1.2.Predictive_imputation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this notebook I have using Predictive Inputation for imputing REPAYMENT_TENURE missing values in train_main_loan.csv\n",
    "### key steps are:\n",
    "#### 1. *Use the same wrangling functions as of part 1.1*\n",
    "#### 2. *filtered a the train_all_loan according to the ACCOUNT_TYPE in train_main_all*\n",
    "#### 3. *only selected the ongoing loan that have not been closed*\n",
    "#### 4. *Essentially trained this model on 98000 (approx) datapoints*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('train_all_loan.csv')\n",
    "\n",
    "def date_opened_to_month(df):\n",
    "    # Convert 'DATE_OPENED' column to datetime\n",
    "    df['DATE_OPENED'] = pd.to_datetime(df['DATE_OPENED'], format='%Y-%m-%d')  # Updated date format\n",
    "\n",
    "    # Define the constant 'Reported_Date'\n",
    "    constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "    # Calculate the number of months until the constant_reported_date\n",
    "    df['Months_Since_Opened'] = (constant_reported_date - df['DATE_OPENED']).dt.days // 30\n",
    "\n",
    "    # Replace 'DATE_OPENED' with the calculated number of months\n",
    "    df['DATE_OPENED'] = df['Months_Since_Opened']\n",
    "\n",
    "    # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "    df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "    \n",
    "    # Rename 'DATE_OPENED' to 'MONTH_OPENED'\n",
    "    df.rename(columns={'DATE_OPENED': 'MONTH_OPENED'}, inplace=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "wrangled_df = date_opened_to_month(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "def history_start_end_dates(df):\n",
    "    # Convert 'PAYMENT_HISTORY_START_DATE' and 'PAYMENT_HISTORY_END_DATE' to datetime\n",
    "    df['PAYMENT_HISTORY_START_DATE'] = pd.to_datetime(df['PAYMENT_HISTORY_START_DATE'], format='%Y-%m-%d')\n",
    "    df['PAYMENT_HISTORY_END_DATE'] = pd.to_datetime(df['PAYMENT_HISTORY_END_DATE'], format='%Y-%m-%d')\n",
    "\n",
    "\n",
    "    # Example operation: Calculate the number of months between 'PAYMENT_HISTORY_START_DATE' and a constant date\n",
    "    constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "    df['Months_Since_Payment_Start'] = (constant_reported_date - df['PAYMENT_HISTORY_START_DATE']).dt.days // 30\n",
    "\n",
    "    # Replace 'PAYMENT_HISTORY_START_DATE' with the calculated number of months\n",
    "    df['PAYMENT_HISTORY_START_DATE'] = df['Months_Since_Payment_Start']\n",
    "\n",
    "    df.drop(columns=['Months_Since_Payment_Start'], inplace=True)\n",
    "\n",
    "    df.rename(columns={'PAYMENT_HISTORY_START_DATE': 'MONTH_PAYMENT_HISTORY_START'}, inplace=True)\n",
    "\n",
    "    \n",
    "    df['Months_Since_Payment_End'] = (constant_reported_date - df['PAYMENT_HISTORY_END_DATE']).dt.days // 30\n",
    "\n",
    "    df['PAYMENT_HISTORY_END_DATE'] = df['Months_Since_Payment_End']\n",
    "\n",
    "    # Drop the 'Months_Since_Payment_End' column if you don't need it anymore\n",
    "    df.drop(columns=['Months_Since_Payment_End'], inplace=True)\n",
    "\n",
    "    # Rename 'PAYMENT_HISTORY_END_DATE' to 'MONTH_PAYMENT_HISTORY_END'\n",
    "    df.rename(columns={'PAYMENT_HISTORY_END_DATE': 'MONTH_PAYMENT_HISTORY_END'}, inplace=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "wrangled_df = history_start_end_dates(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_of_last_payment(df):\n",
    "  df['DATE_OF_LAST_PAYMENT'] = pd.to_datetime(df['DATE_OF_LAST_PAYMENT'], format='%Y-%m-%d')\n",
    "\n",
    "  # Define the constant 'Reported_Date'\n",
    "  constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "  # Calculate the number of months\n",
    "  df['Months_Since_Opened'] = (constant_reported_date - df['DATE_OF_LAST_PAYMENT']).dt.days // 30\n",
    "\n",
    "  # Replace 'DATE_OPENED' with the calculated number of months\n",
    "  df['DATE_OF_LAST_PAYMENT'] = df['Months_Since_Opened']\n",
    "\n",
    "  # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "  df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "  df.rename(columns={'DATE_OF_LAST_PAYMENT': 'MONTH_LAST_PAYMENT'}, inplace=True)\n",
    "\n",
    "  return df\n",
    "\n",
    "wrangled_df = date_of_last_payment(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['EMI_AMOUNT'] = wrangled_df['EMI_AMOUNT'].fillna(wrangled_df['ACTUAL_PAYMT_AMT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_certified_to_month(df):\n",
    "  df['DATE_REPORTED_AND_CERTIFIED'] = pd.to_datetime(df['DATE_REPORTED_AND_CERTIFIED'], format='%Y-%m-%d')\n",
    "\n",
    "  # Define the constant 'Reported_Date'\n",
    "  constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "  # Calculate the number of months\n",
    "  df['Months_Since_Opened'] = (constant_reported_date - df['DATE_REPORTED_AND_CERTIFIED']).dt.days // 30\n",
    "\n",
    "  # Replace 'DATE_OPENED' with the calculated number of months\n",
    "  df['DATE_REPORTED_AND_CERTIFIED'] = df['Months_Since_Opened']\n",
    "\n",
    "  # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "  df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "  df.rename(columns={'DATE_REPORTED_AND_CERTIFIED': 'MONTH_CERTIFIED'}, inplace=True)\n",
    "\n",
    "  return df\n",
    "wrangled_df=date_certified_to_month(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=wrangled_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = df[df['DATE_CLOSED'].isna() \n",
    "                 & ~df['REPAYMENT_TENURE'].isna()\n",
    "                 & (df['CURRENT_BALANCE'] != 0)]\n",
    "count_personal_loans = len(filtered_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(147441, 19)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "property_loan = filtered_df[filtered_df['ACCOUNT_TYPE'] == 'Property Loan']\n",
    "housing_loan = filtered_df[filtered_df['ACCOUNT_TYPE'] == 'Housing Loan']\n",
    "personal_loan = filtered_df[filtered_df['ACCOUNT_TYPE'] == 'Personal Loan']\n",
    "business_loan = filtered_df[\n",
    "    (filtered_df['ACCOUNT_TYPE'] == 'Business Loan - Unsecured') | \n",
    "    (filtered_df['ACCOUNT_TYPE'] == 'Business Loan - General')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "business_loan.loc[:, 'ACCOUNT_TYPE'] = 'Business Loan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.concat([property_loan, housing_loan, personal_loan, business_loan])\n",
    "\n",
    "shuffled_df = merged_df.sample(frac=1, random_state=42).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>DATE_CLOSED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>PAYMENT_HISTORY_1</th>\n",
       "      <th>PAYMENT_HISTORY_2</th>\n",
       "      <th>OWNERSHIP_TYPE</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_START</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_END</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002246602</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1408044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>900900900900874843812782751721690659629598XXX5...</td>\n",
       "      <td>4474173863563252942642332031721441130820520210...</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>662</td>\n",
       "      <td>43</td>\n",
       "      <td>79</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A001899803</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDS...</td>\n",
       "      <td>STDSTDSTDSTDSTDSTDSTDSTD</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>731</td>\n",
       "      <td>-1</td>\n",
       "      <td>25</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A001103373</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1375083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STDSTDSTDSTDSTDSTDSTDSTDSTD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>729</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A001360993</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>323818</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>Joint</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>774</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001041151</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>750654</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>000</td>\n",
       "      <td>Joint</td>\n",
       "      <td>NaN</td>\n",
       "      <td>740</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98042</th>\n",
       "      <td>A001669017</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>617671</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0000220000000000000000000000000000000000000520...</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>Joint</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>707</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>B000054368</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>000000000000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>779</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>D1000167424</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>000000000000000000000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>743</td>\n",
       "      <td>-2</td>\n",
       "      <td>6</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>A000114235</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045152</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>0000000000000000000000000000000000000000000000...</td>\n",
       "      <td>Joint</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>700</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>A002083044</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>855192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>9009009009009009009009009009009009009009009009...</td>\n",
       "      <td>9009009009009009009009009009009009009009009009...</td>\n",
       "      <td>Joint</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>644</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98047 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0       A002246602   Housing Loan                         1600000.0   \n",
       "1       A001899803   Housing Loan                          500000.0   \n",
       "2       A001103373  Personal Loan                         1488000.0   \n",
       "3       A001360993   Housing Loan                         1087470.0   \n",
       "4       A001041151  Business Loan                         1015604.0   \n",
       "...            ...            ...                               ...   \n",
       "98042   A001669017  Property Loan                          798459.0   \n",
       "98043   B000054368  Personal Loan                           18000.0   \n",
       "98044  D1000167424  Personal Loan                           30000.0   \n",
       "98045   A000114235  Property Loan                         1103746.0   \n",
       "98046   A002083044   Housing Loan                         2340000.0   \n",
       "\n",
       "       MONTH_OPENED DATE_CLOSED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  \\\n",
       "0             102.0         NaN          1408044               NaN   \n",
       "1              46.0         NaN           387902               NaN   \n",
       "2               7.0         NaN          1375083               NaN   \n",
       "3              70.0         NaN           323818            2079.0   \n",
       "4              18.0         NaN           750654           31998.0   \n",
       "...             ...         ...              ...               ...   \n",
       "98042          87.0         NaN           617671           11354.0   \n",
       "98043           3.0         NaN            15648               NaN   \n",
       "98044           5.0         NaN            10567               NaN   \n",
       "98045          66.0         NaN          1045152           13609.0   \n",
       "98046         188.0         NaN           855192               NaN   \n",
       "\n",
       "       EMI_AMOUNT  REPAYMENT_TENURE  AMOUNT_OVERDUE  \\\n",
       "0         23420.0             123.0        769070.0   \n",
       "1          5361.0             156.0             NaN   \n",
       "2         28706.0              72.0             NaN   \n",
       "3          9581.0              56.0             NaN   \n",
       "4         31998.0              48.0             NaN   \n",
       "...           ...               ...             ...   \n",
       "98042     11354.0             106.0             NaN   \n",
       "98043      1011.0              24.0             NaN   \n",
       "98044      3900.0               9.0             NaN   \n",
       "98045     13609.0             175.0             NaN   \n",
       "98046      2206.0              53.0        855192.0   \n",
       "\n",
       "                                       PAYMENT_HISTORY_1  \\\n",
       "0      900900900900874843812782751721690659629598XXX5...   \n",
       "1      STDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDSTDS...   \n",
       "2                            STDSTDSTDSTDSTDSTDSTDSTDSTD   \n",
       "3      0000000000000000000000000000000000000000000000...   \n",
       "4      0000000000000000000000000000000000000000000000...   \n",
       "...                                                  ...   \n",
       "98042  0000220000000000000000000000000000000000000520...   \n",
       "98043                                    000000000000000   \n",
       "98044                           000000000000000000000000   \n",
       "98045  0000000000000000000000000000000000000000000000...   \n",
       "98046  9009009009009009009009009009009009009009009009...   \n",
       "\n",
       "                                       PAYMENT_HISTORY_2 OWNERSHIP_TYPE  \\\n",
       "0      4474173863563252942642332031721441130820520210...     Individual   \n",
       "1                               STDSTDSTDSTDSTDSTDSTDSTD     Individual   \n",
       "2                                                    NaN     Individual   \n",
       "3      0000000000000000000000000000000000000000000000...          Joint   \n",
       "4                                                    000          Joint   \n",
       "...                                                  ...            ...   \n",
       "98042  0000000000000000000000000000000000000000000000...          Joint   \n",
       "98043                                                NaN     Individual   \n",
       "98044                                                NaN     Individual   \n",
       "98045  0000000000000000000000000000000000000000000000...          Joint   \n",
       "98046  9009009009009009009009009009009009009009009009...          Joint   \n",
       "\n",
       "       COLLATERALVALUE  TU_SCORE  MONTH_PAYMENT_HISTORY_START  \\\n",
       "0            1890000.0       662                           43   \n",
       "1            1250000.0       731                           -1   \n",
       "2                  NaN       729                           -1   \n",
       "3            2409000.0       774                            0   \n",
       "4                  NaN       740                            0   \n",
       "...                ...       ...                          ...   \n",
       "98042        4424222.0       707                            0   \n",
       "98043              NaN       779                           -1   \n",
       "98044              NaN       743                           -2   \n",
       "98045        3017700.0       700                            0   \n",
       "98046        3124820.0       644                            0   \n",
       "\n",
       "       MONTH_PAYMENT_HISTORY_END  MONTH_CERTIFIED  MONTH_LAST_PAYMENT  \n",
       "0                             79               42                73.0  \n",
       "1                             25               -2                 NaN  \n",
       "2                              8               -2                 NaN  \n",
       "3                             36                0                 0.0  \n",
       "4                             19                0                 0.0  \n",
       "...                          ...              ...                 ...  \n",
       "98042                         36                0                 0.0  \n",
       "98043                          4               -2                -1.0  \n",
       "98044                          6               -2                -2.0  \n",
       "98045                         36                0                 0.0  \n",
       "98046                         36                0                56.0  \n",
       "\n",
       "[98047 rows x 19 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffled_df['PAYMENT_HISTORY_LENGTH'] = abs(shuffled_df['MONTH_PAYMENT_HISTORY_END'] - shuffled_df['MONTH_PAYMENT_HISTORY_START'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = ['HIGH_CREDIT_OR_SANCTIONED_AMOUNT', 'MONTH_OPENED', 'CURRENT_BALANCE', 'TU_SCORE','REPAYMENT_TENURE','ACCOUNT_TYPE','AMOUNT_OVERDUE','ACTUAL_PAYMT_AMT','EMI_AMOUNT','COLLATERALVALUE','PAYMENT_HISTORY_LENGTH','MONTH_CERTIFIED','MONTH_LAST_PAYMENT']\n",
    "\n",
    "shuffled_df = shuffled_df[selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1408044</td>\n",
       "      <td>662</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>387902</td>\n",
       "      <td>731</td>\n",
       "      <td>156.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1375083</td>\n",
       "      <td>729</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>323818</td>\n",
       "      <td>774</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>750654</td>\n",
       "      <td>740</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98042</th>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>617671</td>\n",
       "      <td>707</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15648</td>\n",
       "      <td>779</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10567</td>\n",
       "      <td>743</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1045152</td>\n",
       "      <td>700</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>855192</td>\n",
       "      <td>644</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98047 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HIGH_CREDIT_OR_SANCTIONED_AMOUNT  MONTH_OPENED  CURRENT_BALANCE  \\\n",
       "0                             1600000.0         102.0          1408044   \n",
       "1                              500000.0          46.0           387902   \n",
       "2                             1488000.0           7.0          1375083   \n",
       "3                             1087470.0          70.0           323818   \n",
       "4                             1015604.0          18.0           750654   \n",
       "...                                 ...           ...              ...   \n",
       "98042                          798459.0          87.0           617671   \n",
       "98043                           18000.0           3.0            15648   \n",
       "98044                           30000.0           5.0            10567   \n",
       "98045                         1103746.0          66.0          1045152   \n",
       "98046                         2340000.0         188.0           855192   \n",
       "\n",
       "       TU_SCORE  REPAYMENT_TENURE   ACCOUNT_TYPE  AMOUNT_OVERDUE  \\\n",
       "0           662             123.0   Housing Loan        769070.0   \n",
       "1           731             156.0   Housing Loan             NaN   \n",
       "2           729              72.0  Personal Loan             NaN   \n",
       "3           774              56.0   Housing Loan             NaN   \n",
       "4           740              48.0  Business Loan             NaN   \n",
       "...         ...               ...            ...             ...   \n",
       "98042       707             106.0  Property Loan             NaN   \n",
       "98043       779              24.0  Personal Loan             NaN   \n",
       "98044       743               9.0  Personal Loan             NaN   \n",
       "98045       700             175.0  Property Loan             NaN   \n",
       "98046       644              53.0   Housing Loan        855192.0   \n",
       "\n",
       "       ACTUAL_PAYMT_AMT  EMI_AMOUNT  COLLATERALVALUE  PAYMENT_HISTORY_LENGTH  \\\n",
       "0                   NaN     23420.0        1890000.0                      36   \n",
       "1                   NaN      5361.0        1250000.0                      26   \n",
       "2                   NaN     28706.0              NaN                       9   \n",
       "3                2079.0      9581.0        2409000.0                      36   \n",
       "4               31998.0     31998.0              NaN                      19   \n",
       "...                 ...         ...              ...                     ...   \n",
       "98042           11354.0     11354.0        4424222.0                      36   \n",
       "98043               NaN      1011.0              NaN                       5   \n",
       "98044               NaN      3900.0              NaN                       8   \n",
       "98045           13609.0     13609.0        3017700.0                      36   \n",
       "98046               NaN      2206.0        3124820.0                      36   \n",
       "\n",
       "       MONTH_CERTIFIED  MONTH_LAST_PAYMENT  \n",
       "0                   42                73.0  \n",
       "1                   -2                 NaN  \n",
       "2                   -2                 NaN  \n",
       "3                    0                 0.0  \n",
       "4                    0                 0.0  \n",
       "...                ...                 ...  \n",
       "98042                0                 0.0  \n",
       "98043               -2                -1.0  \n",
       "98044               -2                -2.0  \n",
       "98045                0                 0.0  \n",
       "98046                0                56.0  \n",
       "\n",
       "[98047 rows x 13 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIGH_CREDIT_OR_SANCTIONED_AMOUNT       24\n",
       "MONTH_OPENED                            1\n",
       "CURRENT_BALANCE                         0\n",
       "TU_SCORE                                0\n",
       "REPAYMENT_TENURE                        0\n",
       "ACCOUNT_TYPE                            0\n",
       "AMOUNT_OVERDUE                      81540\n",
       "ACTUAL_PAYMT_AMT                    41653\n",
       "EMI_AMOUNT                           9324\n",
       "COLLATERALVALUE                     58709\n",
       "PAYMENT_HISTORY_LENGTH                  0\n",
       "MONTH_CERTIFIED                         0\n",
       "MONTH_LAST_PAYMENT                  11480\n",
       "dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1465158498.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['AMOUNT_OVERDUE'].fillna(0,inplace=True)\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1465158498.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['ACTUAL_PAYMT_AMT'].fillna(0,inplace=True)\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1465158498.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['COLLATERALVALUE'].fillna(0,inplace=True)\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1465158498.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['MONTH_LAST_PAYMENT'].fillna(0,inplace=True)\n"
     ]
    }
   ],
   "source": [
    "shuffled_df['AMOUNT_OVERDUE'].fillna(0,inplace=True)\n",
    "shuffled_df['ACTUAL_PAYMT_AMT'].fillna(0,inplace=True)\n",
    "shuffled_df['COLLATERALVALUE'].fillna(0,inplace=True)\n",
    "shuffled_df['MONTH_LAST_PAYMENT'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1407977898.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['PAID']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']-shuffled_df['CURRENT_BALANCE']\n"
     ]
    }
   ],
   "source": [
    "shuffled_df['PAID']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']-shuffled_df['CURRENT_BALANCE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\2716784534.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['EMI_AMOUNT'] = shuffled_df['EMI_AMOUNT'].fillna(avg_emi_amount)\n"
     ]
    }
   ],
   "source": [
    "avg_emi_amount = shuffled_df['EMI_AMOUNT'].mean()\n",
    "shuffled_df['EMI_AMOUNT'] = shuffled_df['EMI_AMOUNT'].fillna(avg_emi_amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\2396456767.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "shuffled_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98022, 14)"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1160694562.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['new-feature']=shuffled_df['CURRENT_BALANCE']/(shuffled_df['ACTUAL_PAYMT_AMT']+1)\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1160694562.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['new-feature-1']=shuffled_df['COLLATERALVALUE']/(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1160694562.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['new-feature-2']=shuffled_df['COLLATERALVALUE']-(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
      "C:\\Users\\risha\\AppData\\Local\\Temp\\ipykernel_13236\\1160694562.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shuffled_df['new-feature-3']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']/(shuffled_df['MONTH_OPENED']+1.5)\n"
     ]
    }
   ],
   "source": [
    "shuffled_df['new-feature']=shuffled_df['CURRENT_BALANCE']/(shuffled_df['ACTUAL_PAYMT_AMT']+1)\n",
    "shuffled_df['new-feature-1']=shuffled_df['COLLATERALVALUE']/(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "shuffled_df['new-feature-2']=shuffled_df['COLLATERALVALUE']-(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "shuffled_df['new-feature-3']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']/(shuffled_df['MONTH_OPENED']+1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>PAID</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1408044</td>\n",
       "      <td>662</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "      <td>191956.0</td>\n",
       "      <td>1.408044e+06</td>\n",
       "      <td>1.181250</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>15458.937198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>387902</td>\n",
       "      <td>731</td>\n",
       "      <td>156.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112098.0</td>\n",
       "      <td>3.879020e+05</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10526.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1375083</td>\n",
       "      <td>729</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112917.0</td>\n",
       "      <td>1.375083e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1488000.0</td>\n",
       "      <td>175058.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>323818</td>\n",
       "      <td>774</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>763652.0</td>\n",
       "      <td>1.556817e+02</td>\n",
       "      <td>2.215234</td>\n",
       "      <td>1321530.0</td>\n",
       "      <td>15209.370629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>750654</td>\n",
       "      <td>740</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>264950.0</td>\n",
       "      <td>2.345867e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1015604.0</td>\n",
       "      <td>52082.256410</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98042</th>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>617671</td>\n",
       "      <td>707</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180788.0</td>\n",
       "      <td>5.439639e+01</td>\n",
       "      <td>5.540951</td>\n",
       "      <td>3625763.0</td>\n",
       "      <td>9022.135593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15648</td>\n",
       "      <td>779</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>1.564800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18000.0</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10567</td>\n",
       "      <td>743</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>19433.0</td>\n",
       "      <td>1.056700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30000.0</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1045152</td>\n",
       "      <td>700</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58594.0</td>\n",
       "      <td>7.679295e+01</td>\n",
       "      <td>2.734053</td>\n",
       "      <td>1913954.0</td>\n",
       "      <td>16351.792593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>855192</td>\n",
       "      <td>644</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1484808.0</td>\n",
       "      <td>8.551920e+05</td>\n",
       "      <td>1.335393</td>\n",
       "      <td>784820.0</td>\n",
       "      <td>12348.284960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98022 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HIGH_CREDIT_OR_SANCTIONED_AMOUNT  MONTH_OPENED  CURRENT_BALANCE  \\\n",
       "0                             1600000.0         102.0          1408044   \n",
       "1                              500000.0          46.0           387902   \n",
       "2                             1488000.0           7.0          1375083   \n",
       "3                             1087470.0          70.0           323818   \n",
       "4                             1015604.0          18.0           750654   \n",
       "...                                 ...           ...              ...   \n",
       "98042                          798459.0          87.0           617671   \n",
       "98043                           18000.0           3.0            15648   \n",
       "98044                           30000.0           5.0            10567   \n",
       "98045                         1103746.0          66.0          1045152   \n",
       "98046                         2340000.0         188.0           855192   \n",
       "\n",
       "       TU_SCORE  REPAYMENT_TENURE   ACCOUNT_TYPE  AMOUNT_OVERDUE  \\\n",
       "0           662             123.0   Housing Loan        769070.0   \n",
       "1           731             156.0   Housing Loan             0.0   \n",
       "2           729              72.0  Personal Loan             0.0   \n",
       "3           774              56.0   Housing Loan             0.0   \n",
       "4           740              48.0  Business Loan             0.0   \n",
       "...         ...               ...            ...             ...   \n",
       "98042       707             106.0  Property Loan             0.0   \n",
       "98043       779              24.0  Personal Loan             0.0   \n",
       "98044       743               9.0  Personal Loan             0.0   \n",
       "98045       700             175.0  Property Loan             0.0   \n",
       "98046       644              53.0   Housing Loan        855192.0   \n",
       "\n",
       "       ACTUAL_PAYMT_AMT  EMI_AMOUNT  COLLATERALVALUE  PAYMENT_HISTORY_LENGTH  \\\n",
       "0                   0.0     23420.0        1890000.0                      36   \n",
       "1                   0.0      5361.0        1250000.0                      26   \n",
       "2                   0.0     28706.0              0.0                       9   \n",
       "3                2079.0      9581.0        2409000.0                      36   \n",
       "4               31998.0     31998.0              0.0                      19   \n",
       "...                 ...         ...              ...                     ...   \n",
       "98042           11354.0     11354.0        4424222.0                      36   \n",
       "98043               0.0      1011.0              0.0                       5   \n",
       "98044               0.0      3900.0              0.0                       8   \n",
       "98045           13609.0     13609.0        3017700.0                      36   \n",
       "98046               0.0      2206.0        3124820.0                      36   \n",
       "\n",
       "       MONTH_CERTIFIED  MONTH_LAST_PAYMENT       PAID   new-feature  \\\n",
       "0                   42                73.0   191956.0  1.408044e+06   \n",
       "1                   -2                 0.0   112098.0  3.879020e+05   \n",
       "2                   -2                 0.0   112917.0  1.375083e+06   \n",
       "3                    0                 0.0   763652.0  1.556817e+02   \n",
       "4                    0                 0.0   264950.0  2.345867e+01   \n",
       "...                ...                 ...        ...           ...   \n",
       "98042                0                 0.0   180788.0  5.439639e+01   \n",
       "98043               -2                -1.0     2352.0  1.564800e+04   \n",
       "98044               -2                -2.0    19433.0  1.056700e+04   \n",
       "98045                0                 0.0    58594.0  7.679295e+01   \n",
       "98046                0                56.0  1484808.0  8.551920e+05   \n",
       "\n",
       "       new-feature-1  new-feature-2  new-feature-3  \n",
       "0           1.181250       290000.0   15458.937198  \n",
       "1           2.500000       750000.0   10526.315789  \n",
       "2           0.000000     -1488000.0  175058.823529  \n",
       "3           2.215234      1321530.0   15209.370629  \n",
       "4           0.000000     -1015604.0   52082.256410  \n",
       "...              ...            ...            ...  \n",
       "98042       5.540951      3625763.0    9022.135593  \n",
       "98043       0.000000       -18000.0    4000.000000  \n",
       "98044       0.000000       -30000.0    4615.384615  \n",
       "98045       2.734053      1913954.0   16351.792593  \n",
       "98046       1.335393       784820.0   12348.284960  \n",
       "\n",
       "[98022 rows x 18 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIGH_CREDIT_OR_SANCTIONED_AMOUNT    0\n",
       "MONTH_OPENED                        0\n",
       "CURRENT_BALANCE                     0\n",
       "TU_SCORE                            0\n",
       "REPAYMENT_TENURE                    0\n",
       "ACCOUNT_TYPE                        0\n",
       "AMOUNT_OVERDUE                      0\n",
       "ACTUAL_PAYMT_AMT                    0\n",
       "EMI_AMOUNT                          0\n",
       "COLLATERALVALUE                     0\n",
       "PAYMENT_HISTORY_LENGTH              0\n",
       "MONTH_CERTIFIED                     0\n",
       "MONTH_LAST_PAYMENT                  0\n",
       "PAID                                0\n",
       "new-feature                         0\n",
       "new-feature-1                       0\n",
       "new-feature-2                       0\n",
       "new-feature-3                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select only the numerical features\n",
    "numerical_features = shuffled_df.select_dtypes(include=['float64', 'int64'])\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = numerical_features.corr()\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Draw the heatmap using seaborn\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Assuming 'TARGET_COLUMN' is your target variable\n",
    "target_column = 'REPAYMENT_TENURE'\n",
    "\n",
    "# Features and target for Personal Loans\n",
    "X = shuffled_df.drop(columns=[target_column])\n",
    "y = shuffled_df[target_column]\n",
    "\n",
    "# Perform one-hot encoding for the \"ACCOUNT_TYPE\" column\n",
    "X_encoded = pd.get_dummies(X, columns=['ACCOUNT_TYPE'],drop_first=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>PAID</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "      <th>ACCOUNT_TYPE_Housing Loan</th>\n",
       "      <th>ACCOUNT_TYPE_Personal Loan</th>\n",
       "      <th>ACCOUNT_TYPE_Property Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1408044</td>\n",
       "      <td>662</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "      <td>191956.0</td>\n",
       "      <td>1.408044e+06</td>\n",
       "      <td>1.181250</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>15458.937198</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>387902</td>\n",
       "      <td>731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112098.0</td>\n",
       "      <td>3.879020e+05</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10526.315789</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1375083</td>\n",
       "      <td>729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112917.0</td>\n",
       "      <td>1.375083e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1488000.0</td>\n",
       "      <td>175058.823529</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>323818</td>\n",
       "      <td>774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>763652.0</td>\n",
       "      <td>1.556817e+02</td>\n",
       "      <td>2.215234</td>\n",
       "      <td>1321530.0</td>\n",
       "      <td>15209.370629</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>750654</td>\n",
       "      <td>740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>264950.0</td>\n",
       "      <td>2.345867e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1015604.0</td>\n",
       "      <td>52082.256410</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>98042</th>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>617671</td>\n",
       "      <td>707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180788.0</td>\n",
       "      <td>5.439639e+01</td>\n",
       "      <td>5.540951</td>\n",
       "      <td>3625763.0</td>\n",
       "      <td>9022.135593</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15648</td>\n",
       "      <td>779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>1.564800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18000.0</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10567</td>\n",
       "      <td>743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>19433.0</td>\n",
       "      <td>1.056700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30000.0</td>\n",
       "      <td>4615.384615</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1045152</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58594.0</td>\n",
       "      <td>7.679295e+01</td>\n",
       "      <td>2.734053</td>\n",
       "      <td>1913954.0</td>\n",
       "      <td>16351.792593</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>855192</td>\n",
       "      <td>644</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1484808.0</td>\n",
       "      <td>8.551920e+05</td>\n",
       "      <td>1.335393</td>\n",
       "      <td>784820.0</td>\n",
       "      <td>12348.284960</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98022 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HIGH_CREDIT_OR_SANCTIONED_AMOUNT  MONTH_OPENED  CURRENT_BALANCE  \\\n",
       "0                             1600000.0         102.0          1408044   \n",
       "1                              500000.0          46.0           387902   \n",
       "2                             1488000.0           7.0          1375083   \n",
       "3                             1087470.0          70.0           323818   \n",
       "4                             1015604.0          18.0           750654   \n",
       "...                                 ...           ...              ...   \n",
       "98042                          798459.0          87.0           617671   \n",
       "98043                           18000.0           3.0            15648   \n",
       "98044                           30000.0           5.0            10567   \n",
       "98045                         1103746.0          66.0          1045152   \n",
       "98046                         2340000.0         188.0           855192   \n",
       "\n",
       "       TU_SCORE  AMOUNT_OVERDUE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0           662        769070.0               0.0     23420.0   \n",
       "1           731             0.0               0.0      5361.0   \n",
       "2           729             0.0               0.0     28706.0   \n",
       "3           774             0.0            2079.0      9581.0   \n",
       "4           740             0.0           31998.0     31998.0   \n",
       "...         ...             ...               ...         ...   \n",
       "98042       707             0.0           11354.0     11354.0   \n",
       "98043       779             0.0               0.0      1011.0   \n",
       "98044       743             0.0               0.0      3900.0   \n",
       "98045       700             0.0           13609.0     13609.0   \n",
       "98046       644        855192.0               0.0      2206.0   \n",
       "\n",
       "       COLLATERALVALUE  PAYMENT_HISTORY_LENGTH  MONTH_CERTIFIED  \\\n",
       "0            1890000.0                      36               42   \n",
       "1            1250000.0                      26               -2   \n",
       "2                  0.0                       9               -2   \n",
       "3            2409000.0                      36                0   \n",
       "4                  0.0                      19                0   \n",
       "...                ...                     ...              ...   \n",
       "98042        4424222.0                      36                0   \n",
       "98043              0.0                       5               -2   \n",
       "98044              0.0                       8               -2   \n",
       "98045        3017700.0                      36                0   \n",
       "98046        3124820.0                      36                0   \n",
       "\n",
       "       MONTH_LAST_PAYMENT       PAID   new-feature  new-feature-1  \\\n",
       "0                    73.0   191956.0  1.408044e+06       1.181250   \n",
       "1                     0.0   112098.0  3.879020e+05       2.500000   \n",
       "2                     0.0   112917.0  1.375083e+06       0.000000   \n",
       "3                     0.0   763652.0  1.556817e+02       2.215234   \n",
       "4                     0.0   264950.0  2.345867e+01       0.000000   \n",
       "...                   ...        ...           ...            ...   \n",
       "98042                 0.0   180788.0  5.439639e+01       5.540951   \n",
       "98043                -1.0     2352.0  1.564800e+04       0.000000   \n",
       "98044                -2.0    19433.0  1.056700e+04       0.000000   \n",
       "98045                 0.0    58594.0  7.679295e+01       2.734053   \n",
       "98046                56.0  1484808.0  8.551920e+05       1.335393   \n",
       "\n",
       "       new-feature-2  new-feature-3  ACCOUNT_TYPE_Housing Loan  \\\n",
       "0           290000.0   15458.937198                       True   \n",
       "1           750000.0   10526.315789                       True   \n",
       "2         -1488000.0  175058.823529                      False   \n",
       "3          1321530.0   15209.370629                       True   \n",
       "4         -1015604.0   52082.256410                      False   \n",
       "...              ...            ...                        ...   \n",
       "98042      3625763.0    9022.135593                      False   \n",
       "98043       -18000.0    4000.000000                      False   \n",
       "98044       -30000.0    4615.384615                      False   \n",
       "98045      1913954.0   16351.792593                      False   \n",
       "98046       784820.0   12348.284960                       True   \n",
       "\n",
       "       ACCOUNT_TYPE_Personal Loan  ACCOUNT_TYPE_Property Loan  \n",
       "0                           False                       False  \n",
       "1                           False                       False  \n",
       "2                            True                       False  \n",
       "3                           False                       False  \n",
       "4                           False                       False  \n",
       "...                           ...                         ...  \n",
       "98042                       False                        True  \n",
       "98043                        True                       False  \n",
       "98044                        True                       False  \n",
       "98045                       False                        True  \n",
       "98046                       False                       False  \n",
       "\n",
       "[98022 rows x 19 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.009065 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3533\n",
      "[LightGBM] [Info] Number of data points in the train set: 93120, number of used features: 19\n",
      "[LightGBM] [Info] Start training from score 101.317193\n",
      "XGBoost:\n",
      "Mean Squared Error: 908.7835\n",
      "R2 Score (Test): 0.9074\n",
      "R2 Score (Train): 0.9378\n",
      "------------------------\n",
      "LGBM:\n",
      "Mean Squared Error: 1021.6097\n",
      "R2 Score (Test): 0.8959\n",
      "R2 Score (Train): 0.9072\n",
      "------------------------\n"
     ]
    }
   ],
   "source": [
    "# Normalize the features\n",
    "scaler = StandardScaler()\n",
    "X_normalized = scaler.fit_transform(X_encoded)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_normalized, y, test_size=0.05, random_state=42)\n",
    "\n",
    "# Train XGBoost model\n",
    "xgb_model = XGBRegressor(random_state=42)\n",
    "xgb_model.fit(X_train, y_train)\n",
    "\n",
    "# Train LGBM model\n",
    "lgbm_model = LGBMRegressor(random_state=42)\n",
    "lgbm_model.fit(X_train, y_train)\n",
    "\n",
    "# Train Linear Regression model\n",
    "\n",
    "# Make predictions\n",
    "xgb_predictions = xgb_model.predict(X_test)\n",
    "lgbm_predictions = lgbm_model.predict(X_test)\n",
    "\n",
    "# Evaluate models\n",
    "def evaluate_model(predictions, model_name, model):\n",
    "    mse = mean_squared_error(y_test, predictions)\n",
    "    r2 = r2_score(y_test, predictions)\n",
    "    train_r2 = model.score(X_train, y_train)\n",
    "    print(f'{model_name}:')\n",
    "    print(f'Mean Squared Error: {mse:.4f}')\n",
    "    print(f'R2 Score (Test): {r2:.4f}')\n",
    "    print(f'R2 Score (Train): {train_r2:.4f}')\n",
    "    print('------------------------')\n",
    "\n",
    "evaluate_model(xgb_predictions, 'XGBoost', xgb_model)\n",
    "evaluate_model(lgbm_predictions, 'LGBM', lgbm_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>PAID</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "      <th>ACCOUNT_TYPE_Business Loan</th>\n",
       "      <th>ACCOUNT_TYPE_Housing Loan</th>\n",
       "      <th>ACCOUNT_TYPE_Personal Loan</th>\n",
       "      <th>ACCOUNT_TYPE_Property Loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1408044</td>\n",
       "      <td>662</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "      <td>191956.0</td>\n",
       "      <td>1.408044e+06</td>\n",
       "      <td>1.181250</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>15458.937198</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>387902</td>\n",
       "      <td>731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112098.0</td>\n",
       "      <td>3.879020e+05</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10526.315789</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1375083</td>\n",
       "      <td>729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112917.0</td>\n",
       "      <td>1.375083e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1488000.0</td>\n",
       "      <td>175058.823529</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>323818</td>\n",
       "      <td>774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>763652.0</td>\n",
       "      <td>1.556817e+02</td>\n",
       "      <td>2.215234</td>\n",
       "      <td>1321530.0</td>\n",
       "      <td>15209.370629</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>750654</td>\n",
       "      <td>740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>98042</th>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>617671</td>\n",
       "      <td>707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180788.0</td>\n",
       "      <td>5.439639e+01</td>\n",
       "      <td>5.540951</td>\n",
       "      <td>3625763.0</td>\n",
       "      <td>9022.135593</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15648</td>\n",
       "      <td>779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>1.564800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18000.0</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10567</td>\n",
       "      <td>743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>19433.0</td>\n",
       "      <td>1.056700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30000.0</td>\n",
       "      <td>4615.384615</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1045152</td>\n",
       "      <td>700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58594.0</td>\n",
       "      <td>7.679295e+01</td>\n",
       "      <td>2.734053</td>\n",
       "      <td>1913954.0</td>\n",
       "      <td>16351.792593</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>855192</td>\n",
       "      <td>644</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1484808.0</td>\n",
       "      <td>8.551920e+05</td>\n",
       "      <td>1.335393</td>\n",
       "      <td>784820.0</td>\n",
       "      <td>12348.284960</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98022 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HIGH_CREDIT_OR_SANCTIONED_AMOUNT  MONTH_OPENED  CURRENT_BALANCE  \\\n",
       "0                             1600000.0         102.0          1408044   \n",
       "1                              500000.0          46.0           387902   \n",
       "2                             1488000.0           7.0          1375083   \n",
       "3                             1087470.0          70.0           323818   \n",
       "4                             1015604.0          18.0           750654   \n",
       "...                                 ...           ...              ...   \n",
       "98042                          798459.0          87.0           617671   \n",
       "98043                           18000.0           3.0            15648   \n",
       "98044                           30000.0           5.0            10567   \n",
       "98045                         1103746.0          66.0          1045152   \n",
       "98046                         2340000.0         188.0           855192   \n",
       "\n",
       "       TU_SCORE  AMOUNT_OVERDUE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0           662        769070.0               0.0     23420.0   \n",
       "1           731             0.0               0.0      5361.0   \n",
       "2           729             0.0               0.0     28706.0   \n",
       "3           774             0.0            2079.0      9581.0   \n",
       "4           740             0.0           31998.0     31998.0   \n",
       "...         ...             ...               ...         ...   \n",
       "98042       707             0.0           11354.0     11354.0   \n",
       "98043       779             0.0               0.0      1011.0   \n",
       "98044       743             0.0               0.0      3900.0   \n",
       "98045       700             0.0           13609.0     13609.0   \n",
       "98046       644        855192.0               0.0      2206.0   \n",
       "\n",
       "       COLLATERALVALUE  PAYMENT_HISTORY_LENGTH  MONTH_CERTIFIED  \\\n",
       "0            1890000.0                      36               42   \n",
       "1            1250000.0                      26               -2   \n",
       "2                  0.0                       9               -2   \n",
       "3            2409000.0                      36                0   \n",
       "4                  0.0                      19                0   \n",
       "...                ...                     ...              ...   \n",
       "98042        4424222.0                      36                0   \n",
       "98043              0.0                       5               -2   \n",
       "98044              0.0                       8               -2   \n",
       "98045        3017700.0                      36                0   \n",
       "98046        3124820.0                      36                0   \n",
       "\n",
       "       MONTH_LAST_PAYMENT       PAID   new-feature  new-feature-1  \\\n",
       "0                    73.0   191956.0  1.408044e+06       1.181250   \n",
       "1                     0.0   112098.0  3.879020e+05       2.500000   \n",
       "2                     0.0   112917.0  1.375083e+06       0.000000   \n",
       "3                     0.0   763652.0  1.556817e+02       2.215234   \n",
       "4                     0.0   264950.0  2.345867e+01       0.000000   \n",
       "...                   ...        ...           ...            ...   \n",
       "98042                 0.0   180788.0  5.439639e+01       5.540951   \n",
       "98043                -1.0     2352.0  1.564800e+04       0.000000   \n",
       "98044                -2.0    19433.0  1.056700e+04       0.000000   \n",
       "98045                 0.0    58594.0  7.679295e+01       2.734053   \n",
       "98046                56.0  1484808.0  8.551920e+05       1.335393   \n",
       "\n",
       "       new-feature-2  new-feature-3  ACCOUNT_TYPE_Business Loan  \\\n",
       "0           290000.0   15458.937198                       False   \n",
       "1           750000.0   10526.315789                       False   \n",
       "2         -1488000.0  175058.823529                       False   \n",
       "3          1321530.0   15209.370629                       False   \n",
       "4         -1015604.0   52082.256410                        True   \n",
       "...              ...            ...                         ...   \n",
       "98042      3625763.0    9022.135593                       False   \n",
       "98043       -18000.0    4000.000000                       False   \n",
       "98044       -30000.0    4615.384615                       False   \n",
       "98045      1913954.0   16351.792593                       False   \n",
       "98046       784820.0   12348.284960                       False   \n",
       "\n",
       "       ACCOUNT_TYPE_Housing Loan  ACCOUNT_TYPE_Personal Loan  \\\n",
       "0                           True                       False   \n",
       "1                           True                       False   \n",
       "2                          False                        True   \n",
       "3                           True                       False   \n",
       "4                          False                       False   \n",
       "...                          ...                         ...   \n",
       "98042                      False                       False   \n",
       "98043                      False                        True   \n",
       "98044                      False                        True   \n",
       "98045                      False                       False   \n",
       "98046                       True                       False   \n",
       "\n",
       "       ACCOUNT_TYPE_Property Loan  \n",
       "0                           False  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                           False  \n",
       "...                           ...  \n",
       "98042                        True  \n",
       "98043                       False  \n",
       "98044                       False  \n",
       "98045                        True  \n",
       "98046                       False  \n",
       "\n",
       "[98022 rows x 20 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('train_main_loan.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>DATE_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>LOAN_CLASSIFICATION</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>PAYMENT_HISTORY_START_DATE</th>\n",
       "      <th>PAYMENT_HISTORY_END_DATE</th>\n",
       "      <th>DATE_REPORTED_AND_CERTIFIED</th>\n",
       "      <th>DATE_OF_LAST_PAYMENT</th>\n",
       "      <th>Reported_Date</th>\n",
       "      <th>DATE_OF_BIRTH</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ACTUAL_ROI</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002338349</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>818517</td>\n",
       "      <td>13-06-2014</td>\n",
       "      <td>640742</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>681</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>21-05-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002000537</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1243755</td>\n",
       "      <td>05-12-2012</td>\n",
       "      <td>949446</td>\n",
       "      <td>500.0</td>\n",
       "      <td>12210.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>784</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>12-11-1984</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002421579</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1826422</td>\n",
       "      <td>16-03-2017</td>\n",
       "      <td>1296849</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>748</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>11-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-08-1986</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002152345</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1847916</td>\n",
       "      <td>24-12-2022</td>\n",
       "      <td>1724256</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>786</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2022</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-07-1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001952834</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2318386</td>\n",
       "      <td>21-10-2014</td>\n",
       "      <td>1790937</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>539</td>\n",
       "      <td>431551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>27-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>28-10-1974</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.97</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>69953</th>\n",
       "      <td>A000195582</td>\n",
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       "      <td>2600000</td>\n",
       "      <td>15-03-2018</td>\n",
       "      <td>2381437</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>744</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-02-1989</td>\n",
       "      <td>SENP</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69954</th>\n",
       "      <td>A001735798</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1016188</td>\n",
       "      <td>26-08-2022</td>\n",
       "      <td>790486</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>673</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-08-2022</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-08-1992</td>\n",
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       "      <td>Male</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69955</th>\n",
       "      <td>A002435973</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>335475</td>\n",
       "      <td>22-03-2022</td>\n",
       "      <td>281106</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240</td>\n",
       "      <td>82468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>567</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-03-2022</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>19-06-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>13-05-1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69956</th>\n",
       "      <td>A000027755</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1585160</td>\n",
       "      <td>12-09-2015</td>\n",
       "      <td>1255235</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>798</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>14-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-06-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69957</th>\n",
       "      <td>A000500401</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>542973</td>\n",
       "      <td>17-06-2023</td>\n",
       "      <td>357217</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>757</td>\n",
       "      <td>01-11-2023</td>\n",
       "      <td>01-06-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-11-2023</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>03-02-1989</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69958 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0      A002338349   Housing Loan                            818517   \n",
       "1      A002000537   Housing Loan                           1243755   \n",
       "2      A002421579   Housing Loan                           1826422   \n",
       "3      A002152345   Housing Loan                           1847916   \n",
       "4      A001952834   Housing Loan                           2318386   \n",
       "...           ...            ...                               ...   \n",
       "69953  A000195582  Property Loan                           2600000   \n",
       "69954  A001735798  Business Loan                           1016188   \n",
       "69955  A002435973  Business Loan                            335475   \n",
       "69956  A000027755   Housing Loan                           1585160   \n",
       "69957  A000500401   Housing Loan                            542973   \n",
       "\n",
       "      DATE_OPENED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0      13-06-2014           640742            8627.0      8627.0   \n",
       "1      05-12-2012           949446             500.0     12210.0   \n",
       "2      16-03-2017          1296849           16036.0     16036.0   \n",
       "3      24-12-2022          1724256           26996.0         NaN   \n",
       "4      21-10-2014          1790937           25131.0         NaN   \n",
       "...           ...              ...               ...         ...   \n",
       "69953  15-03-2018          2381437           30762.0     30762.0   \n",
       "69954  26-08-2022           790486           29851.0         NaN   \n",
       "69955  22-03-2022           281106           10388.0         NaN   \n",
       "69956  12-09-2015          1255235           16739.0         NaN   \n",
       "69957  17-06-2023           357217            2878.0         NaN   \n",
       "\n",
       "       REPAYMENT_TENURE  LOAN_CLASSIFICATION  AMOUNT_OVERDUE  ... TU_SCORE  \\\n",
       "0                 142.0                    0             NaN  ...      681   \n",
       "1                 156.0                    0             NaN  ...      784   \n",
       "2                 148.0                    0             NaN  ...      748   \n",
       "3                   NaN                    0             NaN  ...      786   \n",
       "4                 116.0                  539        431551.0  ...      664   \n",
       "...                 ...                  ...             ...  ...      ...   \n",
       "69953             162.0                   21         30762.0  ...      744   \n",
       "69954              48.0                    0             NaN  ...      673   \n",
       "69955              48.0                  240         82468.0  ...      567   \n",
       "69956             143.0                    0             NaN  ...      798   \n",
       "69957               NaN                    0             NaN  ...      757   \n",
       "\n",
       "      PAYMENT_HISTORY_START_DATE PAYMENT_HISTORY_END_DATE  \\\n",
       "0                     01-11-2023               01-12-2020   \n",
       "1                     01-11-2023               01-12-2020   \n",
       "2                     01-11-2023               01-12-2020   \n",
       "3                     01-11-2023               01-12-2022   \n",
       "4                     01-11-2023               01-12-2020   \n",
       "...                          ...                      ...   \n",
       "69953                 01-11-2023               01-12-2020   \n",
       "69954                 01-11-2023               01-08-2022   \n",
       "69955                 01-11-2023               01-03-2022   \n",
       "69956                 01-11-2023               01-12-2020   \n",
       "69957                 01-11-2023               01-06-2023   \n",
       "\n",
       "       DATE_REPORTED_AND_CERTIFIED  DATE_OF_LAST_PAYMENT Reported_Date  \\\n",
       "0                       30-11-2023            30-11-2023    30-11-2023   \n",
       "1                       30-11-2023            30-11-2023    30-11-2023   \n",
       "2                       30-11-2023            11-11-2023    30-11-2023   \n",
       "3                       30-11-2023            05-11-2023    30-11-2023   \n",
       "4                       30-11-2023            27-11-2023    30-11-2023   \n",
       "...                            ...                   ...           ...   \n",
       "69953                   30-11-2023            01-11-2023    30-11-2023   \n",
       "69954                   30-11-2023            05-11-2023    30-11-2023   \n",
       "69955                   30-11-2023            19-06-2023    30-11-2023   \n",
       "69956                   30-11-2023            14-11-2023    30-11-2023   \n",
       "69957                   30-11-2023            05-11-2023    30-11-2023   \n",
       "\n",
       "      DATE_OF_BIRTH OCCUPATION_TYPE  GENDER ACTUAL_ROI  \n",
       "0        21-05-1971        SALARIED    Male      12.32  \n",
       "1        12-11-1984        SALARIED  Female      12.42  \n",
       "2        01-08-1986        SALARIED    Male      10.87  \n",
       "3        22-07-1969             NaN  Female      10.00  \n",
       "4        28-10-1974        SALARIED    Male      11.97  \n",
       "...             ...             ...     ...        ...  \n",
       "69953    05-02-1989            SENP  Female      12.97  \n",
       "69954    22-08-1992             NaN    Male      18.00  \n",
       "69955    13-05-1987             NaN    Male      21.00  \n",
       "69956    01-06-1971        SALARIED    Male      12.07  \n",
       "69957    03-02-1989          OTHERS  Female      12.85  \n",
       "\n",
       "[69958 rows x 24 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the better estimate for the emi will be if the current last payment amount is less than the (principal) with intrest rate minnimum of that loan type with tenure maximum of that loan type then its a good estimation choose high or current using hit and trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shuffled_df['new-feature']=shuffled_df['CURRENT_BALANCE']/(shuffled_df['ACTUAL_PAYMT_AMT']+1)\n",
    "shuffled_df['new-feature-1']=shuffled_df['COLLATERALVALUE']/(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "shuffled_df['new-feature-2']=shuffled_df['COLLATERALVALUE']-(shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "shuffled_df['new-feature-3']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']/(shuffled_df['MONTH_OPENED']+1.5)\n",
    "shuffled_df['PAID']=shuffled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']-shuffled_df['CURRENT_BALANCE']\n",
    "wrangled_df['EMI_AMOUNT'] = wrangled_df['EMI_AMOUNT'].fillna(wrangled_df['ACTUAL_PAYMT_AMT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>PAID</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1600000.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1408044</td>\n",
       "      <td>662</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>769070.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23420.0</td>\n",
       "      <td>1890000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>73.0</td>\n",
       "      <td>191956.0</td>\n",
       "      <td>1.408044e+06</td>\n",
       "      <td>1.181250</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>15458.937198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500000.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>387902</td>\n",
       "      <td>731</td>\n",
       "      <td>156.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112098.0</td>\n",
       "      <td>3.879020e+05</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>750000.0</td>\n",
       "      <td>10526.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1488000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1375083</td>\n",
       "      <td>729</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28706.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>-2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112917.0</td>\n",
       "      <td>1.375083e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1488000.0</td>\n",
       "      <td>175058.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1087470.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>323818</td>\n",
       "      <td>774</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2409000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>763652.0</td>\n",
       "      <td>1.556817e+02</td>\n",
       "      <td>2.215234</td>\n",
       "      <td>1321530.0</td>\n",
       "      <td>15209.370629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1015604.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>750654</td>\n",
       "      <td>740</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Business Loan - General</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>31998.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>264950.0</td>\n",
       "      <td>2.345867e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1015604.0</td>\n",
       "      <td>52082.256410</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98042</th>\n",
       "      <td>798459.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>617671</td>\n",
       "      <td>707</td>\n",
       "      <td>106.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>4424222.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180788.0</td>\n",
       "      <td>5.439639e+01</td>\n",
       "      <td>5.540951</td>\n",
       "      <td>3625763.0</td>\n",
       "      <td>9022.135593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98043</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15648</td>\n",
       "      <td>779</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>1.564800e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18000.0</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98044</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10567</td>\n",
       "      <td>743</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Personal Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>19433.0</td>\n",
       "      <td>1.056700e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-30000.0</td>\n",
       "      <td>4615.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98045</th>\n",
       "      <td>1103746.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1045152</td>\n",
       "      <td>700</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>13609.0</td>\n",
       "      <td>3017700.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58594.0</td>\n",
       "      <td>7.679295e+01</td>\n",
       "      <td>2.734053</td>\n",
       "      <td>1913954.0</td>\n",
       "      <td>16351.792593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98046</th>\n",
       "      <td>2340000.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>855192</td>\n",
       "      <td>644</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>855192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>3124820.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1484808.0</td>\n",
       "      <td>8.551920e+05</td>\n",
       "      <td>1.335393</td>\n",
       "      <td>784820.0</td>\n",
       "      <td>12348.284960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98022 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HIGH_CREDIT_OR_SANCTIONED_AMOUNT  MONTH_OPENED  CURRENT_BALANCE  \\\n",
       "0                             1600000.0         102.0          1408044   \n",
       "1                              500000.0          46.0           387902   \n",
       "2                             1488000.0           7.0          1375083   \n",
       "3                             1087470.0          70.0           323818   \n",
       "4                             1015604.0          18.0           750654   \n",
       "...                                 ...           ...              ...   \n",
       "98042                          798459.0          87.0           617671   \n",
       "98043                           18000.0           3.0            15648   \n",
       "98044                           30000.0           5.0            10567   \n",
       "98045                         1103746.0          66.0          1045152   \n",
       "98046                         2340000.0         188.0           855192   \n",
       "\n",
       "       TU_SCORE  REPAYMENT_TENURE             ACCOUNT_TYPE  AMOUNT_OVERDUE  \\\n",
       "0           662             123.0             Housing Loan        769070.0   \n",
       "1           731             156.0             Housing Loan             0.0   \n",
       "2           729              72.0            Personal Loan             0.0   \n",
       "3           774              56.0             Housing Loan             0.0   \n",
       "4           740              48.0  Business Loan - General             0.0   \n",
       "...         ...               ...                      ...             ...   \n",
       "98042       707             106.0            Property Loan             0.0   \n",
       "98043       779              24.0            Personal Loan             0.0   \n",
       "98044       743               9.0            Personal Loan             0.0   \n",
       "98045       700             175.0            Property Loan             0.0   \n",
       "98046       644              53.0             Housing Loan        855192.0   \n",
       "\n",
       "       ACTUAL_PAYMT_AMT  EMI_AMOUNT  COLLATERALVALUE  PAYMENT_HISTORY_LENGTH  \\\n",
       "0                   0.0     23420.0        1890000.0                      36   \n",
       "1                   0.0      5361.0        1250000.0                      26   \n",
       "2                   0.0     28706.0              0.0                       9   \n",
       "3                2079.0      9581.0        2409000.0                      36   \n",
       "4               31998.0     31998.0              0.0                      19   \n",
       "...                 ...         ...              ...                     ...   \n",
       "98042           11354.0     11354.0        4424222.0                      36   \n",
       "98043               0.0      1011.0              0.0                       5   \n",
       "98044               0.0      3900.0              0.0                       8   \n",
       "98045           13609.0     13609.0        3017700.0                      36   \n",
       "98046               0.0      2206.0        3124820.0                      36   \n",
       "\n",
       "       MONTH_CERTIFIED  MONTH_LAST_PAYMENT       PAID   new-feature  \\\n",
       "0                   42                73.0   191956.0  1.408044e+06   \n",
       "1                   -2                 0.0   112098.0  3.879020e+05   \n",
       "2                   -2                 0.0   112917.0  1.375083e+06   \n",
       "3                    0                 0.0   763652.0  1.556817e+02   \n",
       "4                    0                 0.0   264950.0  2.345867e+01   \n",
       "...                ...                 ...        ...           ...   \n",
       "98042                0                 0.0   180788.0  5.439639e+01   \n",
       "98043               -2                -1.0     2352.0  1.564800e+04   \n",
       "98044               -2                -2.0    19433.0  1.056700e+04   \n",
       "98045                0                 0.0    58594.0  7.679295e+01   \n",
       "98046                0                56.0  1484808.0  8.551920e+05   \n",
       "\n",
       "       new-feature-1  new-feature-2  new-feature-3  \n",
       "0           1.181250       290000.0   15458.937198  \n",
       "1           2.500000       750000.0   10526.315789  \n",
       "2           0.000000     -1488000.0  175058.823529  \n",
       "3           2.215234      1321530.0   15209.370629  \n",
       "4           0.000000     -1015604.0   52082.256410  \n",
       "...              ...            ...            ...  \n",
       "98042       5.540951      3625763.0    9022.135593  \n",
       "98043       0.000000       -18000.0    4000.000000  \n",
       "98044       0.000000       -30000.0    4615.384615  \n",
       "98045       2.734053      1913954.0   16351.792593  \n",
       "98046       1.335393       784820.0   12348.284960  \n",
       "\n",
       "[98022 rows x 18 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shuffled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_opened_to_month(df):\n",
    "    # Convert 'DATE_OPENED' column to datetime\n",
    "    df['DATE_OPENED'] = pd.to_datetime(df['DATE_OPENED'], format='%d-%m-%Y')  # Updated date format\n",
    "\n",
    "    # Define the constant 'Reported_Date'\n",
    "    constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "    # Calculate the number of months until the constant_reported_date\n",
    "    df['Months_Since_Opened'] = (constant_reported_date - df['DATE_OPENED']).dt.days // 30\n",
    "\n",
    "    # Replace 'DATE_OPENED' with the calculated number of months\n",
    "    df['DATE_OPENED'] = df['Months_Since_Opened']\n",
    "\n",
    "    # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "    df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "    \n",
    "    # Rename 'DATE_OPENED' to 'MONTH_OPENED'\n",
    "    df.rename(columns={'DATE_OPENED': 'MONTH_OPENED'}, inplace=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "wrangled_df = date_opened_to_month(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "def history_start_end_dates(df):\n",
    "    # Convert 'PAYMENT_HISTORY_START_DATE' and 'PAYMENT_HISTORY_END_DATE' to datetime\n",
    "    df['PAYMENT_HISTORY_START_DATE'] = pd.to_datetime(df['PAYMENT_HISTORY_START_DATE'], format='%d-%m-%Y')\n",
    "    df['PAYMENT_HISTORY_END_DATE'] = pd.to_datetime(df['PAYMENT_HISTORY_END_DATE'], format='%d-%m-%Y')\n",
    "\n",
    "\n",
    "    # Example operation: Calculate the number of months between 'PAYMENT_HISTORY_START_DATE' and a constant date\n",
    "    constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "    df['Months_Since_Payment_Start'] = (constant_reported_date - df['PAYMENT_HISTORY_START_DATE']).dt.days // 30\n",
    "\n",
    "    # Replace 'PAYMENT_HISTORY_START_DATE' with the calculated number of months\n",
    "    df['PAYMENT_HISTORY_START_DATE'] = df['Months_Since_Payment_Start']\n",
    "\n",
    "    df.drop(columns=['Months_Since_Payment_Start'], inplace=True)\n",
    "\n",
    "    df.rename(columns={'PAYMENT_HISTORY_START_DATE': 'MONTH_PAYMENT_HISTORY_START'}, inplace=True)\n",
    "\n",
    "    \n",
    "    df['Months_Since_Payment_End'] = (constant_reported_date - df['PAYMENT_HISTORY_END_DATE']).dt.days // 30\n",
    "\n",
    "    df['PAYMENT_HISTORY_END_DATE'] = df['Months_Since_Payment_End']\n",
    "\n",
    "    # Drop the 'Months_Since_Payment_End' column if you don't need it anymore\n",
    "    df.drop(columns=['Months_Since_Payment_End'], inplace=True)\n",
    "\n",
    "    # Rename 'PAYMENT_HISTORY_END_DATE' to 'MONTH_PAYMENT_HISTORY_END'\n",
    "    df.rename(columns={'PAYMENT_HISTORY_END_DATE': 'MONTH_PAYMENT_HISTORY_END'}, inplace=True)\n",
    "\n",
    "    return df\n",
    "\n",
    "wrangled_df = history_start_end_dates(wrangled_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_of_last_payment(df):\n",
    "  df['DATE_OF_LAST_PAYMENT'] = pd.to_datetime(df['DATE_OF_LAST_PAYMENT'], format='%d-%m-%Y')\n",
    "\n",
    "  # Define the constant 'Reported_Date'\n",
    "  constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "  # Calculate the number of months\n",
    "  df['Months_Since_Opened'] = (constant_reported_date - df['DATE_OF_LAST_PAYMENT']).dt.days // 30\n",
    "\n",
    "  # Replace 'DATE_OPENED' with the calculated number of months\n",
    "  df['DATE_OF_LAST_PAYMENT'] = df['Months_Since_Opened']\n",
    "\n",
    "  # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "  df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "  df.rename(columns={'DATE_OF_LAST_PAYMENT': 'MONTH_LAST_PAYMENT'}, inplace=True)\n",
    "\n",
    "  return df\n",
    "\n",
    "wrangled_df = date_of_last_payment(wrangled_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wrangled_df['EMI_AMOUNT'] = wrangled_df['EMI_AMOUNT'].fillna(wrangled_df['ACTUAL_PAYMT_AMT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>LOAN_CLASSIFICATION</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_START</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_END</th>\n",
       "      <th>DATE_REPORTED_AND_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>Reported_Date</th>\n",
       "      <th>DATE_OF_BIRTH</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ACTUAL_ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002338349</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>818517</td>\n",
       "      <td>115</td>\n",
       "      <td>640742</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>21-05-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002000537</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1243755</td>\n",
       "      <td>133</td>\n",
       "      <td>949446</td>\n",
       "      <td>500.0</td>\n",
       "      <td>12210.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>12-11-1984</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002421579</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1826422</td>\n",
       "      <td>81</td>\n",
       "      <td>1296849</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>748</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-08-1986</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002152345</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1847916</td>\n",
       "      <td>11</td>\n",
       "      <td>1724256</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-07-1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001952834</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2318386</td>\n",
       "      <td>110</td>\n",
       "      <td>1790937</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>539</td>\n",
       "      <td>431551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>28-10-1974</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.97</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>69953</th>\n",
       "      <td>A000195582</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>2600000</td>\n",
       "      <td>69</td>\n",
       "      <td>2381437</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>744</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-02-1989</td>\n",
       "      <td>SENP</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69954</th>\n",
       "      <td>A001735798</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1016188</td>\n",
       "      <td>15</td>\n",
       "      <td>790486</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>673</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-08-1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69955</th>\n",
       "      <td>A002435973</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>335475</td>\n",
       "      <td>20</td>\n",
       "      <td>281106</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240</td>\n",
       "      <td>82468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>567</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>13-05-1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69956</th>\n",
       "      <td>A000027755</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1585160</td>\n",
       "      <td>100</td>\n",
       "      <td>1255235</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>798</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-06-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69957</th>\n",
       "      <td>A000500401</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>542973</td>\n",
       "      <td>5</td>\n",
       "      <td>357217</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>757</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>03-02-1989</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69958 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0      A002338349   Housing Loan                            818517   \n",
       "1      A002000537   Housing Loan                           1243755   \n",
       "2      A002421579   Housing Loan                           1826422   \n",
       "3      A002152345   Housing Loan                           1847916   \n",
       "4      A001952834   Housing Loan                           2318386   \n",
       "...           ...            ...                               ...   \n",
       "69953  A000195582  Property Loan                           2600000   \n",
       "69954  A001735798  Business Loan                           1016188   \n",
       "69955  A002435973  Business Loan                            335475   \n",
       "69956  A000027755   Housing Loan                           1585160   \n",
       "69957  A000500401   Housing Loan                            542973   \n",
       "\n",
       "       MONTH_OPENED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0               115           640742            8627.0      8627.0   \n",
       "1               133           949446             500.0     12210.0   \n",
       "2                81          1296849           16036.0     16036.0   \n",
       "3                11          1724256           26996.0         NaN   \n",
       "4               110          1790937           25131.0         NaN   \n",
       "...             ...              ...               ...         ...   \n",
       "69953            69          2381437           30762.0     30762.0   \n",
       "69954            15           790486           29851.0         NaN   \n",
       "69955            20           281106           10388.0         NaN   \n",
       "69956           100          1255235           16739.0         NaN   \n",
       "69957             5           357217            2878.0         NaN   \n",
       "\n",
       "       REPAYMENT_TENURE  LOAN_CLASSIFICATION  AMOUNT_OVERDUE  ... TU_SCORE  \\\n",
       "0                 142.0                    0             NaN  ...      681   \n",
       "1                 156.0                    0             NaN  ...      784   \n",
       "2                 148.0                    0             NaN  ...      748   \n",
       "3                   NaN                    0             NaN  ...      786   \n",
       "4                 116.0                  539        431551.0  ...      664   \n",
       "...                 ...                  ...             ...  ...      ...   \n",
       "69953             162.0                   21         30762.0  ...      744   \n",
       "69954              48.0                    0             NaN  ...      673   \n",
       "69955              48.0                  240         82468.0  ...      567   \n",
       "69956             143.0                    0             NaN  ...      798   \n",
       "69957               NaN                    0             NaN  ...      757   \n",
       "\n",
       "      MONTH_PAYMENT_HISTORY_START MONTH_PAYMENT_HISTORY_END  \\\n",
       "0                               0                        36   \n",
       "1                               0                        36   \n",
       "2                               0                        36   \n",
       "3                               0                        12   \n",
       "4                               0                        36   \n",
       "...                           ...                       ...   \n",
       "69953                           0                        36   \n",
       "69954                           0                        16   \n",
       "69955                           0                        21   \n",
       "69956                           0                        36   \n",
       "69957                           0                         6   \n",
       "\n",
       "       DATE_REPORTED_AND_CERTIFIED  MONTH_LAST_PAYMENT  Reported_Date  \\\n",
       "0                       30-11-2023                 0.0     30-11-2023   \n",
       "1                       30-11-2023                 0.0     30-11-2023   \n",
       "2                       30-11-2023                 0.0     30-11-2023   \n",
       "3                       30-11-2023                 0.0     30-11-2023   \n",
       "4                       30-11-2023                 0.0     30-11-2023   \n",
       "...                            ...                 ...            ...   \n",
       "69953                   30-11-2023                 0.0     30-11-2023   \n",
       "69954                   30-11-2023                 0.0     30-11-2023   \n",
       "69955                   30-11-2023                 5.0     30-11-2023   \n",
       "69956                   30-11-2023                 0.0     30-11-2023   \n",
       "69957                   30-11-2023                 0.0     30-11-2023   \n",
       "\n",
       "       DATE_OF_BIRTH OCCUPATION_TYPE  GENDER ACTUAL_ROI  \n",
       "0         21-05-1971        SALARIED    Male      12.32  \n",
       "1         12-11-1984        SALARIED  Female      12.42  \n",
       "2         01-08-1986        SALARIED    Male      10.87  \n",
       "3         22-07-1969             NaN  Female      10.00  \n",
       "4         28-10-1974        SALARIED    Male      11.97  \n",
       "...              ...             ...     ...        ...  \n",
       "69953     05-02-1989            SENP  Female      12.97  \n",
       "69954     22-08-1992             NaN    Male      18.00  \n",
       "69955     13-05-1987             NaN    Male      21.00  \n",
       "69956     01-06-1971        SALARIED    Male      12.07  \n",
       "69957     03-02-1989          OTHERS  Female      12.85  \n",
       "\n",
       "[69958 rows x 24 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_certified_to_month(df):\n",
    "  df['DATE_REPORTED_AND_CERTIFIED'] = pd.to_datetime(df['DATE_REPORTED_AND_CERTIFIED'], format='%d-%m-%Y')\n",
    "\n",
    "  # Define the constant 'Reported_Date'\n",
    "  constant_reported_date = pd.to_datetime('30-11-2023', format='%d-%m-%Y')\n",
    "\n",
    "  # Calculate the number of months\n",
    "  df['Months_Since_Opened'] = (constant_reported_date - df['DATE_REPORTED_AND_CERTIFIED']).dt.days // 30\n",
    "\n",
    "  # Replace 'DATE_OPENED' with the calculated number of months\n",
    "  df['DATE_REPORTED_AND_CERTIFIED'] = df['Months_Since_Opened']\n",
    "\n",
    "  # Drop the 'Months_Since_Opened' column if you don't need it anymore\n",
    "  df.drop(columns=['Months_Since_Opened'], inplace=True)\n",
    "  df.rename(columns={'DATE_REPORTED_AND_CERTIFIED': 'MONTH_CERTIFIED'}, inplace=True)\n",
    "\n",
    "  return df\n",
    "wrangled_df=date_certified_to_month(wrangled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>LOAN_CLASSIFICATION</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_START</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_END</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>Reported_Date</th>\n",
       "      <th>DATE_OF_BIRTH</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ACTUAL_ROI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002338349</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>818517</td>\n",
       "      <td>115</td>\n",
       "      <td>640742</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>21-05-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002000537</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1243755</td>\n",
       "      <td>133</td>\n",
       "      <td>949446</td>\n",
       "      <td>500.0</td>\n",
       "      <td>12210.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>12-11-1984</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002421579</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1826422</td>\n",
       "      <td>81</td>\n",
       "      <td>1296849</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>748</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-08-1986</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002152345</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1847916</td>\n",
       "      <td>11</td>\n",
       "      <td>1724256</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>786</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-07-1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001952834</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2318386</td>\n",
       "      <td>110</td>\n",
       "      <td>1790937</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>539</td>\n",
       "      <td>431551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>28-10-1974</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.97</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>69953</th>\n",
       "      <td>A000195582</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>2600000</td>\n",
       "      <td>69</td>\n",
       "      <td>2381437</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>744</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-02-1989</td>\n",
       "      <td>SENP</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69954</th>\n",
       "      <td>A001735798</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1016188</td>\n",
       "      <td>15</td>\n",
       "      <td>790486</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>673</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-08-1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69955</th>\n",
       "      <td>A002435973</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>335475</td>\n",
       "      <td>20</td>\n",
       "      <td>281106</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240</td>\n",
       "      <td>82468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>567</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>13-05-1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69956</th>\n",
       "      <td>A000027755</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1585160</td>\n",
       "      <td>100</td>\n",
       "      <td>1255235</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>798</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-06-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69957</th>\n",
       "      <td>A000500401</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>542973</td>\n",
       "      <td>5</td>\n",
       "      <td>357217</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>757</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>03-02-1989</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69958 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0      A002338349   Housing Loan                            818517   \n",
       "1      A002000537   Housing Loan                           1243755   \n",
       "2      A002421579   Housing Loan                           1826422   \n",
       "3      A002152345   Housing Loan                           1847916   \n",
       "4      A001952834   Housing Loan                           2318386   \n",
       "...           ...            ...                               ...   \n",
       "69953  A000195582  Property Loan                           2600000   \n",
       "69954  A001735798  Business Loan                           1016188   \n",
       "69955  A002435973  Business Loan                            335475   \n",
       "69956  A000027755   Housing Loan                           1585160   \n",
       "69957  A000500401   Housing Loan                            542973   \n",
       "\n",
       "       MONTH_OPENED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0               115           640742            8627.0      8627.0   \n",
       "1               133           949446             500.0     12210.0   \n",
       "2                81          1296849           16036.0     16036.0   \n",
       "3                11          1724256           26996.0         NaN   \n",
       "4               110          1790937           25131.0         NaN   \n",
       "...             ...              ...               ...         ...   \n",
       "69953            69          2381437           30762.0     30762.0   \n",
       "69954            15           790486           29851.0         NaN   \n",
       "69955            20           281106           10388.0         NaN   \n",
       "69956           100          1255235           16739.0         NaN   \n",
       "69957             5           357217            2878.0         NaN   \n",
       "\n",
       "       REPAYMENT_TENURE  LOAN_CLASSIFICATION  AMOUNT_OVERDUE  ... TU_SCORE  \\\n",
       "0                 142.0                    0             NaN  ...      681   \n",
       "1                 156.0                    0             NaN  ...      784   \n",
       "2                 148.0                    0             NaN  ...      748   \n",
       "3                   NaN                    0             NaN  ...      786   \n",
       "4                 116.0                  539        431551.0  ...      664   \n",
       "...                 ...                  ...             ...  ...      ...   \n",
       "69953             162.0                   21         30762.0  ...      744   \n",
       "69954              48.0                    0             NaN  ...      673   \n",
       "69955              48.0                  240         82468.0  ...      567   \n",
       "69956             143.0                    0             NaN  ...      798   \n",
       "69957               NaN                    0             NaN  ...      757   \n",
       "\n",
       "      MONTH_PAYMENT_HISTORY_START MONTH_PAYMENT_HISTORY_END  MONTH_CERTIFIED  \\\n",
       "0                               0                        36                0   \n",
       "1                               0                        36                0   \n",
       "2                               0                        36                0   \n",
       "3                               0                        12                0   \n",
       "4                               0                        36                0   \n",
       "...                           ...                       ...              ...   \n",
       "69953                           0                        36                0   \n",
       "69954                           0                        16                0   \n",
       "69955                           0                        21                0   \n",
       "69956                           0                        36                0   \n",
       "69957                           0                         6                0   \n",
       "\n",
       "       MONTH_LAST_PAYMENT  Reported_Date  DATE_OF_BIRTH  OCCUPATION_TYPE  \\\n",
       "0                     0.0     30-11-2023     21-05-1971         SALARIED   \n",
       "1                     0.0     30-11-2023     12-11-1984         SALARIED   \n",
       "2                     0.0     30-11-2023     01-08-1986         SALARIED   \n",
       "3                     0.0     30-11-2023     22-07-1969              NaN   \n",
       "4                     0.0     30-11-2023     28-10-1974         SALARIED   \n",
       "...                   ...            ...            ...              ...   \n",
       "69953                 0.0     30-11-2023     05-02-1989             SENP   \n",
       "69954                 0.0     30-11-2023     22-08-1992              NaN   \n",
       "69955                 5.0     30-11-2023     13-05-1987              NaN   \n",
       "69956                 0.0     30-11-2023     01-06-1971         SALARIED   \n",
       "69957                 0.0     30-11-2023     03-02-1989           OTHERS   \n",
       "\n",
       "       GENDER ACTUAL_ROI  \n",
       "0        Male      12.32  \n",
       "1      Female      12.42  \n",
       "2        Male      10.87  \n",
       "3      Female      10.00  \n",
       "4        Male      11.97  \n",
       "...       ...        ...  \n",
       "69953  Female      12.97  \n",
       "69954    Male      18.00  \n",
       "69955    Male      21.00  \n",
       "69956    Male      12.07  \n",
       "69957  Female      12.85  \n",
       "\n",
       "[69958 rows x 24 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['PAYMENT_HISTORY_LENGTH'] = abs(wrangled_df['MONTH_PAYMENT_HISTORY_END'] - wrangled_df['MONTH_PAYMENT_HISTORY_START'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>LOAN_CLASSIFICATION</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_START</th>\n",
       "      <th>MONTH_PAYMENT_HISTORY_END</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>Reported_Date</th>\n",
       "      <th>DATE_OF_BIRTH</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ACTUAL_ROI</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002338349</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>818517</td>\n",
       "      <td>115</td>\n",
       "      <td>640742</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>21-05-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.32</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002000537</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1243755</td>\n",
       "      <td>133</td>\n",
       "      <td>949446</td>\n",
       "      <td>500.0</td>\n",
       "      <td>12210.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>12-11-1984</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.42</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002421579</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1826422</td>\n",
       "      <td>81</td>\n",
       "      <td>1296849</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-08-1986</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.87</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002152345</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1847916</td>\n",
       "      <td>11</td>\n",
       "      <td>1724256</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-07-1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001952834</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2318386</td>\n",
       "      <td>110</td>\n",
       "      <td>1790937</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>539</td>\n",
       "      <td>431551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>28-10-1974</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.97</td>\n",
       "      <td>36</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>69953</th>\n",
       "      <td>A000195582</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>2600000</td>\n",
       "      <td>69</td>\n",
       "      <td>2381437</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>05-02-1989</td>\n",
       "      <td>SENP</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.97</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69954</th>\n",
       "      <td>A001735798</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1016188</td>\n",
       "      <td>15</td>\n",
       "      <td>790486</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>22-08-1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69955</th>\n",
       "      <td>A002435973</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>335475</td>\n",
       "      <td>20</td>\n",
       "      <td>281106</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240</td>\n",
       "      <td>82468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>13-05-1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69956</th>\n",
       "      <td>A000027755</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1585160</td>\n",
       "      <td>100</td>\n",
       "      <td>1255235</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>01-06-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.07</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69957</th>\n",
       "      <td>A000500401</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>542973</td>\n",
       "      <td>5</td>\n",
       "      <td>357217</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30-11-2023</td>\n",
       "      <td>03-02-1989</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.85</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69958 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0      A002338349   Housing Loan                            818517   \n",
       "1      A002000537   Housing Loan                           1243755   \n",
       "2      A002421579   Housing Loan                           1826422   \n",
       "3      A002152345   Housing Loan                           1847916   \n",
       "4      A001952834   Housing Loan                           2318386   \n",
       "...           ...            ...                               ...   \n",
       "69953  A000195582  Property Loan                           2600000   \n",
       "69954  A001735798  Business Loan                           1016188   \n",
       "69955  A002435973  Business Loan                            335475   \n",
       "69956  A000027755   Housing Loan                           1585160   \n",
       "69957  A000500401   Housing Loan                            542973   \n",
       "\n",
       "       MONTH_OPENED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0               115           640742            8627.0      8627.0   \n",
       "1               133           949446             500.0     12210.0   \n",
       "2                81          1296849           16036.0     16036.0   \n",
       "3                11          1724256           26996.0         NaN   \n",
       "4               110          1790937           25131.0         NaN   \n",
       "...             ...              ...               ...         ...   \n",
       "69953            69          2381437           30762.0     30762.0   \n",
       "69954            15           790486           29851.0         NaN   \n",
       "69955            20           281106           10388.0         NaN   \n",
       "69956           100          1255235           16739.0         NaN   \n",
       "69957             5           357217            2878.0         NaN   \n",
       "\n",
       "       REPAYMENT_TENURE  LOAN_CLASSIFICATION  AMOUNT_OVERDUE  ...  \\\n",
       "0                 142.0                    0             NaN  ...   \n",
       "1                 156.0                    0             NaN  ...   \n",
       "2                 148.0                    0             NaN  ...   \n",
       "3                   NaN                    0             NaN  ...   \n",
       "4                 116.0                  539        431551.0  ...   \n",
       "...                 ...                  ...             ...  ...   \n",
       "69953             162.0                   21         30762.0  ...   \n",
       "69954              48.0                    0             NaN  ...   \n",
       "69955              48.0                  240         82468.0  ...   \n",
       "69956             143.0                    0             NaN  ...   \n",
       "69957               NaN                    0             NaN  ...   \n",
       "\n",
       "      MONTH_PAYMENT_HISTORY_START MONTH_PAYMENT_HISTORY_END MONTH_CERTIFIED  \\\n",
       "0                               0                        36               0   \n",
       "1                               0                        36               0   \n",
       "2                               0                        36               0   \n",
       "3                               0                        12               0   \n",
       "4                               0                        36               0   \n",
       "...                           ...                       ...             ...   \n",
       "69953                           0                        36               0   \n",
       "69954                           0                        16               0   \n",
       "69955                           0                        21               0   \n",
       "69956                           0                        36               0   \n",
       "69957                           0                         6               0   \n",
       "\n",
       "       MONTH_LAST_PAYMENT  Reported_Date  DATE_OF_BIRTH  OCCUPATION_TYPE  \\\n",
       "0                     0.0     30-11-2023     21-05-1971         SALARIED   \n",
       "1                     0.0     30-11-2023     12-11-1984         SALARIED   \n",
       "2                     0.0     30-11-2023     01-08-1986         SALARIED   \n",
       "3                     0.0     30-11-2023     22-07-1969              NaN   \n",
       "4                     0.0     30-11-2023     28-10-1974         SALARIED   \n",
       "...                   ...            ...            ...              ...   \n",
       "69953                 0.0     30-11-2023     05-02-1989             SENP   \n",
       "69954                 0.0     30-11-2023     22-08-1992              NaN   \n",
       "69955                 5.0     30-11-2023     13-05-1987              NaN   \n",
       "69956                 0.0     30-11-2023     01-06-1971         SALARIED   \n",
       "69957                 0.0     30-11-2023     03-02-1989           OTHERS   \n",
       "\n",
       "       GENDER  ACTUAL_ROI PAYMENT_HISTORY_LENGTH  \n",
       "0        Male       12.32                     36  \n",
       "1      Female       12.42                     36  \n",
       "2        Male       10.87                     36  \n",
       "3      Female       10.00                     12  \n",
       "4        Male       11.97                     36  \n",
       "...       ...         ...                    ...  \n",
       "69953  Female       12.97                     36  \n",
       "69954    Male       18.00                     16  \n",
       "69955    Male       21.00                     21  \n",
       "69956    Male       12.07                     36  \n",
       "69957  Female       12.85                      6  \n",
       "\n",
       "[69958 rows x 25 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['AMOUNT_OVERDUE'].fillna(0,inplace=True)\n",
    "wrangled_df['ACTUAL_PAYMT_AMT'].fillna(0,inplace=True)\n",
    "wrangled_df['COLLATERALVALUE'].fillna(0,inplace=True)\n",
    "wrangled_df['MONTH_LAST_PAYMENT'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                      0\n",
       "ACCOUNT_TYPE                            0\n",
       "HIGH_CREDIT_OR_SANCTIONED_AMOUNT        0\n",
       "MONTH_OPENED                            0\n",
       "CURRENT_BALANCE                         0\n",
       "ACTUAL_PAYMT_AMT                        0\n",
       "EMI_AMOUNT                          49766\n",
       "REPAYMENT_TENURE                    37176\n",
       "LOAN_CLASSIFICATION                     0\n",
       "AMOUNT_OVERDUE                          0\n",
       "PAYMENT_HISTORY_1                       0\n",
       "PAYMENT_HISTORY_2                   39673\n",
       "OWNERSHIP_TYPE                          0\n",
       "COLLATERALVALUE                         0\n",
       "TU_SCORE                                0\n",
       "MONTH_PAYMENT_HISTORY_START             0\n",
       "MONTH_PAYMENT_HISTORY_END               0\n",
       "MONTH_CERTIFIED                         0\n",
       "MONTH_LAST_PAYMENT                      0\n",
       "Reported_Date                           0\n",
       "DATE_OF_BIRTH                           0\n",
       "OCCUPATION_TYPE                     19113\n",
       "GENDER                                  0\n",
       "ACTUAL_ROI                              0\n",
       "PAYMENT_HISTORY_LENGTH                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['EMI_AMOUNT'] = wrangled_df['EMI_AMOUNT'].fillna(wrangled_df['ACTUAL_PAYMT_AMT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                      0\n",
       "ACCOUNT_TYPE                            0\n",
       "HIGH_CREDIT_OR_SANCTIONED_AMOUNT        0\n",
       "MONTH_OPENED                            0\n",
       "CURRENT_BALANCE                         0\n",
       "ACTUAL_PAYMT_AMT                        0\n",
       "EMI_AMOUNT                              0\n",
       "REPAYMENT_TENURE                    37176\n",
       "LOAN_CLASSIFICATION                     0\n",
       "AMOUNT_OVERDUE                          0\n",
       "PAYMENT_HISTORY_1                       0\n",
       "PAYMENT_HISTORY_2                   39673\n",
       "OWNERSHIP_TYPE                          0\n",
       "COLLATERALVALUE                         0\n",
       "TU_SCORE                                0\n",
       "MONTH_PAYMENT_HISTORY_START             0\n",
       "MONTH_PAYMENT_HISTORY_END               0\n",
       "MONTH_CERTIFIED                         0\n",
       "MONTH_LAST_PAYMENT                      0\n",
       "Reported_Date                           0\n",
       "DATE_OF_BIRTH                           0\n",
       "OCCUPATION_TYPE                     19113\n",
       "GENDER                                  0\n",
       "ACTUAL_ROI                              0\n",
       "PAYMENT_HISTORY_LENGTH                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_columns = ['HIGH_CREDIT_OR_SANCTIONED_AMOUNT', 'MONTH_OPENED', 'CURRENT_BALANCE', 'TU_SCORE','REPAYMENT_TENURE','ACCOUNT_TYPE','AMOUNT_OVERDUE','ACTUAL_PAYMT_AMT','EMI_AMOUNT','COLLATERALVALUE','PAYMENT_HISTORY_LENGTH','MONTH_CERTIFIED','MONTH_LAST_PAYMENT']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['new-feature'] = wrangled_df['CURRENT_BALANCE'] / (wrangled_df['ACTUAL_PAYMT_AMT'] + 1)\n",
    "wrangled_df['new-feature-1'] = wrangled_df['COLLATERALVALUE'] / (wrangled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "wrangled_df['new-feature-2'] = wrangled_df['COLLATERALVALUE'] - (wrangled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'])\n",
    "wrangled_df['new-feature-3'] = wrangled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT'] / (wrangled_df['MONTH_OPENED'] + 1.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrangled_df['PAID']=wrangled_df['HIGH_CREDIT_OR_SANCTIONED_AMOUNT']-wrangled_df['CURRENT_BALANCE']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ACCOUNT_TYPE</th>\n",
       "      <th>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>REPAYMENT_TENURE</th>\n",
       "      <th>LOAN_CLASSIFICATION</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>...</th>\n",
       "      <th>DATE_OF_BIRTH</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>ACTUAL_ROI</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "      <th>PAID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A002338349</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>818517</td>\n",
       "      <td>115</td>\n",
       "      <td>640742</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>8627.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21-05-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.32</td>\n",
       "      <td>36</td>\n",
       "      <td>74.263097</td>\n",
       "      <td>2.548512</td>\n",
       "      <td>1267483.0</td>\n",
       "      <td>7025.896996</td>\n",
       "      <td>177775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A002000537</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1243755</td>\n",
       "      <td>133</td>\n",
       "      <td>949446</td>\n",
       "      <td>500.0</td>\n",
       "      <td>12210.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12-11-1984</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.42</td>\n",
       "      <td>36</td>\n",
       "      <td>1895.101796</td>\n",
       "      <td>1.234970</td>\n",
       "      <td>292245.0</td>\n",
       "      <td>9247.249071</td>\n",
       "      <td>294309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A002421579</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1826422</td>\n",
       "      <td>81</td>\n",
       "      <td>1296849</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>16036.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>01-08-1986</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.87</td>\n",
       "      <td>36</td>\n",
       "      <td>80.866060</td>\n",
       "      <td>1.964332</td>\n",
       "      <td>1761278.0</td>\n",
       "      <td>22138.448485</td>\n",
       "      <td>529573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A002152345</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1847916</td>\n",
       "      <td>11</td>\n",
       "      <td>1724256</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>26996.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22-07-1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12</td>\n",
       "      <td>63.868430</td>\n",
       "      <td>1.650291</td>\n",
       "      <td>1201684.0</td>\n",
       "      <td>147833.280000</td>\n",
       "      <td>123660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A001952834</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>2318386</td>\n",
       "      <td>110</td>\n",
       "      <td>1790937</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>25131.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>539</td>\n",
       "      <td>431551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28-10-1974</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>11.97</td>\n",
       "      <td>36</td>\n",
       "      <td>71.261221</td>\n",
       "      <td>1.201439</td>\n",
       "      <td>467014.0</td>\n",
       "      <td>20792.699552</td>\n",
       "      <td>527449</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>69953</th>\n",
       "      <td>A000195582</td>\n",
       "      <td>Property Loan</td>\n",
       "      <td>2600000</td>\n",
       "      <td>69</td>\n",
       "      <td>2381437</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>21</td>\n",
       "      <td>30762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>05-02-1989</td>\n",
       "      <td>SENP</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.97</td>\n",
       "      <td>36</td>\n",
       "      <td>77.412379</td>\n",
       "      <td>6.282692</td>\n",
       "      <td>13735000.0</td>\n",
       "      <td>36879.432624</td>\n",
       "      <td>218563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69954</th>\n",
       "      <td>A001735798</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>1016188</td>\n",
       "      <td>15</td>\n",
       "      <td>790486</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>29851.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22-08-1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>16</td>\n",
       "      <td>26.480169</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1016188.0</td>\n",
       "      <td>61587.151515</td>\n",
       "      <td>225702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69955</th>\n",
       "      <td>A002435973</td>\n",
       "      <td>Business Loan</td>\n",
       "      <td>335475</td>\n",
       "      <td>20</td>\n",
       "      <td>281106</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>10388.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>240</td>\n",
       "      <td>82468.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13-05-1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>21.00</td>\n",
       "      <td>21</td>\n",
       "      <td>27.058042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-335475.0</td>\n",
       "      <td>15603.488372</td>\n",
       "      <td>54369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69956</th>\n",
       "      <td>A000027755</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>1585160</td>\n",
       "      <td>100</td>\n",
       "      <td>1255235</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>16739.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>01-06-1971</td>\n",
       "      <td>SALARIED</td>\n",
       "      <td>Male</td>\n",
       "      <td>12.07</td>\n",
       "      <td>36</td>\n",
       "      <td>74.984170</td>\n",
       "      <td>5.997313</td>\n",
       "      <td>7921540.0</td>\n",
       "      <td>15617.339901</td>\n",
       "      <td>329925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69957</th>\n",
       "      <td>A000500401</td>\n",
       "      <td>Housing Loan</td>\n",
       "      <td>542973</td>\n",
       "      <td>5</td>\n",
       "      <td>357217</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>03-02-1989</td>\n",
       "      <td>OTHERS</td>\n",
       "      <td>Female</td>\n",
       "      <td>12.85</td>\n",
       "      <td>6</td>\n",
       "      <td>124.076763</td>\n",
       "      <td>3.280139</td>\n",
       "      <td>1238054.0</td>\n",
       "      <td>83534.307692</td>\n",
       "      <td>185756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69958 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID   ACCOUNT_TYPE  HIGH_CREDIT_OR_SANCTIONED_AMOUNT  \\\n",
       "0      A002338349   Housing Loan                            818517   \n",
       "1      A002000537   Housing Loan                           1243755   \n",
       "2      A002421579   Housing Loan                           1826422   \n",
       "3      A002152345   Housing Loan                           1847916   \n",
       "4      A001952834   Housing Loan                           2318386   \n",
       "...           ...            ...                               ...   \n",
       "69953  A000195582  Property Loan                           2600000   \n",
       "69954  A001735798  Business Loan                           1016188   \n",
       "69955  A002435973  Business Loan                            335475   \n",
       "69956  A000027755   Housing Loan                           1585160   \n",
       "69957  A000500401   Housing Loan                            542973   \n",
       "\n",
       "       MONTH_OPENED  CURRENT_BALANCE  ACTUAL_PAYMT_AMT  EMI_AMOUNT  \\\n",
       "0               115           640742            8627.0      8627.0   \n",
       "1               133           949446             500.0     12210.0   \n",
       "2                81          1296849           16036.0     16036.0   \n",
       "3                11          1724256           26996.0     26996.0   \n",
       "4               110          1790937           25131.0     25131.0   \n",
       "...             ...              ...               ...         ...   \n",
       "69953            69          2381437           30762.0     30762.0   \n",
       "69954            15           790486           29851.0     29851.0   \n",
       "69955            20           281106           10388.0     10388.0   \n",
       "69956           100          1255235           16739.0     16739.0   \n",
       "69957             5           357217            2878.0      2878.0   \n",
       "\n",
       "       REPAYMENT_TENURE  LOAN_CLASSIFICATION  AMOUNT_OVERDUE  ...  \\\n",
       "0                 142.0                    0             0.0  ...   \n",
       "1                 156.0                    0             0.0  ...   \n",
       "2                 148.0                    0             0.0  ...   \n",
       "3                   NaN                    0             0.0  ...   \n",
       "4                 116.0                  539        431551.0  ...   \n",
       "...                 ...                  ...             ...  ...   \n",
       "69953             162.0                   21         30762.0  ...   \n",
       "69954              48.0                    0             0.0  ...   \n",
       "69955              48.0                  240         82468.0  ...   \n",
       "69956             143.0                    0             0.0  ...   \n",
       "69957               NaN                    0             0.0  ...   \n",
       "\n",
       "      DATE_OF_BIRTH OCCUPATION_TYPE  GENDER  ACTUAL_ROI  \\\n",
       "0        21-05-1971        SALARIED    Male       12.32   \n",
       "1        12-11-1984        SALARIED  Female       12.42   \n",
       "2        01-08-1986        SALARIED    Male       10.87   \n",
       "3        22-07-1969             NaN  Female       10.00   \n",
       "4        28-10-1974        SALARIED    Male       11.97   \n",
       "...             ...             ...     ...         ...   \n",
       "69953    05-02-1989            SENP  Female       12.97   \n",
       "69954    22-08-1992             NaN    Male       18.00   \n",
       "69955    13-05-1987             NaN    Male       21.00   \n",
       "69956    01-06-1971        SALARIED    Male       12.07   \n",
       "69957    03-02-1989          OTHERS  Female       12.85   \n",
       "\n",
       "       PAYMENT_HISTORY_LENGTH  new-feature  new-feature-1  new-feature-2  \\\n",
       "0                          36    74.263097       2.548512      1267483.0   \n",
       "1                          36  1895.101796       1.234970       292245.0   \n",
       "2                          36    80.866060       1.964332      1761278.0   \n",
       "3                          12    63.868430       1.650291      1201684.0   \n",
       "4                          36    71.261221       1.201439       467014.0   \n",
       "...                       ...          ...            ...            ...   \n",
       "69953                      36    77.412379       6.282692     13735000.0   \n",
       "69954                      16    26.480169       0.000000     -1016188.0   \n",
       "69955                      21    27.058042       0.000000      -335475.0   \n",
       "69956                      36    74.984170       5.997313      7921540.0   \n",
       "69957                       6   124.076763       3.280139      1238054.0   \n",
       "\n",
       "       new-feature-3    PAID  \n",
       "0        7025.896996  177775  \n",
       "1        9247.249071  294309  \n",
       "2       22138.448485  529573  \n",
       "3      147833.280000  123660  \n",
       "4       20792.699552  527449  \n",
       "...              ...     ...  \n",
       "69953   36879.432624  218563  \n",
       "69954   61587.151515  225702  \n",
       "69955   15603.488372   54369  \n",
       "69956   15617.339901  329925  \n",
       "69957   83534.307692  185756  \n",
       "\n",
       "[69958 rows x 30 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrangled_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_list = [\n",
    "    'HIGH_CREDIT_OR_SANCTIONED_AMOUNT',\n",
    "    'MONTH_OPENED',\n",
    "    'CURRENT_BALANCE',\n",
    "    'TU_SCORE',\n",
    "    'ACCOUNT_TYPE',\n",
    "    'AMOUNT_OVERDUE',\n",
    "    'ACTUAL_PAYMT_AMT',\n",
    "    'EMI_AMOUNT',\n",
    "    'COLLATERALVALUE',\n",
    "    'PAYMENT_HISTORY_LENGTH',\n",
    "    'MONTH_CERTIFIED',\n",
    "    'MONTH_LAST_PAYMENT',\n",
    "    'PAID',\n",
    "    'new-feature',\n",
    "    'new-feature-1',\n",
    "    'new-feature-2',\n",
    "    'new-feature-3',\n",
    "    'ACCOUNT_TYPE_Housing Loan','ACCOUNT_TYPE_Personal Loan','ACCOUNT_TYPE_Property Loan'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming 'TARGET_COLUMN' is your target variable\n",
    "target_column = 'REPAYMENT_TENURE'\n",
    "\n",
    "# Features for prediction\n",
    "X_nan = shuffled_df[shuffled_df[target_column].isna()].drop(columns=[target_column])\n",
    "\n",
    "# Perform one-hot encoding for the \"ACCOUNT_TYPE\" column\n",
    "X_nan_encoded = pd.get_dummies(X_nan, columns=['ACCOUNT_TYPE'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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>HIGH_CREDIT_OR_SANCTIONED_AMOUNT</th>\n",
       "      <th>MONTH_OPENED</th>\n",
       "      <th>CURRENT_BALANCE</th>\n",
       "      <th>TU_SCORE</th>\n",
       "      <th>AMOUNT_OVERDUE</th>\n",
       "      <th>ACTUAL_PAYMT_AMT</th>\n",
       "      <th>EMI_AMOUNT</th>\n",
       "      <th>COLLATERALVALUE</th>\n",
       "      <th>PAYMENT_HISTORY_LENGTH</th>\n",
       "      <th>MONTH_CERTIFIED</th>\n",
       "      <th>MONTH_LAST_PAYMENT</th>\n",
       "      <th>PAID</th>\n",
       "      <th>new-feature</th>\n",
       "      <th>new-feature-1</th>\n",
       "      <th>new-feature-2</th>\n",
       "      <th>new-feature-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [HIGH_CREDIT_OR_SANCTIONED_AMOUNT, MONTH_OPENED, CURRENT_BALANCE, TU_SCORE, AMOUNT_OVERDUE, ACTUAL_PAYMT_AMT, EMI_AMOUNT, COLLATERALVALUE, PAYMENT_HISTORY_LENGTH, MONTH_CERTIFIED, MONTH_LAST_PAYMENT, PAID, new-feature, new-feature-1, new-feature-2, new-feature-3]\n",
       "Index: []"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_nan_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The feature names should match those that were passed during fit.\nFeature names seen at fit time, yet now missing:\n- ACCOUNT_TYPE_Housing Loan\n- ACCOUNT_TYPE_Personal Loan\n- ACCOUNT_TYPE_Property Loan\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[236], line 14\u001b[0m\n\u001b[0;32m     11\u001b[0m X_nan_encoded \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mget_dummies(X_nan, columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mACCOUNT_TYPE\u001b[39m\u001b[38;5;124m'\u001b[39m],drop_first\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Normalize the features using the same scaler used during training\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m X_nan_normalized \u001b[38;5;241m=\u001b[39m \u001b[43mscaler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_nan_encoded\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Assuming 'scaler' was previously defined during training\u001b[39;00m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;66;03m# Make predictions using your trained XGBoost model (assuming 'xgb_model' is already trained)\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, nan_row \u001b[38;5;129;01min\u001b[39;00m X_nan\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;66;03m# Extract the features for the current NaN row (excluding the target column)\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\risha\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:295\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m    293\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m    294\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 295\u001b[0m     data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    297\u001b[0m         \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m    298\u001b[0m         return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    299\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    300\u001b[0m             \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m    301\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\risha\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\preprocessing\\_data.py:1043\u001b[0m, in \u001b[0;36mStandardScaler.transform\u001b[1;34m(self, X, copy)\u001b[0m\n\u001b[0;32m   1040\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m   1042\u001b[0m copy \u001b[38;5;241m=\u001b[39m copy \u001b[38;5;28;01mif\u001b[39;00m copy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy\n\u001b[1;32m-> 1043\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1044\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1045\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m   1046\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1047\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1048\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mFLOAT_DTYPES\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1049\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1050\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1052\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sparse\u001b[38;5;241m.\u001b[39missparse(X):\n\u001b[0;32m   1053\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwith_mean:\n",
      "File \u001b[1;32mc:\\Users\\risha\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:608\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[0;32m    537\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m    538\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    539\u001b[0m     X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m    545\u001b[0m ):\n\u001b[0;32m    546\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m    547\u001b[0m \n\u001b[0;32m    548\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    606\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 608\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    610\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m    611\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    612\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    613\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    614\u001b[0m         )\n",
      "File \u001b[1;32mc:\\Users\\risha\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:535\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    530\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m    531\u001b[0m     message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    532\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    533\u001b[0m     )\n\u001b[1;32m--> 535\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n",
      "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names seen at fit time, yet now missing:\n- ACCOUNT_TYPE_Housing Loan\n- ACCOUNT_TYPE_Personal Loan\n- ACCOUNT_TYPE_Property Loan\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Normalize the features using the same scaler used during training\n",
    "X_nan_normalized = scaler.transform(X_nan_encoded)  # Assuming 'scaler' was previously defined during training\n",
    "\n",
    "# Make predictions using your trained XGBoost model (assuming 'xgb_model' is already trained)\n",
    "for index, nan_row in X_nan.iterrows():\n",
    "    # Extract the features for the current NaN row (excluding the target column)\n",
    "    features = [nan_row[column] for column in columns_list if column != target_column]\n",
    "    \n",
    "    # Make predictions for the current row\n",
    "    prediction = xgb_model.predict([features])[0]\n",
    "    \n",
    "    # Fill the NaN value in 'REPAYMENT_TENURE' with the predicted value\n",
    "    shuffled_df.at[index, target_column] = prediction\n",
    "\n",
    "# Now 'shuffled_df' should have the NaN values in 'REPAYMENT_TENURE' filled with predictions from the XGBoost model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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