Customer Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3cfc82e2",
   "metadata": {},
   "source": [
    "# `Data Description`\n",
    "\n",
    "Dataset consists customer information for a customer churn prediction problem. It includes the following columns:\n",
    "\n",
    "**CustomerID**: Unique identifier for each customer.\n",
    "\n",
    "\n",
    "**Name**: Name of the customer.\n",
    "\n",
    "\n",
    "**Age: Age of the customer.**\n",
    "\n",
    "\n",
    "**Gender**: Gender of the customer (Male or Female).\n",
    "\n",
    "\n",
    "**Location**: Location where the customer is based, with options including Houston, Los Angeles, Miami, Chicago, and New York.\n",
    "\n",
    "\n",
    "**Subscription_Length_Months**: The number of months the customer has been subscribed.\n",
    "\n",
    "\n",
    "**Monthly_Bill**: Monthly bill amount for the customer.\n",
    "\n",
    "\n",
    "**Total_Usage_GB**: Total usage in gigabytes.\n",
    "\n",
    "\n",
    "**Churn**: A binary indicator (1 or 0) representing whether the customer has churned (1) or not (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e11fb6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "853c8571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Customer_1</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Customer_2</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>New York</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Customer_3</td>\n",
       "      <td>24</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Customer_4</td>\n",
       "      <td>36</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Customer_5</td>\n",
       "      <td>46</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID        Name  Age  Gender     Location  \\\n",
       "0           1  Customer_1   63    Male  Los Angeles   \n",
       "1           2  Customer_2   62  Female     New York   \n",
       "2           3  Customer_3   24  Female  Los Angeles   \n",
       "3           4  Customer_4   36  Female        Miami   \n",
       "4           5  Customer_5   46  Female        Miami   \n",
       "\n",
       "   Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "0                          17         73.36             236      0  \n",
       "1                           1         48.76             172      0  \n",
       "2                           5         85.47             460      0  \n",
       "3                           3         97.94             297      1  \n",
       "4                          19         58.14             266      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'C:\\Users\\HP\\Desktop\\Data Science\\customer_churn_large_dataset.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feaafb09",
   "metadata": {},
   "source": [
    "# `Exploratory Data Analysis (EDA)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98e6927d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>CustomerID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45759</th>\n",
       "      <td>45760</td>\n",
       "      <td>Customer_45760</td>\n",
       "      <td>52</td>\n",
       "      <td>Female</td>\n",
       "      <td>Houston</td>\n",
       "      <td>3</td>\n",
       "      <td>46.50</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19977</th>\n",
       "      <td>19978</td>\n",
       "      <td>Customer_19978</td>\n",
       "      <td>59</td>\n",
       "      <td>Female</td>\n",
       "      <td>Miami</td>\n",
       "      <td>10</td>\n",
       "      <td>37.06</td>\n",
       "      <td>389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84826</th>\n",
       "      <td>84827</td>\n",
       "      <td>Customer_84827</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>Miami</td>\n",
       "      <td>4</td>\n",
       "      <td>53.13</td>\n",
       "      <td>167</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68506</th>\n",
       "      <td>68507</td>\n",
       "      <td>Customer_68507</td>\n",
       "      <td>48</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>13</td>\n",
       "      <td>73.74</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14285</th>\n",
       "      <td>14286</td>\n",
       "      <td>Customer_14286</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>Miami</td>\n",
       "      <td>20</td>\n",
       "      <td>80.93</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43236</th>\n",
       "      <td>43237</td>\n",
       "      <td>Customer_43237</td>\n",
       "      <td>41</td>\n",
       "      <td>Female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2</td>\n",
       "      <td>45.77</td>\n",
       "      <td>402</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63916</th>\n",
       "      <td>63917</td>\n",
       "      <td>Customer_63917</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>Miami</td>\n",
       "      <td>3</td>\n",
       "      <td>42.62</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5379</th>\n",
       "      <td>5380</td>\n",
       "      <td>Customer_5380</td>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>1</td>\n",
       "      <td>38.91</td>\n",
       "      <td>362</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CustomerID            Name  Age  Gender     Location  \\\n",
       "45759       45760  Customer_45760   52  Female      Houston   \n",
       "19977       19978  Customer_19978   59  Female        Miami   \n",
       "84826       84827  Customer_84827   66    Male        Miami   \n",
       "68506       68507  Customer_68507   48    Male  Los Angeles   \n",
       "14285       14286  Customer_14286   38    Male        Miami   \n",
       "43236       43237  Customer_43237   41  Female  Los Angeles   \n",
       "63916       63917  Customer_63917   65    Male        Miami   \n",
       "5379         5380   Customer_5380   64    Male  Los Angeles   \n",
       "\n",
       "       Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \n",
       "45759                           3         46.50             148      1  \n",
       "19977                          10         37.06             389      1  \n",
       "84826                           4         53.13             167      0  \n",
       "68506                          13         73.74             171      1  \n",
       "14285                          20         80.93              63      1  \n",
       "43236                           2         45.77             402      0  \n",
       "63916                           3         42.62              54      0  \n",
       "5379                            1         38.91             362      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12046ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8649918b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CustomerID', 'Name', 'Age', 'Gender', 'Location',\n",
       "       'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB',\n",
       "       'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "768c1a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 9 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   CustomerID                  100000 non-null  int64  \n",
      " 1   Name                        100000 non-null  object \n",
      " 2   Age                         100000 non-null  int64  \n",
      " 3   Gender                      100000 non-null  object \n",
      " 4   Location                    100000 non-null  object \n",
      " 5   Subscription_Length_Months  100000 non-null  int64  \n",
      " 6   Monthly_Bill                100000 non-null  float64\n",
      " 7   Total_Usage_GB              100000 non-null  int64  \n",
      " 8   Churn                       100000 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(3)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87b2b8a",
   "metadata": {},
   "source": [
    "**All variables has correct data type.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13edb16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(CustomerID                    0.0\n",
       " Name                          0.0\n",
       " Age                           0.0\n",
       " Gender                        0.0\n",
       " Location                      0.0\n",
       " Subscription_Length_Months    0.0\n",
       " Monthly_Bill                  0.0\n",
       " Total_Usage_GB                0.0\n",
       " Churn                         0.0\n",
       " dtype: float64,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f7d2a0",
   "metadata": {},
   "source": [
    "**There is no null value in any of variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62b833ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928e183a",
   "metadata": {},
   "source": [
    "**There is no duplicate record in datset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c5ad6a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50000.500000</td>\n",
       "      <td>44.027020</td>\n",
       "      <td>12.490100</td>\n",
       "      <td>65.053197</td>\n",
       "      <td>274.393650</td>\n",
       "      <td>0.497790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28867.657797</td>\n",
       "      <td>15.280283</td>\n",
       "      <td>6.926461</td>\n",
       "      <td>20.230696</td>\n",
       "      <td>130.463063</td>\n",
       "      <td>0.499998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25000.750000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>47.540000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50000.500000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>65.010000</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75000.250000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>82.640000</td>\n",
       "      <td>387.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100000.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CustomerID            Age  Subscription_Length_Months  \\\n",
       "count  100000.000000  100000.000000               100000.000000   \n",
       "mean    50000.500000      44.027020                   12.490100   \n",
       "std     28867.657797      15.280283                    6.926461   \n",
       "min         1.000000      18.000000                    1.000000   \n",
       "25%     25000.750000      31.000000                    6.000000   \n",
       "50%     50000.500000      44.000000                   12.000000   \n",
       "75%     75000.250000      57.000000                   19.000000   \n",
       "max    100000.000000      70.000000                   24.000000   \n",
       "\n",
       "        Monthly_Bill  Total_Usage_GB          Churn  \n",
       "count  100000.000000   100000.000000  100000.000000  \n",
       "mean       65.053197      274.393650       0.497790  \n",
       "std        20.230696      130.463063       0.499998  \n",
       "min        30.000000       50.000000       0.000000  \n",
       "25%        47.540000      161.000000       0.000000  \n",
       "50%        65.010000      274.000000       0.000000  \n",
       "75%        82.640000      387.000000       1.000000  \n",
       "max       100.000000      500.000000       1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary of numerical variables\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09996b54",
   "metadata": {},
   "source": [
    "### `Age` \n",
    "\n",
    "* **The average age of the customers is approximately 44 years.**\n",
    "\n",
    "\n",
    "* **The ages of the people varies widely. The youngest person is 18 years old and the oldest person is 70 years old.**\n",
    "\n",
    "\n",
    "* **The majority of customers fall within the range of 31 to 57 years.**\n",
    "\n",
    "\n",
    "* **The spread of ages is represented by a standard deviation of about 15 years.**\n",
    "\n",
    "\n",
    "### `Subscription_Length_Months` \n",
    "\n",
    "* **On average, customers have a subscription length of around 12.5 months.**\n",
    "\n",
    "\n",
    "* **Subscription lengths vary between 1 and 24 months.**\n",
    "\n",
    "\n",
    "* **The majority of customers have subscription lengths ranging from 6 to 19 months.**\n",
    "\n",
    "\n",
    "* **The standard deviation is approximately 6.9 months, indicating some variation in subscription lengths.**\n",
    "\n",
    "\n",
    "### `Monthly_Bill`\n",
    "\n",
    "* **Customers pay an average monthly bill of roughly $65.**\n",
    "\n",
    "\n",
    "* **Monthly bills range from 30 Dollar (minimum) to 100 Dollar (maximum).**\n",
    "\n",
    "\n",
    "* **The majority of customers pay between approximately 47.50 Dollar to 82.64 Dollar per month.**\n",
    "\n",
    "\n",
    "* **Monthly bill amounts have a standard deviation of about $20.23, showing some variability.**\n",
    "\n",
    "\n",
    "### `Total_Usage_GB`\n",
    "\n",
    "* **The average total usage of customers is about 274.4 GB.**\n",
    "\n",
    "\n",
    "* **Total usage varies between 50 GB (minimum) and 500 GB (maximum).**\n",
    "\n",
    "\n",
    "* **Most customers have total usage between 161 GB and 387 GB.**\n",
    "\n",
    "\n",
    "* **Total usage has a standard deviation of approximately 130.46 GB, indicating a significant range.**\n",
    "\n",
    "### `Churn`\n",
    "\n",
    "* **About half of the customers have churned (Churn value = 1), while the other half haven't (Churn value = 0).**\n",
    "\n",
    "\n",
    "* **This is represented by a mean churn rate of approximately 0.5.**\n",
    "\n",
    "\n",
    "* **Churn values are binary (0 or 1), indicating whether a customer has churned or not.**\n",
    "\n",
    "\n",
    "* **Churn rate is evenly distributed due to a mean close to 0.5.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0fda8cd0",
   "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>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>100000</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Customer_1</td>\n",
       "      <td>Female</td>\n",
       "      <td>Houston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>50216</td>\n",
       "      <td>20157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name  Gender Location\n",
       "count       100000  100000   100000\n",
       "unique      100000       2        5\n",
       "top     Customer_1  Female  Houston\n",
       "freq             1   50216    20157"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical summary of categoical variables\n",
    "\n",
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68616266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    50216\n",
       "Male      49784\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4dbf1bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Houston        20157\n",
       "Los Angeles    20041\n",
       "Miami          20031\n",
       "Chicago        19958\n",
       "New York       19813\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190386dc",
   "metadata": {},
   "source": [
    "### `Gender`\n",
    "\n",
    "* **The dataset includes information about two unique genders.**\n",
    "\n",
    "\n",
    "* **The most common gender is \"Female,\" which appears 50,216 times.**\n",
    "\n",
    "\n",
    "* **The frequency of females in the dataset is greater than that Male.**\n",
    "\n",
    "\n",
    "### `Location`\n",
    "\n",
    "* **The dataset contains information about five unique locations[Houston, Los Angeles, Miami, Chicago, New York].**\n",
    "\n",
    "\n",
    "* **The most frequent location is \"Houston,\" occurring 20,157 times.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "804da930",
   "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>Age</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001085</td>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.001265</td>\n",
       "      <td>-0.004025</td>\n",
       "      <td>-0.004586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.001085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>0.001559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <td>0.005444</td>\n",
       "      <td>0.003382</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>-0.002203</td>\n",
       "      <td>0.002328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <td>0.001265</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>-0.005294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>-0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <td>-0.004025</td>\n",
       "      <td>0.001927</td>\n",
       "      <td>-0.002203</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>-0.004586</td>\n",
       "      <td>0.001559</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>-0.000211</td>\n",
       "      <td>-0.002842</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            CustomerID       Age  Subscription_Length_Months  \\\n",
       "CustomerID                    1.000000 -0.001085                    0.005444   \n",
       "Age                          -0.001085  1.000000                    0.003382   \n",
       "Subscription_Length_Months    0.005444  0.003382                    1.000000   \n",
       "Monthly_Bill                  0.001265  0.001110                   -0.005294   \n",
       "Total_Usage_GB               -0.004025  0.001927                   -0.002203   \n",
       "Churn                        -0.004586  0.001559                    0.002328   \n",
       "\n",
       "                            Monthly_Bill  Total_Usage_GB     Churn  \n",
       "CustomerID                      0.001265       -0.004025 -0.004586  \n",
       "Age                             0.001110        0.001927  0.001559  \n",
       "Subscription_Length_Months     -0.005294       -0.002203  0.002328  \n",
       "Monthly_Bill                    1.000000        0.003187 -0.000211  \n",
       "Total_Usage_GB                  0.003187        1.000000 -0.002842  \n",
       "Churn                          -0.000211       -0.002842  1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking correlation between numerical variables\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f29e0eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.corr()\n",
    "plt.figure(figsize=(10,5)) #(width,height)\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', square=True)\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6531a4",
   "metadata": {},
   "source": [
    "### `Age and Subscription Length` \n",
    "\n",
    "* **Age and subscription length have a very low positive correlation of approximately 0.0034.**\n",
    "\n",
    "\n",
    "* **This indicates that there is almost no linear relationship between a customer's age and the length of their subscription. It means customer's age does not have a strong impact on how long they stay subscribed.**\n",
    "\n",
    "\n",
    "### `Age and Monthly Bill`\n",
    "\n",
    "* **Age and monthly bill amount have an extremely low positive correlation of about 0.0011.**\n",
    "\n",
    "\n",
    "* **This suggests that there is almost no linear relationship between a customer's age and the amount they are billed monthly. It means customer's age does not have a strong impact on the amount they are billed monthly.**\n",
    "\n",
    "\n",
    "### `Age and Total Usage`\n",
    "\n",
    "* **Age and total usage in GB have a very low positive correlation of around 0.0019.**\n",
    "\n",
    "\n",
    "* **This implies that there is almost no linear relationship between a customer's age and the amount of data they use. it means  customer's age does not have a strong impact on how much data they use.**\n",
    "\n",
    "\n",
    "### `Age and Churn`\n",
    "\n",
    "* **Age and churn have a very low positive correlation of about 0.0016.**\n",
    "\n",
    "\n",
    "* **This means that there is almost no linear relationship between a customer's age and their likelihood to churn means customer's age is not a good predictor of whether they will churn.**\n",
    "\n",
    "\n",
    "### `Subscription Length and Monthly Bill`\n",
    "\n",
    "* **Subscription length and monthly bill amount have a very low negative correlation of approximately -0.0053.**\n",
    "\n",
    "\n",
    "* **There is a weak relationship between how long a customer has been subscribed and how much they pay each month.**\n",
    "\n",
    "\n",
    "### `Subscription Length and Total Usage`\n",
    "\n",
    "* **Subscription length and total usage have a very low negative correlation of around -0.0022.**\n",
    "\n",
    "\n",
    "* **This suggests that changes in subscription length are not strongly associated with changes in total data usage.**\n",
    "\n",
    "\n",
    "### `Subscription Length and Churn`\n",
    "\n",
    "* **Subscription length and churn have a very low positive correlation of about 0.0023.** \n",
    "\n",
    "\n",
    "* **There is almost no linear relationship between subscription length and churn behavior means length of a subscription does not have a strong impact on whether a customer churns.**\n",
    "\n",
    "\n",
    "### `Monthly Bill and Total Usage`\n",
    "\n",
    "* **Monthly bill amount and total usage have an extremely low positive correlation of about 0.0032.**\n",
    "\n",
    "\n",
    "* **This indicates that there is almost no linear relationship between a customer's monthly bill and their total data usage. It means the amount of data a customer uses does not have a strong impact on their monthly bill.**\n",
    "\n",
    "### `Monthly Bill and Churn`\n",
    "\n",
    "* **Monthly bill amount and churn have an extremely low negative correlation of approximately -0.0002.**\n",
    "\n",
    "\n",
    "* **This suggests that there is hardly any linear relationship between monthly bill amount and churn behavior.** \n",
    "\n",
    "\n",
    "* **The amount a customer is billed monthly does not have a strong impact on whether they churn.**\n",
    "\n",
    "### `Total Usage and Churn`\n",
    "\n",
    "* **Total usage and churn have a very low negative correlation of about -0.0028.**\n",
    "\n",
    "\n",
    "* **There is almost no linear relationship between a customer's data usage and their likelihood to churn.**\n",
    "\n",
    "\n",
    "* **The amount of data a customer uses does not have a strong impact on whether they churn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb1f3054",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['CustomerID', 'Name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82275164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Gender', 'Location', 'Subscription_Length_Months',\n",
       "       'Monthly_Bill', 'Total_Usage_GB', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d88a955b",
   "metadata": {},
   "source": [
    "**Removed ['CustomerID' & 'Name'] variables becuase they are irrelevant for analysis. Now dataset left with 6 variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c306b752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in 'Age': [63 62 24 36 46 67 30 20 53 50 44 34 28 42 55 43 31 41 58 64 70 18 25 27\n",
      " 66 29 60 26 45 19 37 49 38 57 47 48 40 21 33 51 35 69 61 52 39 56 59 65\n",
      " 22 54 68 32 23]\n",
      "\n",
      "Unique values in 'Gender': ['Male' 'Female']\n",
      "\n",
      "Unique values in 'Location': ['Los Angeles' 'New York' 'Miami' 'Chicago' 'Houston']\n",
      "\n",
      "Unique values in 'Subscription_Length_Months': [17  1  5  3 19 15 10 12 20 13  8 23  2  4 18  9 14 16  6  7 24 22 11 21]\n",
      "\n",
      "Unique values in 'Monthly_Bill': [73.36 48.76 85.47 ... 75.21 41.98 83.55]\n",
      "\n",
      "Unique values in 'Total_Usage_GB': [236 172 460 297 266 456 269 396 150 383 442 295 148 239 233 231 198 354\n",
      " 429 243 270 389 226 393  89 183 135 185 151 115 194 278 423 202 495 162\n",
      " 101 294 122 305 212 220 253 411 447 218 221 166  64  67 391 232 298 334\n",
      " 326 459 371 299 376 214 329  50 444 113  76 134 184 319 235  85 332  73\n",
      " 369 308 446 321  83 114 296 102 215 169 225 140  84 463 402 468 375 366\n",
      " 404 471 204 320 178 149 361 304 281 177 117 137 322 427 403 237 439 336\n",
      " 344 288 257 196 365 419 111 192 244 287 395 213 109 286 283 230 491 274\n",
      " 335 462 407 126 199 351 261 496 480 400 303 197 181 474 348 112 457  54\n",
      " 397 100 318 242 327 476 256 241 466 438  51 165 120 493  59 188 339 167\n",
      " 301 209 311 190 345 284 390  72 258 358 136 280  55 309 388  52 478 401\n",
      " 472  65 163 488 497  71 216 229 271 223 263 255 145 203 372 443 306 307\n",
      " 386 448 377 482 324 357 187 394 123 106  80 211  78 499 498 424 325 119\n",
      " 143 153 437 492 406  69 272 352 125  82 160 333 104 247 405 353  63 289\n",
      "  88 346  91 385 380 431 364  79 300 374  86 290 265 433 469 453 224 421\n",
      " 251 485 180 171  95  93 350  58 200 420 132 428 479  96 103 129 417  99\n",
      " 430  75  53 382 412 268 410  94 315 387 116 489 108 208 313 127 222 451\n",
      "  56 139 282 138 141 156 373 157 467  68 414 360 144 273 217 189 118 155\n",
      "  62 413 154 458 449 490 464 379 254 131  92 195 450 347 191 425 432 384\n",
      "  57 494 173 210 363 130 487 455 105 399 323  61 341 426 240 302 176 152\n",
      " 331 440 276  87 291 193  90 500 170 174 182 359 408 454 158 328 436 312\n",
      " 338  60 362 205 416 483 415 477 484 252 250 164 146 161 234 473 317  77\n",
      " 267 342 110 343 133 330 367 147  81 277 310 392 349 314 107 259 228 168\n",
      " 246 219  98 121 142 238 206  97 381 245 175 124 292 340 316 465 470 475\n",
      " 248 445 452 249 337  66 418 201 227 422 481 355 279 435 275 260  70 186\n",
      " 159 368 179 262 370  74 378 128 461 293 441 434 207 356 398 285 409 486\n",
      " 264]\n",
      "\n",
      "Unique values in 'Churn': [0 1]\n",
      "\n",
      "--------------------------------------------------\n",
      "Missing values:\n",
      "Age                           0\n",
      "Gender                        0\n",
      "Location                      0\n",
      "Subscription_Length_Months    0\n",
      "Monthly_Bill                  0\n",
      "Total_Usage_GB                0\n",
      "Churn                         0\n",
      "dtype: int64\n",
      "\n",
      "--------------------------------------------------\n",
      "Duplicate values: 0\n",
      "\n",
      "--------------------------------------------------\n",
      "Shape of Dataset: (100000, 7)\n",
      "\n",
      "--------------------------------------------------\n",
      "Columns of Datset: Index(['Age', 'Gender', 'Location', 'Subscription_Length_Months',\n",
      "       'Monthly_Bill', 'Total_Usage_GB', 'Churn'],\n",
      "      dtype='object')\n",
      "\n",
      "--------------------------------------------------\n",
      "\n",
      "Data types:\n",
      "Age                             int64\n",
      "Gender                         object\n",
      "Location                       object\n",
      "Subscription_Length_Months      int64\n",
      "Monthly_Bill                  float64\n",
      "Total_Usage_GB                  int64\n",
      "Churn                           int64\n",
      "dtype: object\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check for typo's & suspicious values and rechecking dataset \n",
    "\n",
    "for column in df.columns:\n",
    "    unique_values = df[column].unique()\n",
    "    print(f\"Unique values in '{column}': {unique_values}\")\n",
    "    print()\n",
    "\n",
    "missing_values = df.isnull().sum()\n",
    "data_types = df.dtypes\n",
    "\n",
    "print('-'*50)\n",
    "print(\"Missing values:\")\n",
    "print(missing_values)\n",
    "print()\n",
    "\n",
    "x = df.duplicated().sum()\n",
    "print('-'*50)\n",
    "print(\"Duplicate values:\", x)\n",
    "print()\n",
    "\n",
    "\n",
    "y = df.shape\n",
    "print('-'*50)\n",
    "print(\"Shape of Dataset:\", y)\n",
    "print()\n",
    "\n",
    "z = df.columns\n",
    "print('-'*50)\n",
    "print(\"Columns of Datset:\", z)\n",
    "print()\n",
    "\n",
    "print('-'*50)\n",
    "print(\"\\nData types:\")\n",
    "print(data_types)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb7859c",
   "metadata": {},
   "source": [
    "**The dataset has been cleaned and no suspicious values or typos have been found.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9a9a41",
   "metadata": {},
   "source": [
    "# `Outliers Treatment`\n",
    "\n",
    "It is important to find and remove unusual or extreme data points, also known as outliers. Outliers can affect the model's understanding of the data and lead to wrong predictions. By identifying and removing outliers, the model becomes more accurate and reliable, and can better capture the actual patterns in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1f660d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust figure size based on the number of columns\n",
    "num_columns = len(df.columns)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "if num_columns > 16:\n",
    "    fig.set_size_inches(10, num_columns * 0.6)\n",
    "else:\n",
    "    fig.set_size_inches(num_columns, 6)\n",
    "\n",
    "# Create a boxplot for each column\n",
    "df.boxplot(ax=ax)\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title(\"Boxplot of Columns\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Values\")\n",
    "\n",
    "# Rotate the x-axis labels for better readability (optional)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3de4af",
   "metadata": {},
   "source": [
    "**There is no outlier present in any of the variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6193a6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of cleaned data\n",
    "\n",
    "df_cleaned = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09da425",
   "metadata": {},
   "source": [
    "# `Feature Encoding`\n",
    "\n",
    "Feature encoding techniques converts categorical data (like labels) into numbers that algorithms understand. This is essential because algorithms work with numbers, ensuring valuable info from categories isn't lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "868b4e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Location'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = df.select_dtypes(include=['object']).columns\n",
    "categorical_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68a4328",
   "metadata": {},
   "source": [
    "**There are two variables, which needs to be encode.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a7d7273",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['Gender', 'Location'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2bdab30",
   "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>Age</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Location_Houston</th>\n",
       "      <th>Location_Los Angeles</th>\n",
       "      <th>Location_Miami</th>\n",
       "      <th>Location_New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \\\n",
       "0   63                          17         73.36             236      0   \n",
       "1   62                           1         48.76             172      0   \n",
       "2   24                           5         85.47             460      0   \n",
       "3   36                           3         97.94             297      1   \n",
       "4   46                          19         58.14             266      0   \n",
       "\n",
       "   Gender_Male  Location_Houston  Location_Los Angeles  Location_Miami  \\\n",
       "0            1                 0                     1               0   \n",
       "1            0                 0                     0               0   \n",
       "2            0                 0                     1               0   \n",
       "3            0                 0                     0               1   \n",
       "4            0                 0                     0               1   \n",
       "\n",
       "   Location_New York  \n",
       "0                  0  \n",
       "1                  1  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3536f146",
   "metadata": {},
   "source": [
    "**Here I apply One Hot Encoding becuase both variables have less number of categories.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e7ce710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Age                         100000 non-null  int64  \n",
      " 1   Subscription_Length_Months  100000 non-null  int64  \n",
      " 2   Monthly_Bill                100000 non-null  float64\n",
      " 3   Total_Usage_GB              100000 non-null  int64  \n",
      " 4   Churn                       100000 non-null  int64  \n",
      " 5   Gender_Male                 100000 non-null  uint8  \n",
      " 6   Location_Houston            100000 non-null  uint8  \n",
      " 7   Location_Los Angeles        100000 non-null  uint8  \n",
      " 8   Location_Miami              100000 non-null  uint8  \n",
      " 9   Location_New York           100000 non-null  uint8  \n",
      "dtypes: float64(1), int64(4), uint8(5)\n",
      "memory usage: 4.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "004d0106",
   "metadata": {},
   "source": [
    "**Data types of all variables is fine.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e3847f",
   "metadata": {},
   "source": [
    "# `Checking Distribution of data`\n",
    "\n",
    "* In machine learning, it is important to check how the data is spread across different values. This is called the distribution of data.\n",
    "\n",
    "\n",
    "* If the data is skewed, meaning that there are more data points in one area than others, it can affect the performance of our models. For example, if the data is skewed towards the majority class, the model might perform well on the majority class but poorly on the minority class.\n",
    "\n",
    "\n",
    "* By understanding the data distribution, we can choose appropriate strategies to handle imbalances, outliers, or other issues. This will ensure that our model works well across all scenarios and accurately represents the underlying patterns in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "252af253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5dc90af4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of bins according to Sturges' Rule: 17\n"
     ]
    }
   ],
   "source": [
    "# Select appropriate bin size using sturges_rule\n",
    "\n",
    "import math\n",
    "\n",
    "def sturges_rule(num_data_points):\n",
    "    k = 1 + math.log2(num_data_points)\n",
    "    return int(k)\n",
    "\n",
    "# Example usage\n",
    "num_data_points = 100000\n",
    "bins = sturges_rule(num_data_points)\n",
    "print(\"Number of bins according to Sturges' Rule:\", bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1b92b6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check distribution of all continuous variables\n",
    "continuous_vars = df.select_dtypes(include=['float64', 'int64']).columns\n",
    "\n",
    "# Exclude binary variables from the list\n",
    "binary_vars = [var for var in continuous_vars if df[var].nunique() == 2]\n",
    "\n",
    "# Exclude binary variables from the continuous variables\n",
    "continuous_vars = [var for var in continuous_vars if var not in binary_vars]\n",
    "\n",
    "# Plot the distribution of each continuous variable\n",
    "for var in continuous_vars:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.histplot(data=df, x=var, bins=17, kde=True)\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.title(f'Distribution of {var}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8bd2974f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          -0.002689\n",
       "Subscription_Length_Months   -0.001655\n",
       "Monthly_Bill                 -0.000326\n",
       "Total_Usage_GB                0.007113\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check skewness of all continuous variables\n",
    "df[continuous_vars].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b766e7a",
   "metadata": {},
   "source": [
    "**All variables are approx normally distributed.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e2fd60",
   "metadata": {},
   "source": [
    "# `Check colinearity between variables`\n",
    "\n",
    "* In machine learning, it is important to check if there are variables that are highly correlated with each other. If there are, then we should remove one of them to improve the stability and interpretability of the model.\n",
    "\n",
    "\n",
    "* Highly correlated variables are redundant, meaning that they provide the same information. This can cause the model to give too much importance to one variable, leading to overfitting. Overfitting is when the model learns the training data too well and does not generalize well to new data.\n",
    "\n",
    "\n",
    "* By removing highly correlated variables, we can make the model focus on distinct and meaningful information. This makes the model more reliable and understandable. It also helps to prevent multicollinearity, which is a problem that can distort the model's predictions and insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b36ffdb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking colinearity between variables\n",
    "\n",
    "corr_matrix = df.corr()\n",
    "\n",
    "plt.figure(figsize=(14,8)) #(width,height)\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', square=True)\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb5a9f9",
   "metadata": {},
   "source": [
    "**No pair of variable is highly corrleated.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4305d7b8",
   "metadata": {},
   "source": [
    "# `Diving data into train and test set` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "03a06ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"Churn\",axis=1)\n",
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b609f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6f0cc2",
   "metadata": {},
   "source": [
    "**Take 70% data for training and 30% for testing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7ac957d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 9), (70000,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de22f658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30000, 9), (30000,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bb2d34",
   "metadata": {},
   "source": [
    "# `Feature Scaling` \n",
    "\n",
    "* In machine learning, it is important to make sure that all features are on the same scale. This is called feature scaling.\n",
    "\n",
    "\n",
    "* Feature scaling is important because it ensures that no single feature dominates the learning process. This is because many machine learning algorithms use distance-based calculations, and if features are on different scales, those with larger values could disproportionately influence the results.\n",
    "\n",
    "\n",
    "* Feature scaling also helps to improve the convergence of gradient-based optimization algorithms, which can lead to faster training.\n",
    "\n",
    "\n",
    "* By scaling features, we create a balanced playing field for each feature. This allows the algorithm to make fair and accurate comparisons, which can lead to better model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "244aeef4",
   "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>Age</th>\n",
       "      <th>Subscription_Length_Months</th>\n",
       "      <th>Monthly_Bill</th>\n",
       "      <th>Total_Usage_GB</th>\n",
       "      <th>Churn</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>Location_Houston</th>\n",
       "      <th>Location_Los Angeles</th>\n",
       "      <th>Location_Miami</th>\n",
       "      <th>Location_New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>17</td>\n",
       "      <td>73.36</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>48.76</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>85.47</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>97.94</td>\n",
       "      <td>297</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>58.14</td>\n",
       "      <td>266</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  Churn  \\\n",
       "0   63                          17         73.36             236      0   \n",
       "1   62                           1         48.76             172      0   \n",
       "2   24                           5         85.47             460      0   \n",
       "3   36                           3         97.94             297      1   \n",
       "4   46                          19         58.14             266      0   \n",
       "\n",
       "   Gender_Male  Location_Houston  Location_Los Angeles  Location_Miami  \\\n",
       "0            1                 0                     1               0   \n",
       "1            0                 0                     0               0   \n",
       "2            0                 0                     1               0   \n",
       "3            0                 0                     0               1   \n",
       "4            0                 0                     0               1   \n",
       "\n",
       "   Location_New York  \n",
       "0                  0  \n",
       "1                  1  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d0cfed04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with values greater than 1 or less than 0:\n",
      "['Age', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB']\n"
     ]
    }
   ],
   "source": [
    "columns_to_check = df.columns\n",
    "columns_needs_to_be_scaled = []\n",
    "\n",
    "for column in columns_to_check:\n",
    "    if (df[column] > 1).any() or (df[column] < 0).any():\n",
    "        columns_needs_to_be_scaled.append(column)\n",
    "\n",
    "print(\"Columns with values greater than 1 or less than 0:\")\n",
    "print(columns_needs_to_be_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f05c1f9",
   "metadata": {},
   "source": [
    "**These four columns need scaling, ['Age', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB']. I decide to use MinMax Scaler becuase majority of variables in binary.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f39c5dbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "columns_to_scale = ['Age', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train[columns_to_scale] = scaler.fit_transform(X_train[columns_to_scale])\n",
    "\n",
    "X_test[columns_to_scale] = scaler.transform(X_test[columns_to_scale])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edb5d53",
   "metadata": {},
   "source": [
    "* We use the .fit_transform() method on training data to calculate the scaling parameters (such as mean and standard deviation) and then apply the transformation. This ensures that the training data is scaled properly.\n",
    "\n",
    "\n",
    "* However, we use the .transform() method on test data because we do not want to change the scaling parameters based on the test data. Instead, we want to apply the same scaling that was learned from the training data. This maintains the consistency of scaling between training and testing data, which is more realistic and prevents the model from learning information from the test data during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cef1ac85",
   "metadata": {},
   "outputs": [
    {
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       "            Age  Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  \\\n",
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       "76513                  0  \n",
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      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5e234ecc",
   "metadata": {},
   "outputs": [
    {
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       "            Age  Subscription_Length_Months  Monthly_Bill  Total_Usage_GB  \\\n",
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       "92991  0.096154                    1.000000      0.745964        0.564444   \n",
       "\n",
       "       Gender_Male  Location_Houston  Location_Los Angeles  Location_Miami  \\\n",
       "75721            0                 1                     0               0   \n",
       "80184            1                 0                     0               0   \n",
       "19864            0                 0                     1               0   \n",
       "76699            1                 0                     0               0   \n",
       "92991            0                 0                     1               0   \n",
       "\n",
       "       Location_New York  \n",
       "75721                  0  \n",
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b47457",
   "metadata": {},
   "source": [
    "# `Check for the class imbalance`\n",
    "\n",
    "* In machine learning, it is important to check if the data is balanced or not. This is called class imbalance.\n",
    "\n",
    "\n",
    "* Class imbalance happens when there are more data points in one class than in the other class. This can affect the performance of the model. For example, if the majority class has more data points than the minority class, the model might perform well on the majority class but poorly on the minority class.\n",
    "\n",
    "\n",
    "* By identifying class imbalance, we can employ strategies to address the issue. These strategies include resampling, adjusting class weights, or using different evaluation metrics. This will help us create a fair and accurate model that performs well across all classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6a543d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50221\n",
       "1    49779\n",
       "Name: Churn, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_counts = df['Churn'].value_counts()\n",
    "class_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9f5d311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6)) \n",
    "class_counts.plot(kind='bar')\n",
    "plt.title('Class Distribution')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12836a5",
   "metadata": {},
   "source": [
    "**The churn variables are almost evenly distributed, which means that there is no problem of class imbalance.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a216c9b",
   "metadata": {},
   "source": [
    "# `Feature Selection using Random Forest Feature Importance Method`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "119b2b21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monthly_Bill</td>\n",
       "      <td>0.316383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total_Usage_GB</td>\n",
       "      <td>0.290353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.194396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subscription_Length_Months</td>\n",
       "      <td>0.142624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>0.016683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Los Angeles</td>\n",
       "      <td>0.010595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Houston</td>\n",
       "      <td>0.010007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Miami</td>\n",
       "      <td>0.009792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_New York</td>\n",
       "      <td>0.009166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Feature  Importance\n",
       "0                Monthly_Bill    0.316383\n",
       "1              Total_Usage_GB    0.290353\n",
       "2                         Age    0.194396\n",
       "3  Subscription_Length_Months    0.142624\n",
       "4                 Gender_Male    0.016683\n",
       "5        Location_Los Angeles    0.010595\n",
       "6            Location_Houston    0.010007\n",
       "7              Location_Miami    0.009792\n",
       "8           Location_New York    0.009166"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_classifier = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "importances = rf_classifier.feature_importances_\n",
    "\n",
    "feature_importance_df = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances})\n",
    "\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False).reset_index(drop=True)\n",
    "\n",
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed2cabfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check optimal number of features\n",
    "\n",
    "sorted_indices = np.argsort(importances)[::-1]\n",
    "cumulative_importance = np.cumsum(importances[sorted_indices])\n",
    "\n",
    "plt.plot(range(1, len(importances) + 1), cumulative_importance, 'b-')\n",
    "plt.xlabel('Number of Features')\n",
    "plt.ylabel('Cumulative Importance')\n",
    "plt.title('Cumulative Importance of Features')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d712fa",
   "metadata": {},
   "source": [
    "**The graph shows that only 4 variables are important for the analysis. These variables are:**\n",
    "\n",
    "* **Monthly bill**\n",
    "\n",
    "\n",
    "* **Total usage in GB**\n",
    "\n",
    "\n",
    "* **Age**\n",
    "\n",
    "\n",
    "* **Subscription length in months**\n",
    "\n",
    "\n",
    "* **These 4 variables together account for approximately 94% of the importance for the analysis. This means that they are the most important variables to consider when trying to predict customer churn.**\n",
    "\n",
    "\n",
    "* **Here, I will use all the variables for analysis because there are not many variables. If the model does not perform well or overfits, I will remove variables and build a model on the features that contribute more to the model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65c055c6",
   "metadata": {},
   "source": [
    "# `Check multi-colinearity between variables`\n",
    "\n",
    "* In machine learning, it is important to check if there are variables that are highly correlated with each other. This is called multicollinearity.\n",
    "\n",
    "\n",
    "* Multicollinearity can cause problems with the model, such as making it unstable and difficult to interpret. This is because correlated variables are redundant, meaning that they provide the same information. This can cause the model to give too much importance to one variable, leading to overfitting.\n",
    "\n",
    "\n",
    "* By identifying and addressing multicollinearity, we can make the model more reliable and interpretable. This improves the model's predictive power and makes it easier to understand how the variables affect the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "40457ee0",
   "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>Variable</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monthly_Bill</td>\n",
       "      <td>3.340229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total_Usage_GB</td>\n",
       "      <td>3.304982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>3.252427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subscription_Length_Months</td>\n",
       "      <td>3.152641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>1.880472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Miami</td>\n",
       "      <td>1.729954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Houston</td>\n",
       "      <td>1.728491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Los Angeles</td>\n",
       "      <td>1.718256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_New York</td>\n",
       "      <td>1.715994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Variable       VIF\n",
       "2                Monthly_Bill  3.340229\n",
       "3              Total_Usage_GB  3.304982\n",
       "0                         Age  3.252427\n",
       "1  Subscription_Length_Months  3.152641\n",
       "4                 Gender_Male  1.880472\n",
       "7              Location_Miami  1.729954\n",
       "5            Location_Houston  1.728491\n",
       "6        Location_Los Angeles  1.718256\n",
       "8           Location_New York  1.715994"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking multicolinearity of X_train_resampled\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"Variable\"] = X_train.columns\n",
    "vif[\"VIF\"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif = vif.sort_values(by='VIF', ascending=False)\n",
    "\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec6c0fa",
   "metadata": {},
   "source": [
    "**The training data does not contain multicollinearity because all of the variables have a VIF value less than 5.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a540b882",
   "metadata": {},
   "source": [
    "# `Model Building: Machine Learning Algorithms`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2e1185dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing algorithms, metrics and time\n",
    "\n",
    "import time\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "81daac2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Building Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.503214</td>\n",
       "      <td>0.503138</td>\n",
       "      <td>0.503214</td>\n",
       "      <td>0.500180</td>\n",
       "      <td>7.496585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.781101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.687743</td>\n",
       "      <td>0.687744</td>\n",
       "      <td>0.687743</td>\n",
       "      <td>0.687743</td>\n",
       "      <td>4.527633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.504471</td>\n",
       "      <td>0.504419</td>\n",
       "      <td>0.504471</td>\n",
       "      <td>0.503437</td>\n",
       "      <td>0.125011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.516557</td>\n",
       "      <td>0.516552</td>\n",
       "      <td>0.516557</td>\n",
       "      <td>0.516205</td>\n",
       "      <td>2.527126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.541729</td>\n",
       "      <td>0.541847</td>\n",
       "      <td>0.541729</td>\n",
       "      <td>0.541213</td>\n",
       "      <td>6.869250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.974446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.665023</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.664863</td>\n",
       "      <td>3.413992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.516986</td>\n",
       "      <td>0.517164</td>\n",
       "      <td>0.516986</td>\n",
       "      <td>0.514768</td>\n",
       "      <td>889.043590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score  \\\n",
       "0          LogisticRegression  0.503214   0.503138  0.503214  0.500180   \n",
       "1      DecisionTreeClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "2        KNeighborsClassifier  0.687743   0.687744  0.687743  0.687743   \n",
       "3                  GaussianNB  0.504471   0.504419  0.504471  0.503437   \n",
       "4          AdaBoostClassifier  0.516557   0.516552  0.516557  0.516205   \n",
       "5  GradientBoostingClassifier  0.541729   0.541847  0.541729  0.541213   \n",
       "6      RandomForestClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "7               XGBClassifier  0.664929   0.665023  0.664929  0.664863   \n",
       "8                         SVC  0.516986   0.517164  0.516986  0.514768   \n",
       "\n",
       "   Building Time (s)  \n",
       "0           7.496585  \n",
       "1           0.781101  \n",
       "2           4.527633  \n",
       "3           0.125011  \n",
       "4           2.527126  \n",
       "5           6.869250  \n",
       "6           4.974446  \n",
       "7           3.413992  \n",
       "8         889.043590  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algorithms = [\n",
    "    LogisticRegression(n_jobs=-1, random_state=42),\n",
    "    DecisionTreeClassifier(random_state=42),\n",
    "    KNeighborsClassifier(n_jobs=-1),\n",
    "    GaussianNB(),\n",
    "    AdaBoostClassifier(random_state=42),\n",
    "    GradientBoostingClassifier(random_state=42),\n",
    "    RandomForestClassifier(n_jobs=-1, random_state=42),\n",
    "    XGBClassifier(n_jobs=-1, random_state=42),\n",
    "    SVC(random_state=42)\n",
    "]\n",
    "\n",
    "# Initialize the results dictionary for training data\n",
    "results_train = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': [],\n",
    "    'Building Time (s)': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for training data\n",
    "for algorithm in algorithms:\n",
    "    start_time = time.time()  # Start timer\n",
    "\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    algorithm.fit(X_train, y_train)\n",
    "    y_train_pred = algorithm.predict(X_train)\n",
    "\n",
    "    accuracy = accuracy_score(y_train, y_train_pred)\n",
    "    precision = precision_score(y_train, y_train_pred, average='weighted')\n",
    "    recall = recall_score(y_train, y_train_pred, average='weighted')\n",
    "    f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "\n",
    "    end_time = time.time()  # End timer\n",
    "    building_time = end_time - start_time\n",
    "\n",
    "    results_train['Algorithm'].append(algorithm_name)\n",
    "    results_train['Accuracy'].append(accuracy)\n",
    "    results_train['Precision'].append(precision)\n",
    "    results_train['Recall'].append(recall)\n",
    "    results_train['F1-score'].append(f1)\n",
    "    results_train['Building Time (s)'].append(building_time)\n",
    "\n",
    "# Create a dataframe for the training data results\n",
    "df_train = pd.DataFrame(results_train)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c4b923",
   "metadata": {},
   "source": [
    "* **Decision Tree, Random Forest, and Gradient Boosting achieved perfect accuracy on the training data, suggesting potential overfitting.**\n",
    "\n",
    "\n",
    "* **Logistic Regression, GaussianNB, and AdaBoost have relatively low accuracy and F1-score.**\n",
    "\n",
    "\n",
    "* **KNeighborsClassifier and XGBClassifier have moderate accuracy and F1-score.**\n",
    "\n",
    "\n",
    "* **SVC has moderate accuracy but high building time.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c1c1a933",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.504733</td>\n",
       "      <td>0.504061</td>\n",
       "      <td>0.504733</td>\n",
       "      <td>0.501456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.499100</td>\n",
       "      <td>0.499157</td>\n",
       "      <td>0.499100</td>\n",
       "      <td>0.499113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.496500</td>\n",
       "      <td>0.496527</td>\n",
       "      <td>0.496500</td>\n",
       "      <td>0.496510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.505267</td>\n",
       "      <td>0.504866</td>\n",
       "      <td>0.505267</td>\n",
       "      <td>0.504104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.504200</td>\n",
       "      <td>0.504030</td>\n",
       "      <td>0.504200</td>\n",
       "      <td>0.503962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.499567</td>\n",
       "      <td>0.499291</td>\n",
       "      <td>0.499567</td>\n",
       "      <td>0.499067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.497267</td>\n",
       "      <td>0.497076</td>\n",
       "      <td>0.497267</td>\n",
       "      <td>0.497004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.500500</td>\n",
       "      <td>0.500438</td>\n",
       "      <td>0.500500</td>\n",
       "      <td>0.500451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.500933</td>\n",
       "      <td>0.500248</td>\n",
       "      <td>0.500933</td>\n",
       "      <td>0.498261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score\n",
       "0          LogisticRegression  0.504733   0.504061  0.504733  0.501456\n",
       "1      DecisionTreeClassifier  0.499100   0.499157  0.499100  0.499113\n",
       "2        KNeighborsClassifier  0.496500   0.496527  0.496500  0.496510\n",
       "3                  GaussianNB  0.505267   0.504866  0.505267  0.504104\n",
       "4          AdaBoostClassifier  0.504200   0.504030  0.504200  0.503962\n",
       "5  GradientBoostingClassifier  0.499567   0.499291  0.499567  0.499067\n",
       "6      RandomForestClassifier  0.497267   0.497076  0.497267  0.497004\n",
       "7               XGBClassifier  0.500500   0.500438  0.500500  0.500451\n",
       "8                         SVC  0.500933   0.500248  0.500933  0.498261"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_test = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for test data\n",
    "for algorithm in algorithms:\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    y_test_pred = algorithm.predict(X_test)\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    precision = precision_score(y_test, y_test_pred, average='weighted')\n",
    "    recall = recall_score(y_test, y_test_pred, average='weighted')\n",
    "    f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "\n",
    "    results_test['Algorithm'].append(algorithm_name)\n",
    "    results_test['Accuracy'].append(accuracy)\n",
    "    results_test['Precision'].append(precision)\n",
    "    results_test['Recall'].append(recall)\n",
    "    results_test['F1-score'].append(f1)\n",
    "\n",
    "# Create a dataframe for the test data results\n",
    "df_test = pd.DataFrame(results_test)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9ff4f2e",
   "metadata": {},
   "source": [
    "* **Most algorithms have mediocre performance on the test data, with accuracy close to random guessing (around 0.5).**\n",
    "\n",
    "\n",
    "* **Decision Tree, Random Forest, and Gradient Boosting also show subpar results, indicating overfitting.**\n",
    "\n",
    "\n",
    "* **No algorithm is performing well, so I decided to take only 4 variables that contribute more to the target variable and build the model again.**\n",
    "\n",
    "\n",
    "* **I removed the age and location variables because they are not good predictors of whether a customer will churn or not.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "74bb15cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[['Monthly_Bill', 'Total_Usage_GB', 'Age', 'Subscription_Length_Months']]\n",
    "\n",
    "X_test = X_test[['Monthly_Bill', 'Total_Usage_GB', 'Age', 'Subscription_Length_Months']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "24fda754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train columns: Index(['Monthly_Bill', 'Total_Usage_GB', 'Age', 'Subscription_Length_Months'], dtype='object')\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "X_test columns: Index(['Monthly_Bill', 'Total_Usage_GB', 'Age', 'Subscription_Length_Months'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print('X_train columns:', X_train.columns)\n",
    "print('-'*120)\n",
    "print('X_test columns:', X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fa3ef4e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Building Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.502671</td>\n",
       "      <td>0.502584</td>\n",
       "      <td>0.502671</td>\n",
       "      <td>0.495610</td>\n",
       "      <td>6.616779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.501294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.686600</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>0.686600</td>\n",
       "      <td>0.686596</td>\n",
       "      <td>2.191296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.501486</td>\n",
       "      <td>0.501325</td>\n",
       "      <td>0.501486</td>\n",
       "      <td>0.493915</td>\n",
       "      <td>0.095272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.513000</td>\n",
       "      <td>0.512984</td>\n",
       "      <td>0.513000</td>\n",
       "      <td>0.512904</td>\n",
       "      <td>2.078163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.538557</td>\n",
       "      <td>0.538573</td>\n",
       "      <td>0.538557</td>\n",
       "      <td>0.538399</td>\n",
       "      <td>5.318027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.958342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.659214</td>\n",
       "      <td>0.659225</td>\n",
       "      <td>0.659214</td>\n",
       "      <td>0.659201</td>\n",
       "      <td>2.676721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.517729</td>\n",
       "      <td>0.518011</td>\n",
       "      <td>0.517729</td>\n",
       "      <td>0.514761</td>\n",
       "      <td>861.455037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score  \\\n",
       "0          LogisticRegression  0.502671   0.502584  0.502671  0.495610   \n",
       "1      DecisionTreeClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "2        KNeighborsClassifier  0.686600   0.686602  0.686600  0.686596   \n",
       "3                  GaussianNB  0.501486   0.501325  0.501486  0.493915   \n",
       "4          AdaBoostClassifier  0.513000   0.512984  0.513000  0.512904   \n",
       "5  GradientBoostingClassifier  0.538557   0.538573  0.538557  0.538399   \n",
       "6      RandomForestClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "7               XGBClassifier  0.659214   0.659225  0.659214  0.659201   \n",
       "8                         SVC  0.517729   0.518011  0.517729  0.514761   \n",
       "\n",
       "   Building Time (s)  \n",
       "0           6.616779  \n",
       "1           0.501294  \n",
       "2           2.191296  \n",
       "3           0.095272  \n",
       "4           2.078163  \n",
       "5           5.318027  \n",
       "6           4.958342  \n",
       "7           2.676721  \n",
       "8         861.455037  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algorithms = [\n",
    "    LogisticRegression(n_jobs=-1, random_state=42),\n",
    "    DecisionTreeClassifier(random_state=42),\n",
    "    KNeighborsClassifier(n_jobs=-1),\n",
    "    GaussianNB(),\n",
    "    AdaBoostClassifier(random_state=42),\n",
    "    GradientBoostingClassifier(random_state=42),\n",
    "    RandomForestClassifier(n_jobs=-1, random_state=42),\n",
    "    XGBClassifier(n_jobs=-1, random_state=42),\n",
    "    SVC(random_state=42)\n",
    "]\n",
    "\n",
    "# Initialize the results dictionary for training data\n",
    "results_train = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': [],\n",
    "    'Building Time (s)': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for training data\n",
    "for algorithm in algorithms:\n",
    "    start_time = time.time()  # Start timer\n",
    "\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    algorithm.fit(X_train, y_train)\n",
    "    y_train_pred = algorithm.predict(X_train)\n",
    "\n",
    "    accuracy = accuracy_score(y_train, y_train_pred)\n",
    "    precision = precision_score(y_train, y_train_pred, average='weighted')\n",
    "    recall = recall_score(y_train, y_train_pred, average='weighted')\n",
    "    f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "\n",
    "    end_time = time.time()  # End timer\n",
    "    building_time = end_time - start_time\n",
    "\n",
    "    results_train['Algorithm'].append(algorithm_name)\n",
    "    results_train['Accuracy'].append(accuracy)\n",
    "    results_train['Precision'].append(precision)\n",
    "    results_train['Recall'].append(recall)\n",
    "    results_train['F1-score'].append(f1)\n",
    "    results_train['Building Time (s)'].append(building_time)\n",
    "\n",
    "# Create a dataframe for the training data results\n",
    "df_train = pd.DataFrame(results_train)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b51c921",
   "metadata": {},
   "source": [
    "* **Decision Tree and Random Forest still achieve perfect accuracy, likely overfitting again.**\n",
    "\n",
    "\n",
    "* **Logistic Regression, GaussianNB, and AdaBoost have improved slightly.**\n",
    "\n",
    "\n",
    "* **KNeighborsClassifier and XGBClassifier remain moderate.**\n",
    "\n",
    "\n",
    "* **SVC still has high building time.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b3fa9f01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.498433</td>\n",
       "      <td>0.497171</td>\n",
       "      <td>0.498433</td>\n",
       "      <td>0.491696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.500400</td>\n",
       "      <td>0.500360</td>\n",
       "      <td>0.500400</td>\n",
       "      <td>0.500372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497918</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.499333</td>\n",
       "      <td>0.498084</td>\n",
       "      <td>0.499333</td>\n",
       "      <td>0.492268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.498467</td>\n",
       "      <td>0.498457</td>\n",
       "      <td>0.498467</td>\n",
       "      <td>0.498461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.501906</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.501909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.505767</td>\n",
       "      <td>0.505583</td>\n",
       "      <td>0.505767</td>\n",
       "      <td>0.505489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.497667</td>\n",
       "      <td>0.497633</td>\n",
       "      <td>0.497667</td>\n",
       "      <td>0.497644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.501733</td>\n",
       "      <td>0.501010</td>\n",
       "      <td>0.501733</td>\n",
       "      <td>0.498641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score\n",
       "0          LogisticRegression  0.498433   0.497171  0.498433  0.491696\n",
       "1      DecisionTreeClassifier  0.500400   0.500360  0.500400  0.500372\n",
       "2        KNeighborsClassifier  0.497900   0.497918  0.497900  0.497907\n",
       "3                  GaussianNB  0.499333   0.498084  0.499333  0.492268\n",
       "4          AdaBoostClassifier  0.498467   0.498457  0.498467  0.498461\n",
       "5  GradientBoostingClassifier  0.502000   0.501906  0.502000  0.501909\n",
       "6      RandomForestClassifier  0.505767   0.505583  0.505767  0.505489\n",
       "7               XGBClassifier  0.497667   0.497633  0.497667  0.497644\n",
       "8                         SVC  0.501733   0.501010  0.501733  0.498641"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_test = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for test data\n",
    "for algorithm in algorithms:\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    y_test_pred = algorithm.predict(X_test)\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    precision = precision_score(y_test, y_test_pred, average='weighted')\n",
    "    recall = recall_score(y_test, y_test_pred, average='weighted')\n",
    "    f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "\n",
    "    results_test['Algorithm'].append(algorithm_name)\n",
    "    results_test['Accuracy'].append(accuracy)\n",
    "    results_test['Precision'].append(precision)\n",
    "    results_test['Recall'].append(recall)\n",
    "    results_test['F1-score'].append(f1)\n",
    "\n",
    "# Create a dataframe for the test data results\n",
    "df_test = pd.DataFrame(results_test)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03dcb884",
   "metadata": {},
   "source": [
    "* **Decision Tree and Random Forest overfits.**\n",
    "\n",
    "\n",
    "* **No algorithm is performing well, so I decided to build model using neural networks.**\n",
    "\n",
    "\n",
    "* **If the neural network does not provide better results than machine learning algorithms, then I will build ensembles of random forests because random forests achieve perfect scores in all metrics in the training data. Decision trees also achieve perfect scores in all metrics in the training data, but I have already tried and tested multiple ensembles that use decision trees. Therefore, I will now build ensembles using random forests**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cd508fc",
   "metadata": {},
   "source": [
    "# `Model Building: Neural Network`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d56b5604",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.layers import Dense, BatchNormalization, Activation, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abacd02c",
   "metadata": {},
   "source": [
    "## `Defining Callbacks for Early Stopping and Model Checkpoint`\n",
    "\n",
    "* Best model will save to pwd (present working directory) as 'Churn Classifier.h5'\n",
    "\n",
    "\n",
    "* Early stopping is a regularization technique that stops the training of a neural network before it reaches the maximum number of epochs or iterations. It is important because it helps to prevent overfitting.\n",
    "\n",
    "\n",
    "* Early stopping is a technique that stops the training of a model when the validation loss stops improving. If you set restore_best_weights to False, the model will be saved at the end of the training, even if the validation loss has not improved. This could result in the model being saved at a point where it is overfitting the training data. By setting restore_best_weights to True, the model will be saved at the epoch with the best validation loss. This ensures that the model that is saved is the one that is most likely to generalize well to new data.\n",
    "\n",
    "\n",
    "* When you set restore_best_weights=True in the EarlyStopping callback, it ensures that the model's weights are restored to the state they were in at the epoch with the lowest validation loss. This means that when you evaluate the model after training, the evaluation metrics, including accuracy and other metrics, are computed based on the weights of the model that performed the best on the validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0d997187",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the EarlyStopping and ModelCheckpoint callbacks\n",
    "\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss',        # The metric to monitor. This is usually the validation loss.\n",
    "    min_delta=0.001,           # The minimum amount of improvement required to consider the model to have improved.\n",
    "    patience=10,               # The number of epochs to wait without improvement before stopping the training.\n",
    "    verbose=1,                 # Print output\n",
    "    mode='auto',               # Consider mode of the metric as min or max.\n",
    "    restore_best_weights=True  # Whether to restore the model weights at the epoch with the best validation loss.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa0fb849",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = ModelCheckpoint('ChurnClassifier.h5', monitor='val_loss', verbose=1, save_best_only=True, mode='min')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ee0c8bf",
   "metadata": {},
   "source": [
    "## `Build Neural Network Architectures & Fit Model on Training Data`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10399651",
   "metadata": {},
   "source": [
    "### `Architecture I`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "af7bb570",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model]\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "36a16a03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "363/383 [===========================>..] - ETA: 0s - loss: 0.6937 - accuracy: 0.4993\n",
      "Epoch 1: val_loss improved from inf to 0.69363, saving model to ChurnClassifier.h5\n",
      "383/383 [==============================] - 2s 3ms/step - loss: 0.6937 - accuracy: 0.4993 - val_loss: 0.6936 - val_accuracy: 0.4971\n",
      "Epoch 2/100\n",
      " 35/383 [=>............................] - ETA: 1s - loss: 0.6927 - accuracy: 0.5152"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "370/383 [===========================>..] - ETA: 0s - loss: 0.6933 - accuracy: 0.5038\n",
      "Epoch 2: val_loss improved from 0.69363 to 0.69333, saving model to ChurnClassifier.h5\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6933 - accuracy: 0.5037 - val_loss: 0.6933 - val_accuracy: 0.4981\n",
      "Epoch 3/100\n",
      "360/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5036\n",
      "Epoch 3: val_loss improved from 0.69333 to 0.69316, saving model to ChurnClassifier.h5\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6933 - accuracy: 0.5032 - val_loss: 0.6932 - val_accuracy: 0.5034\n",
      "Epoch 4/100\n",
      "364/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5070\n",
      "Epoch 4: val_loss improved from 0.69316 to 0.69313, saving model to ChurnClassifier.h5\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5062 - val_loss: 0.6931 - val_accuracy: 0.5016\n",
      "Epoch 5/100\n",
      "370/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5017\n",
      "Epoch 5: val_loss improved from 0.69313 to 0.69309, saving model to ChurnClassifier.h5\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5007 - val_loss: 0.6931 - val_accuracy: 0.5046\n",
      "Epoch 6/100\n",
      "383/383 [==============================] - ETA: 0s - loss: 0.6931 - accuracy: 0.5032\n",
      "Epoch 6: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6931 - accuracy: 0.5032 - val_loss: 0.6932 - val_accuracy: 0.5035\n",
      "Epoch 7/100\n",
      "360/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5042\n",
      "Epoch 7: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5041 - val_loss: 0.6933 - val_accuracy: 0.4995\n",
      "Epoch 8/100\n",
      "364/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5071\n",
      "Epoch 8: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5064 - val_loss: 0.6932 - val_accuracy: 0.5011\n",
      "Epoch 9/100\n",
      "368/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5029\n",
      "Epoch 9: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5032 - val_loss: 0.6935 - val_accuracy: 0.4972\n",
      "Epoch 10/100\n",
      "364/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5066\n",
      "Epoch 10: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5063 - val_loss: 0.6932 - val_accuracy: 0.5013\n",
      "Epoch 11/100\n",
      "361/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5043Restoring model weights from the end of the best epoch: 1.\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6931 - accuracy: 0.5041 - val_loss: 0.6931 - val_accuracy: 0.5037\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.3, callbacks=[early_stopping, checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174e1ecb",
   "metadata": {},
   "source": [
    "### `Architecture II`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fbb03b77",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# layers\n",
    "model.add(Dense(units = 32, kernel_initializer = 'uniform', activation = 'relu', input_dim = 4))\n",
    "model.add(Dense(units = 32, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "\n",
    "# Compile the model]\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7efcf0ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "359/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5017\n",
      "Epoch 1: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 2s 3ms/step - loss: 0.6931 - accuracy: 0.5007 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 2/100\n",
      "379/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5029\n",
      "Epoch 2: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 3/100\n",
      "361/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5011\n",
      "Epoch 3: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5021 - val_loss: 0.6933 - val_accuracy: 0.4967\n",
      "Epoch 4/100\n",
      "368/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5034\n",
      "Epoch 4: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 5/100\n",
      "367/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5024\n",
      "Epoch 5: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5022 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 6/100\n",
      "369/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5012\n",
      "Epoch 6: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5014 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 7/100\n",
      "371/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5025\n",
      "Epoch 7: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 8/100\n",
      "368/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5031\n",
      "Epoch 8: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 9/100\n",
      "363/383 [===========================>..] - ETA: 0s - loss: 0.6931 - accuracy: 0.5002\n",
      "Epoch 9: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.4998 - val_loss: 0.6933 - val_accuracy: 0.4967\n",
      "Epoch 10/100\n",
      "366/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5007\n",
      "Epoch 10: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5013 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 11/100\n",
      "363/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5025Restoring model weights from the end of the best epoch: 1.\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.3, callbacks=[early_stopping, checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77f8ba9",
   "metadata": {},
   "source": [
    "### `Architecture III`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7083bca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the NN\n",
    "model = Sequential()\n",
    "\n",
    "# layers\n",
    "model.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 4))\n",
    "model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(units = 4, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "# Compiling the ANN\n",
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8087721a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "365/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.4993\n",
      "Epoch 1: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 2s 3ms/step - loss: 0.6932 - accuracy: 0.4992 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 2/100\n",
      "373/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5027\n",
      "Epoch 2: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 3/100\n",
      "373/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5006\n",
      "Epoch 3: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5007 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 4/100\n",
      "371/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5031\n",
      "Epoch 4: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 5/100\n",
      "374/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5029\n",
      "Epoch 5: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 6/100\n",
      "371/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5027\n",
      "Epoch 6: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 7/100\n",
      "358/383 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.4971\n",
      "Epoch 7: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.4994 - val_loss: 0.6933 - val_accuracy: 0.4967\n",
      "Epoch 8/100\n",
      "376/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5032\n",
      "Epoch 8: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 9/100\n",
      "376/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5022\n",
      "Epoch 9: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 10/100\n",
      "374/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5026\n",
      "Epoch 10: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 11/100\n",
      "375/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5024Restoring model weights from the end of the best epoch: 1.\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 2ms/step - loss: 0.6931 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.4967\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.3, callbacks=[early_stopping, checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1267af1",
   "metadata": {},
   "source": [
    "### `Architecture IV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6e160692",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# Input layer with BatchNormalization and Activation (ReLU)\n",
    "model.add(Dense(10, input_dim=4, kernel_initializer='he_normal'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# First hidden layer with BatchNormalization, Activation (ReLU), and Dropout\n",
    "model.add(Dense(10, kernel_initializer='he_normal'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))  # 20% dropout\n",
    "\n",
    "# Second hidden layer with BatchNormalization, Activation (ReLU), and Dropout\n",
    "model.add(Dense(5, kernel_initializer='he_normal'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.1))  # 10% dropout\n",
    "\n",
    "# Output layer with Sigmoid activation\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff07c03",
   "metadata": {},
   "source": [
    "**`Batch Normalization`** is used before the activation function in neural networks to enhance training stability and speed up convergence. By normalizing inputs within each batch, it maintains stable distribution and prevents extreme activations, leading to faster learning and better performance.\n",
    "\n",
    "\n",
    "**`\"He_normal\"`** is a weight initialization technique in neural networks. It sets initial weights to encourage effective learning, especially with ReLU activation. It helps prevent vanishing gradient and works well in deep networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2fb5b655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "364/383 [===========================>..] - ETA: 0s - loss: 0.7160 - accuracy: 0.5003\n",
      "Epoch 1: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 3s 3ms/step - loss: 0.7152 - accuracy: 0.5002 - val_loss: 0.6943 - val_accuracy: 0.5035\n",
      "Epoch 2/100\n",
      "376/383 [============================>.] - ETA: 0s - loss: 0.7018 - accuracy: 0.5022\n",
      "Epoch 2: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.7018 - accuracy: 0.5024 - val_loss: 0.6939 - val_accuracy: 0.5026\n",
      "Epoch 3/100\n",
      "380/383 [============================>.] - ETA: 0s - loss: 0.6978 - accuracy: 0.5001\n",
      "Epoch 3: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6978 - accuracy: 0.4997 - val_loss: 0.6936 - val_accuracy: 0.5019\n",
      "Epoch 4/100\n",
      "377/383 [============================>.] - ETA: 0s - loss: 0.6956 - accuracy: 0.5030\n",
      "Epoch 4: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6956 - accuracy: 0.5031 - val_loss: 0.6933 - val_accuracy: 0.5055\n",
      "Epoch 5/100\n",
      "369/383 [===========================>..] - ETA: 0s - loss: 0.6954 - accuracy: 0.4979\n",
      "Epoch 5: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6953 - accuracy: 0.4979 - val_loss: 0.6935 - val_accuracy: 0.5002\n",
      "Epoch 6/100\n",
      "376/383 [============================>.] - ETA: 0s - loss: 0.6945 - accuracy: 0.5031\n",
      "Epoch 6: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6945 - accuracy: 0.5027 - val_loss: 0.6933 - val_accuracy: 0.4997\n",
      "Epoch 7/100\n",
      "374/383 [============================>.] - ETA: 0s - loss: 0.6941 - accuracy: 0.5002\n",
      "Epoch 7: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6941 - accuracy: 0.5005 - val_loss: 0.6932 - val_accuracy: 0.5007\n",
      "Epoch 8/100\n",
      "371/383 [============================>.] - ETA: 0s - loss: 0.6937 - accuracy: 0.5002\n",
      "Epoch 8: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6937 - accuracy: 0.4997 - val_loss: 0.6934 - val_accuracy: 0.4980\n",
      "Epoch 9/100\n",
      "365/383 [===========================>..] - ETA: 0s - loss: 0.6937 - accuracy: 0.5005\n",
      "Epoch 9: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6937 - accuracy: 0.5008 - val_loss: 0.6933 - val_accuracy: 0.4990\n",
      "Epoch 10/100\n",
      "375/383 [============================>.] - ETA: 0s - loss: 0.6936 - accuracy: 0.5025\n",
      "Epoch 10: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6936 - accuracy: 0.5022 - val_loss: 0.6933 - val_accuracy: 0.4979\n",
      "Epoch 11/100\n",
      "377/383 [============================>.] - ETA: 0s - loss: 0.6933 - accuracy: 0.5042\n",
      "Epoch 11: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6933 - accuracy: 0.5042 - val_loss: 0.6934 - val_accuracy: 0.4966\n",
      "Epoch 12/100\n",
      "375/383 [============================>.] - ETA: 0s - loss: 0.6933 - accuracy: 0.5021\n",
      "Epoch 12: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6933 - accuracy: 0.5028 - val_loss: 0.6932 - val_accuracy: 0.5001\n",
      "Epoch 13/100\n",
      "377/383 [============================>.] - ETA: 0s - loss: 0.6931 - accuracy: 0.5024\n",
      "Epoch 13: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5019 - val_loss: 0.6933 - val_accuracy: 0.4984\n",
      "Epoch 14/100\n",
      "376/383 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5027Restoring model weights from the end of the best epoch: 4.\n",
      "\n",
      "Epoch 14: val_loss did not improve from 0.69309\n",
      "383/383 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5025 - val_loss: 0.6933 - val_accuracy: 0.4990\n",
      "Epoch 14: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.3, callbacks=[early_stopping, checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2c0da8",
   "metadata": {},
   "source": [
    "### `Architecture V`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4099a888",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the neural network\n",
    "model = Sequential([\n",
    "    Dense(64, activation = 'relu', input_dim = 4),\n",
    "    Dropout(0.5),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f8f20a8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "427/438 [============================>.] - ETA: 0s - loss: 0.6953 - accuracy: 0.5012\n",
      "Epoch 1: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 2s 3ms/step - loss: 0.6952 - accuracy: 0.5012 - val_loss: 0.6932 - val_accuracy: 0.4995\n",
      "Epoch 2/100\n",
      "433/438 [============================>.] - ETA: 0s - loss: 0.6936 - accuracy: 0.4992\n",
      "Epoch 2: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.6936 - accuracy: 0.4996 - val_loss: 0.6932 - val_accuracy: 0.4988\n",
      "Epoch 3/100\n",
      "417/438 [===========================>..] - ETA: 0s - loss: 0.6933 - accuracy: 0.5024\n",
      "Epoch 3: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.6933 - accuracy: 0.5017 - val_loss: 0.6932 - val_accuracy: 0.4964\n",
      "Epoch 4/100\n",
      "419/438 [===========================>..] - ETA: 0s - loss: 0.6934 - accuracy: 0.4980\n",
      "Epoch 4: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.6934 - accuracy: 0.4979 - val_loss: 0.6932 - val_accuracy: 0.4957\n",
      "Epoch 5/100\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5006\n",
      "Epoch 5: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5006 - val_loss: 0.6932 - val_accuracy: 0.4979\n",
      "Epoch 6/100\n",
      "429/438 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5015\n",
      "Epoch 6: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5014 - val_loss: 0.6932 - val_accuracy: 0.4956\n",
      "Epoch 7/100\n",
      "420/438 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5014\n",
      "Epoch 7: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.5013 - val_loss: 0.6932 - val_accuracy: 0.4958\n",
      "Epoch 8/100\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5032\n",
      "Epoch 8: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5031 - val_loss: 0.6931 - val_accuracy: 0.5047\n",
      "Epoch 9/100\n",
      "416/438 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.4978\n",
      "Epoch 9: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 2ms/step - loss: 0.6932 - accuracy: 0.4984 - val_loss: 0.6932 - val_accuracy: 0.4975\n",
      "Epoch 10/100\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.6932 - accuracy: 0.5036\n",
      "Epoch 10: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4919\n",
      "Epoch 11/100\n",
      "417/438 [===========================>..] - ETA: 0s - loss: 0.6932 - accuracy: 0.5006Restoring model weights from the end of the best epoch: 1.\n",
      "\n",
      "Epoch 11: val_loss did not improve from 0.69309\n",
      "438/438 [==============================] - 1s 3ms/step - loss: 0.6932 - accuracy: 0.5009 - val_loss: 0.6932 - val_accuracy: 0.4954\n",
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.2, callbacks=[early_stopping, checkpoint],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a931ea6",
   "metadata": {},
   "source": [
    "* **I have tried multiple ANN architectures, but none of them have provided good results.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86a95d2",
   "metadata": {},
   "source": [
    "## `Ensembles of Random Forest`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1d7fb4d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Results:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Building Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.420235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.530471</td>\n",
       "      <td>0.530660</td>\n",
       "      <td>0.530471</td>\n",
       "      <td>0.529365</td>\n",
       "      <td>2.834267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.616929</td>\n",
       "      <td>0.616943</td>\n",
       "      <td>0.616929</td>\n",
       "      <td>0.616902</td>\n",
       "      <td>0.923865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Accuracy  Precision    Recall  F1 Score  Building Time\n",
       "AdaBoost           1.000000   1.000000  1.000000  1.000000      18.420235\n",
       "Gradient Boosting  0.530471   0.530660  0.530471  0.529365       2.834267\n",
       "XGBoost            0.616929   0.616943  0.616929  0.616902       0.923865"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize base estimator (Random Forest)\n",
    "base_estimator = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Initialize models\n",
    "adaboost_model = AdaBoostClassifier(base_estimator=base_estimator, n_estimators=50, random_state=42)\n",
    "gradient_boost_model = GradientBoostingClassifier(n_estimators=50, random_state=42)\n",
    "xgboost_model = XGBClassifier(n_estimators=50, random_state=42)\n",
    "\n",
    "# Initialize lists to store metrics\n",
    "models = ['AdaBoost', 'Gradient Boosting', 'XGBoost']\n",
    "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score', 'Building Time']\n",
    "results_train = {metric: [] for metric in metrics}\n",
    "results_test = {metric: [] for metric in metrics}\n",
    "\n",
    "# Train and evaluate models\n",
    "for model in [adaboost_model, gradient_boost_model, xgboost_model]:\n",
    "    start_time = time.time()\n",
    "    model.fit(X_train, y_train)\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Predict on the training set\n",
    "    y_train_pred = model.predict(X_train)\n",
    "\n",
    "    # Calculate metrics on training data\n",
    "    accuracy_train = accuracy_score(y_train, y_train_pred)\n",
    "    precision_train = precision_score(y_train, y_train_pred, average='weighted')\n",
    "    recall_train = recall_score(y_train, y_train_pred, average='weighted')\n",
    "    f1_train = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    building_time = end_time - start_time\n",
    "\n",
    "    # Predict on the test set\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # Calculate metrics on test data\n",
    "    accuracy_test = accuracy_score(y_test, y_test_pred)\n",
    "    precision_test = precision_score(y_test, y_test_pred, average='weighted')\n",
    "    recall_test = recall_score(y_test, y_test_pred, average='weighted')\n",
    "    f1_test = f1_score(y_test, y_test_pred, average='weighted')\n",
    "\n",
    "    # Append metrics to the results dictionaries\n",
    "    results_train['Accuracy'].append(accuracy_train)\n",
    "    results_train['Precision'].append(precision_train)\n",
    "    results_train['Recall'].append(recall_train)\n",
    "    results_train['F1 Score'].append(f1_train)\n",
    "    results_train['Building Time'].append(building_time)\n",
    "\n",
    "    results_test['Accuracy'].append(accuracy_test)\n",
    "    results_test['Precision'].append(precision_test)\n",
    "    results_test['Recall'].append(recall_test)\n",
    "    results_test['F1 Score'].append(f1_test)\n",
    "    results_test['Building Time'].append(building_time)\n",
    "\n",
    "# Create DataFrames from the results\n",
    "results_train_df = pd.DataFrame(results_train, index=models)\n",
    "results_test_df = pd.DataFrame(results_test, index=models)\n",
    "\n",
    "# Display the DataFrames\n",
    "print(\"Training Data Results:\")\n",
    "results_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "db7b3d7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Data Results:\n"
     ]
    },
    {
     "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>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Building Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>0.502333</td>\n",
       "      <td>0.502113</td>\n",
       "      <td>0.502333</td>\n",
       "      <td>0.501976</td>\n",
       "      <td>18.420235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.500767</td>\n",
       "      <td>0.500344</td>\n",
       "      <td>0.500767</td>\n",
       "      <td>0.499672</td>\n",
       "      <td>2.834267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497872</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497882</td>\n",
       "      <td>0.923865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Accuracy  Precision    Recall  F1 Score  Building Time\n",
       "AdaBoost           0.502333   0.502113  0.502333  0.501976      18.420235\n",
       "Gradient Boosting  0.500767   0.500344  0.500767  0.499672       2.834267\n",
       "XGBoost            0.497900   0.497872  0.497900  0.497882       0.923865"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nTest Data Results:\")\n",
    "results_test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "667932e4",
   "metadata": {},
   "source": [
    "* **The performance of ensembles can vary, but they are usually not much better than individual algorithms.**\n",
    "\n",
    "\n",
    "* **None of the ensembles provided the desired results. So, I decided to trial and error using a different approach.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d764cee",
   "metadata": {},
   "source": [
    "# `Model Building: PCA`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "94366989",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = pd.get_dummies(df_cleaned, columns=['Gender', 'Location'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b7040da2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_cleaned.drop('Churn', axis=1)\n",
    "y = df_cleaned['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c08cf20c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1369be14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "16d2a470",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert scaled numpy arrays back to DataFrames\n",
    "\n",
    "X_train = pd.DataFrame(X_train)\n",
    "\n",
    "X_test = pd.DataFrame(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "16766486",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>-1.243589</td>\n",
       "      <td>0.074501</td>\n",
       "      <td>1.632642</td>\n",
       "      <td>1.481066</td>\n",
       "      <td>1.005731</td>\n",
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       "      <td>1.998751</td>\n",
       "      <td>-0.502499</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.654284</td>\n",
       "      <td>-1.515230</td>\n",
       "      <td>0.336478</td>\n",
       "      <td>0.829765</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>1.989613</td>\n",
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       "      <td>-0.502499</td>\n",
       "      <td>-0.498169</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.785482</td>\n",
       "      <td>1.086147</td>\n",
       "      <td>0.027632</td>\n",
       "      <td>1.166909</td>\n",
       "      <td>1.005731</td>\n",
       "      <td>-0.502610</td>\n",
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       "      <th>3</th>\n",
       "      <td>1.112391</td>\n",
       "      <td>-0.070020</td>\n",
       "      <td>0.894871</td>\n",
       "      <td>-1.614532</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>1.989613</td>\n",
       "      <td>-0.500312</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>-0.498169</td>\n",
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       "      <th>4</th>\n",
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       "      <td>-1.081667</td>\n",
       "      <td>-1.144499</td>\n",
       "      <td>-1.346349</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>-0.502610</td>\n",
       "      <td>-0.500312</td>\n",
       "      <td>-0.502499</td>\n",
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       "          0         1         2         3         4         5         6  \\\n",
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       "2 -0.785482  1.086147  0.027632  1.166909  1.005731 -0.502610 -0.500312   \n",
       "3  1.112391 -0.070020  0.894871 -1.614532 -0.994302  1.989613 -0.500312   \n",
       "4  1.701386 -1.081667 -1.144499 -1.346349 -0.994302 -0.502610 -0.500312   \n",
       "\n",
       "          7         8  \n",
       "0 -0.502499 -0.498169  \n",
       "1 -0.502499 -0.498169  \n",
       "2 -0.502499 -0.498169  \n",
       "3 -0.502499 -0.498169  \n",
       "4 -0.502499  2.007351  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b4452a34",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.664963</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>1.989613</td>\n",
       "      <td>-0.500312</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>-0.498169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.327064</td>\n",
       "      <td>0.074501</td>\n",
       "      <td>-1.209728</td>\n",
       "      <td>1.136259</td>\n",
       "      <td>1.005731</td>\n",
       "      <td>-0.502610</td>\n",
       "      <td>-0.500312</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>2.007351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.850926</td>\n",
       "      <td>-1.081667</td>\n",
       "      <td>-1.585284</td>\n",
       "      <td>0.009891</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>-0.502610</td>\n",
       "      <td>1.998751</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>-0.498169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.588840</td>\n",
       "      <td>-1.226188</td>\n",
       "      <td>1.461171</td>\n",
       "      <td>0.492620</td>\n",
       "      <td>1.005731</td>\n",
       "      <td>-0.502610</td>\n",
       "      <td>-0.500312</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>2.007351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.374477</td>\n",
       "      <td>1.664231</td>\n",
       "      <td>0.845950</td>\n",
       "      <td>0.224437</td>\n",
       "      <td>-0.994302</td>\n",
       "      <td>-0.502610</td>\n",
       "      <td>1.998751</td>\n",
       "      <td>-0.502499</td>\n",
       "      <td>-0.498169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.261620 -0.214541  1.155784  1.664963 -0.994302  1.989613 -0.500312   \n",
       "1  0.327064  0.074501 -1.209728  1.136259  1.005731 -0.502610 -0.500312   \n",
       "2 -0.850926 -1.081667 -1.585284  0.009891 -0.994302 -0.502610  1.998751   \n",
       "3  0.588840 -1.226188  1.461171  0.492620  1.005731 -0.502610 -0.500312   \n",
       "4 -1.374477  1.664231  0.845950  0.224437 -0.994302 -0.502610  1.998751   \n",
       "\n",
       "          7         8  \n",
       "0 -0.502499 -0.498169  \n",
       "1 -0.502499  2.007351  \n",
       "2 -0.502499 -0.498169  \n",
       "3 -0.502499  2.007351  \n",
       "4 -0.502499 -0.498169  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ef9075d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "\n",
    "X_train_pca = pca.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f85ebff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 9)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "11681537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the explained variance ratio\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "\n",
    "# Calculate the cumulative explained variance ratio\n",
    "cumulative_variance_ratio = np.cumsum(explained_variance_ratio)\n",
    "\n",
    "# Plot the scree plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, len(cumulative_variance_ratio) + 1), cumulative_variance_ratio, marker='o')\n",
    "plt.title('Scree Plot')\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cumulative Explained Variance Ratio')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3cfb5a",
   "metadata": {},
   "source": [
    "**8 out of 9 principal components (PCs) capture approximately 98% of the variance of the data. Therefore, I will select 8 PCs for further analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fad67fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=8)  # Select 8 principal components\n",
    "\n",
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "20bae1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 8), (30000, 8))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca.shape, X_test_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "16cbbe8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Building Time (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.504100</td>\n",
       "      <td>0.504041</td>\n",
       "      <td>0.504100</td>\n",
       "      <td>0.502468</td>\n",
       "      <td>8.944818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.820117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.688157</td>\n",
       "      <td>0.688157</td>\n",
       "      <td>0.688157</td>\n",
       "      <td>0.688157</td>\n",
       "      <td>2.678202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.505600</td>\n",
       "      <td>0.505607</td>\n",
       "      <td>0.505600</td>\n",
       "      <td>0.501428</td>\n",
       "      <td>0.109698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.517357</td>\n",
       "      <td>0.518133</td>\n",
       "      <td>0.517357</td>\n",
       "      <td>0.510504</td>\n",
       "      <td>5.871627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.539629</td>\n",
       "      <td>0.540417</td>\n",
       "      <td>0.539629</td>\n",
       "      <td>0.536908</td>\n",
       "      <td>21.214955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.480596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.655871</td>\n",
       "      <td>0.656014</td>\n",
       "      <td>0.655871</td>\n",
       "      <td>0.655770</td>\n",
       "      <td>5.292027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.522286</td>\n",
       "      <td>0.522553</td>\n",
       "      <td>0.522286</td>\n",
       "      <td>0.520140</td>\n",
       "      <td>882.968847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score  \\\n",
       "0          LogisticRegression  0.504100   0.504041  0.504100  0.502468   \n",
       "1      DecisionTreeClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "2        KNeighborsClassifier  0.688157   0.688157  0.688157  0.688157   \n",
       "3                  GaussianNB  0.505600   0.505607  0.505600  0.501428   \n",
       "4          AdaBoostClassifier  0.517357   0.518133  0.517357  0.510504   \n",
       "5  GradientBoostingClassifier  0.539629   0.540417  0.539629  0.536908   \n",
       "6      RandomForestClassifier  1.000000   1.000000  1.000000  1.000000   \n",
       "7               XGBClassifier  0.655871   0.656014  0.655871  0.655770   \n",
       "8                         SVC  0.522286   0.522553  0.522286  0.520140   \n",
       "\n",
       "   Building Time (s)  \n",
       "0           8.944818  \n",
       "1           3.820117  \n",
       "2           2.678202  \n",
       "3           0.109698  \n",
       "4           5.871627  \n",
       "5          21.214955  \n",
       "6          13.480596  \n",
       "7           5.292027  \n",
       "8         882.968847  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algorithms = [\n",
    "    LogisticRegression(n_jobs=-1, random_state=42),\n",
    "    DecisionTreeClassifier(random_state=42),\n",
    "    KNeighborsClassifier(n_jobs=-1),\n",
    "    GaussianNB(),\n",
    "    AdaBoostClassifier(random_state=42),\n",
    "    GradientBoostingClassifier(random_state=42),\n",
    "    RandomForestClassifier(n_jobs=-1, random_state=42),\n",
    "    XGBClassifier(n_jobs=-1, random_state=42),\n",
    "    SVC(random_state=42)\n",
    "]\n",
    "\n",
    "# Initialize the results dictionary for training data\n",
    "results_train = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': [],\n",
    "    'Building Time (s)': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for training data using X_train_pca\n",
    "for algorithm in algorithms:\n",
    "    start_time = time.time()  # Start timer\n",
    "\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    algorithm.fit(X_train_pca, y_train)\n",
    "    y_train_pred = algorithm.predict(X_train_pca)\n",
    "\n",
    "    accuracy = accuracy_score(y_train, y_train_pred)\n",
    "    precision = precision_score(y_train, y_train_pred, average='weighted')\n",
    "    recall = recall_score(y_train, y_train_pred, average='weighted')\n",
    "    f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "\n",
    "    end_time = time.time()  # End timer\n",
    "    building_time = end_time - start_time\n",
    "\n",
    "    results_train['Algorithm'].append(algorithm_name)\n",
    "    results_train['Accuracy'].append(accuracy)\n",
    "    results_train['Precision'].append(precision)\n",
    "    results_train['Recall'].append(recall)\n",
    "    results_train['F1-score'].append(f1)\n",
    "    results_train['Building Time (s)'].append(building_time)\n",
    "\n",
    "# Create a DataFrame for the training data results\n",
    "df_train = pd.DataFrame(results_train)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "651353c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "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>Algorithm</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.503667</td>\n",
       "      <td>0.503184</td>\n",
       "      <td>0.503667</td>\n",
       "      <td>0.502102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.495833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.496733</td>\n",
       "      <td>0.496756</td>\n",
       "      <td>0.496733</td>\n",
       "      <td>0.496742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.503667</td>\n",
       "      <td>0.502901</td>\n",
       "      <td>0.503667</td>\n",
       "      <td>0.499713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.506067</td>\n",
       "      <td>0.505211</td>\n",
       "      <td>0.506067</td>\n",
       "      <td>0.499374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.500200</td>\n",
       "      <td>0.499450</td>\n",
       "      <td>0.500200</td>\n",
       "      <td>0.497172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.497533</td>\n",
       "      <td>0.497356</td>\n",
       "      <td>0.497533</td>\n",
       "      <td>0.497298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497803</td>\n",
       "      <td>0.497900</td>\n",
       "      <td>0.497807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.502667</td>\n",
       "      <td>0.502032</td>\n",
       "      <td>0.502667</td>\n",
       "      <td>0.500129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Algorithm  Accuracy  Precision    Recall  F1-score\n",
       "0          LogisticRegression  0.503667   0.503184  0.503667  0.502102\n",
       "1      DecisionTreeClassifier  0.495833   0.495833  0.495833  0.495833\n",
       "2        KNeighborsClassifier  0.496733   0.496756  0.496733  0.496742\n",
       "3                  GaussianNB  0.503667   0.502901  0.503667  0.499713\n",
       "4          AdaBoostClassifier  0.506067   0.505211  0.506067  0.499374\n",
       "5  GradientBoostingClassifier  0.500200   0.499450  0.500200  0.497172\n",
       "6      RandomForestClassifier  0.497533   0.497356  0.497533  0.497298\n",
       "7               XGBClassifier  0.497900   0.497803  0.497900  0.497807\n",
       "8                         SVC  0.502667   0.502032  0.502667  0.500129"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_test = {\n",
    "    'Algorithm': [],\n",
    "    'Accuracy': [],\n",
    "    'Precision': [],\n",
    "    'Recall': [],\n",
    "    'F1-score': []\n",
    "}\n",
    "\n",
    "# Apply the algorithms and calculate performance metrics for test data using X_test_pca\n",
    "for algorithm in algorithms:\n",
    "    algorithm_name = type(algorithm).__name__\n",
    "    y_test_pred = algorithm.predict(X_test_pca)\n",
    "\n",
    "    accuracy = accuracy_score(y_test, y_test_pred)\n",
    "    precision = precision_score(y_test, y_test_pred, average='weighted')\n",
    "    recall = recall_score(y_test, y_test_pred, average='weighted')\n",
    "    f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "\n",
    "    results_test['Algorithm'].append(algorithm_name)\n",
    "    results_test['Accuracy'].append(accuracy)\n",
    "    results_test['Precision'].append(precision)\n",
    "    results_test['Recall'].append(recall)\n",
    "    results_test['F1-score'].append(f1)\n",
    "\n",
    "# Create a DataFrame for the test data results\n",
    "df_test = pd.DataFrame(results_test)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01aa5ff2",
   "metadata": {},
   "source": [
    "**There's no significant improvement in performance with PCA.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003e0238",
   "metadata": {},
   "source": [
    "**`Final Model:` XGBClassifier is the best-performing algorithm on both the training and test data, using 9 variables, 4 variables, or PCA. It has the highest accuracy, precision, recall, and F1-score on all three. Therefore, I will build the final model using XGBoost Classifier using 9 variables.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551aff0a",
   "metadata": {},
   "source": [
    "# `Final Model: XGBoost Using 9 variables`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "017bf4e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_cleaned.drop(\"Churn\",axis=1)\n",
    "y = df_cleaned['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1ad161be",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b636f4b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70000, 9), (30000, 9))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5eeffc6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB',\n",
       "       'Gender_Male', 'Location_Houston', 'Location_Los Angeles',\n",
       "       'Location_Miami', 'Location_New York'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7af7dc73",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_scale = ['Age', 'Subscription_Length_Months', 'Monthly_Bill', 'Total_Usage_GB']\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train[columns_to_scale] = scaler.fit_transform(X_train[columns_to_scale])\n",
    "\n",
    "X_test[columns_to_scale] = scaler.transform(X_test[columns_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bd3ce919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=42, ...)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the XGBoost classifier\n",
    "xgb_classifier = XGBClassifier(\n",
    "    objective='binary:logistic',  # For binary classification\n",
    "    random_state=42               # Random seed for reproducibility\n",
    ")\n",
    "\n",
    "# Train (fit) the model\n",
    "xgb_classifier.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "dc09c244",
   "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>Metric</th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.500500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Precision</td>\n",
       "      <td>0.668665</td>\n",
       "      <td>0.495329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Recall</td>\n",
       "      <td>0.651227</td>\n",
       "      <td>0.489224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F1-Score</td>\n",
       "      <td>0.659831</td>\n",
       "      <td>0.492258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Metric     Train      Test\n",
       "0   Accuracy  0.664929  0.500500\n",
       "1  Precision  0.668665  0.495329\n",
       "2     Recall  0.651227  0.489224\n",
       "3   F1-Score  0.659831  0.492258"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred = xgb_classifier.predict(X_train)\n",
    "y_test_pred = xgb_classifier.predict(X_test)\n",
    "\n",
    "# Calculate metrics for train set\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "train_precision = precision_score(y_train, y_train_pred)\n",
    "train_recall = recall_score(y_train, y_train_pred)\n",
    "train_f1 = f1_score(y_train, y_train_pred)\n",
    "\n",
    "# Calculate metrics for test set\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_precision = precision_score(y_test, y_test_pred)\n",
    "test_recall = recall_score(y_test, y_test_pred)\n",
    "test_f1 = f1_score(y_test, y_test_pred)\n",
    "\n",
    "# Create a DataFrame to store the results\n",
    "results = pd.DataFrame({\n",
    "    'Metric': ['Accuracy', 'Precision', 'Recall', 'F1-Score'],\n",
    "    'Train': [train_accuracy, train_precision, train_recall, train_f1],\n",
    "    'Test': [test_accuracy, test_precision, test_recall, test_f1]\n",
    "})\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20b997f",
   "metadata": {},
   "source": [
    "# `Hyperparameter Tunning`\n",
    "\n",
    "Hyperparameters are settings in machine learning algorithms that control how the algorithm works. They are like the knobs and dials on a machine, and they can be adjusted to improve the performance of the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "123a8187",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid for hyperparameter tuning\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [3, 4, 5],\n",
    "    'learning_rate': [0.1, 0.01, 0.001],\n",
    "    'n_estimators': [50, 100, 150]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9866b820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     gpu_id=None, grow_policy=None,\n",
       "                                     importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, predictor=None,\n",
       "                                     random_state=42, ...),\n",
       "             param_grid={'learning_rate': [0.1, 0.01, 0.001],\n",
       "                         'max_depth': [3, 4, 5],\n",
       "                         'n_estimators': [50, 100, 150]},\n",
       "             scoring='recall')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize GridSearchCV with recall as the scoring metric\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=xgb_classifier,\n",
    "    param_grid=param_grid,\n",
    "    scoring='recall',\n",
    "    cv=5\n",
    ")\n",
    "\n",
    "# Fit the GridSearchCV on the training data\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77426fe",
   "metadata": {},
   "source": [
    "**According to the problem statement, false negatives are more important to reduce. Therefore, I selected recall as a metric to focus on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b4bc2604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 150}\n",
      "Test Recall with Best Model: 0.46619073275862066\n"
     ]
    }
   ],
   "source": [
    "# Get the best model from the search\n",
    "best_xgb = grid_search.best_estimator_\n",
    "\n",
    "# Make predictions on the test set using the best model\n",
    "y_test_pred = best_xgb.predict(X_test)\n",
    "\n",
    "# Calculate recall for the test set\n",
    "test_recall = recall_score(y_test, y_test_pred)\n",
    "\n",
    "print(\"Best Parameters:\", grid_search.best_params_)\n",
    "print(\"Test Recall with Best Model:\", test_recall)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbae3723",
   "metadata": {},
   "source": [
    "**After hyperparameter tuning, the performance of the model did not improve. Hence, I choose XGBoost CLassifier without hyperparameter tunning and perform cross-validation on it.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e29ae7",
   "metadata": {},
   "source": [
    "# `Cross Validation`\n",
    "\n",
    "* Cross-validation is a technique used to evaluate the performance of a machine learning model. It works by dividing the data into several parts, called folds. The model is then trained on a subset of the data and tested on the remaining folds. This process is repeated several times, and the results are averaged to get an estimate of the model's performance.\n",
    "\n",
    "\n",
    "* Cross-validation is necessary because it helps us avoid overfitting the model to the training data.\n",
    "\n",
    "\n",
    "* Cross-validation is a way to make sure that our model is not just memorizing the training data, but that it is actually learning to make accurate predictions on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c2a1e598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores (Accuracy): [0.49692857 0.50057143 0.49892857 0.50478571 0.505     ]\n",
      "\n",
      "Mean Accuracy Score: 0.5012428571428571\n"
     ]
    }
   ],
   "source": [
    "#cross validation of accuracy\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(xgb_classifier, X_train, y_train, cv=5, scoring='accuracy',n_jobs=-1)\n",
    "\n",
    "print(\"Cross-Validation Scores (Accuracy):\", scores)\n",
    "print()\n",
    "print(\"Mean Accuracy Score:\", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a6c7b3d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores (Recall): [0.48990983 0.49398798 0.48869167 0.50171772 0.49427426]\n",
      "\n",
      "Mean Recall Score: 0.4937162923036775\n"
     ]
    }
   ],
   "source": [
    "#cross validation of recall\n",
    "\n",
    "scores = cross_val_score(xgb_classifier, X_train, y_train, cv=5, scoring='recall',n_jobs=-1)\n",
    "\n",
    "print(\"Cross-Validation Scores (Recall):\", scores)\n",
    "print()\n",
    "print(\"Mean Recall Score:\", scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54677bf9",
   "metadata": {},
   "source": [
    "# `Finding Optimal Threshold`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ad4e4573",
   "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>Probability</th>\n",
       "      <th>Actual_Label</th>\n",
       "      <th>Predicted_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43364</th>\n",
       "      <td>0.474755</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52617</th>\n",
       "      <td>0.490060</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83194</th>\n",
       "      <td>0.420003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79395</th>\n",
       "      <td>0.481040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78962</th>\n",
       "      <td>0.469605</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32846</th>\n",
       "      <td>0.477571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88132</th>\n",
       "      <td>0.498932</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22788</th>\n",
       "      <td>0.528793</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63261</th>\n",
       "      <td>0.456429</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48217</th>\n",
       "      <td>0.518680</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Probability  Actual_Label  Predicted_Label\n",
       "43364     0.474755             1                0\n",
       "52617     0.490060             1                0\n",
       "83194     0.420003             0                0\n",
       "79395     0.481040             0                0\n",
       "78962     0.469605             0                0\n",
       "32846     0.477571             0                0\n",
       "88132     0.498932             1                0\n",
       "22788     0.528793             0                1\n",
       "63261     0.456429             0                0\n",
       "48217     0.518680             0                1"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a dataframe of probabilites, actual labels, predicted labels for genral check\n",
    "\n",
    "probabilities = xgb_classifier.predict_proba(X_train)\n",
    "\n",
    "predicted_labels = xgb_classifier.predict(X_train)\n",
    "\n",
    "results = pd.DataFrame({'Probability': probabilities[:, 1],\n",
    "                        'Actual_Label': y_train,\n",
    "                        'Predicted_Label': predicted_labels})\n",
    "\n",
    "results.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5d000988",
   "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>Probability</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.499214</td>\n",
       "      <td>0.499114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.665879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.501929</td>\n",
       "      <td>0.500473</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.514757</td>\n",
       "      <td>0.506998</td>\n",
       "      <td>0.999714</td>\n",
       "      <td>0.672793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.569243</td>\n",
       "      <td>0.537409</td>\n",
       "      <td>0.982508</td>\n",
       "      <td>0.694786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.668665</td>\n",
       "      <td>0.651227</td>\n",
       "      <td>0.659831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.569614</td>\n",
       "      <td>0.888422</td>\n",
       "      <td>0.157281</td>\n",
       "      <td>0.267250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.517100</td>\n",
       "      <td>0.986207</td>\n",
       "      <td>0.032750</td>\n",
       "      <td>0.063395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.504286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006613</td>\n",
       "      <td>0.013139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.501371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.001545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Probability  Accuracy  Precision    Recall  F1 Score\n",
       "0          0.1  0.499214   0.499114  1.000000  0.665879\n",
       "1          0.2  0.501929   0.500473  1.000000  0.667087\n",
       "2          0.3  0.514757   0.506998  0.999714  0.672793\n",
       "3          0.4  0.569243   0.537409  0.982508  0.694786\n",
       "4          0.5  0.664929   0.668665  0.651227  0.659831\n",
       "5          0.6  0.569614   0.888422  0.157281  0.267250\n",
       "6          0.7  0.517100   0.986207  0.032750  0.063395\n",
       "7          0.8  0.504286   1.000000  0.006613  0.013139\n",
       "8          0.9  0.501371   1.000000  0.000773  0.001545\n",
       "9          1.0  0.500986   1.000000  0.000000  0.000000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check evaulation metrics for different thresholds\n",
    "\n",
    "thresholds = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
    "\n",
    "# Creating an empty DataFrame to store the evaluation metrics\n",
    "metrics_df = pd.DataFrame(columns=['Probability', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])\n",
    "\n",
    "# Iterating over the probability thresholds\n",
    "for threshold in thresholds:\n",
    "    # Converting probabilities to predicted labels based on the threshold\n",
    "    predicted_labels = (probabilities[:, 1] >= threshold).astype(int)\n",
    "\n",
    "    # Calculating evaluation metrics\n",
    "    accuracy = accuracy_score(y_train, predicted_labels)\n",
    "    precision = precision_score(y_train, predicted_labels, zero_division=1)\n",
    "    recall = recall_score(y_train, predicted_labels, zero_division=1)\n",
    "    f1 = f1_score(y_train, predicted_labels, zero_division=1)\n",
    "\n",
    "    # Adding the metrics to the DataFrame\n",
    "    metrics_df = pd.concat([metrics_df, pd.DataFrame({'Probability': [threshold],\n",
    "                                                      'Accuracy': [accuracy],\n",
    "                                                      'Precision': [precision],\n",
    "                                                      'Recall': [recall],\n",
    "                                                      'F1 Score': [f1]})], ignore_index=True)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5513a7",
   "metadata": {},
   "source": [
    "## `Check the Trade-Off Between Accuracy, Sensitivity, Specitivity, F-1 Score and Probabilities (Thresholds)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c5ad2048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Calculate predicted probabilities for the train data\n",
    "train_probabilities = xgb_classifier.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Apply different thresholds to obtain binary predictions for train data\n",
    "thresholds = np.arange(0, 1.1, 0.1)\n",
    "sensitivity = []\n",
    "specificity = []\n",
    "accuracy = []\n",
    "f1_scores = []\n",
    "\n",
    "for threshold in thresholds:\n",
    "    train_predictions = (train_probabilities >= threshold).astype(int)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_train, train_predictions).ravel()\n",
    "    sensitivity.append(tp / (tp + fn))\n",
    "    specificity.append(tn / (tn + fp))\n",
    "    accuracy.append((tp + tn) / (tp + tn + fp + fn))\n",
    "    f1_scores.append(f1_score(y_train, train_predictions))\n",
    "\n",
    "# Plot the sensitivity, specificity, accuracy, and F1 score against probability thresholds\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(thresholds, sensitivity, label='Sensitivity')\n",
    "plt.plot(thresholds, specificity, label='Specificity')\n",
    "plt.plot(thresholds, accuracy, label='Accuracy')\n",
    "plt.plot(thresholds, f1_scores, label='F1 Score')\n",
    "plt.xlabel('Probability Threshold')\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.ylabel('Metric Value')\n",
    "plt.title('Metrics vs. Probability Threshold')\n",
    "plt.legend(loc='lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33900411",
   "metadata": {},
   "source": [
    "**The graph indicates that 0.5 is the best threshold point, where accuracy, sensitivity, specificity, and F1 score are all stable.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c81360d",
   "metadata": {},
   "source": [
    "# `Model Evaulation (XG Boost)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2cb8fe",
   "metadata": {},
   "source": [
    "## `Train & Test Data Metrics`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "bd89a37d",
   "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>Dataset</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train</td>\n",
       "      <td>0.664929</td>\n",
       "      <td>0.665030</td>\n",
       "      <td>0.664902</td>\n",
       "      <td>0.664853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test</td>\n",
       "      <td>0.500500</td>\n",
       "      <td>0.500387</td>\n",
       "      <td>0.500387</td>\n",
       "      <td>0.500368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Dataset  Accuracy  Precision    Recall  F1-score\n",
       "0   Train  0.664929   0.665030  0.664902  0.664853\n",
       "1    Test  0.500500   0.500387  0.500387  0.500368"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate evaluation metrics for train data\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "train_precision = precision_score(y_train, y_train_pred, average='macro')\n",
    "train_recall = recall_score(y_train, y_train_pred, average='macro')\n",
    "train_f1_score = f1_score(y_train, y_train_pred, average='macro')\n",
    "\n",
    "# Predict on the test data\n",
    "y_test_pred = xgb_classifier.predict(X_test)\n",
    "\n",
    "# Calculate evaluation metrics for test data\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "test_precision = precision_score(y_test, y_test_pred, average='macro')\n",
    "test_recall = recall_score(y_test, y_test_pred, average='macro')\n",
    "test_f1_score = f1_score(y_test, y_test_pred, average='macro')\n",
    "\n",
    "# Create a DataFrame to store the evaluation metrics\n",
    "metrics_data = {\n",
    "    'Dataset': ['Train', 'Test'],\n",
    "    'Accuracy': [train_accuracy, test_accuracy],\n",
    "    'Precision': [train_precision, test_precision],\n",
    "    'Recall': [train_recall, test_recall],\n",
    "    'F1-score': [train_f1_score, test_f1_score]\n",
    "}\n",
    "\n",
    "metrics_df = pd.DataFrame(metrics_data)\n",
    "metrics_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed2be82",
   "metadata": {},
   "source": [
    "## `Confusion Matrix`\n",
    "\n",
    "Confusion matrix is a table that is used to evaluate the performance of a machine learning model. It shows how many instances were correctly classified as positive (true positives), incorrectly classified as positive (false positives), correctly classified as negative (true negatives), and incorrectly classified as negative (false negatives)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0d130517",
   "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>Training Set</th>\n",
       "      <th>Test Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True Positive (%)</th>\n",
       "      <td>33.995714</td>\n",
       "      <td>25.836667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative (%)</th>\n",
       "      <td>16.102857</td>\n",
       "      <td>24.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive (%)</th>\n",
       "      <td>17.404286</td>\n",
       "      <td>25.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative (%)</th>\n",
       "      <td>32.497143</td>\n",
       "      <td>24.213333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Training Set   Test Set\n",
       "True Positive (%)      33.995714  25.836667\n",
       "True Negative (%)      16.102857  24.670000\n",
       "False Positive (%)     17.404286  25.280000\n",
       "False Negative (%)     32.497143  24.213333"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Calculate predictions for the training set using a threshold of 0.50\n",
    "train_predictions = (xgb_classifier.predict_proba(X_train)[:, 1] >= 0.50).astype(int)\n",
    "train_confusion_matrix = confusion_matrix(y_train, train_predictions)\n",
    "\n",
    "# Calculate predictions for the test set using a threshold of 0.50\n",
    "test_predictions = (xgb_classifier.predict_proba(X_test)[:, 1] >= 0.50).astype(int)\n",
    "test_confusion_matrix = confusion_matrix(y_test, test_predictions)\n",
    "\n",
    "# Calculate the total number of samples in each set\n",
    "train_total = len(y_train)\n",
    "test_total = len(y_test)\n",
    "\n",
    "# Calculate the confusion matrix in percentage form for the training set\n",
    "train_confusion_matrix_percent = train_confusion_matrix / train_total * 100\n",
    "\n",
    "# Calculate the confusion matrix in percentage form for the test set\n",
    "test_confusion_matrix_percent = test_confusion_matrix / test_total * 100\n",
    "\n",
    "# Create a dataframe for the confusion matrix\n",
    "confusion_matrix_df = pd.DataFrame({'Training Set': train_confusion_matrix_percent.flatten(),\n",
    "                                    'Test Set': test_confusion_matrix_percent.flatten()},\n",
    "                                   index=['True Positive (%)', 'True Negative (%)', 'False Positive (%)', 'False Negative (%)', ])\n",
    "#\n",
    "confusion_matrix_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b520c0",
   "metadata": {},
   "source": [
    "## `ROC-AUC Curve`\n",
    "\n",
    "ROC-AUC curve, or Receiver Operating Characteristic-Area Under Curve, is a graph used in machine learning to measure the performance of a binary classifier. It plots the true positive rate (TPR) against the false positive rate (FPR) at different classification thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "be8d8208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "# Calculate predicted probabilities for the train data\n",
    "train_probabilities = xgb_classifier.predict_proba(X_train)[:, 1]\n",
    "\n",
    "# Apply the threshold of 0.50 to obtain binary predictions for train data\n",
    "train_predictions = (train_probabilities >= 0.50).astype(int)\n",
    "\n",
    "# Calculate the false positive rate, true positive rate, and thresholds for train data\n",
    "fpr_train, tpr_train, thresholds_train = roc_curve(y_train, train_probabilities)\n",
    "\n",
    "# Calculate the ROC-AUC score for train data\n",
    "roc_auc_train = roc_auc_score(y_train, train_predictions)\n",
    "\n",
    "# Calculate predicted probabilities for the test data\n",
    "test_probabilities = xgb_classifier.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Apply the threshold of 0.50 to obtain binary predictions for test data\n",
    "test_predictions = (test_probabilities >= 0.50).astype(int)\n",
    "\n",
    "# Calculate the false positive rate, true positive rate, and thresholds for test data\n",
    "fpr_test, tpr_test, thresholds_test = roc_curve(y_test, test_probabilities)\n",
    "\n",
    "# Calculate the ROC-AUC score for test data\n",
    "roc_auc_test = roc_auc_score(y_test, test_predictions)\n",
    "\n",
    "# Plot the ROC curves\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(fpr_train, tpr_train, label='Train ROC curve (area = {:.2f})'.format(roc_auc_train))\n",
    "plt.plot(fpr_test, tpr_test, label='Test ROC curve (area = {:.2f})'.format(roc_auc_test))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b272b92",
   "metadata": {},
   "source": [
    "# `Important Features in Model Building`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a90c65f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monthly_Bill</td>\n",
       "      <td>0.316383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total_Usage_GB</td>\n",
       "      <td>0.290353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.194396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subscription_Length_Months</td>\n",
       "      <td>0.142624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>0.016683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Location_Los Angeles</td>\n",
       "      <td>0.010595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Location_Houston</td>\n",
       "      <td>0.010007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Location_Miami</td>\n",
       "      <td>0.009792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Location_New York</td>\n",
       "      <td>0.009166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Feature  Importance\n",
       "0                Monthly_Bill    0.316383\n",
       "1              Total_Usage_GB    0.290353\n",
       "2                         Age    0.194396\n",
       "3  Subscription_Length_Months    0.142624\n",
       "4                 Gender_Male    0.016683\n",
       "5        Location_Los Angeles    0.010595\n",
       "6            Location_Houston    0.010007\n",
       "7              Location_Miami    0.009792\n",
       "8           Location_New York    0.009166"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91105e19",
   "metadata": {},
   "source": [
    "**The four variables ['Monthly_Bill', 'Total_Usage_GB', 'Age', and 'Subscription_Length_Months'] are the most important variables in the analysis because they contain 94% of the feature importance or information gain in predicting whether a customer will churn or not.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f58250a",
   "metadata": {},
   "source": [
    "# `Save model in a pickle file`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "82acf819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['customer_churn_classifier.pkl']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "\n",
    "joblib.dump(xgb_classifier, 'customer_churn_classifier.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbbf8995",
   "metadata": {},
   "source": [
    "# `Outcome`\n",
    "\n",
    "**The outcome of this customer churn prediction project involves developing a machine learning model to predict whether customers are likely to churn or not. This prediction is based on various customer attributes such as age, gender, location, subscription length, monthly bill, and total usage. The model's primary purpose is to assist in identifying customers who are at a higher risk of churning, enabling the business to take proactive measures to retain them. By using the trained model to predict churn, the company can allocate resources more effectively, personalize engagement strategies, and implement targeted retention efforts. Ultimately, the project's success is measured by the model's ability to make predictions, helping the company reduce churn rates, improve customer satisfaction, and optimize its customer retention strategies.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e585340d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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