{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "Bank Churn Model\n",
    "</h1>\n",
    "\n",
    "<h5>\n",
    "Objective: Compare the Logistic Regression and Gaussian Naive Bayes algorithm, then use the best performing algorithm for the model\n",
    "</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will load the dataset using pandas library. df will be defined as the placeholder for the csv dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BankChurners.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CLIENTNUM</th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>...</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1</th>\n",
       "      <th>Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>768805383</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.000093</td>\n",
       "      <td>0.99991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>818770008</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.99994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>713982108</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.99998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>769911858</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>34</td>\n",
       "      <td>...</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.99987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>709106358</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.99998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLIENTNUM     Attrition_Flag  Customer_Age Gender  Dependent_count  \\\n",
       "0  768805383  Existing Customer            45      M                3   \n",
       "1  818770008  Existing Customer            49      F                5   \n",
       "2  713982108  Existing Customer            51      M                3   \n",
       "3  769911858  Existing Customer            40      F                4   \n",
       "4  709106358  Existing Customer            40      M                3   \n",
       "\n",
       "  Education_Level Marital_Status Income_Category Card_Category  \\\n",
       "0     High School        Married     $60K - $80K          Blue   \n",
       "1        Graduate         Single  Less than $40K          Blue   \n",
       "2        Graduate        Married    $80K - $120K          Blue   \n",
       "3     High School        Unknown  Less than $40K          Blue   \n",
       "4      Uneducated        Married     $60K - $80K          Blue   \n",
       "\n",
       "   Months_on_book  ...  Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  \\\n",
       "0              39  ...       12691.0                  777          11914.0   \n",
       "1              44  ...        8256.0                  864           7392.0   \n",
       "2              36  ...        3418.0                    0           3418.0   \n",
       "3              34  ...        3313.0                 2517            796.0   \n",
       "4              21  ...        4716.0                    0           4716.0   \n",
       "\n",
       "   Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "0                 1.335             1144              42                1.625   \n",
       "1                 1.541             1291              33                3.714   \n",
       "2                 2.594             1887              20                2.333   \n",
       "3                 1.405             1171              20                2.333   \n",
       "4                 2.175              816              28                2.500   \n",
       "\n",
       "   Avg_Utilization_Ratio  \\\n",
       "0                  0.061   \n",
       "1                  0.105   \n",
       "2                  0.000   \n",
       "3                  0.760   \n",
       "4                  0.000   \n",
       "\n",
       "   Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1  \\\n",
       "0                                           0.000093                                                                                    \n",
       "1                                           0.000057                                                                                    \n",
       "2                                           0.000021                                                                                    \n",
       "3                                           0.000134                                                                                    \n",
       "4                                           0.000022                                                                                    \n",
       "\n",
       "   Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2  \n",
       "0                                            0.99991                                                                                   \n",
       "1                                            0.99994                                                                                   \n",
       "2                                            0.99998                                                                                   \n",
       "3                                            0.99987                                                                                   \n",
       "4                                            0.99998                                                                                   \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Judging from data and columns, we can infer the following:\n",
    "\n",
    "1. CLIENTNUM does not hold any value, it is a unique ID used to identify the details of each account\n",
    "\n",
    "2. Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 are redundant data\n",
    "3. Attrition_Flag contains the target variable, which we can identify the customer activity (If the account is closed, it will be 1, else false)\n",
    "4. Too many data for input in application later on, we will deduct the columns such as Months_Inactive_12_mon, Contacts_Count_12_mon, Avg_Open_To_Buy, Total_Ct_Chng_Q4_Q1, Total_Relationship_Count, Total_Trans_Amt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['CLIENTNUM', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1', 'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By removing the columns, we will be able to make the model predictions accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10127 entries, 0 to 10126\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Attrition_Flag            10127 non-null  object \n",
      " 1   Customer_Age              10127 non-null  int64  \n",
      " 2   Gender                    10127 non-null  object \n",
      " 3   Dependent_count           10127 non-null  int64  \n",
      " 4   Education_Level           10127 non-null  object \n",
      " 5   Marital_Status            10127 non-null  object \n",
      " 6   Income_Category           10127 non-null  object \n",
      " 7   Card_Category             10127 non-null  object \n",
      " 8   Months_on_book            10127 non-null  int64  \n",
      " 9   Total_Relationship_Count  10127 non-null  int64  \n",
      " 10  Months_Inactive_12_mon    10127 non-null  int64  \n",
      " 11  Contacts_Count_12_mon     10127 non-null  int64  \n",
      " 12  Credit_Limit              10127 non-null  float64\n",
      " 13  Total_Revolving_Bal       10127 non-null  int64  \n",
      " 14  Avg_Open_To_Buy           10127 non-null  float64\n",
      " 15  Total_Amt_Chng_Q4_Q1      10127 non-null  float64\n",
      " 16  Total_Trans_Amt           10127 non-null  int64  \n",
      " 17  Total_Trans_Ct            10127 non-null  int64  \n",
      " 18  Total_Ct_Chng_Q4_Q1       10127 non-null  float64\n",
      " 19  Avg_Utilization_Ratio     10127 non-null  float64\n",
      "dtypes: float64(5), int64(9), object(6)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition_Flag                 2\n",
       "Customer_Age                  45\n",
       "Gender                         2\n",
       "Dependent_count                6\n",
       "Education_Level                7\n",
       "Marital_Status                 4\n",
       "Income_Category                6\n",
       "Card_Category                  4\n",
       "Months_on_book                44\n",
       "Total_Relationship_Count       6\n",
       "Months_Inactive_12_mon         7\n",
       "Contacts_Count_12_mon          7\n",
       "Credit_Limit                6205\n",
       "Total_Revolving_Bal         1974\n",
       "Avg_Open_To_Buy             6813\n",
       "Total_Amt_Chng_Q4_Q1        1158\n",
       "Total_Trans_Amt             5033\n",
       "Total_Trans_Ct               126\n",
       "Total_Ct_Chng_Q4_Q1          830\n",
       "Avg_Utilization_Ratio        964\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using the .info() function, we can infer that the data still has 'object' data types. These data types are not numbers, but strings. However, seeing as some of these columns have binary data, we can turn these string values into numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = []\n",
    "for var in df.columns:\n",
    "    if df[var].dtype != 'O':\n",
    "        numerical.append(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Customer_Age',\n",
       " 'Dependent_count',\n",
       " 'Months_on_book',\n",
       " 'Total_Relationship_Count',\n",
       " 'Months_Inactive_12_mon',\n",
       " 'Contacts_Count_12_mon',\n",
       " 'Credit_Limit',\n",
       " 'Total_Revolving_Bal',\n",
       " 'Avg_Open_To_Buy',\n",
       " 'Total_Amt_Chng_Q4_Q1',\n",
       " 'Total_Trans_Amt',\n",
       " 'Total_Trans_Ct',\n",
       " 'Total_Ct_Chng_Q4_Q1',\n",
       " 'Avg_Utilization_Ratio']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_labels = {}\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == object:  # Check if the column contains categorical variables\n",
    "        unique_values = df[column].unique()\n",
    "        label_mapping = {}\n",
    "        \n",
    "        for index, category in enumerate(unique_values):\n",
    "            label_mapping[category] = index\n",
    "            \n",
    "        new_labels[column] = label_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for col in df.columns:\n",
    "    if df[str(col)].dtype == object:\n",
    "        df[str(col)].replace(new_labels[str(col)].keys(), new_labels[str(col)].values(), inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
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       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
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       "      <td>1.625</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
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       "      <td>1</td>\n",
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       "      <td>8256.0</td>\n",
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       "      <td>0.105</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10122</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4003.0</td>\n",
       "      <td>1851</td>\n",
       "      <td>2152.0</td>\n",
       "      <td>0.703</td>\n",
       "      <td>15476</td>\n",
       "      <td>117</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10123</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4277.0</td>\n",
       "      <td>2186</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>0.804</td>\n",
       "      <td>8764</td>\n",
       "      <td>69</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10124</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5409.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5409.0</td>\n",
       "      <td>0.819</td>\n",
       "      <td>10291</td>\n",
       "      <td>60</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>0.535</td>\n",
       "      <td>8395</td>\n",
       "      <td>62</td>\n",
       "      <td>0.722</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>1961</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>0.703</td>\n",
       "      <td>10294</td>\n",
       "      <td>61</td>\n",
       "      <td>0.649</td>\n",
       "      <td>0.189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10127 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Attrition_Flag  Customer_Age  Gender  Dependent_count  Education_Level  \\\n",
       "0                   0            45       0                3                0   \n",
       "1                   0            49       1                5                1   \n",
       "2                   0            51       0                3                1   \n",
       "3                   0            40       1                4                0   \n",
       "4                   0            40       0                3                2   \n",
       "...               ...           ...     ...              ...              ...   \n",
       "10122               0            50       0                2                1   \n",
       "10123               1            41       0                2                3   \n",
       "10124               1            44       1                1                0   \n",
       "10125               1            30       0                2                1   \n",
       "10126               1            43       1                2                1   \n",
       "\n",
       "       Marital_Status  Income_Category  Card_Category  Months_on_book  \\\n",
       "0                   0                0              0              39   \n",
       "1                   1                1              0              44   \n",
       "2                   0                2              0              36   \n",
       "3                   2                1              0              34   \n",
       "4                   0                0              0              21   \n",
       "...               ...              ...            ...             ...   \n",
       "10122               1                3              0              40   \n",
       "10123               3                3              0              25   \n",
       "10124               0                1              0              36   \n",
       "10125               2                3              0              36   \n",
       "10126               0                1              2              25   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "0                             5                       1   \n",
       "1                             6                       1   \n",
       "2                             4                       1   \n",
       "3                             3                       4   \n",
       "4                             5                       1   \n",
       "...                         ...                     ...   \n",
       "10122                         3                       2   \n",
       "10123                         4                       2   \n",
       "10124                         5                       3   \n",
       "10125                         4                       3   \n",
       "10126                         6                       2   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "0                          3       12691.0                  777   \n",
       "1                          2        8256.0                  864   \n",
       "2                          0        3418.0                    0   \n",
       "3                          1        3313.0                 2517   \n",
       "4                          0        4716.0                    0   \n",
       "...                      ...           ...                  ...   \n",
       "10122                      3        4003.0                 1851   \n",
       "10123                      3        4277.0                 2186   \n",
       "10124                      4        5409.0                    0   \n",
       "10125                      3        5281.0                    0   \n",
       "10126                      4       10388.0                 1961   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "0              11914.0                 1.335             1144              42   \n",
       "1               7392.0                 1.541             1291              33   \n",
       "2               3418.0                 2.594             1887              20   \n",
       "3                796.0                 1.405             1171              20   \n",
       "4               4716.0                 2.175              816              28   \n",
       "...                ...                   ...              ...             ...   \n",
       "10122           2152.0                 0.703            15476             117   \n",
       "10123           2091.0                 0.804             8764              69   \n",
       "10124           5409.0                 0.819            10291              60   \n",
       "10125           5281.0                 0.535             8395              62   \n",
       "10126           8427.0                 0.703            10294              61   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0                    1.625                  0.061  \n",
       "1                    3.714                  0.105  \n",
       "2                    2.333                  0.000  \n",
       "3                    2.333                  0.760  \n",
       "4                    2.500                  0.000  \n",
       "...                    ...                    ...  \n",
       "10122                0.857                  0.462  \n",
       "10123                0.683                  0.511  \n",
       "10124                0.818                  0.000  \n",
       "10125                0.722                  0.000  \n",
       "10126                0.649                  0.189  \n",
       "\n",
       "[10127 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attrition_Flag: {'Existing Customer': 0, 'Attrited Customer': 1}\n",
      "Gender: {'M': 0, 'F': 1}\n",
      "Education_Level: {'High School': 0, 'Graduate': 1, 'Uneducated': 2, 'Unknown': 3, 'College': 4, 'Post-Graduate': 5, 'Doctorate': 6}\n",
      "Marital_Status: {'Married': 0, 'Single': 1, 'Unknown': 2, 'Divorced': 3}\n",
      "Income_Category: {'$60K - $80K': 0, 'Less than $40K': 1, '$80K - $120K': 2, '$40K - $60K': 3, '$120K +': 4, 'Unknown': 5}\n",
      "Card_Category: {'Blue': 0, 'Gold': 1, 'Silver': 2, 'Platinum': 3}\n"
     ]
    }
   ],
   "source": [
    "for col, categoricals in new_labels.items():\n",
    "    print(f\"{col}: {categoricals}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), cmap= 'Oranges_r', annot= True)\n",
    "plt.suptitle('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation matrix measures the linear relationship between pairs of variables. The correlation coefficient (ranging from -1 to +1) indicates how strongly two variables are related.\n",
    "\n",
    "+1 indicates a perfect positive correlation (when one variable increases, the other also increases).\n",
    "\n",
    "-1 indicates a perfect negative correlation (when one variable increases, the other decreases).\n",
    "\n",
    "0 indicates no linear correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1> Training the Model\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Gaussian Naive Bayes\n",
    "</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Attrition_Flag'])\n",
    "y = df['Attrition_Flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer_Age  Gender  Dependent_count  Education_Level  Marital_Status  \\\n",
       "0            45       0                3                0               0   \n",
       "1            49       1                5                1               1   \n",
       "2            51       0                3                1               0   \n",
       "3            40       1                4                0               2   \n",
       "4            40       0                3                2               0   \n",
       "\n",
       "   Income_Category  Card_Category  Months_on_book  Total_Relationship_Count  \\\n",
       "0                0              0              39                         5   \n",
       "1                1              0              44                         6   \n",
       "2                2              0              36                         4   \n",
       "3                1              0              34                         3   \n",
       "4                0              0              21                         5   \n",
       "\n",
       "   Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "0                       1                      3       12691.0   \n",
       "1                       1                      2        8256.0   \n",
       "2                       1                      0        3418.0   \n",
       "3                       4                      1        3313.0   \n",
       "4                       1                      0        4716.0   \n",
       "\n",
       "   Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0                  777          11914.0                 1.335   \n",
       "1                  864           7392.0                 1.541   \n",
       "2                    0           3418.0                 2.594   \n",
       "3                 2517            796.0                 1.405   \n",
       "4                    0           4716.0                 2.175   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0             1144              42                1.625                  0.061  \n",
       "1             1291              33                3.714                  0.105  \n",
       "2             1887              20                2.333                  0.000  \n",
       "3             1171              20                2.333                  0.760  \n",
       "4              816              28                2.500                  0.000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Attrition_Flag, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussianNB_model = GaussianNB()\n",
    "gaussianNB_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gaussianNB_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Evaluating the Gaussian Naive Bayes Model\n",
    "</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.88\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['True 0', 'True 1'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Explanation of the terms:\n",
    "\n",
    "True Positive (TP):\n",
    "The model correctly predicted the positive class (e.g., predicted 1 when the actual label was 1).\n",
    "\n",
    "\n",
    "True Negative (TN):\n",
    "The model correctly predicted the negative class (e.g., predicted 0 when the actual label was 0).\n",
    "\n",
    "\n",
    "False Positive (FP):\n",
    "The model incorrectly predicted the positive class (e.g., predicted 1 when the actual label was 0).\n",
    "\n",
    "\n",
    "False Negative (FN):\n",
    "The model incorrectly predicted the negative class (e.g., predicted 0 when the actual label was 1).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "joblib.dump(gaussianNB_model, 'GNB_bank_churn_model.pkl')\n",
    "print('Model saved')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Logistic Regression\n",
    "</h5>\n",
    "\n",
    "Logistic regression is a supervised machine learning algorithm used for classification tasks where the goal is to predict the probability that an instance belongs to a given class or not. Logistic regression is a statistical algorithm which analyze the relationship between two data factors. The article explores the fundamentals of logistic regression, it’s types and implementations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5250)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=5250)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=5250)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train the model\n",
    "LogisticRegression_model = LogisticRegression(max_iter=5250)  # You can adjust max_iter if needed\n",
    "LogisticRegression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = LogisticRegression_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Evaluating the Logistic Regression Model\n",
    "</h5>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.90\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a heatmap for the confusion matrix\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['True 0', 'True 1'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Explanation of the terms:\n",
    "\n",
    "True Positive (TP):\n",
    "The model correctly predicted the positive class (e.g., predicted 1 when the actual label was 1).\n",
    "\n",
    "\n",
    "True Negative (TN):\n",
    "The model correctly predicted the negative class (e.g., predicted 0 when the actual label was 0).\n",
    "\n",
    "\n",
    "False Positive (FP):\n",
    "The model incorrectly predicted the positive class (e.g., predicted 1 when the actual label was 0).\n",
    "\n",
    "\n",
    "False Negative (FN):\n",
    "The model incorrectly predicted the negative class (e.g., predicted 0 when the actual label was 1).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Contacts_Count_12_mon'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Testing the Model </h5>\n",
    "\n",
    "So, after examining the accuracy of both models, we would like to test the model by having the user input values into different fields for the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved\n"
     ]
    }
   ],
   "source": [
    "joblib.dump(LogisticRegression_model, 'LR_bank_churn_model.pkl')\n",
    "print('Model saved')"
   ]
  }
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