Feature_engineer_&_Over_sampling_&_Scaling.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "52118fd6",
   "metadata": {},
   "source": [
    "# <center><font color = 'blue'> Feature Engineering, Oversampling and Scaling</font> <center>\n",
    "\n",
    "---\n",
    "This project will go through the following steps to build a Machine Learning model for our chosen dataset:\n",
    "1. EDA and Visualization\n",
    "2. **Feature Engineering, Oversampling and Scaling**\n",
    "3. Model Buiding and Hyperparameter Tuning\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "00df9692",
   "metadata": {},
   "source": [
    "# **Introduction**\n",
    "\n",
    "In our previous notebook(EDA and Visualization.ipynb), we performed exploratory data analysis (EDA) and visualization on our chosen dataset to gain a better understanding of our target variable and the different variables available.\n",
    "\n",
    "However, as our dataset contains categorical columns and is unbalanced, we need to apply specific techniques to preprocess our data before training our machine learning model. Specifically, we need to encode our categorical variables, balance our dataset using oversampling techniques, and normalize our data to ensure optimal performance of our model.\n",
    "\n",
    "In this notebook, we will explore various encoding, oversampling, and normalization methods to determine which techniques work best for our dataset. By comparing different approaches and measuring their performance using appropriate evaluation metrics, we aim to identify the most effective techniques for our specific problem and improve the performance of our machine learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81869ca9",
   "metadata": {},
   "source": [
    "## Load Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d45b8f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN, BorderlineSMOTE, SVMSMOTE, KMeansSMOTE\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_validate, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score, classification_report, accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder, MinMaxScaler\n",
    "\n",
    "import category_encoders as ce"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0aa19a",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41014aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data from CSV file\n",
    "df = pd.read_csv('BankChurners.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc227326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10127, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79efe24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unuseful columns\n",
    "df.drop(['CLIENTNUM',\n",
    "        'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
    "     'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'],\n",
    "        axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d4fff1d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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>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>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>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>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>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>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>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>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>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": [
       "      Attrition_Flag  Customer_Age Gender  Dependent_count Education_Level  \\\n",
       "0  Existing Customer            45      M                3     High School   \n",
       "1  Existing Customer            49      F                5        Graduate   \n",
       "2  Existing Customer            51      M                3        Graduate   \n",
       "3  Existing Customer            40      F                4     High School   \n",
       "4  Existing Customer            40      M                3      Uneducated   \n",
       "\n",
       "  Marital_Status Income_Category Card_Category  Months_on_book  \\\n",
       "0        Married     $60K - $80K          Blue              39   \n",
       "1         Single  Less than $40K          Blue              44   \n",
       "2        Married    $80K - $120K          Blue              36   \n",
       "3        Unknown  Less than $40K          Blue              34   \n",
       "4        Married     $60K - $80K          Blue              21   \n",
       "\n",
       "   Total_Relationship_Count  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "0                         5                       1                      3   \n",
       "1                         6                       1                      2   \n",
       "2                         4                       1                      0   \n",
       "3                         3                       4                      1   \n",
       "4                         5                       1                      0   \n",
       "\n",
       "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0       12691.0                  777          11914.0                 1.335   \n",
       "1        8256.0                  864           7392.0                 1.541   \n",
       "2        3418.0                    0           3418.0                 2.594   \n",
       "3        3313.0                 2517            796.0                 1.405   \n",
       "4        4716.0                    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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f2f158",
   "metadata": {},
   "source": [
    "### Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5788c0e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Gender</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
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       "      <th>Card_Category</th>\n",
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       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>M</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10123</th>\n",
       "      <td>M</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10124</th>\n",
       "      <td>F</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>M</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126</th>\n",
       "      <td>F</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10127 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender Education_Level Marital_Status Income_Category Card_Category\n",
       "0          M     High School        Married     $60K - $80K          Blue\n",
       "1          F        Graduate         Single  Less than $40K          Blue\n",
       "2          M        Graduate        Married    $80K - $120K          Blue\n",
       "3          F     High School        Unknown  Less than $40K          Blue\n",
       "4          M      Uneducated        Married     $60K - $80K          Blue\n",
       "...      ...             ...            ...             ...           ...\n",
       "10122      M        Graduate         Single     $40K - $60K          Blue\n",
       "10123      M         Unknown       Divorced     $40K - $60K          Blue\n",
       "10124      F     High School        Married  Less than $40K          Blue\n",
       "10125      M        Graduate        Unknown     $40K - $60K          Blue\n",
       "10126      F        Graduate        Married  Less than $40K        Silver\n",
       "\n",
       "[10127 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the non-numeric columns\n",
    "df_cat = df.select_dtypes(exclude=[np.number])\n",
    "\n",
    "# Dropping Attrition_Flag as it's the target.\n",
    "df_cat.drop(columns='Attrition_Flag', inplace=True)\n",
    "df_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c2b2ba",
   "metadata": {},
   "source": [
    "### Numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b92683c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Months_Inactive_12_mon</th>\n",
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       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
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       "      <th>0</th>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
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       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>5</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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>3</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>4</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>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10122</th>\n",
       "      <td>50</td>\n",
       "      <td>2</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>41</td>\n",
       "      <td>2</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>44</td>\n",
       "      <td>1</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>30</td>\n",
       "      <td>2</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>43</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 × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age  Dependent_count  Months_on_book  \\\n",
       "0                45                3              39   \n",
       "1                49                5              44   \n",
       "2                51                3              36   \n",
       "3                40                4              34   \n",
       "4                40                3              21   \n",
       "...             ...              ...             ...   \n",
       "10122            50                2              40   \n",
       "10123            41                2              25   \n",
       "10124            44                1              36   \n",
       "10125            30                2              36   \n",
       "10126            43                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 14 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = df.select_dtypes(exclude=[np.object])\n",
    "df_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c24695a",
   "metadata": {},
   "source": [
    "There are 5 categorical variables, and 14 numerical variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4fc39d9",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219ed88b",
   "metadata": {},
   "source": [
    "## Splitting train and test data\n",
    "\n",
    "We are splitting the train and test data before performing feature engineering techniques to avoid data-leakage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "479b1778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10127, 19)\n",
      "(10127,)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns='Attrition_Flag')\n",
    "y = df['Attrition_Flag']\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4046c4fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04b37bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training X data points: 6785\n",
      "testing X data points: 3342\n",
      "training y data points: 6785\n",
      "testing y data points: 3342\n"
     ]
    }
   ],
   "source": [
    "print (\"training X data points: {}\".format(len(X_train)))\n",
    "print (\"testing X data points: {}\".format(len(X_test)))\n",
    "\n",
    "print (\"training y data points: {}\".format(len(y_train)))\n",
    "print (\"testing y data points: {}\".format(len(y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741c503d",
   "metadata": {},
   "source": [
    "## Create a function to evaluate model performance\n",
    "* ML algorithm used: Random Forest\n",
    "* Metrics used: ROC-AUC score (performs better than Accuracy for imbalanced dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "de42cb4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RF_ROC(X_train, X_test, y_train, y_test):\n",
    "    rf = RandomForestClassifier(n_jobs=-1, random_state= 42, max_depth = 5)\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    print(\"Train set\")\n",
    "    train_pred = rf.predict_proba(X_train)\n",
    "    print(\"Random Forests roc-auc: {}\".format(roc_auc_score(y_train, train_pred[:,1])))\n",
    "    \n",
    "    print(\"Test set\")\n",
    "    test_pred = rf.predict_proba(X_test)\n",
    "    print(\"Random Forests roc-auc: {}\".format(roc_auc_score(y_test, test_pred[:, 1])))\n",
    "    \n",
    "    return roc_auc_score(y_train, train_pred[:,1]), roc_auc_score(y_test, test_pred[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131545a5",
   "metadata": {},
   "source": [
    "## Find the best encoder for the categorical variables\n",
    "**Categorical Encoding techniques tested:**\n",
    "* One-Hot Encoding\n",
    "* Ordinal Encoding\n",
    "* CatBoost Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8eb92f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Education_Level', 'Marital_Status', 'Income_Category',\n",
       "       'Card_Category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical columns\n",
    "df_cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74688436",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender has 2 unique values.\n",
      "['M' 'F']\n",
      "------------------------------------------------------------\n",
      "Education_Level has 7 unique values.\n",
      "['High School' 'Graduate' 'Uneducated' 'Unknown' 'College' 'Post-Graduate'\n",
      " 'Doctorate']\n",
      "------------------------------------------------------------\n",
      "Marital_Status has 4 unique values.\n",
      "['Married' 'Single' 'Unknown' 'Divorced']\n",
      "------------------------------------------------------------\n",
      "Income_Category has 6 unique values.\n",
      "['$60K - $80K' 'Less than $40K' '$80K - $120K' '$40K - $60K' '$120K +'\n",
      " 'Unknown']\n",
      "------------------------------------------------------------\n",
      "Card_Category has 4 unique values.\n",
      "['Blue' 'Gold' 'Silver' 'Platinum']\n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# How many unique values does each categorical variable has? \n",
    "# What are they?\n",
    "\n",
    "for col in df_cat.columns:\n",
    "    print(col,'has {} unique values.'.format(df_cat[col].nunique()))\n",
    "    print(df_cat[col].unique())\n",
    "    print('------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe1bae4",
   "metadata": {},
   "source": [
    "### One-Hot Encoding\n",
    "Applying One-Hot Encoding using pd.get_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb698e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of the original dataset\n",
    "X_train_ohe = X_train.copy()\n",
    "X_test_ohe = X_test.copy()\n",
    "\n",
    "X_train_OHE = pd.get_dummies(data = X_train_ohe)\n",
    "X_test_OHE = pd.get_dummies(data = X_test_ohe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40e2045e",
   "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>Dependent_count</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>...</th>\n",
       "      <th>Income_Category_$120K +</th>\n",
       "      <th>Income_Category_$40K - $60K</th>\n",
       "      <th>Income_Category_$60K - $80K</th>\n",
       "      <th>Income_Category_$80K - $120K</th>\n",
       "      <th>Income_Category_Less than $40K</th>\n",
       "      <th>Income_Category_Unknown</th>\n",
       "      <th>Card_Category_Blue</th>\n",
       "      <th>Card_Category_Gold</th>\n",
       "      <th>Card_Category_Platinum</th>\n",
       "      <th>Card_Category_Silver</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>21782.0</td>\n",
       "      <td>1006</td>\n",
       "      <td>20776.0</td>\n",
       "      <td>0.654</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5965.0</td>\n",
       "      <td>688</td>\n",
       "      <td>5277.0</td>\n",
       "      <td>0.673</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6332</th>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>1870</td>\n",
       "      <td>8435.0</td>\n",
       "      <td>0.722</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>26516.0</td>\n",
       "      <td>1434</td>\n",
       "      <td>25082.0</td>\n",
       "      <td>0.472</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6006</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2763.0</td>\n",
       "      <td>1913</td>\n",
       "      <td>850.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4142.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>0.809</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7939.0</td>\n",
       "      <td>0.551</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2314.0</td>\n",
       "      <td>1547</td>\n",
       "      <td>767.0</td>\n",
       "      <td>0.804</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>1707</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>0.506</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3858.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3858.0</td>\n",
       "      <td>0.670</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6785 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Age  Dependent_count  Months_on_book  Total_Relationship_Count  \\\n",
       "1163            52                2              42                         5   \n",
       "1584            50                2              36                         5   \n",
       "6332            43                3              25                         5   \n",
       "3643            44                3              38                         4   \n",
       "6006            34                2              36                         5   \n",
       "...            ...              ...             ...                       ...   \n",
       "5734            44                1              38                         3   \n",
       "5191            53                3              36                         4   \n",
       "5390            42                4              32                         3   \n",
       "860             40                3              28                         4   \n",
       "7270            53                4              49                         5   \n",
       "\n",
       "      Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "1163                       3                      2       21782.0   \n",
       "1584                       3                      2        5965.0   \n",
       "6332                       1                      2       10305.0   \n",
       "3643                       6                      4       26516.0   \n",
       "6006                       1                      3        2763.0   \n",
       "...                      ...                    ...           ...   \n",
       "5734                       2                      5        4142.0   \n",
       "5191                       3                      6        7939.0   \n",
       "5390                       1                      2        2314.0   \n",
       "860                        1                      1        3563.0   \n",
       "7270                       1                      2        3858.0   \n",
       "\n",
       "      Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  ...  \\\n",
       "1163                 1006          20776.0                 0.654  ...   \n",
       "1584                  688           5277.0                 0.673  ...   \n",
       "6332                 1870           8435.0                 0.722  ...   \n",
       "3643                 1434          25082.0                 0.472  ...   \n",
       "6006                 1913            850.0                 0.598  ...   \n",
       "...                   ...              ...                   ...  ...   \n",
       "5734                 2517           1625.0                 0.809  ...   \n",
       "5191                    0           7939.0                 0.551  ...   \n",
       "5390                 1547            767.0                 0.804  ...   \n",
       "860                  1707           1856.0                 0.506  ...   \n",
       "7270                    0           3858.0                 0.670  ...   \n",
       "\n",
       "      Income_Category_$120K +  Income_Category_$40K - $60K  \\\n",
       "1163                        0                            0   \n",
       "1584                        0                            0   \n",
       "6332                        0                            0   \n",
       "3643                        0                            0   \n",
       "6006                        0                            0   \n",
       "...                       ...                          ...   \n",
       "5734                        0                            1   \n",
       "5191                        0                            0   \n",
       "5390                        0                            0   \n",
       "860                         0                            1   \n",
       "7270                        0                            0   \n",
       "\n",
       "      Income_Category_$60K - $80K  Income_Category_$80K - $120K  \\\n",
       "1163                            0                             1   \n",
       "1584                            1                             0   \n",
       "6332                            0                             0   \n",
       "3643                            0                             0   \n",
       "6006                            1                             0   \n",
       "...                           ...                           ...   \n",
       "5734                            0                             0   \n",
       "5191                            0                             0   \n",
       "5390                            0                             0   \n",
       "860                             0                             0   \n",
       "7270                            1                             0   \n",
       "\n",
       "      Income_Category_Less than $40K  Income_Category_Unknown  \\\n",
       "1163                               0                        0   \n",
       "1584                               0                        0   \n",
       "6332                               0                        1   \n",
       "3643                               0                        1   \n",
       "6006                               0                        0   \n",
       "...                              ...                      ...   \n",
       "5734                               0                        0   \n",
       "5191                               0                        1   \n",
       "5390                               1                        0   \n",
       "860                                0                        0   \n",
       "7270                               0                        0   \n",
       "\n",
       "      Card_Category_Blue  Card_Category_Gold  Card_Category_Platinum  \\\n",
       "1163                   1                   0                       0   \n",
       "1584                   1                   0                       0   \n",
       "6332                   1                   0                       0   \n",
       "3643                   1                   0                       0   \n",
       "6006                   1                   0                       0   \n",
       "...                  ...                 ...                     ...   \n",
       "5734                   1                   0                       0   \n",
       "5191                   1                   0                       0   \n",
       "5390                   1                   0                       0   \n",
       "860                    1                   0                       0   \n",
       "7270                   1                   0                       0   \n",
       "\n",
       "      Card_Category_Silver  \n",
       "1163                     0  \n",
       "1584                     0  \n",
       "6332                     0  \n",
       "3643                     0  \n",
       "6006                     0  \n",
       "...                    ...  \n",
       "5734                     0  \n",
       "5191                     0  \n",
       "5390                     0  \n",
       "860                      0  \n",
       "7270                     0  \n",
       "\n",
       "[6785 rows x 37 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_OHE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78786641",
   "metadata": {},
   "source": [
    "### Ordinal Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1ca6e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of the whole dataset. \n",
    "# As applying ordinal encoder doesn't introduce data leakage, to avoid 'unknow category' issue, we will apply it to the whole dataset.\n",
    "X_oe = X.copy()\n",
    "oe = OrdinalEncoder()\n",
    "X_oe = oe.fit_transform(X_oe)\n",
    "\n",
    "X_oe = pd.DataFrame(X_oe,columns=X.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "820aadf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4623.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>4935.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3623.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>3949.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2485.0</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>818.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>818.0</td>\n",
       "      <td>757.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2363.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3089.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>821.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10122</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1382.0</td>\n",
       "      <td>1772.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>4874.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10123</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2139.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>4244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10124</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3348.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>4351.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10125</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3305.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>4160.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10126</th>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4184.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>4235.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10127 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age  Gender  Dependent_count  Education_Level  Marital_Status  \\\n",
       "0              19.0     1.0              3.0              3.0             1.0   \n",
       "1              23.0     0.0              5.0              2.0             2.0   \n",
       "2              25.0     1.0              3.0              2.0             1.0   \n",
       "3              14.0     0.0              4.0              3.0             3.0   \n",
       "4              14.0     1.0              3.0              5.0             1.0   \n",
       "...             ...     ...              ...              ...             ...   \n",
       "10122          24.0     1.0              2.0              2.0             2.0   \n",
       "10123          15.0     1.0              2.0              6.0             0.0   \n",
       "10124          18.0     0.0              1.0              3.0             1.0   \n",
       "10125           4.0     1.0              2.0              2.0             3.0   \n",
       "10126          17.0     0.0              2.0              2.0             1.0   \n",
       "\n",
       "       Income_Category  Card_Category  Months_on_book  \\\n",
       "0                  2.0            0.0            26.0   \n",
       "1                  4.0            0.0            31.0   \n",
       "2                  3.0            0.0            23.0   \n",
       "3                  4.0            0.0            21.0   \n",
       "4                  2.0            0.0             8.0   \n",
       "...                ...            ...             ...   \n",
       "10122              1.0            0.0            27.0   \n",
       "10123              1.0            0.0            12.0   \n",
       "10124              4.0            0.0            23.0   \n",
       "10125              1.0            0.0            23.0   \n",
       "10126              4.0            3.0            12.0   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "0                           4.0                     1.0   \n",
       "1                           5.0                     1.0   \n",
       "2                           3.0                     1.0   \n",
       "3                           2.0                     4.0   \n",
       "4                           4.0                     1.0   \n",
       "...                         ...                     ...   \n",
       "10122                       2.0                     2.0   \n",
       "10123                       3.0                     2.0   \n",
       "10124                       4.0                     3.0   \n",
       "10125                       3.0                     3.0   \n",
       "10126                       5.0                     2.0   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "0                        3.0        4623.0                324.0   \n",
       "1                        2.0        3623.0                408.0   \n",
       "2                        0.0        1623.0                  0.0   \n",
       "3                        1.0        1552.0               1973.0   \n",
       "4                        0.0        2363.0                  0.0   \n",
       "...                      ...           ...                  ...   \n",
       "10122                    3.0        1975.0               1382.0   \n",
       "10123                    3.0        2139.0               1698.0   \n",
       "10124                    4.0        2671.0                  0.0   \n",
       "10125                    3.0        2615.0                  0.0   \n",
       "10126                    4.0        4184.0               1489.0   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "0               4935.0                 987.0            217.0            32.0   \n",
       "1               3949.0                1073.0            338.0            23.0   \n",
       "2               2485.0                1154.0            893.0            10.0   \n",
       "3                686.0                1020.0            236.0            10.0   \n",
       "4               3089.0                1145.0             65.0            18.0   \n",
       "...                ...                   ...              ...             ...   \n",
       "10122           1772.0                 427.0           4874.0           107.0   \n",
       "10123           1738.0                 528.0           4244.0            59.0   \n",
       "10124           3348.0                 543.0           4351.0            50.0   \n",
       "10125           3305.0                 259.0           4160.0            52.0   \n",
       "10126           4235.0                 427.0           4352.0            51.0   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0                    791.0                   58.0  \n",
       "1                    829.0                  102.0  \n",
       "2                    818.0                    0.0  \n",
       "3                    818.0                  757.0  \n",
       "4                    821.0                    0.0  \n",
       "...                    ...                    ...  \n",
       "10122                517.0                  459.0  \n",
       "10123                363.0                  508.0  \n",
       "10124                483.0                    0.0  \n",
       "10125                399.0                    0.0  \n",
       "10126                336.0                  186.0  \n",
       "\n",
       "[10127 rows x 19 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_oe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c75a1c07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then split to train and test \n",
    "X_train_ORD, X_test_ORD, y_train, y_test = train_test_split(X_oe, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1e5b00",
   "metadata": {},
   "source": [
    "### Catboost Encoder\n",
    "\n",
    "Catboost is a recently created target-based categorical encoder.\n",
    "\n",
    "It is intended to overcome target leakage problems inherent in LeaveOneOut Encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55486dd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cbe = X_train.copy()\n",
    "X_test_cbe = X_test.copy()\n",
    "\n",
    "cbe = ce.CatBoostEncoder()\n",
    "\n",
    "# Map strings to integers for the target column.\n",
    "y1=y_train.map({'Existing Customer':0,'Attrited Customer':1 })\n",
    "\n",
    "X_train_CBE = cbe.fit_transform(X_train_cbe,y1)\n",
    "X_test_CBE = cbe.transform(X_test_cbe)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f852384f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>1163</th>\n",
       "      <td>52</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.160059</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>21782.0</td>\n",
       "      <td>1006</td>\n",
       "      <td>20776.0</td>\n",
       "      <td>0.654</td>\n",
       "      <td>1537</td>\n",
       "      <td>35</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>50</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>2</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5965.0</td>\n",
       "      <td>688</td>\n",
       "      <td>5277.0</td>\n",
       "      <td>0.673</td>\n",
       "      <td>3382</td>\n",
       "      <td>58</td>\n",
       "      <td>1.071</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6332</th>\n",
       "      <td>43</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>3</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.160059</td>\n",
       "      <td>0.053353</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>1870</td>\n",
       "      <td>8435.0</td>\n",
       "      <td>0.722</td>\n",
       "      <td>4018</td>\n",
       "      <td>63</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3643</th>\n",
       "      <td>44</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>3</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.040015</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>26516.0</td>\n",
       "      <td>1434</td>\n",
       "      <td>25082.0</td>\n",
       "      <td>0.472</td>\n",
       "      <td>3609</td>\n",
       "      <td>76</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6006</th>\n",
       "      <td>34</td>\n",
       "      <td>0.053353</td>\n",
       "      <td>2</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.053353</td>\n",
       "      <td>0.080029</td>\n",
       "      <td>0.032012</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2763.0</td>\n",
       "      <td>1913</td>\n",
       "      <td>850.0</td>\n",
       "      <td>0.598</td>\n",
       "      <td>4136</td>\n",
       "      <td>69</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.692</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>44</td>\n",
       "      <td>0.176186</td>\n",
       "      <td>1</td>\n",
       "      <td>0.154432</td>\n",
       "      <td>0.164896</td>\n",
       "      <td>0.147362</td>\n",
       "      <td>0.160872</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4142.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>1625.0</td>\n",
       "      <td>0.809</td>\n",
       "      <td>2104</td>\n",
       "      <td>44</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>53</td>\n",
       "      <td>0.176417</td>\n",
       "      <td>3</td>\n",
       "      <td>0.153171</td>\n",
       "      <td>0.170234</td>\n",
       "      <td>0.179531</td>\n",
       "      <td>0.161005</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7939.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7939.0</td>\n",
       "      <td>0.551</td>\n",
       "      <td>2269</td>\n",
       "      <td>42</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>42</td>\n",
       "      <td>0.176647</td>\n",
       "      <td>4</td>\n",
       "      <td>0.154832</td>\n",
       "      <td>0.157899</td>\n",
       "      <td>0.171364</td>\n",
       "      <td>0.161137</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2314.0</td>\n",
       "      <td>1547</td>\n",
       "      <td>767.0</td>\n",
       "      <td>0.804</td>\n",
       "      <td>4678</td>\n",
       "      <td>74</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>40</td>\n",
       "      <td>0.141750</td>\n",
       "      <td>3</td>\n",
       "      <td>0.163687</td>\n",
       "      <td>0.165212</td>\n",
       "      <td>0.148066</td>\n",
       "      <td>0.161112</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>1707</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>0.506</td>\n",
       "      <td>1482</td>\n",
       "      <td>42</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>53</td>\n",
       "      <td>0.141706</td>\n",
       "      <td>4</td>\n",
       "      <td>0.153794</td>\n",
       "      <td>0.165150</td>\n",
       "      <td>0.136867</td>\n",
       "      <td>0.161086</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3858.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3858.0</td>\n",
       "      <td>0.670</td>\n",
       "      <td>4472</td>\n",
       "      <td>92</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6785 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Age    Gender  Dependent_count  Education_Level  \\\n",
       "1163            52  0.160059                2         0.160059   \n",
       "1584            50  0.080029                2         0.160059   \n",
       "6332            43  0.160059                3         0.160059   \n",
       "3643            44  0.080029                3         0.080029   \n",
       "6006            34  0.053353                2         0.080029   \n",
       "...            ...       ...              ...              ...   \n",
       "5734            44  0.176186                1         0.154432   \n",
       "5191            53  0.176417                3         0.153171   \n",
       "5390            42  0.176647                4         0.154832   \n",
       "860             40  0.141750                3         0.163687   \n",
       "7270            53  0.141706                4         0.153794   \n",
       "\n",
       "      Marital_Status  Income_Category  Card_Category  Months_on_book  \\\n",
       "1163        0.160059         0.160059       0.160059              42   \n",
       "1584        0.160059         0.160059       0.080029              36   \n",
       "6332        0.080029         0.160059       0.053353              25   \n",
       "3643        0.080029         0.080029       0.040015              38   \n",
       "6006        0.053353         0.080029       0.032012              36   \n",
       "...              ...              ...            ...             ...   \n",
       "5734        0.164896         0.147362       0.160872              38   \n",
       "5191        0.170234         0.179531       0.161005              36   \n",
       "5390        0.157899         0.171364       0.161137              32   \n",
       "860         0.165212         0.148066       0.161112              28   \n",
       "7270        0.165150         0.136867       0.161086              49   \n",
       "\n",
       "      Total_Relationship_Count  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "1163                         5                       3                      2   \n",
       "1584                         5                       3                      2   \n",
       "6332                         5                       1                      2   \n",
       "3643                         4                       6                      4   \n",
       "6006                         5                       1                      3   \n",
       "...                        ...                     ...                    ...   \n",
       "5734                         3                       2                      5   \n",
       "5191                         4                       3                      6   \n",
       "5390                         3                       1                      2   \n",
       "860                          4                       1                      1   \n",
       "7270                         5                       1                      2   \n",
       "\n",
       "      Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  \\\n",
       "1163       21782.0                 1006          20776.0   \n",
       "1584        5965.0                  688           5277.0   \n",
       "6332       10305.0                 1870           8435.0   \n",
       "3643       26516.0                 1434          25082.0   \n",
       "6006        2763.0                 1913            850.0   \n",
       "...            ...                  ...              ...   \n",
       "5734        4142.0                 2517           1625.0   \n",
       "5191        7939.0                    0           7939.0   \n",
       "5390        2314.0                 1547            767.0   \n",
       "860         3563.0                 1707           1856.0   \n",
       "7270        3858.0                    0           3858.0   \n",
       "\n",
       "      Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "1163                 0.654             1537              35   \n",
       "1584                 0.673             3382              58   \n",
       "6332                 0.722             4018              63   \n",
       "3643                 0.472             3609              76   \n",
       "6006                 0.598             4136              69   \n",
       "...                    ...              ...             ...   \n",
       "5734                 0.809             2104              44   \n",
       "5191                 0.551             2269              42   \n",
       "5390                 0.804             4678              74   \n",
       "860                  0.506             1482              42   \n",
       "7270                 0.670             4472              92   \n",
       "\n",
       "      Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "1163                0.591                  0.046  \n",
       "1584                1.071                  0.115  \n",
       "6332                0.800                  0.181  \n",
       "3643                0.551                  0.054  \n",
       "6006                0.568                  0.692  \n",
       "...                   ...                    ...  \n",
       "5734                0.833                  0.608  \n",
       "5191                0.312                  0.000  \n",
       "5390                1.000                  0.669  \n",
       "860                 0.312                  0.479  \n",
       "7270                0.614                  0.000  \n",
       "\n",
       "[6785 rows x 19 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_CBE = pd.DataFrame(X_train_CBE,columns=X_train_cbe.columns)\n",
    "X_train_CBE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bde2bbc2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>3781</th>\n",
       "      <td>48</td>\n",
       "      <td>0.176598</td>\n",
       "      <td>3</td>\n",
       "      <td>0.159436</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.171292</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2991.0</td>\n",
       "      <td>1508</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>0.703</td>\n",
       "      <td>3734</td>\n",
       "      <td>64</td>\n",
       "      <td>0.882</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>59</td>\n",
       "      <td>0.141662</td>\n",
       "      <td>1</td>\n",
       "      <td>0.159436</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.171292</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2192.0</td>\n",
       "      <td>1569</td>\n",
       "      <td>623.0</td>\n",
       "      <td>0.706</td>\n",
       "      <td>4010</td>\n",
       "      <td>79</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5070</th>\n",
       "      <td>37</td>\n",
       "      <td>0.176598</td>\n",
       "      <td>2</td>\n",
       "      <td>0.154759</td>\n",
       "      <td>0.171852</td>\n",
       "      <td>0.171292</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>987</td>\n",
       "      <td>747.0</td>\n",
       "      <td>0.879</td>\n",
       "      <td>4727</td>\n",
       "      <td>67</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7246</th>\n",
       "      <td>47</td>\n",
       "      <td>0.141662</td>\n",
       "      <td>3</td>\n",
       "      <td>0.228436</td>\n",
       "      <td>0.171852</td>\n",
       "      <td>0.147944</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4786.0</td>\n",
       "      <td>1516</td>\n",
       "      <td>3270.0</td>\n",
       "      <td>0.940</td>\n",
       "      <td>4973</td>\n",
       "      <td>74</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>42</td>\n",
       "      <td>0.141662</td>\n",
       "      <td>3</td>\n",
       "      <td>0.163525</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.156224</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3714.0</td>\n",
       "      <td>2170</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>1454</td>\n",
       "      <td>35</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.584</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>60</td>\n",
       "      <td>0.141662</td>\n",
       "      <td>1</td>\n",
       "      <td>0.154759</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.156224</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11524.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11524.0</td>\n",
       "      <td>0.682</td>\n",
       "      <td>3598</td>\n",
       "      <td>72</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838</th>\n",
       "      <td>55</td>\n",
       "      <td>0.141662</td>\n",
       "      <td>1</td>\n",
       "      <td>0.228436</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.136723</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15221.0</td>\n",
       "      <td>1166</td>\n",
       "      <td>14055.0</td>\n",
       "      <td>0.839</td>\n",
       "      <td>3848</td>\n",
       "      <td>68</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>54</td>\n",
       "      <td>0.176598</td>\n",
       "      <td>3</td>\n",
       "      <td>0.153681</td>\n",
       "      <td>0.154311</td>\n",
       "      <td>0.171292</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3161.0</td>\n",
       "      <td>1220</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>0.820</td>\n",
       "      <td>2412</td>\n",
       "      <td>62</td>\n",
       "      <td>0.824</td>\n",
       "      <td>0.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>36</td>\n",
       "      <td>0.176598</td>\n",
       "      <td>4</td>\n",
       "      <td>0.163525</td>\n",
       "      <td>0.154311</td>\n",
       "      <td>0.180661</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15700.0</td>\n",
       "      <td>1276</td>\n",
       "      <td>14424.0</td>\n",
       "      <td>1.389</td>\n",
       "      <td>2229</td>\n",
       "      <td>48</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4385</th>\n",
       "      <td>52</td>\n",
       "      <td>0.176598</td>\n",
       "      <td>0</td>\n",
       "      <td>0.154759</td>\n",
       "      <td>0.165087</td>\n",
       "      <td>0.171292</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2537.0</td>\n",
       "      <td>1097</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>0.874</td>\n",
       "      <td>4964</td>\n",
       "      <td>84</td>\n",
       "      <td>0.909</td>\n",
       "      <td>0.432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3342 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer_Age    Gender  Dependent_count  Education_Level  \\\n",
       "3781            48  0.176598                3         0.159436   \n",
       "2922            59  0.141662                1         0.159436   \n",
       "5070            37  0.176598                2         0.154759   \n",
       "7246            47  0.141662                3         0.228436   \n",
       "623             42  0.141662                3         0.163525   \n",
       "...            ...       ...              ...              ...   \n",
       "4205            60  0.141662                1         0.154759   \n",
       "3838            55  0.141662                1         0.228436   \n",
       "1952            54  0.176598                3         0.153681   \n",
       "555             36  0.176598                4         0.163525   \n",
       "4385            52  0.176598                0         0.154759   \n",
       "\n",
       "      Marital_Status  Income_Category  Card_Category  Months_on_book  \\\n",
       "3781        0.165087         0.171292       0.161061              39   \n",
       "2922        0.165087         0.171292       0.161061              53   \n",
       "5070        0.171852         0.171292       0.161061              36   \n",
       "7246        0.171852         0.147944       0.161061              36   \n",
       "623         0.165087         0.156224       0.161061              33   \n",
       "...              ...              ...            ...             ...   \n",
       "4205        0.165087         0.156224       0.161061              55   \n",
       "3838        0.165087         0.136723       0.161061              36   \n",
       "1952        0.154311         0.171292       0.161061              41   \n",
       "555         0.154311         0.180661       0.161061              31   \n",
       "4385        0.165087         0.171292       0.161061              36   \n",
       "\n",
       "      Total_Relationship_Count  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "3781                         4                       3                      4   \n",
       "2922                         5                       5                      4   \n",
       "5070                         4                       3                      3   \n",
       "7246                         4                       2                      3   \n",
       "623                          3                       3                      2   \n",
       "...                        ...                     ...                    ...   \n",
       "4205                         6                       2                      1   \n",
       "3838                         3                       3                      3   \n",
       "1952                         5                       4                      4   \n",
       "555                          5                       2                      0   \n",
       "4385                         5                       1                      2   \n",
       "\n",
       "      Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  \\\n",
       "3781        2991.0                 1508           1483.0   \n",
       "2922        2192.0                 1569            623.0   \n",
       "5070        1734.0                  987            747.0   \n",
       "7246        4786.0                 1516           3270.0   \n",
       "623         3714.0                 2170           1544.0   \n",
       "...            ...                  ...              ...   \n",
       "4205       11524.0                    0          11524.0   \n",
       "3838       15221.0                 1166          14055.0   \n",
       "1952        3161.0                 1220           1941.0   \n",
       "555        15700.0                 1276          14424.0   \n",
       "4385        2537.0                 1097           1440.0   \n",
       "\n",
       "      Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "3781                 0.703             3734              64   \n",
       "2922                 0.706             4010              79   \n",
       "5070                 0.879             4727              67   \n",
       "7246                 0.940             4973              74   \n",
       "623                  0.524             1454              35   \n",
       "...                    ...              ...             ...   \n",
       "4205                 0.682             3598              72   \n",
       "3838                 0.839             3848              68   \n",
       "1952                 0.820             2412              62   \n",
       "555                  1.389             2229              48   \n",
       "4385                 0.874             4964              84   \n",
       "\n",
       "      Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "3781                0.882                  0.504  \n",
       "2922                0.717                  0.716  \n",
       "5070                0.914                  0.569  \n",
       "7246                0.850                  0.317  \n",
       "623                 0.522                  0.584  \n",
       "...                   ...                    ...  \n",
       "4205                0.532                  0.000  \n",
       "3838                0.619                  0.077  \n",
       "1952                0.824                  0.386  \n",
       "555                 0.778                  0.081  \n",
       "4385                0.909                  0.432  \n",
       "\n",
       "[3342 rows x 19 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_CBE = pd.DataFrame(X_test_CBE,columns=X_test_cbe.columns)\n",
    "X_test_CBE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422bf4d3",
   "metadata": {},
   "source": [
    "### Evaluate model performance with the 3 encoders above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c26fe425",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "Random Forests roc-auc: 0.9690782881039192\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9531834748746321\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9764366919077593\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9661765899556601\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9764080933070549\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9658235341088244\n"
     ]
    }
   ],
   "source": [
    "encoders = {\"OHE\": RF_ROC(X_train_OHE, X_test_OHE, y_train, y_test),\n",
    "               \"ORD\": RF_ROC(X_train_ORD, X_test_ORD, y_train, y_test),\n",
    "               \"CBE\": RF_ROC(X_train_CBE, X_test_CBE, y_train, y_test)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b5f2c6de",
   "metadata": {
    "scrolled": true
   },
   "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>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHE</th>\n",
       "      <td>0.969078</td>\n",
       "      <td>0.953183</td>\n",
       "      <td>0.015895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORD</th>\n",
       "      <td>0.976437</td>\n",
       "      <td>0.966177</td>\n",
       "      <td>0.010260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBE</th>\n",
       "      <td>0.976408</td>\n",
       "      <td>0.965824</td>\n",
       "      <td>0.010585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Train      Test      diff\n",
       "OHE  0.969078  0.953183  0.015895\n",
       "ORD  0.976437  0.966177  0.010260\n",
       "CBE  0.976408  0.965824  0.010585"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Encoders = pd.DataFrame(encoders,index=['Train','Test']).T\n",
    "Encoders['diff']=Encoders['Train']-Encoders['Test']\n",
    "Encoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da6910",
   "metadata": {},
   "source": [
    "* **Observation:** Ordinal encoder and Catboost encoder has similar performance which is better than Onehot encoder's.\n",
    "\n",
    "* We will go with **Ordinal encoder** as it's slightly better, and it doesn't introduce any data leakage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1d95cc",
   "metadata": {},
   "source": [
    "## Over Sampling\n",
    "**Over-sampling method tested:**\n",
    "* RandomOverSampler\n",
    "* Synthetic Minority Oversampling Technique (SMOTE)\n",
    "* Adaptive Synthetic (ADASYN)\n",
    "* BorderlineSMOTE\n",
    "* SVMSMOTE\n",
    "* KMeansSMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618584f2",
   "metadata": {},
   "source": [
    "### RandomOverSampler\n",
    "Object to over-sample the minority class(es) by picking samples at random with replacement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bef54666",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_ros = X_train_ORD.copy()\n",
    "X_test_ros = X_test_ORD.copy()\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "X_resampled_ros, y_resampled_ros = ros.fit_resample(X_train_ros, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc75ca71",
   "metadata": {},
   "source": [
    "### Synthetic Minority Oversampling Technique (SMOTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "726ca146",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_smo = X_train_ORD.copy()\n",
    "X_test_smo = X_test_ORD.copy()\n",
    "\n",
    "smo = SMOTE(random_state=42)\n",
    "\n",
    "X_resampled_smo, y_resampled_smo = smo.fit_resample(X_train_smo, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618fb738",
   "metadata": {},
   "source": [
    "### Adaptive Synthetic (ADASYN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "03424167",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_ada = X_train_ORD.copy()\n",
    "X_test_ada = X_test_ORD.copy()\n",
    "\n",
    "ada = ADASYN(random_state=42)\n",
    "\n",
    "X_resampled_ada, y_resampled_ada = ada.fit_resample(X_train_ada, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9542178",
   "metadata": {},
   "source": [
    "### BorderlineSMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4c54a601",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_bor = X_train_ORD.copy()\n",
    "X_test_bor = X_test_ORD.copy()\n",
    "\n",
    "bor = BorderlineSMOTE(random_state=42)\n",
    "\n",
    "X_resampled_bor, y_resampled_bor = bor.fit_resample(X_train_bor, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4241b39e",
   "metadata": {},
   "source": [
    "### SVMSMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "889b174e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_svm = X_train_ORD.copy()\n",
    "X_test_svm = X_test_ORD.copy()\n",
    "\n",
    "svm = SVMSMOTE(random_state=42)\n",
    "\n",
    "X_resampled_svm, y_resampled_svm = svm.fit_resample(X_train_svm, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e12f7047",
   "metadata": {},
   "source": [
    "### KMeansSMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "54274542",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_km = X_train_ORD.copy()\n",
    "X_test_km = X_test_ORD.copy()\n",
    "\n",
    "km = KMeansSMOTE(kmeans_estimator=20, random_state=42)\n",
    "\n",
    "X_resampled_km, y_resampled_km = km.fit_resample(X_train_km, y_train) # y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ce77c3",
   "metadata": {},
   "source": [
    "### Evaluate model performance with the different over-sampling methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ef3be0a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "Random Forests roc-auc: 0.9743244482728797\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9604075914266162\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9851052081953899\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9609573026797269\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9846413119853318\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9551767555949453\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9857642575183578\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9540994403239931\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9822913862576779\n",
      "Test set\n",
      "Random Forests roc-auc: 0.960953673133638\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9922691251387337\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9441195084142777\n"
     ]
    }
   ],
   "source": [
    "oversampler= {'random':RF_ROC(X_resampled_ros, X_test_ros, y_resampled_ros, y_test),\n",
    "                'SMOTE':RF_ROC(X_resampled_smo, X_test_smo, y_resampled_smo, y_test),\n",
    "                'ADASYN':RF_ROC(X_resampled_ada, X_test_ada, y_resampled_ada, y_test),\n",
    "             'BorderlineSMOTE':RF_ROC(X_resampled_bor, X_test_bor, y_resampled_bor, y_test),\n",
    "              'SVMSMOTE':RF_ROC(X_resampled_svm, X_test_svm, y_resampled_svm, y_test),\n",
    "             'KMeansSMOTE':RF_ROC(X_resampled_km, X_test_km, y_resampled_km, y_test),}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ab18f1ce",
   "metadata": {
    "scrolled": false
   },
   "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>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <td>0.974324</td>\n",
       "      <td>0.960408</td>\n",
       "      <td>0.013917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOTE</th>\n",
       "      <td>0.985105</td>\n",
       "      <td>0.960957</td>\n",
       "      <td>0.024148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADASYN</th>\n",
       "      <td>0.984641</td>\n",
       "      <td>0.955177</td>\n",
       "      <td>0.029465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BorderlineSMOTE</th>\n",
       "      <td>0.985764</td>\n",
       "      <td>0.954099</td>\n",
       "      <td>0.031665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVMSMOTE</th>\n",
       "      <td>0.982291</td>\n",
       "      <td>0.960954</td>\n",
       "      <td>0.021338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KMeansSMOTE</th>\n",
       "      <td>0.992269</td>\n",
       "      <td>0.944120</td>\n",
       "      <td>0.048150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Train      Test      diff\n",
       "random           0.974324  0.960408  0.013917\n",
       "SMOTE            0.985105  0.960957  0.024148\n",
       "ADASYN           0.984641  0.955177  0.029465\n",
       "BorderlineSMOTE  0.985764  0.954099  0.031665\n",
       "SVMSMOTE         0.982291  0.960954  0.021338\n",
       "KMeansSMOTE      0.992269  0.944120  0.048150"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversampler = pd.DataFrame(oversampler,index=['Train', 'Test']).T\n",
    "oversampler['diff']=oversampler['Train']-oversampler['Test']\n",
    "oversampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0be268",
   "metadata": {},
   "source": [
    "* **Observation:** KMeansSMOTE got the highest score on train set, while SMOTE has the highest score on test set. Randomoversampler offers the smallest difference between train and test.\n",
    "* We will go with **SMOTE** for its overall performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01973875",
   "metadata": {},
   "source": [
    "## Numerical Data Scaling\n",
    "**Scaling techniques tested:**\n",
    "* Standard scaler\n",
    "* Minmax scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "50033256",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A quick look of the ditribution of the data\n",
    "X_oe.hist(figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03371ccb",
   "metadata": {},
   "source": [
    "### Standardization\n",
    "StandardScaler is used to resize the distribution of values so that the mean of the observed values is 0 and the standard deviation is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "14a0a6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_st = X_resampled_smo.copy()\n",
    "X_test_st = X_test_ORD.copy()\n",
    "\n",
    "# Initialize the scaler\n",
    "st_scaler = StandardScaler()\n",
    "\n",
    "# Fit it to the train set\n",
    "st_scaler.fit(X_train_st)\n",
    "\n",
    "# Transform to train and test set\n",
    "X_train_ST = st_scaler.transform(X_train_st)\n",
    "X_test_ST =st_scaler.transform(X_test_st)\n",
    "\n",
    "# Create dataframe\n",
    "X_train_ST = pd.DataFrame(X_train_ST, columns=X_train_st.columns)\n",
    "X_test_ST = pd.DataFrame(X_test_ST, columns=X_test_st.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "46e7d5bf",
   "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>0.734189</td>\n",
       "      <td>1.170097</td>\n",
       "      <td>-0.288757</td>\n",
       "      <td>-0.637725</td>\n",
       "      <td>-0.654686</td>\n",
       "      <td>0.071386</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>0.790379</td>\n",
       "      <td>0.958497</td>\n",
       "      <td>0.554118</td>\n",
       "      <td>-0.634471</td>\n",
       "      <td>1.542291</td>\n",
       "      <td>-0.164826</td>\n",
       "      <td>1.520616</td>\n",
       "      <td>-0.401104</td>\n",
       "      <td>-1.110146</td>\n",
       "      <td>-0.976744</td>\n",
       "      <td>-0.294071</td>\n",
       "      <td>-0.662142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.463065</td>\n",
       "      <td>1.170097</td>\n",
       "      <td>-0.288757</td>\n",
       "      <td>-1.804946</td>\n",
       "      <td>0.803409</td>\n",
       "      <td>-0.636041</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-0.013882</td>\n",
       "      <td>0.958497</td>\n",
       "      <td>0.554118</td>\n",
       "      <td>-0.634471</td>\n",
       "      <td>0.179367</td>\n",
       "      <td>-0.603433</td>\n",
       "      <td>0.202263</td>\n",
       "      <td>-0.301854</td>\n",
       "      <td>-0.003432</td>\n",
       "      <td>0.044156</td>\n",
       "      <td>1.967580</td>\n",
       "      <td>-0.409086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.485870</td>\n",
       "      <td>-0.945513</td>\n",
       "      <td>0.543468</td>\n",
       "      <td>-0.054115</td>\n",
       "      <td>0.803409</td>\n",
       "      <td>1.486240</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-1.488360</td>\n",
       "      <td>0.958497</td>\n",
       "      <td>-1.585363</td>\n",
       "      <td>-0.634471</td>\n",
       "      <td>0.811419</td>\n",
       "      <td>1.061835</td>\n",
       "      <td>0.679853</td>\n",
       "      <td>-0.045891</td>\n",
       "      <td>0.387714</td>\n",
       "      <td>0.266091</td>\n",
       "      <td>0.821514</td>\n",
       "      <td>-0.167033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.350308</td>\n",
       "      <td>-0.945513</td>\n",
       "      <td>0.543468</td>\n",
       "      <td>-0.054115</td>\n",
       "      <td>-0.654686</td>\n",
       "      <td>1.486240</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>0.254205</td>\n",
       "      <td>0.280187</td>\n",
       "      <td>3.763339</td>\n",
       "      <td>1.267305</td>\n",
       "      <td>1.682747</td>\n",
       "      <td>0.446347</td>\n",
       "      <td>1.661594</td>\n",
       "      <td>-1.351822</td>\n",
       "      <td>0.126950</td>\n",
       "      <td>0.843121</td>\n",
       "      <td>-0.507434</td>\n",
       "      <td>-0.632802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.705929</td>\n",
       "      <td>1.170097</td>\n",
       "      <td>-0.288757</td>\n",
       "      <td>-0.637725</td>\n",
       "      <td>-0.654686</td>\n",
       "      <td>-0.636041</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-0.013882</td>\n",
       "      <td>0.958497</td>\n",
       "      <td>-1.585363</td>\n",
       "      <td>0.316417</td>\n",
       "      <td>-0.721056</td>\n",
       "      <td>1.123671</td>\n",
       "      <td>-1.101787</td>\n",
       "      <td>-0.693633</td>\n",
       "      <td>0.475914</td>\n",
       "      <td>0.532412</td>\n",
       "      <td>-0.415992</td>\n",
       "      <td>1.707044</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11393</th>\n",
       "      <td>-1.946351</td>\n",
       "      <td>-0.924681</td>\n",
       "      <td>-1.104593</td>\n",
       "      <td>-0.637725</td>\n",
       "      <td>-0.640329</td>\n",
       "      <td>0.764882</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-1.726089</td>\n",
       "      <td>-1.748066</td>\n",
       "      <td>-0.494556</td>\n",
       "      <td>0.307054</td>\n",
       "      <td>-0.269254</td>\n",
       "      <td>-0.921541</td>\n",
       "      <td>-0.101910</td>\n",
       "      <td>0.257159</td>\n",
       "      <td>1.514528</td>\n",
       "      <td>0.306107</td>\n",
       "      <td>0.450460</td>\n",
       "      <td>-0.623750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11394</th>\n",
       "      <td>-0.124352</td>\n",
       "      <td>-0.945513</td>\n",
       "      <td>-0.685038</td>\n",
       "      <td>1.001972</td>\n",
       "      <td>-0.654686</td>\n",
       "      <td>0.778813</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>0.209542</td>\n",
       "      <td>0.474010</td>\n",
       "      <td>-0.260933</td>\n",
       "      <td>-0.634471</td>\n",
       "      <td>-0.476836</td>\n",
       "      <td>-0.951443</td>\n",
       "      <td>-0.224736</td>\n",
       "      <td>-0.036447</td>\n",
       "      <td>1.357799</td>\n",
       "      <td>0.642322</td>\n",
       "      <td>0.937050</td>\n",
       "      <td>-0.819843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11395</th>\n",
       "      <td>-2.400607</td>\n",
       "      <td>-0.565289</td>\n",
       "      <td>-1.654067</td>\n",
       "      <td>0.155662</td>\n",
       "      <td>0.803409</td>\n",
       "      <td>1.104817</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-2.687339</td>\n",
       "      <td>-1.632837</td>\n",
       "      <td>-0.515623</td>\n",
       "      <td>1.096408</td>\n",
       "      <td>0.223046</td>\n",
       "      <td>-0.951443</td>\n",
       "      <td>0.357972</td>\n",
       "      <td>-1.467948</td>\n",
       "      <td>-1.464259</td>\n",
       "      <td>-1.581183</td>\n",
       "      <td>-0.712341</td>\n",
       "      <td>-0.819843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11396</th>\n",
       "      <td>0.712131</td>\n",
       "      <td>-0.945513</td>\n",
       "      <td>-0.946779</td>\n",
       "      <td>-0.149076</td>\n",
       "      <td>0.803409</td>\n",
       "      <td>-0.751148</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-0.013882</td>\n",
       "      <td>-0.256139</td>\n",
       "      <td>0.554118</td>\n",
       "      <td>0.117375</td>\n",
       "      <td>0.663173</td>\n",
       "      <td>0.885683</td>\n",
       "      <td>0.530314</td>\n",
       "      <td>0.320350</td>\n",
       "      <td>-0.283017</td>\n",
       "      <td>-0.444100</td>\n",
       "      <td>0.447204</td>\n",
       "      <td>-0.126268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11397</th>\n",
       "      <td>-0.526446</td>\n",
       "      <td>-0.945513</td>\n",
       "      <td>0.573834</td>\n",
       "      <td>-1.383209</td>\n",
       "      <td>-0.303463</td>\n",
       "      <td>0.949217</td>\n",
       "      <td>-0.263129</td>\n",
       "      <td>-0.756509</td>\n",
       "      <td>-1.239825</td>\n",
       "      <td>0.296441</td>\n",
       "      <td>-0.176375</td>\n",
       "      <td>0.192395</td>\n",
       "      <td>-0.951443</td>\n",
       "      <td>0.331903</td>\n",
       "      <td>-1.715462</td>\n",
       "      <td>-1.234858</td>\n",
       "      <td>-0.869180</td>\n",
       "      <td>-0.720663</td>\n",
       "      <td>-0.819843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11398 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age    Gender  Dependent_count  Education_Level  \\\n",
       "0          0.734189  1.170097        -0.288757        -0.637725   \n",
       "1          0.463065  1.170097        -0.288757        -1.804946   \n",
       "2         -0.485870 -0.945513         0.543468        -0.054115   \n",
       "3         -0.350308 -0.945513         0.543468        -0.054115   \n",
       "4         -1.705929  1.170097        -0.288757        -0.637725   \n",
       "...             ...       ...              ...              ...   \n",
       "11393     -1.946351 -0.924681        -1.104593        -0.637725   \n",
       "11394     -0.124352 -0.945513        -0.685038         1.001972   \n",
       "11395     -2.400607 -0.565289        -1.654067         0.155662   \n",
       "11396      0.712131 -0.945513        -0.946779        -0.149076   \n",
       "11397     -0.526446 -0.945513         0.573834        -1.383209   \n",
       "\n",
       "       Marital_Status  Income_Category  Card_Category  Months_on_book  \\\n",
       "0           -0.654686         0.071386      -0.263129        0.790379   \n",
       "1            0.803409        -0.636041      -0.263129       -0.013882   \n",
       "2            0.803409         1.486240      -0.263129       -1.488360   \n",
       "3           -0.654686         1.486240      -0.263129        0.254205   \n",
       "4           -0.654686        -0.636041      -0.263129       -0.013882   \n",
       "...               ...              ...            ...             ...   \n",
       "11393       -0.640329         0.764882      -0.263129       -1.726089   \n",
       "11394       -0.654686         0.778813      -0.263129        0.209542   \n",
       "11395        0.803409         1.104817      -0.263129       -2.687339   \n",
       "11396        0.803409        -0.751148      -0.263129       -0.013882   \n",
       "11397       -0.303463         0.949217      -0.263129       -0.756509   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "0                      0.958497                0.554118   \n",
       "1                      0.958497                0.554118   \n",
       "2                      0.958497               -1.585363   \n",
       "3                      0.280187                3.763339   \n",
       "4                      0.958497               -1.585363   \n",
       "...                         ...                     ...   \n",
       "11393                 -1.748066               -0.494556   \n",
       "11394                  0.474010               -0.260933   \n",
       "11395                 -1.632837               -0.515623   \n",
       "11396                 -0.256139                0.554118   \n",
       "11397                 -1.239825                0.296441   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "0                  -0.634471      1.542291            -0.164826   \n",
       "1                  -0.634471      0.179367            -0.603433   \n",
       "2                  -0.634471      0.811419             1.061835   \n",
       "3                   1.267305      1.682747             0.446347   \n",
       "4                   0.316417     -0.721056             1.123671   \n",
       "...                      ...           ...                  ...   \n",
       "11393               0.307054     -0.269254            -0.921541   \n",
       "11394              -0.634471     -0.476836            -0.951443   \n",
       "11395               1.096408      0.223046            -0.951443   \n",
       "11396               0.117375      0.663173             0.885683   \n",
       "11397              -0.176375      0.192395            -0.951443   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "0             1.520616             -0.401104        -1.110146       -0.976744   \n",
       "1             0.202263             -0.301854        -0.003432        0.044156   \n",
       "2             0.679853             -0.045891         0.387714        0.266091   \n",
       "3             1.661594             -1.351822         0.126950        0.843121   \n",
       "4            -1.101787             -0.693633         0.475914        0.532412   \n",
       "...                ...                   ...              ...             ...   \n",
       "11393        -0.101910              0.257159         1.514528        0.306107   \n",
       "11394        -0.224736             -0.036447         1.357799        0.642322   \n",
       "11395         0.357972             -1.467948        -1.464259       -1.581183   \n",
       "11396         0.530314              0.320350        -0.283017       -0.444100   \n",
       "11397         0.331903             -1.715462        -1.234858       -0.869180   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0                -0.294071              -0.662142  \n",
       "1                 1.967580              -0.409086  \n",
       "2                 0.821514              -0.167033  \n",
       "3                -0.507434              -0.632802  \n",
       "4                -0.415992               1.707044  \n",
       "...                    ...                    ...  \n",
       "11393             0.450460              -0.623750  \n",
       "11394             0.937050              -0.819843  \n",
       "11395            -0.712341              -0.819843  \n",
       "11396             0.447204              -0.126268  \n",
       "11397            -0.720663              -0.819843  \n",
       "\n",
       "[11398 rows x 19 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_ST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556a4ed6",
   "metadata": {},
   "source": [
    "### Min-Max Normalization \n",
    "Min-max scalling rescales the range of features to scale the range in [0, 1] or [−1, 1]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f612953f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of datasets\n",
    "X_train_minmax = X_resampled_smo.copy()\n",
    "X_test_minmax = X_test_ORD.copy()\n",
    "\n",
    "# Initialize the scaler\n",
    "minmax_scaler = MinMaxScaler()\n",
    "\n",
    "# Fit it to the train set\n",
    "minmax_scaler.fit(X_train_minmax)\n",
    "\n",
    "# Transform to train and test set\n",
    "X_train_MINMAX = minmax_scaler.transform(X_train_minmax)\n",
    "X_test_MINMAX =minmax_scaler.transform(X_test_minmax)\n",
    "\n",
    "# Create dataframe\n",
    "X_train_MINMAX = pd.DataFrame(X_train_MINMAX, columns=X_train_minmax.columns)\n",
    "X_test_MINMAX = pd.DataFrame(X_test_MINMAX, columns=X_test_minmax.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8a11d1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0.619048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.906190</td>\n",
       "      <td>0.277243</td>\n",
       "      <td>0.863917</td>\n",
       "      <td>0.327556</td>\n",
       "      <td>0.112679</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.343788</td>\n",
       "      <td>0.044652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.468246</td>\n",
       "      <td>0.122656</td>\n",
       "      <td>0.485026</td>\n",
       "      <td>0.344021</td>\n",
       "      <td>0.399444</td>\n",
       "      <td>0.384000</td>\n",
       "      <td>0.791315</td>\n",
       "      <td>0.116303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.404762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.671341</td>\n",
       "      <td>0.709579</td>\n",
       "      <td>0.622284</td>\n",
       "      <td>0.386482</td>\n",
       "      <td>0.500795</td>\n",
       "      <td>0.424000</td>\n",
       "      <td>0.564536</td>\n",
       "      <td>0.184839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.581395</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.951322</td>\n",
       "      <td>0.492651</td>\n",
       "      <td>0.904433</td>\n",
       "      <td>0.169844</td>\n",
       "      <td>0.433227</td>\n",
       "      <td>0.528000</td>\n",
       "      <td>0.301568</td>\n",
       "      <td>0.052960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.190476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.178917</td>\n",
       "      <td>0.731374</td>\n",
       "      <td>0.110247</td>\n",
       "      <td>0.279029</td>\n",
       "      <td>0.523649</td>\n",
       "      <td>0.472000</td>\n",
       "      <td>0.319662</td>\n",
       "      <td>0.715472</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11393</th>\n",
       "      <td>0.148249</td>\n",
       "      <td>0.009847</td>\n",
       "      <td>0.203939</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.336616</td>\n",
       "      <td>0.796061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.237825</td>\n",
       "      <td>0.001969</td>\n",
       "      <td>0.336616</td>\n",
       "      <td>0.498359</td>\n",
       "      <td>0.324092</td>\n",
       "      <td>0.010539</td>\n",
       "      <td>0.397608</td>\n",
       "      <td>0.436754</td>\n",
       "      <td>0.792768</td>\n",
       "      <td>0.431212</td>\n",
       "      <td>0.491113</td>\n",
       "      <td>0.055523</td>\n",
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       "    <tr>\n",
       "      <th>11394</th>\n",
       "      <td>0.468257</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.304766</td>\n",
       "      <td>0.801596</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573647</td>\n",
       "      <td>0.657149</td>\n",
       "      <td>0.373014</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.257391</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.362308</td>\n",
       "      <td>0.388048</td>\n",
       "      <td>0.752157</td>\n",
       "      <td>0.491809</td>\n",
       "      <td>0.587397</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11395</th>\n",
       "      <td>0.068466</td>\n",
       "      <td>0.179723</td>\n",
       "      <td>0.071889</td>\n",
       "      <td>0.559908</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.892166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.071053</td>\n",
       "      <td>0.035945</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.636713</td>\n",
       "      <td>0.482281</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.529777</td>\n",
       "      <td>0.150580</td>\n",
       "      <td>0.020923</td>\n",
       "      <td>0.091060</td>\n",
       "      <td>0.261022</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11396</th>\n",
       "      <td>0.615174</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.241864</td>\n",
       "      <td>0.472881</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.367457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534884</td>\n",
       "      <td>0.441864</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.465113</td>\n",
       "      <td>0.623706</td>\n",
       "      <td>0.647495</td>\n",
       "      <td>0.579307</td>\n",
       "      <td>0.447237</td>\n",
       "      <td>0.326999</td>\n",
       "      <td>0.296000</td>\n",
       "      <td>0.490468</td>\n",
       "      <td>0.196381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11397</th>\n",
       "      <td>0.397635</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.607298</td>\n",
       "      <td>0.120439</td>\n",
       "      <td>0.413626</td>\n",
       "      <td>0.848176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.406042</td>\n",
       "      <td>0.151824</td>\n",
       "      <td>0.459854</td>\n",
       "      <td>0.413626</td>\n",
       "      <td>0.472432</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.522285</td>\n",
       "      <td>0.109521</td>\n",
       "      <td>0.080364</td>\n",
       "      <td>0.219387</td>\n",
       "      <td>0.259375</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11398 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer_Age    Gender  Dependent_count  Education_Level  \\\n",
       "0          0.619048  1.000000         0.400000         0.333333   \n",
       "1          0.571429  1.000000         0.400000         0.000000   \n",
       "2          0.404762  0.000000         0.600000         0.500000   \n",
       "3          0.428571  0.000000         0.600000         0.500000   \n",
       "4          0.190476  1.000000         0.400000         0.333333   \n",
       "...             ...       ...              ...              ...   \n",
       "11393      0.148249  0.009847         0.203939         0.333333   \n",
       "11394      0.468257  0.000000         0.304766         0.801596   \n",
       "11395      0.068466  0.179723         0.071889         0.559908   \n",
       "11396      0.615174  0.000000         0.241864         0.472881   \n",
       "11397      0.397635  0.000000         0.607298         0.120439   \n",
       "\n",
       "       Marital_Status  Income_Category  Card_Category  Months_on_book  \\\n",
       "0            0.333333         0.600000            0.0        0.674419   \n",
       "1            0.666667         0.400000            0.0        0.534884   \n",
       "2            0.666667         1.000000            0.0        0.279070   \n",
       "3            0.333333         1.000000            0.0        0.581395   \n",
       "4            0.333333         0.400000            0.0        0.534884   \n",
       "...               ...              ...            ...             ...   \n",
       "11393        0.336616         0.796061            0.0        0.237825   \n",
       "11394        0.333333         0.800000            0.0        0.573647   \n",
       "11395        0.666667         0.892166            0.0        0.071053   \n",
       "11396        0.666667         0.367457            0.0        0.534884   \n",
       "11397        0.413626         0.848176            0.0        0.406042   \n",
       "\n",
       "       Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "0                      0.800000                0.500000   \n",
       "1                      0.800000                0.500000   \n",
       "2                      0.800000                0.166667   \n",
       "3                      0.600000                1.000000   \n",
       "4                      0.800000                0.166667   \n",
       "...                         ...                     ...   \n",
       "11393                  0.001969                0.336616   \n",
       "11394                  0.657149                0.373014   \n",
       "11395                  0.035945                0.333333   \n",
       "11396                  0.441864                0.500000   \n",
       "11397                  0.151824                0.459854   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "0                   0.333333      0.906190             0.277243   \n",
       "1                   0.333333      0.468246             0.122656   \n",
       "2                   0.333333      0.671341             0.709579   \n",
       "3                   0.666667      0.951322             0.492651   \n",
       "4                   0.500000      0.178917             0.731374   \n",
       "...                      ...           ...                  ...   \n",
       "11393               0.498359      0.324092             0.010539   \n",
       "11394               0.333333      0.257391             0.000000   \n",
       "11395               0.636713      0.482281             0.000000   \n",
       "11396               0.465113      0.623706             0.647495   \n",
       "11397               0.413626      0.472432             0.000000   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "0             0.863917              0.327556         0.112679        0.200000   \n",
       "1             0.485026              0.344021         0.399444        0.384000   \n",
       "2             0.622284              0.386482         0.500795        0.424000   \n",
       "3             0.904433              0.169844         0.433227        0.528000   \n",
       "4             0.110247              0.279029         0.523649        0.472000   \n",
       "...                ...                   ...              ...             ...   \n",
       "11393         0.397608              0.436754         0.792768        0.431212   \n",
       "11394         0.362308              0.388048         0.752157        0.491809   \n",
       "11395         0.529777              0.150580         0.020923        0.091060   \n",
       "11396         0.579307              0.447237         0.326999        0.296000   \n",
       "11397         0.522285              0.109521         0.080364        0.219387   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0                 0.343788               0.044652  \n",
       "1                 0.791315               0.116303  \n",
       "2                 0.564536               0.184839  \n",
       "3                 0.301568               0.052960  \n",
       "4                 0.319662               0.715472  \n",
       "...                    ...                    ...  \n",
       "11393             0.491113               0.055523  \n",
       "11394             0.587397               0.000000  \n",
       "11395             0.261022               0.000000  \n",
       "11396             0.490468               0.196381  \n",
       "11397             0.259375               0.000000  \n",
       "\n",
       "[11398 rows x 19 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_MINMAX"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ef3cae",
   "metadata": {},
   "source": [
    "### Evaluate model performance with the 2 scalers above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3ad8b702",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set\n",
      "Random Forests roc-auc: 0.9851059471434744\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9609942580580875\n",
      "Train set\n",
      "Random Forests roc-auc: 0.9851047771423406\n",
      "Test set\n",
      "Random Forests roc-auc: 0.9609869989659092\n"
     ]
    }
   ],
   "source": [
    "scaler = {'standard':RF_ROC(X_train_ST, X_test_ST, y_resampled_smo, y_test),\n",
    "         'minmax':RF_ROC(X_train_MINMAX, X_test_MINMAX, y_resampled_smo, y_test)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1d86dcad",
   "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>Train</th>\n",
       "      <th>Test</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>standard</th>\n",
       "      <td>0.985106</td>\n",
       "      <td>0.960994</td>\n",
       "      <td>0.024112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minmax</th>\n",
       "      <td>0.985105</td>\n",
       "      <td>0.960987</td>\n",
       "      <td>0.024118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Train      Test      diff\n",
       "standard  0.985106  0.960994  0.024112\n",
       "minmax    0.985105  0.960987  0.024118"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = pd.DataFrame(scaler,index=['Train', 'Test']).T\n",
    "scaler['diff']=scaler['Train']-scaler['Test']\n",
    "scaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f802098c",
   "metadata": {},
   "source": [
    "* **Observation:** The performance difference of the 2 scalers is minor.\n",
    "* We are choosing **standard scaler** as it provides slightly better performance and consistency between train and test set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b569400",
   "metadata": {},
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "68f7f159",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a random forest model\n",
    "rf_ = RandomForestClassifier(random_state=42, max_depth=5, n_jobs=-1)\n",
    "model = rf_.fit(X_train_ST, y_resampled_smo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c423e0e5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "pd.Series(model.feature_importances_, index=X.columns).sort_values(ascending=False).head(15).plot(kind='bar', figsize=(15,10),color='DarkMagenta');\n",
    "plt.title('Feature Importance After Over-Sampling and Scaling', fontsize=18)\n",
    "plt.rcParams['font.size'] = 15"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ab5d8f",
   "metadata": {},
   "source": [
    "* **Observation:** **Total_Trans_Ct (Total Transaction Count)** seems to be the most important feature."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9ce0ad",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
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   "cell_type": "markdown",
   "id": "2e5c572f",
   "metadata": {},
   "source": [
    "In this notebook, we focused on optimizing our random forest model by experimenting with different encoding, oversampling, and scaling techniques. Specifically, we explored various methods for encoding categorical features, oversampling our imbalanced dataset, and scaling our data to ensure optimal performance.\n",
    "\n",
    "After applying these techniques, we plotted the feature importance of our model to identify which variables had the greatest impact on predicting customer churn. Based on our results, we have identified the following methods for our model building:\n",
    "* Ordinal Encoder for encoding categorical features\n",
    "* SMOTE for over-sampling for our imbalanced dataset\n",
    "* Standard Scaler for normalizing\n"
   ]
  }
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