",
    "    bootstrap=True,\n",
    "    random_state=1 \n",
    ")\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(best_random_forest, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c8531f",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> Differences Observed in Performance:\n",
    "    \n",
    "* `Overfitting Reduction`: The performance improvement observed after grid search suggests that the hyperparameter tuning helped reduce overfitting. The previous model had a high accuracy on the training data but lower accuracy on the test data, indicating overfitting. With grid search, the model's generalization to unseen data improved, resulting in a more balanced performance on both training and test sets.\n",
    "* `Model Generalization`: The second model with hyperparameter tuning has better generalization capability as indicated by the improved performance on the test data. It is more likely to perform well on new, unseen data.\n",
    "* `Trade-off`: It's important to note that while the second model performs better on the test data, it may not achieve the same level of performance on the training data as the first model. This is because the second model is tuned for better generalization, which may result in slightly lower training set performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf546dfe",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 e. Train a model using Adaboost and check the performance of the model on train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0d64e60f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.808839190628328\n",
      "  Precision: 0.6825657894736842\n",
      "  Recall: 0.5456936226166995\n",
      "  F1 Score: 0.6065034709535989\n",
      "  Confusion Matrix:\n",
      " [[3727  386]\n",
      " [ 691  830]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.8133427963094393\n",
      "  Precision: 0.6402640264026402\n",
      "  Recall: 0.5574712643678161\n",
      "  F1 Score: 0.5960061443932412 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the AdaBoost classifier\n",
    "ada_cl = AdaBoostClassifier()\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(ada_cl, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15985527",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 f. Use grid search and improve the performance of the Adaboost model , check the performance of the model on train and test data , provide the differences observed in performance in Q3.e and Q3.f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93c1bc1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "# Create the base estimator (weak learner)\n",
    "base_estimator = DecisionTreeClassifier(random_state=1)\n",
    "\n",
    "# Create the AdaBoost Classifier\n",
    "ada_clf = AdaBoostClassifier(estimator=base_estimator)\n",
    "\n",
    "# Define the grid of hyperparameters to search\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200, 300], # Number of estimators (trees)\n",
    "    'learning_rate': [0.1, 0.5, 1.0], # Learning rate\n",
    "    'algorithm': ['SAMME', 'SAMME.R'] # Boosting algorithm\n",
    "}\n",
    "\n",
    "# Create the GridSearchCV object\n",
    "grid_search = GridSearchCV(ada_clf, param_grid, cv=10, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Perform the grid search on your data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters and the best model\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22efd9d",
   "metadata": {},
   "source": [
    "##### These are the best parameter combination so lets now check the performance of the model on train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d2d341c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.8029818956336529\n",
      "  Precision: 0.6929577464788732\n",
      "  Recall: 0.48520710059171596\n",
      "  F1 Score: 0.5707656612529002\n",
      "  Confusion Matrix:\n",
      " [[3786  327]\n",
      " [ 783  738]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.8097941802696949\n",
      "  Precision: 0.644927536231884\n",
      "  Recall: 0.5114942528735632\n",
      "  F1 Score: 0.5705128205128206 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the AdaBoost classifier with the best hyperparameters\n",
    "best_ada = AdaBoostClassifier(\n",
    "    n_estimators=100,\n",
    "    algorithm='SAMME.R',\n",
    "    learning_rate=0.1,\n",
    "    random_state=1\n",
    ")\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(best_ada, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a280e965",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> Differences Observed in Performance:\n",
    "    \n",
    "* The performance of the AdaBoost model slightly decreased after grid search on both the training and test data due to slight decrease in the model's accuracy, precision, recall, and F1 score on both the training and test data after grid search.\n",
    "* The confusion matrices for both training and test data after grid search were similar to those before grid search, with only slight changes in the number of true positives, true negatives, false positives, and false negatives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9581192",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 g. Train a model using GradientBoost and check the performance of the model on train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3ffbdce9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.8219737309194178\n",
      "  Precision: 0.7144039735099338\n",
      "  Recall: 0.5673898750821827\n",
      "  F1 Score: 0.6324661048002932\n",
      "  Confusion Matrix:\n",
      " [[3768  345]\n",
      " [ 658  863]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.8076650106458482\n",
      "  Precision: 0.6222222222222222\n",
      "  Recall: 0.5632183908045977\n",
      "  F1 Score: 0.5912518853695324 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the GradientBoostingClassifier\n",
    "gbcl = GradientBoostingClassifier(random_state=1)\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(gbcl, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55165ee7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3 h. Use grid search and improve the performance of the GradientBoost model , check the performance of the model on train and test data , provide the differences observed in performance in Q3.g and Q3.h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bc821318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 1.0, 'max_depth': 1, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "# Initialize the GradientBoostingClassifier\n",
    "grad_boost = GradientBoostingClassifier()\n",
    "\n",
    "# Define the hyperparameter grid to search\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'learning_rate': [0.01, 0.1, 0.2, 0.5, 1.0],\n",
    "    'max_depth': list(range(1, 5))\n",
    "}\n",
    "\n",
    "# Initialize the GridSearchCV \n",
    "grid_search = GridSearchCV(grad_boost, param_grid, cv=10, scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "# Perform grid search to find the best hyperparameters\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best hyperparameters from the grid search\n",
    "best_params = grid_search.best_params_\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best Hyperparameters:\", best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4867413f",
   "metadata": {},
   "source": [
    "##### These are the best parameter combination so lets now check the performance of the model on train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ab03bc5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "• Performance on Training Data: \n",
      "\n",
      "  Accuracy: 0.8097266595669151\n",
      "  Precision: 0.6823720552396426\n",
      "  Recall: 0.5522682445759369\n",
      "  F1 Score: 0.6104651162790699\n",
      "  Confusion Matrix:\n",
      " [[3722  391]\n",
      " [ 681  840]] \n",
      "\n",
      "\n",
      "• Performance on Test Data: \n",
      "\n",
      "  Accuracy: 0.8112136266855926\n",
      "  Precision: 0.6339869281045751\n",
      "  Recall: 0.5574712643678161\n",
      "  F1 Score: 0.5932721712538227 \n",
      "\n",
      "  Confusion Matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the GradientBoostingClassifier\n",
    "best_gb = GradientBoostingClassifier(\n",
    "    n_estimators=50,\n",
    "    learning_rate=1.0,\n",
    "    max_depth=1,\n",
    "    random_state=1\n",
    ")\n",
    "\n",
    "# Evaluate the performance \n",
    "evaluate_model(best_gb, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb421667",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> Differences Observed in Performance:\n",
    "    \n",
    "* The model with grid search slightly improved the performance on the training data in terms of accuracy, precision, recall, and F1 score compared to the model without grid search. However, the differences in performance between the two models are relatively small.\n",
    "* On the test data, the model with grid search also shows slight improvements in precision, recall, and F1 score compared to the model without grid search.\n",
    "* Overall, the model with grid search provides marginally better performance on both training and test data, but the differences may not be significant."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8623e0b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 3 i. Provide detailed analysis of the below steps\n",
    "    1. Compare the performance of each model in train stage and test stage\n",
    "    2. Provide your observation on which model performed the best\n",
    "    3. Provide your reasoning on why the model performed best\n",
    "    4. Provide your final conclusion on your observation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8390f0",
   "metadata": {},
   "source": [
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\">1. Performance comparison of each model \n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.6em;\"> i. `Decision Tree` -\n",
    "    \n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » Default Model:\n",
    "\n",
    "* `Training Data`: The default Decision Tree model achieved very high accuracy (approximately 99.8%) on the training data. It shows excellent performance in predicting the majority class (No Churn) but still has a good ability to detect the minority class (Churn) with high precision and recall (approximately 99.9% precision and 99.3% recall).\n",
    "* `Test Data`: However, the model's performance on the test data is not as good as on the training data. It achieved an accuracy of approximately 74.3%, which indicates that it does not generalize well to unseen data. The precision, recall, and F1 score on the test data are significantly lower than on the training data. This suggests that the model is overfitting to the training data.\n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » After Grid Search:\n",
    "\n",
    "* `Training Data`: After performing grid search for hyper-parameter tuning, the model's performance on the training data has improved compared to the default model but is not as high as before. The accuracy is approximately 81.4%, indicating that it has slightly better generalization compared to the default model on the test data.\n",
    "* `Test Data`: The performance on the test data has also improved compared to the default model. The accuracy is approximately 79.5%, indicating better generalization to unseen data. The precision, recall, and F1 score on the test data are higher than in the default model, but they are still not high, indicating that the model may not be capturing the complexity of the data well.   \n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.6em;\"> ii. `Random Forest` -\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » Default Model:\n",
    "    \n",
    "* The model performs very well on the training data with an accuracy of 99.80% and high precision, recall, and F1 score, indicating excellent performance on the training set.\n",
    "* However, on the test data, the performance drops slightly with an accuracy of 79.91%, lower precision, recall, and F1 score compared to the training data. This suggests that the model may be slightly overfitting the training data and not generalizing well to unseen data.\n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » After Grid Search:\n",
    "    \n",
    "* After using grid search to find the best hyperparameters, the model's performance has improved on both training and test data.\n",
    "* The accuracy on the training data is now 85.98%, and the precision, recall, and F1 score have also improved significantly, indicating better overall performance on the training set compared to the previous model.\n",
    "* On the test data, the model's performance has also improved with an accuracy of 81.55% and improved precision, recall, and F1 score compared to the previous model.\n",
    "* The confusion matrix shows that the model is now making better predictions on both the positive and negative classes.    \n",
    "   \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.6em;\">iii. `AdaBoost` -\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » Default Model:\n",
    "    \n",
    "* The AdaBoost model achieved an accuracy of approximately 80.88% on the training data. It had a precision of about 68.26%, recall of 54.57%, and an F1 score of 60.65%. The confusion matrix showed 3727 true negatives, 386 false positives, 691 false negatives, and 830 true positives.\n",
    "* The AdaBoost model achieved an accuracy of approximately 81.33% on the test data. It had a precision of about 64.03%, recall of 55.75%, and an F1 score of 59.60%. The confusion matrix showed 963 true negatives, 98 false positives, 170 false negatives, and 178 true positives.\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » After Grid Search:\n",
    "    \n",
    "* After performing grid search for hyper-parameter tuning, the AdaBoost model achieved an accuracy of approximately 80.30% on the training data. It had a precision of about 69.30%, recall of 48.52%, and an F1 score of 57.08%. The confusion matrix showed 3786 true negatives, 327 false positives, 783 false negatives, and 738 true positives.    \n",
    "* After performing grid search for hyper-parameter tuning, the AdaBoost model achieved an accuracy of approximately 80.98% on the test data. It had a precision of about 64.49%, recall of 51.15%, and an F1 score of 57.05%. The confusion matrix showed 963 true negatives, 98 false positives, 170 false negatives, and 178 true positives.   \n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.6em;\"> iv. `GradientBoost` -\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » Default Model:\n",
    "\n",
    "* The model performed quite well on the training data with an accuracy of approximately 82.20%.\n",
    "* The model achieved a high precision of around 71.44%, indicating that when it predicts a customer will churn (positive class), it is often correct.\n",
    "* The recall score of about 56.74% suggests that the model is capable of identifying a considerable portion of the actual churn cases in the training data.\n",
    "* The F1 score of approximately 63.25% is a harmonic mean of precision and recall, indicating a good balance between the two metrics.\n",
    "* On the test data, the model achieved an accuracy of around 80.77%, which is slightly lower than the training accuracy.\n",
    "* The precision on the test data is approximately 62.22%, indicating that the model's predictions of churn are relatively accurate.\n",
    "* The recall on the test data is approximately 56.32%, showing that the model can identify a substantial portion of the actual churn cases in the test data.\n",
    "* The F1 score on the test data is around 59.13%, which is slightly lower than the F1 score on the training data.\n",
    "\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:1.2em;\"> » After Grid Search:\n",
    "\n",
    "* After performing grid search, the model's performance on the training data slightly improved compared to the previous model.\n",
    "* The accuracy on the training data is approximately 80.97%, which is slightly lower than the previous model.\n",
    "* The precision on the training data is approximately 68.24%, which is slightly lower than the previous model.\n",
    "* The recall on the training data is approximately 55.23%, which is slightly lower than the previous model.\n",
    "* The F1 score on the training data is around 61.05%, which is comparable to the previous model.\n",
    "* On the test data, the model achieved an accuracy of around 81.12%, which is similar to the previous model.\n",
    "* The precision on the test data is approximately 63.40%, which is slightly higher than the previous model.\n",
    "* The recall on the test data is approximately 55.75%, which is similar to the previous model.\n",
    "* The F1 score on the test data is around 59.33%, which is slightly higher than the previous model.    \n",
    "      \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\">2. Best performing Model \n",
    "    \n",
    "* Based on the performance metrics on both the training and test data, the model that performed the best is the Random Forest Model with * Grid Search.\n",
    "* It achieved the highest accuracy, precision, recall, and F1 score on both the training and test data compared to other models.\n",
    "* It showed the best generalization to the test data, indicating reduced overfitting compared to the other models.\n",
    "    \n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> 3. Provide your reasoning on why the model performed best\n",
    "\n",
    "The Random Forest Model with Grid Search performed the best for the following reasons:\n",
    "\n",
    "Grid search allowed us to fine-tune the hyper-parameters of the Random Forest model, leading to better model performance.\n",
    "The Random Forest algorithm is an ensemble learning method that builds multiple decision trees and combines their predictions, resulting in improved accuracy and robustness compared to single decision trees.\n",
    "Random Forest can handle both categorical and continuous features well, making it suitable for the dataset with a mix of both types of features.\n",
    "By reducing overfitting and improving generalization, the model achieved good performance on unseen test data.\n",
    "\n",
    "<span style=\"font-family: Arial; font-weight:bold;font-size:2em;color:#00b3e5;\"> 4. Provide your final conclusion on your observation\n",
    "\n",
    "* In conclusion, after evaluating multiple models and fine-tuning their hyper-parameters using grid search, the Random Forest Model with Grid Search emerged as the best-performing model for predicting customer churn. The model demonstrated strong performance on both the training and test data, achieving high accuracy, precision, recall, and F1 score. Therefore Random Forest model with Grid Search is the recommended choice for making predictions on new customer data, given its better generalization and performance on the test data.\n",
    "    \n",
    "* It is also important to note that the performance of the models can be influenced by various factors, including the dataset, feature engineering, and hyper-parameter tuning. Therefore, further analysis and experimentation may be needed to continuously optimize and improve the model's performance based on specific business requirements."
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    "# <center><span style=\"font-family: Arial; font-weight:bold;font-size:2.4em;color:#FF8C00;\"> End Of Project"
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