{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e8302eda-1005-473f-8c2e-31667cebaeec",
   "metadata": {},
   "source": [
    "# Module Import and Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe664cf2-e5ce-4afe-a0e3-b227b37befc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Core Libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import os\n",
    "import gc\n",
    "import joblib\n",
    "from pathlib import Path\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', 60)\n",
    "pd.set_option('display.float_format', '{:.4f}'.format)\n",
    "#  Visualisation \n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "matplotlib.rcParams.update({\n",
    "    'figure.dpi': 130,\n",
    "    'axes.spines.top': False,\n",
    "    'axes.spines.right': False,\n",
    "    'font.family': 'DejaVu Sans',\n",
    "    'axes.titlesize': 14,\n",
    "    'axes.titleweight': 'bold',\n",
    "})\n",
    "PALETTE = {'default': '#E74C3C', 'paid': '#2ECC71', 'neutral': '#3498DB'}\n",
    "RISK_COLORS = {'Low Risk': '#27AE60', 'Medium Risk': '#F39C12', 'High Risk': '#E74C3C'}\n",
    "#  Sklearn \n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, RobustScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, precision_score, recall_score, f1_score,\n",
    "    roc_auc_score, classification_report, confusion_matrix,\n",
    "    roc_curve, precision_recall_curve, average_precision_score\n",
    ")\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_validate, StratifiedKFold\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, RocCurveDisplay\n",
    "from sklearn.inspection import permutation_importance, PartialDependenceDisplay\n",
    "from sklearn.base import clone\n",
    "from scipy.stats import randint, uniform\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda38a18-1679-4399-8ae2-6036c3256341",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "11ac2641-f1fd-4f98-b303-a7d28233bea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset \n",
    "DATA_PATH = 'loans_full_schema.csv'\n",
    "df_raw = pd.read_csv(DATA_PATH, low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc1c5804-07fe-41b6-b28f-868c866da555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " dataset shape: (10000, 56)\n"
     ]
    }
   ],
   "source": [
    "# Dataset Shape\n",
    "print(f'\\n dataset shape: {df_raw.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "526f7331-0e90-4ffa-95b4-f512c933d7db",
   "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>Unnamed: 0</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>state</th>\n",
       "      <th>homeownership</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>verified_income</th>\n",
       "      <th>debt_to_income</th>\n",
       "      <th>annual_income_joint</th>\n",
       "      <th>verification_income_joint</th>\n",
       "      <th>debt_to_income_joint</th>\n",
       "      <th>delinq_2y</th>\n",
       "      <th>months_since_last_delinq</th>\n",
       "      <th>earliest_credit_line</th>\n",
       "      <th>inquiries_last_12m</th>\n",
       "      <th>total_credit_lines</th>\n",
       "      <th>open_credit_lines</th>\n",
       "      <th>total_credit_limit</th>\n",
       "      <th>total_credit_utilized</th>\n",
       "      <th>num_collections_last_12m</th>\n",
       "      <th>num_historical_failed_to_pay</th>\n",
       "      <th>months_since_90d_late</th>\n",
       "      <th>current_accounts_delinq</th>\n",
       "      <th>total_collection_amount_ever</th>\n",
       "      <th>current_installment_accounts</th>\n",
       "      <th>accounts_opened_24m</th>\n",
       "      <th>months_since_last_credit_inquiry</th>\n",
       "      <th>num_satisfactory_accounts</th>\n",
       "      <th>num_accounts_120d_past_due</th>\n",
       "      <th>num_accounts_30d_past_due</th>\n",
       "      <th>num_active_debit_accounts</th>\n",
       "      <th>total_debit_limit</th>\n",
       "      <th>num_total_cc_accounts</th>\n",
       "      <th>num_open_cc_accounts</th>\n",
       "      <th>num_cc_carrying_balance</th>\n",
       "      <th>num_mort_accounts</th>\n",
       "      <th>account_never_delinq_percent</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>public_record_bankrupt</th>\n",
       "      <th>loan_purpose</th>\n",
       "      <th>application_type</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>term</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>issue_month</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>initial_listing_status</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>balance</th>\n",
       "      <th>paid_total</th>\n",
       "      <th>paid_principal</th>\n",
       "      <th>paid_interest</th>\n",
       "      <th>paid_late_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>global config engineer</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>NJ</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>90000.0000</td>\n",
       "      <td>Verified</td>\n",
       "      <td>18.0100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>2001</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>70795</td>\n",
       "      <td>38767</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11100</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>92.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>moving</td>\n",
       "      <td>individual</td>\n",
       "      <td>28000</td>\n",
       "      <td>60</td>\n",
       "      <td>14.0700</td>\n",
       "      <td>652.5300</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>Mar-2018</td>\n",
       "      <td>Current</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>27015.8600</td>\n",
       "      <td>1999.3300</td>\n",
       "      <td>984.1400</td>\n",
       "      <td>1015.1900</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>warehouse office clerk</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>HI</td>\n",
       "      <td>RENT</td>\n",
       "      <td>40000.0000</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>5.0400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>28800</td>\n",
       "      <td>4321</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16500</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>individual</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>12.6100</td>\n",
       "      <td>167.5400</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Feb-2018</td>\n",
       "      <td>Current</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4651.3700</td>\n",
       "      <td>499.1200</td>\n",
       "      <td>348.6300</td>\n",
       "      <td>150.4900</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>assembly</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>WI</td>\n",
       "      <td>RENT</td>\n",
       "      <td>40000.0000</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>21.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>24193</td>\n",
       "      <td>16000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4300</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>individual</td>\n",
       "      <td>2000</td>\n",
       "      <td>36</td>\n",
       "      <td>17.0900</td>\n",
       "      <td>71.4000</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>Feb-2018</td>\n",
       "      <td>Current</td>\n",
       "      <td>fractional</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1824.6300</td>\n",
       "      <td>281.8000</td>\n",
       "      <td>175.3700</td>\n",
       "      <td>106.4300</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>customer service</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>PA</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.0000</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>10.1600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>25400</td>\n",
       "      <td>4997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>19400</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>individual</td>\n",
       "      <td>21600</td>\n",
       "      <td>36</td>\n",
       "      <td>6.7200</td>\n",
       "      <td>664.1900</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Current</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>18853.2600</td>\n",
       "      <td>3312.8900</td>\n",
       "      <td>2746.7400</td>\n",
       "      <td>566.1500</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>security supervisor</td>\n",
       "      <td>10.0000</td>\n",
       "      <td>CA</td>\n",
       "      <td>RENT</td>\n",
       "      <td>35000.0000</td>\n",
       "      <td>Verified</td>\n",
       "      <td>57.9600</td>\n",
       "      <td>57000.0000</td>\n",
       "      <td>Verified</td>\n",
       "      <td>37.6600</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>69839</td>\n",
       "      <td>52722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>32700</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>joint</td>\n",
       "      <td>23000</td>\n",
       "      <td>36</td>\n",
       "      <td>14.0700</td>\n",
       "      <td>786.8700</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>Mar-2018</td>\n",
       "      <td>Current</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>21430.1500</td>\n",
       "      <td>2324.6500</td>\n",
       "      <td>1569.8500</td>\n",
       "      <td>754.8000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                emp_title  emp_length state homeownership  \\\n",
       "0           1  global config engineer       3.0000    NJ      MORTGAGE   \n",
       "1           2   warehouse office clerk     10.0000    HI          RENT   \n",
       "2           3                 assembly      3.0000    WI          RENT   \n",
       "3           4         customer service      1.0000    PA          RENT   \n",
       "4           5     security supervisor      10.0000    CA          RENT   \n",
       "\n",
       "   annual_income  verified_income  debt_to_income  annual_income_joint  \\\n",
       "0     90000.0000         Verified         18.0100                  NaN   \n",
       "1     40000.0000     Not Verified          5.0400                  NaN   \n",
       "2     40000.0000  Source Verified         21.1500                  NaN   \n",
       "3     30000.0000     Not Verified         10.1600                  NaN   \n",
       "4     35000.0000         Verified         57.9600           57000.0000   \n",
       "\n",
       "  verification_income_joint  debt_to_income_joint  delinq_2y  \\\n",
       "0                       NaN                   NaN          0   \n",
       "1                       NaN                   NaN          0   \n",
       "2                       NaN                   NaN          0   \n",
       "3                       NaN                   NaN          0   \n",
       "4                  Verified               37.6600          0   \n",
       "\n",
       "   months_since_last_delinq  earliest_credit_line  inquiries_last_12m  \\\n",
       "0                   38.0000                  2001                   6   \n",
       "1                       NaN                  1996                   1   \n",
       "2                   28.0000                  2006                   4   \n",
       "3                       NaN                  2007                   0   \n",
       "4                       NaN                  2008                   7   \n",
       "\n",
       "   total_credit_lines  open_credit_lines  total_credit_limit  \\\n",
       "0                  28                 10               70795   \n",
       "1                  30                 14               28800   \n",
       "2                  31                 10               24193   \n",
       "3                   4                  4               25400   \n",
       "4                  22                 16               69839   \n",
       "\n",
       "   total_credit_utilized  num_collections_last_12m  \\\n",
       "0                  38767                         0   \n",
       "1                   4321                         0   \n",
       "2                  16000                         0   \n",
       "3                   4997                         0   \n",
       "4                  52722                         0   \n",
       "\n",
       "   num_historical_failed_to_pay  months_since_90d_late  \\\n",
       "0                             0                38.0000   \n",
       "1                             1                    NaN   \n",
       "2                             0                28.0000   \n",
       "3                             1                    NaN   \n",
       "4                             0                    NaN   \n",
       "\n",
       "   current_accounts_delinq  total_collection_amount_ever  \\\n",
       "0                        0                          1250   \n",
       "1                        0                             0   \n",
       "2                        0                           432   \n",
       "3                        0                             0   \n",
       "4                        0                             0   \n",
       "\n",
       "   current_installment_accounts  accounts_opened_24m  \\\n",
       "0                             2                    5   \n",
       "1                             0                   11   \n",
       "2                             1                   13   \n",
       "3                             1                    1   \n",
       "4                             1                    6   \n",
       "\n",
       "   months_since_last_credit_inquiry  num_satisfactory_accounts  \\\n",
       "0                            5.0000                         10   \n",
       "1                            8.0000                         14   \n",
       "2                            7.0000                         10   \n",
       "3                           15.0000                          4   \n",
       "4                            4.0000                         16   \n",
       "\n",
       "   num_accounts_120d_past_due  num_accounts_30d_past_due  \\\n",
       "0                      0.0000                          0   \n",
       "1                      0.0000                          0   \n",
       "2                      0.0000                          0   \n",
       "3                      0.0000                          0   \n",
       "4                      0.0000                          0   \n",
       "\n",
       "   num_active_debit_accounts  total_debit_limit  num_total_cc_accounts  \\\n",
       "0                          2              11100                     14   \n",
       "1                          3              16500                     24   \n",
       "2                          3               4300                     14   \n",
       "3                          2              19400                      3   \n",
       "4                         10              32700                     20   \n",
       "\n",
       "   num_open_cc_accounts  num_cc_carrying_balance  num_mort_accounts  \\\n",
       "0                     8                        6                  1   \n",
       "1                    14                        4                  0   \n",
       "2                     8                        6                  0   \n",
       "3                     3                        2                  0   \n",
       "4                    15                       13                  0   \n",
       "\n",
       "   account_never_delinq_percent  tax_liens  public_record_bankrupt  \\\n",
       "0                       92.9000          0                       0   \n",
       "1                      100.0000          0                       1   \n",
       "2                       93.5000          0                       0   \n",
       "3                      100.0000          1                       0   \n",
       "4                      100.0000          0                       0   \n",
       "\n",
       "         loan_purpose application_type  loan_amount  term  interest_rate  \\\n",
       "0              moving       individual        28000    60        14.0700   \n",
       "1  debt_consolidation       individual         5000    36        12.6100   \n",
       "2               other       individual         2000    36        17.0900   \n",
       "3  debt_consolidation       individual        21600    36         6.7200   \n",
       "4         credit_card            joint        23000    36        14.0700   \n",
       "\n",
       "   installment grade sub_grade issue_month loan_status initial_listing_status  \\\n",
       "0     652.5300     C        C3    Mar-2018     Current                  whole   \n",
       "1     167.5400     C        C1    Feb-2018     Current                  whole   \n",
       "2      71.4000     D        D1    Feb-2018     Current             fractional   \n",
       "3     664.1900     A        A3    Jan-2018     Current                  whole   \n",
       "4     786.8700     C        C3    Mar-2018     Current                  whole   \n",
       "\n",
       "  disbursement_method    balance  paid_total  paid_principal  paid_interest  \\\n",
       "0                Cash 27015.8600   1999.3300        984.1400      1015.1900   \n",
       "1                Cash  4651.3700    499.1200        348.6300       150.4900   \n",
       "2                Cash  1824.6300    281.8000        175.3700       106.4300   \n",
       "3                Cash 18853.2600   3312.8900       2746.7400       566.1500   \n",
       "4                Cash 21430.1500   2324.6500       1569.8500       754.8000   \n",
       "\n",
       "   paid_late_fees  \n",
       "0          0.0000  \n",
       "1          0.0000  \n",
       "2          0.0000  \n",
       "3          0.0000  \n",
       "4          0.0000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check few rows\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc78d4b2-dd3f-4447-9178-8fd78e047e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " loan_status distribution:\n",
      "loan_status\n",
      "Current               9375\n",
      "Fully Paid             447\n",
      "In Grace Period         67\n",
      "Late (31-120 days)      66\n",
      "Late (16-30 days)       38\n",
      "Charged Off              7\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Target variable distribution \n",
    "print('\\n loan_status distribution:')\n",
    "print(df_raw['loan_status'].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e8c09aa-0d68-4d8c-b25e-864cf9076317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After filtering: 558 rows\n",
      "\n",
      "Target distribution:\n",
      "target\n",
      "0    447\n",
      "1    111\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Default rate: 19.89%\n"
     ]
    }
   ],
   "source": [
    "# Check Class Imbalance\n",
    "# Filter to binary classification rows \n",
    "#  Keep only statuses that map cleanly to default / non-default\n",
    "KEEP_STATUSES = {\n",
    "    'Fully Paid'             : 0,\n",
    "    'Charged Off'            : 1,\n",
    "    'Late (31-120 days)'     : 1,   \n",
    "    'Late (16-30 days)'      : 1\n",
    "}\n",
    "\n",
    "df_raw = df_raw[df_raw['loan_status'].isin(KEEP_STATUSES.keys())].copy()\n",
    "df_raw['target'] = df_raw['loan_status'].map(KEEP_STATUSES)\n",
    "\n",
    "print(f'After filtering: {df_raw.shape[0]:,} rows')\n",
    "print('\\nTarget distribution:')\n",
    "print(df_raw['target'].value_counts())\n",
    "pct = df_raw['target'].mean() * 100\n",
    "print(f'\\nDefault rate: {pct:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfae988a-3219-48aa-8ac8-52c536b87d83",
   "metadata": {},
   "source": [
    "The dataset exhibits a highly imbalanced class distribution, with defaults accounting for only 12.79% of the total observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b511d712-052a-485a-85d7-19beea153ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 46 columns (including target)\n",
      "Working dataframe shape: (558, 46)\n"
     ]
    }
   ],
   "source": [
    "#  Select meaningful features \n",
    "FEATURES = [\n",
    "    # Borrower and application\n",
    "    'emp_length', 'state', 'homeownership', 'annual_income',\n",
    "    'verified_income', 'debt_to_income', 'application_type',\n",
    "    # Credit history\n",
    "    'delinq_2y', 'months_since_last_delinq', 'earliest_credit_line',\n",
    "    'inquiries_last_12m', 'total_credit_lines', 'open_credit_lines',\n",
    "    'total_credit_limit', 'total_credit_utilized',\n",
    "    'num_collections_last_12m', 'num_historical_failed_to_pay',\n",
    "    'months_since_90d_late', 'current_accounts_delinq',\n",
    "    'total_collection_amount_ever', 'current_installment_accounts',\n",
    "    'accounts_opened_24m', 'months_since_last_credit_inquiry',\n",
    "    'num_satisfactory_accounts', 'num_accounts_120d_past_due',\n",
    "    'num_accounts_30d_past_due', 'num_active_debit_accounts',\n",
    "    'total_debit_limit', 'num_total_cc_accounts', 'num_open_cc_accounts',\n",
    "    'num_cc_carrying_balance', 'num_mort_accounts',\n",
    "    'account_never_delinq_percent', 'tax_liens', 'public_record_bankrupt',\n",
    "    # Loan terms known at origination\n",
    "    'loan_purpose', 'loan_amount', 'term', 'interest_rate',\n",
    "    'installment', 'grade', 'sub_grade', 'issue_month',\n",
    "    'initial_listing_status', 'disbursement_method',\n",
    "    # Target\n",
    "    'target'\n",
    "]\n",
    "\n",
    "FEATURES = [c for c in FEATURES if c in df_raw.columns]\n",
    "print(f'Selected {len(FEATURES)} columns (including target)')\n",
    "\n",
    "df = df_raw[FEATURES].copy()\n",
    "del df_raw\n",
    "gc.collect()\n",
    "print(f'Working dataframe shape: {df.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c30ce4e-fd91-4923-8a56-9fe88d74fa0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Missing Values ===\n",
      "                                  Missing Count  Missing %\n",
      "months_since_90d_late                       437    78.3200\n",
      "months_since_last_delinq                    310    55.5600\n",
      "months_since_last_credit_inquiry             52     9.3200\n",
      "emp_length                                   37     6.6300\n",
      "num_accounts_120d_past_due                   20     3.5800\n",
      "debt_to_income                                2     0.3600\n"
     ]
    }
   ],
   "source": [
    "# Identifying missing values\n",
    "missing = df.isnull().sum()\n",
    "missing_pct = (missing / len(df) * 100).round(2)\n",
    "missing_df = pd.DataFrame({'Missing Count': missing, 'Missing %': missing_pct})\n",
    "missing_df = missing_df[missing_df['Missing Count'] > 0].sort_values('Missing %', ascending=False)\n",
    "\n",
    "print('=== Missing Values ===')\n",
    "print(missing_df.to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7afc89d-7194-4449-92de-07efb8d44510",
   "metadata": {},
   "source": [
    "The above columns contains significant missing values thats needs to be treated before machine learning Imnplementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "145766c5-43ac-4c87-af00-206755e892b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean and derive date-related columns\n",
    "if 'term' in df.columns:\n",
    "    df['term'] = pd.to_numeric(df['term'], errors='coerce')\n",
    "\n",
    "if 'emp_length' in df.columns:\n",
    "    df['emp_length'] = pd.to_numeric(df['emp_length'], errors='coerce')\n",
    "\n",
    "if 'interest_rate' in df.columns:\n",
    "    df['interest_rate'] = pd.to_numeric(df['interest_rate'], errors='coerce')\n",
    "\n",
    "# Convert earliest credit year into credit-history length at loan issue\n",
    "if 'issue_month' in df.columns:\n",
    "    issue_dt = pd.to_datetime(df['issue_month'], errors='coerce')\n",
    "    df['issue_year'] = issue_dt.dt.year\n",
    "    df['issue_month_num'] = issue_dt.dt.month\n",
    "else:\n",
    "    issue_dt = pd.Series(pd.NaT, index=df.index)\n",
    "\n",
    "if 'earliest_credit_line' in df.columns:\n",
    "    earliest_year = pd.to_numeric(df['earliest_credit_line'], errors='coerce')\n",
    "    reference_year = issue_dt.dt.year.fillna(pd.Timestamp.today().year)\n",
    "    df['credit_history_years'] = (reference_year - earliest_year).clip(lower=0)\n",
    "\n",
    "df = df.drop(columns=['issue_month', 'earliest_credit_line'], errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47e58665-3e3a-4a41-9bb7-aba5f4d7f4fc",
   "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>emp_length</th>\n",
       "      <th>state</th>\n",
       "      <th>homeownership</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>verified_income</th>\n",
       "      <th>debt_to_income</th>\n",
       "      <th>application_type</th>\n",
       "      <th>delinq_2y</th>\n",
       "      <th>months_since_last_delinq</th>\n",
       "      <th>inquiries_last_12m</th>\n",
       "      <th>total_credit_lines</th>\n",
       "      <th>open_credit_lines</th>\n",
       "      <th>total_credit_limit</th>\n",
       "      <th>total_credit_utilized</th>\n",
       "      <th>num_collections_last_12m</th>\n",
       "      <th>num_historical_failed_to_pay</th>\n",
       "      <th>months_since_90d_late</th>\n",
       "      <th>current_accounts_delinq</th>\n",
       "      <th>total_collection_amount_ever</th>\n",
       "      <th>current_installment_accounts</th>\n",
       "      <th>accounts_opened_24m</th>\n",
       "      <th>months_since_last_credit_inquiry</th>\n",
       "      <th>num_satisfactory_accounts</th>\n",
       "      <th>num_accounts_120d_past_due</th>\n",
       "      <th>num_accounts_30d_past_due</th>\n",
       "      <th>num_active_debit_accounts</th>\n",
       "      <th>total_debit_limit</th>\n",
       "      <th>num_total_cc_accounts</th>\n",
       "      <th>num_open_cc_accounts</th>\n",
       "      <th>num_cc_carrying_balance</th>\n",
       "      <th>num_mort_accounts</th>\n",
       "      <th>account_never_delinq_percent</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>public_record_bankrupt</th>\n",
       "      <th>loan_purpose</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>term</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>initial_listing_status</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>target</th>\n",
       "      <th>issue_year</th>\n",
       "      <th>issue_month_num</th>\n",
       "      <th>credit_history_years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10.0000</td>\n",
       "      <td>IL</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>210000.0000</td>\n",
       "      <td>Verified</td>\n",
       "      <td>9.5300</td>\n",
       "      <td>individual</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>322046</td>\n",
       "      <td>73705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17500</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>medical</td>\n",
       "      <td>5000</td>\n",
       "      <td>36</td>\n",
       "      <td>26.7700</td>\n",
       "      <td>203.5100</td>\n",
       "      <td>E</td>\n",
       "      <td>E5</td>\n",
       "      <td>fractional</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>CA</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>83000.0000</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>18.4400</td>\n",
       "      <td>individual</td>\n",
       "      <td>3</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>555154</td>\n",
       "      <td>39482</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8300</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>63.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>20000</td>\n",
       "      <td>60</td>\n",
       "      <td>15.0500</td>\n",
       "      <td>476.3300</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10.0000</td>\n",
       "      <td>CA</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>140000.0000</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>13.8200</td>\n",
       "      <td>individual</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>631258</td>\n",
       "      <td>69647</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>58100</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>15000</td>\n",
       "      <td>60</td>\n",
       "      <td>9.9300</td>\n",
       "      <td>318.1900</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>MD</td>\n",
       "      <td>OWN</td>\n",
       "      <td>70000.0000</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>individual</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>30400</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>small_business</td>\n",
       "      <td>2400</td>\n",
       "      <td>36</td>\n",
       "      <td>6.0800</td>\n",
       "      <td>73.1000</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>FL</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>44000.0000</td>\n",
       "      <td>Verified</td>\n",
       "      <td>24.7700</td>\n",
       "      <td>individual</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>56476</td>\n",
       "      <td>40706</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>167</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9500</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>7200</td>\n",
       "      <td>36</td>\n",
       "      <td>14.0800</td>\n",
       "      <td>246.3600</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>whole</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     emp_length state homeownership  annual_income  verified_income  \\\n",
       "18      10.0000    IL      MORTGAGE    210000.0000         Verified   \n",
       "19       1.0000    CA      MORTGAGE     83000.0000  Source Verified   \n",
       "34      10.0000    CA      MORTGAGE    140000.0000     Not Verified   \n",
       "35       1.0000    MD           OWN     70000.0000  Source Verified   \n",
       "107      2.0000    FL      MORTGAGE     44000.0000         Verified   \n",
       "\n",
       "     debt_to_income application_type  delinq_2y  months_since_last_delinq  \\\n",
       "18           9.5300       individual          0                       NaN   \n",
       "19          18.4400       individual          3                   14.0000   \n",
       "34          13.8200       individual          0                       NaN   \n",
       "35           0.0000       individual          0                   31.0000   \n",
       "107         24.7700       individual          0                       NaN   \n",
       "\n",
       "     inquiries_last_12m  total_credit_lines  open_credit_lines  \\\n",
       "18                    2                  18                  7   \n",
       "19                    1                  11                  6   \n",
       "34                    2                  21                 12   \n",
       "35                    0                  13                  3   \n",
       "107                   8                  16                  9   \n",
       "\n",
       "     total_credit_limit  total_credit_utilized  num_collections_last_12m  \\\n",
       "18               322046                  73705                         0   \n",
       "19               555154                  39482                         0   \n",
       "34               631258                  69647                         0   \n",
       "35                30400                     92                         0   \n",
       "107               56476                  40706                         0   \n",
       "\n",
       "     num_historical_failed_to_pay  months_since_90d_late  \\\n",
       "18                              0                    NaN   \n",
       "19                              0                22.0000   \n",
       "34                              0                    NaN   \n",
       "35                              0                    NaN   \n",
       "107                             0                    NaN   \n",
       "\n",
       "     current_accounts_delinq  total_collection_amount_ever  \\\n",
       "18                         0                             0   \n",
       "19                         0                             0   \n",
       "34                         0                             0   \n",
       "35                         0                             0   \n",
       "107                        0                           167   \n",
       "\n",
       "     current_installment_accounts  accounts_opened_24m  \\\n",
       "18                              4                    6   \n",
       "19                              1                    1   \n",
       "34                              2                    5   \n",
       "35                              0                    0   \n",
       "107                             3                    7   \n",
       "\n",
       "     months_since_last_credit_inquiry  num_satisfactory_accounts  \\\n",
       "18                             2.0000                          7   \n",
       "19                             8.0000                          6   \n",
       "34                             0.0000                         12   \n",
       "35                                NaN                          3   \n",
       "107                            3.0000                          9   \n",
       "\n",
       "     num_accounts_120d_past_due  num_accounts_30d_past_due  \\\n",
       "18                          NaN                          0   \n",
       "19                       0.0000                          0   \n",
       "34                       0.0000                          0   \n",
       "35                       0.0000                          0   \n",
       "107                      0.0000                          0   \n",
       "\n",
       "     num_active_debit_accounts  total_debit_limit  num_total_cc_accounts  \\\n",
       "18                           2              17500                      6   \n",
       "19                           3               8300                      7   \n",
       "34                           1              58100                     13   \n",
       "35                           0                  0                     11   \n",
       "107                          2               9500                      8   \n",
       "\n",
       "     num_open_cc_accounts  num_cc_carrying_balance  num_mort_accounts  \\\n",
       "18                      2                        2                  5   \n",
       "19                      4                        3                  2   \n",
       "34                      9                        3                  2   \n",
       "35                      3                        1                  0   \n",
       "107                     6                        5                  0   \n",
       "\n",
       "     account_never_delinq_percent  tax_liens  public_record_bankrupt  \\\n",
       "18                       100.0000          0                       0   \n",
       "19                        63.6000          0                       0   \n",
       "34                       100.0000          0                       0   \n",
       "35                        92.3000          0                       0   \n",
       "107                      100.0000          0                       0   \n",
       "\n",
       "           loan_purpose  loan_amount  term  interest_rate  installment grade  \\\n",
       "18              medical         5000    36        26.7700     203.5100     E   \n",
       "19   debt_consolidation        20000    60        15.0500     476.3300     C   \n",
       "34   debt_consolidation        15000    60         9.9300     318.1900     B   \n",
       "35       small_business         2400    36         6.0800      73.1000     A   \n",
       "107    home_improvement         7200    36        14.0800     246.3600     C   \n",
       "\n",
       "    sub_grade initial_listing_status disbursement_method  target  issue_year  \\\n",
       "18         E5             fractional                Cash       0        2018   \n",
       "19         C4                  whole                Cash       0        2018   \n",
       "34         B2                  whole                Cash       0        2018   \n",
       "35         A2                  whole                Cash       0        2018   \n",
       "107        C3                  whole                Cash       0        2018   \n",
       "\n",
       "     issue_month_num  credit_history_years  \n",
       "18                 2                    15  \n",
       "19                 1                    13  \n",
       "34                 1                    25  \n",
       "35                 1                    14  \n",
       "107                2                     7  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing few rows of data after fixing values of column\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cb6cbe1-7dc2-4833-a1f4-5d10db41cc42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded 9 categorical columns.\n"
     ]
    }
   ],
   "source": [
    "# Encode categorical features\n",
    "CATEGORICAL_COLS = [\n",
    "    'state', 'homeownership', 'verified_income', 'application_type',\n",
    "    'loan_purpose', 'grade', 'sub_grade',\n",
    "    'initial_listing_status', 'disbursement_method'\n",
    "]\n",
    "CATEGORICAL_COLS = [c for c in CATEGORICAL_COLS if c in df.columns]\n",
    "\n",
    "le_dict = {}\n",
    "for col in CATEGORICAL_COLS:\n",
    "    le = LabelEncoder()\n",
    "    df[col] = df[col].fillna('Unknown').astype(str)\n",
    "    df[col] = le.fit_transform(df[col])\n",
    "    le_dict[col] = le\n",
    "\n",
    "print(f'Encoded {len(CATEGORICAL_COLS)} categorical columns.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6fdcc38d-790b-46b5-a2c3-1f66611f4b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputed 46 numeric columns with median.\n",
      "Remaining missing values: 0\n"
     ]
    }
   ],
   "source": [
    "# Impute numeric missing values\n",
    "NUM_COLS = df.select_dtypes(include=[np.number]).columns.tolist()\n",
    "NUM_COLS = [c for c in NUM_COLS if c != 'target']\n",
    "\n",
    "# Use median imputation (as it is robust to outliers)\n",
    "for col in NUM_COLS:\n",
    "    median_val = df[col].median()\n",
    "    df[col] = df[col].fillna(median_val)\n",
    "\n",
    "print(f'Imputed {len(NUM_COLS)} numeric columns with median.')\n",
    "print(f'Remaining missing values: {df.isnull().sum().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f72c38ee-dfd9-42e2-a0b9-225e628807d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>5%</th>\n",
       "      <th>50%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>annual_income</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>84925.1308</td>\n",
       "      <td>109017.4491</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>14969.0400</td>\n",
       "      <td>25000.0000</td>\n",
       "      <td>68850.0000</td>\n",
       "      <td>180330.0000</td>\n",
       "      <td>294300.0000</td>\n",
       "      <td>2300000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_credit_utilized</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>53296.0251</td>\n",
       "      <td>62649.4481</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>64.0500</td>\n",
       "      <td>2385.0000</td>\n",
       "      <td>38960.0000</td>\n",
       "      <td>139357.2500</td>\n",
       "      <td>277100.3100</td>\n",
       "      <td>942456.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amount</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>16015.2778</td>\n",
       "      <td>10672.5505</td>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1800.0000</td>\n",
       "      <td>3000.0000</td>\n",
       "      <td>13000.0000</td>\n",
       "      <td>36071.2500</td>\n",
       "      <td>40000.0000</td>\n",
       "      <td>40000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_to_income</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>17.3511</td>\n",
       "      <td>12.9181</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1356</td>\n",
       "      <td>2.3585</td>\n",
       "      <td>16.1600</td>\n",
       "      <td>34.4630</td>\n",
       "      <td>47.2944</td>\n",
       "      <td>209.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>471.7385</td>\n",
       "      <td>307.9726</td>\n",
       "      <td>33.9800</td>\n",
       "      <td>60.0342</td>\n",
       "      <td>103.7505</td>\n",
       "      <td>379.1350</td>\n",
       "      <td>1087.6790</td>\n",
       "      <td>1288.1077</td>\n",
       "      <td>1503.8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_credit_limit</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>202185.4749</td>\n",
       "      <td>228669.5828</td>\n",
       "      <td>1700.0000</td>\n",
       "      <td>6585.0000</td>\n",
       "      <td>18185.0000</td>\n",
       "      <td>125372.5000</td>\n",
       "      <td>561054.7000</td>\n",
       "      <td>898734.8000</td>\n",
       "      <td>2761000.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean         std       min         1%  \\\n",
       "annual_income         558.0000  84925.1308 109017.4491    0.0000 14969.0400   \n",
       "total_credit_utilized 558.0000  53296.0251  62649.4481    0.0000    64.0500   \n",
       "loan_amount           558.0000  16015.2778  10672.5505 1000.0000  1800.0000   \n",
       "debt_to_income        558.0000     17.3511     12.9181    0.0000     0.1356   \n",
       "installment           558.0000    471.7385    307.9726   33.9800    60.0342   \n",
       "total_credit_limit    558.0000 202185.4749 228669.5828 1700.0000  6585.0000   \n",
       "\n",
       "                              5%         50%         95%         99%  \\\n",
       "annual_income         25000.0000  68850.0000 180330.0000 294300.0000   \n",
       "total_credit_utilized  2385.0000  38960.0000 139357.2500 277100.3100   \n",
       "loan_amount            3000.0000  13000.0000  36071.2500  40000.0000   \n",
       "debt_to_income            2.3585     16.1600     34.4630     47.2944   \n",
       "installment             103.7505    379.1350   1087.6790   1288.1077   \n",
       "total_credit_limit    18185.0000 125372.5000 561054.7000 898734.8000   \n",
       "\n",
       "                               max  \n",
       "annual_income         2300000.0000  \n",
       "total_credit_utilized  942456.0000  \n",
       "loan_amount             40000.0000  \n",
       "debt_to_income            209.1000  \n",
       "installment              1503.8900  \n",
       "total_credit_limit    2761000.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier check\n",
    "OUTLIER_COLS = [\n",
    "    'annual_income', 'total_credit_utilized', 'loan_amount',\n",
    "    'debt_to_income', 'installment', 'total_credit_limit'\n",
    "]\n",
    "OUTLIER_COLS = [c for c in OUTLIER_COLS if c in df.columns]\n",
    "\n",
    "display(df[OUTLIER_COLS].describe(percentiles=[0.01, 0.05, 0.95, 0.99]).T)\n",
    "\n",
    "for col in OUTLIER_COLS:\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f\"Boxplot of {col}\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa560a82-02a9-4e30-aa1a-e77187333f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier capping done.\n",
      "Boxplots after outlier capping:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier capping (Winsorization at 1st–99th percentile)\n",
    "OUTLIER_COLS = [\n",
    "    'annual_income', 'total_credit_utilized', 'loan_amount',\n",
    "    'debt_to_income', 'installment', 'total_credit_limit'\n",
    "]\n",
    "OUTLIER_COLS = [c for c in OUTLIER_COLS if c in df.columns]\n",
    "\n",
    "for col in OUTLIER_COLS:\n",
    "    lo, hi = df[col].quantile([0.01, 0.99])\n",
    "    df[col] = df[col].clip(lo, hi)\n",
    "\n",
    "print('Outlier capping done.')\n",
    "print('Boxplots after outlier capping:')\n",
    "\n",
    "for col in OUTLIER_COLS:\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f\"Boxplot of {col}\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4cb49b-bf95-4f62-bc81-9f829ca9af87",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f31f60a1-0517-49b7-bb07-101bdb673841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 780x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target Class Distribution \n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "counts = df['target'].value_counts()\n",
    "labels = ['Not Defaulted (0)', 'Defaulted (1)']\n",
    "\n",
    "plt.bar(labels, counts.values)\n",
    "\n",
    "plt.title('Target Class Distribution', fontsize=12)\n",
    "plt.ylabel('Count', fontsize=10)\n",
    "plt.xticks(fontsize=9)\n",
    "plt.yticks(fontsize=9)\n",
    "\n",
    "for i, v in enumerate(counts.values):\n",
    "    plt.text(\n",
    "        i,\n",
    "        v + counts.max() * 0.01,   # smaller offset above bars\n",
    "        f'{v:,}\\n({v/len(df)*100:.1f}%)',\n",
    "        ha='center',\n",
    "        fontsize=9,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "plt.ylim(0, counts.max() * 1.12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d32d0862-66df-4623-b212-049525cfa7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KlWXJkiUybNgwGTBggBnHr2n9mzdvNhfxniZOnGgCDdOmTTPH0uDIF198YWZD8AwG5MiRw7wPo0ePNhf/8+fPN4EKXa7vc7NmzaLtfQEAILYl0PkFY/2oAAAAAADAa6gZAAAAAACAzRAMAAAAAADAZggGAAAAAABgMwQDAAAAAACwGYIBAAAAAADYDMEAAAAAAABshmAAAAAAAAA2QzAAAAAAAACbIRgAAAAAAIDNEAwAAAAAAMBmCAYAAAAAAGAzBAMAAAAAALAZggEAAAAAANgMwQAAAAAAAGyGYAAAAAAAADZDMAAAAAAAALGX/w/hTvDTG9ENwAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1040x585 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Default Rate by Grade \n",
    "grade_labels = le_dict['grade'].classes_\n",
    "\n",
    "grade_dr = (\n",
    "    df.groupby('grade')['target']\n",
    "      .mean()\n",
    "      .reset_index(name='default_rate')\n",
    ")\n",
    "\n",
    "grade_dr['grade_label'] = grade_labels[grade_dr['grade']]\n",
    "grade_dr = grade_dr.sort_values('grade_label')\n",
    "\n",
    "plt.figure(figsize=(8, 4.5))\n",
    "\n",
    "bars = plt.bar(\n",
    "    grade_dr['grade_label'],\n",
    "    grade_dr['default_rate'] * 100\n",
    ")\n",
    "\n",
    "plt.title('Default Rate by Loan Grade', fontsize=12)\n",
    "plt.xlabel('Loan Grade', fontsize=10)\n",
    "plt.ylabel('Default Rate (%)', fontsize=10)\n",
    "plt.xticks(fontsize=9)\n",
    "plt.yticks(fontsize=9)\n",
    "\n",
    "for bar, rate in zip(bars, grade_dr['default_rate']):\n",
    "    plt.text(\n",
    "        bar.get_x() + bar.get_width() / 2,\n",
    "        bar.get_height() + 0.3,\n",
    "        f'{rate * 100:.1f}%',\n",
    "        ha='center',\n",
    "        fontsize=8,\n",
    "        fontweight='bold'\n",
    "    )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "119c6e70-81e7-4506-b3cd-0e9828b44add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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atRHrLFmyJGpft956a9znBORGZAYAPhQrhS+858abKqdeB/XOeXufvOmHsXpEMkKpiuoBUOaCioSplypYdCtWlfNE9AZkJ/WGBVM9s3oWgfSKVSTLO4xEVAwqM7wpnCoE6fXXX39F9AQFe7Fj3VTxPrPUG6VeHW/PUnqpRzCcetFUSFCFtNTTrZ7rYDaNimqlRTMahKdKq11q3+mnn27ZQb3++gwPGTIkZrHEID0vZVeoAJx6ylS8LHgMUO9/Ij+XsYpbKgPDe+xR2rT3GKV2Hi7ve1/HH28vvjItlOkSpN5zFZ0L79VW5lK8IQLetucUbw+nCgd6j0vqlfX2Fis7Ivz5x6LpO8PFKuAWvo72481mCD8uKIvBWyxO3xXe94UyNTLSK6+Cmt7PtT673varB1xFboO8GRPBfeh4euDAgdD7VgX0ZPXq1e77Td+93u9J77ZiUaZW+GdUw/1UmDGrX69YNKQp3jFKWQ7h7Rw/frwbWqLMDw0h0pC04LAxZU/FykY63O+QzFIGlLfYo94fderUiVgWr7AkkFsxtSDgQ7HS63Vykdr96aE0w2CF7Yx69913XRpk+NjLtKSnYnQyCQ4RyEgw4H//+1/M4QaidMZ77733sNqkMZ5esU4QVc09M7wnbroA9QofjqILydRSnG+++eYMzf4QThfsStlW2q8CTRmlCvOqNK2pOIPvPw1h0S2ocOHCLrX8wQcfTPOi3js0Q2Opa9asadlFw4EUIPn999/jvo/0PBQsyIjD+VweztAfXWwdDlU7180rVi0F7+dGQxvCL5r1PlFgQbMxKOU4SBdwiQoEagiWLq5i0efo3HPPTfXxGzdujDkbSno++/o/6cIvHu9nWJ+LcEqz96aJe48N4ceFRL0vvM853nEt1nI9NngB7r2A16w6CgKEX0ifd9557ns1OA5f9+nzHX7hqgvic845J83nEOsCODter1hS+7/ru0N1NDTUJHiM05An3cIDP40bN3afGX2/pUa1U7xDDdP6DsmseO8Fff7Dg1+xvjOB3IzMAMCHdDHkFd47klovYVoycjEftHXrVjdNUEYfm1aRolgFog6nZ/lwqK0KeATrL6T34lonQ+pVi3XLDXPCe3uvdCKY1TQGXeM/NcZUvT3ek3b1smWGerF0Qv/UU0+5XnKNi/bSe1hBmkaNGsWcdjM1mlYzPQW8Mkrji/X+U6BO0yKGB/60XL124cULgzRmPKOBgOA2Myu7jz2JokBR+OdRtTZUJ8BbcFLHufTWk0iL3n/xjg3eWg7ZzXuh7y2WmNE54JPtfaGx9sqkCA+s6f8d3uuvYIBuQTp2eDMTdJyIdXGb1nE0rWNpVr5e3otzL9WKUOBbQVF9f8X6XtbrpKwiHUsT+bwBZByZAYDP6MJbvVbeE4fwQlneNGqlzqW3aE74hUZ6qWq6t0K1ThQ0Y4BOajWPu4p7xeodCee9OIt1UvPjjz9aTtCFYbBXKllmERAVffJSOr7msg4Xr3p0stJ7QSfsOilVT5YKmYUXZdSwh2DxyowGBNSjpZt6ApX+q9dGPUcqjBXsVdP7VcUEw7NBvNRzq/djcMYODSFRZXx91k455RRLNKU/q1ihhvyEX6QEq7frOYSfaHvT23VBp3nZtQ2dpOtvtVXzfydKrCEcKkpWtmzZLDn2hFOP7fbt293rE07ZJGl9bjR0QoU2Bw8eHFqm46y35759+/aWLPRah88qoSCa3gveC0m9xzN6QZhosd4Xmr0iPVkW4UN2tJ3wY1m841qs5+xtg4azhReSVUAtWEhQgSF9hsPfkwoGeGfnSc8QgZx8vWJJz8W4Zt3QTZkBKnSp11nvNR17w2fb0NAFHXuSYWaNeO8F7+c/1ncmkJvl/KcPQLbZvHmzG+/nHV+nVOHwVNimTZtG3K80Yo0/jNcLpZtS57TdzETtY6U0aqyhqqlXr17dbV9jNtPiTfHVib03pU9VyRMhVu9eatNzZWaIQHaIdUH8wgsvRC1TZersoP+1Lkji3TI6REAX70q/9+rTp0+mAmnh6ah6D+iE//LLL3fb0wwbaU1pGE6fOU01GT6sRpkrGmsc62IkURSEaN26dcSy4MwLqX0uldGik3eNo1Xb9b9Kz/R/GfmsxJpibfr06akee4KvdWaGfnh5A6W6mHn++eejLvy944hFGScKXAbpvRr+HlA2SXhtgZzmfa31/aBq6d5e3PBK+sEU/1jPPysp+OAdvqRMKwWL470vFNRR5f3w3mXvc1Z1fu97WIHp8JoPosCit5faOyuPqutrqJwEh2goGBAMYusz5s3KizWzTzK9XhmlAHz4lJC6yNeQER3TNHzA+15SHYb0TF+cHVS/Rec63veHtz5GZoLIQDIjGADkUeHTq+lLTlMK6aTEW/xGJwfeqZZ0sertWVBvtqYkU3qfAgP6gtSJo+Zs1gWR0h3TKiqVkR49pS6rIJi2qYu59EyjF+sEVVPIqc1Kv1YPRKLmiY/VZqU86vUOFrtToTFvMEAnRtl9Ip0aTbOni5Rw6hHWhY3eK3rd1IsePuYzt7nooovcBWw4nZTrxDcj9N7RSbYKwKmHKzj9pcaF67OgzIBwsVJkvVRXQNNl6QIzSNkF6jH0npgmkoIX3h5gXeiH1zHwvsf13tZYYF3g6r2hwl7pCa7pIsPb86fgkj7bwc9KsM6AjkfeKTfV296uXTs3bEGvtdrx0UcfucwLZThoyE2iPtd9+/Z1r4PS/FVIVYEeTacX7vrrr49Z+EzHzPCCit7pOJOlcGCQCiOGv+9EWR76n+p/o8+8psnUax5Ox4OcSNX2Du9RVooCxirEqboMyrJRJoY+h/q+UjaJvrPCKdvM+75XVpw+zxpWpMCT3lMKJIeLNZe9t1c/vB5BeOZN8HcFM8MD8To+aDrdZH69Mkq96NqOtqfpSPUZ0vFC5wz6XZlFXokI4iWCPq96faZOnereC6+++qr7/MeqWwPkKTk9nQGAw5fR6dXCp9v59ttvY25z5syZbtqxjGzPO9VceqcW1HRiBQsWTHXbZcuWjVrm3ZamAatcuXKq29G0SImYWlD78k6FFW/6tO+++y60rEePHnH/j7GmYPNOB5ZWOzM6taB89tlnoSmw4t2OOuqohLxuktb/MSum1Zw2bVrUepouMSPtHzlyZIY+D4888kiqUwuG/2/Vvnz58kXcr+nINm7cmOHXxNsO7TeWK6+8MmpdTSua2rSB6flcxpo2MNbUcvHeS5reK9Z2U7ul9jmJxzv9mPf1j3U7+uijo6YfC6dp2WI9rkiRIjGnm8wI77E03tSpGTmWjB49OkOv86mnnhr4+++/U30dY33uvZ/TWG1P7fMRnJq1adOmGWpvrP3ce++9GdpG8+bN405bGe/7ZtmyZaF1Jk2aFHMdTSsaS3qnrfW+H7Lq9Urts+q1YsWKDO1P7cvolICx3tveNmVmasHChQun2V4dM4G8hswAwIeUtquedvX6KvU3FhVeU49leJGk1KiHKbNjdtXbOmLEiLhFj9SzqLakRb2PGl4Qb7oiPW/1ACVCcPx0eiTrEIEgVbNWenS8cZtKG3355ZejlqdnWqhkoeEx3ve6ej6DY3wTTWP/ld2SXuoNVwpt+GdAQwXU+6jhCVkh1lCJ8OwA9e6pFzweZbiMHj06XfvSrATpFaxR4p3yLR69ZokomKdt6DgU73OgHkxlgHinHwunLJtYacQalpGe6Sazm3rKx44dm64iduo11WcmM7PFJIKGYOh7QLPOpJemqPV67LHH7JFHHklXdoOmDtXxO1ax0Hhj/vX61K1bN2aWQFqPTcbXK6toaIK+r5OF6sDEygQI0uc61hA6ILcjGADkYTqB0QmsLrbr16/vTkiVyr5u3To3Djatis4KCKiojsZC6ktSJwo6adR2dYGobSr1VeMrNTb/0ksvzXRbVUl99uzZbp9qly7cK1Wq5FJSlbKqfaWHxiYq/VsX3SripBMitVvBD6X7qpp3omjudc3TrvGhGkcbL5gRDAYosKIplZKRCptpyjylOSuoowt9DWlQeqxSJmMVpNKc87mF/jexLn779++f7m0oPXTGjBnuwlYn8hpioUCVLir0OVPRRV08K81UKe3eFOy0KEXbO42kUmw1T3ci5tH20sW2d7YF7U9pxMHXTJ9tBYo0zELTw+l5atz7wIED3VCBWFPxxaLAggqF6rkEiw+mRrVC9H7UOGdV6tffupjWa62LLQ1N0oWaUpE1zEBBjES45557XMFCDS9SoTAdhxQkUEq9jh/puYDTRYVXsg0RCKfCh0rvHjBggDuW6fOvY7zS2DXuXK+/hpqpQF5OF0/Te0Dp2xrnrzHoGmaj92Dwu07HLKX96/2gdcKr+wfpfd2vXz83xEcBO6Xq63OsbQTfW5omUt8jGn6SWhAn1vvh7LPPjnh/6/snWNsiO+oFJPr1yggNRdQ2FHBRgFMBWH3vaX86Huq10LAtfW413CfWNLY5RZ91HW90bqT/ob7Tdb6jwI6OywpQZnQWDCA3SFF6QE43AgDyKlVSDp4I6qRbvXC5kS5wgnNHi4IsKnaXU72EQLLSRVV48FIXl+kpgAog++h7Wd/PQQoQKWME8BumFgSALKR53ZN5iECQUknVA3LZZZe53p1gz5YqyiszxFsoTj2nBAKASDt37owaEqFUfAAAkhHBAADIQqqsrB4HpaZmR1poZillVlPLKW1WQwSUDrl//37bsWNH1LpK9Xz88cdzpJ1AMlK6s6ad1EwQBw4cCC1XfQENMQAAIBlRMwAAspDGNSv1UAGBWPOtJyMFATTneKxAgMZSzps3z9WhAPB/VFtFKcfhgQCNN1ZNkfQU5wOQdTR9sqY/Vr0EBbpVNHfv3r1x11cNJNXHUBBfNUnSa9WqVa4Wg7d+kI4NKsCp+zSEyDsNs2ooeOu3ANmFYAAAwFVR1+wIqnytolIqHqa6ACqsVa9ePVfvQAUeVRyqSpUqOd1cICmpwKEKn2rYjWoHqCAfgJyl4pfK0Jk7d64tXrzYzfqxZcuWmOtqNpUbb7zRFZbMiD179rjgf6wMwJ49e7qCxgoCKCgRXlBUwYYhQ4a4Qq1ATqCAIAAAAADfUGFP9cY/8cQTUTPMaKaU++67z13Y//rrrzFng/DSjEUaYqcpCPV7+OVVrVq13H40k4JmbFF2wO7du0PT0KoGjwLuQE4gMwAAAACALxw8eND++ecfl7YfTpkD48ePt5deeinuVMGxvPzyy7Z06VKXYRdLnTp17IMPPnBZB5piVVM8yoQJE9wyAgHISQQDAAAAAPjCoEGDXDAg/CJcdXJuuukme/HFF61UqVLp3pZ6+jUM4I033ohbH2T48OGhDINZs2bZ888/72bqUS0hBQQGDx5sJ598shuaoGEMQHZiNgEAAAAAed6zzz7rZsN57733rEKFCqHlqhPQrl07a9GiRYaK7SrFX8GFU089Ne565cqVs3fffTdi2eWXX269e/d2wQRlFigI8MUXX9g111zjCpLmloLDyP2oGQAAAAAgT1MPvWb3USDAW+hPwwJUADRIl0dK4dcyBQo0dMBLxf9UcDfe4x566CFXg8BLs4yMHTvW5s+f77IKtK5mPBAVGtRwhdq1ayf42QOxkRmQDkol+u6771zUr1ixYjndHAAAAADp9PDDD9tTTz3l0vRjzfKxYsWKiL9VA0AzEGiMv4oNxqIpdr2PUwZAnz59bPny5TGHG2g4gmbu0QwHCkAocKBbMJDw33//hf4GsgM1A9JBgYDGjRu7nwAA+H2ObhXDCvf1119b06ZNrXTp0q6i9gknnGBdunSxv/76K9Vtq6K2UmU1XWWhQoVcb9iUKVMi1tFJc82aNV0w/sorr7QdO3ZE3K+02sceeyyBzxhAXnLPPfe445jS8atXr26bNm1yt+3bt4fWUYdf+E09/nLSSSdFDCeoUaOGjRo1yv2u6Xe9j1OAILi9WMEAHRfvvvtu1w7RdL6TJ092U5E+88wz7vip+gFAdiEzAAAApDlHd4MGDVyBLM2Hfdlll9mnn35qZ599tltHJ7C33HKLq5KtoMGPP/7oTnp///13e//99+NuWwW8FixY4FJmq1at6nrhbrjhBjv66KPtwgsvdD1k119/vfXq1ctatWpl7du3t0cffTSUUqvAwdq1a+3NN9/MttcDQO7y9NNPu59XXXVVxPImTZrYvHnzMrQtHdu2bduWqXboeLVu3bqI45XapFoBmmLwuOOOs1dffdUFRoHsQs2AdFi4cKHLDNAJi3pHAADws3hzdHtPwJUuq6F2sezbt8/19r/wwguuineQLvrV+69gw9atW13v2t69e90J8pgxY2z69Ok2Y8YM16tXt25dF0BIrXgXAACIjWECAADgsOfoDqfeL/WCNWvWLO46//77r9uWtxdM2QeLFi1y96uYlipxa5yv/v74449Dc3R369bNbr/9dgIBAABkEsEAAABwWHN0BymLThfzqhlQokQJN/d2PMoK0DCDIUOGuDm4NSRAF/0qwHXgwAGXiqsCWxpPq5oA1apVc49RjQFlBqiOT9euXd3wBNUcUEaBinMBAIA8Egz46aefXAXQs846y0qWLOlOBJQWqDGDKjwUTtOF6MQh1k3TiQAAgMOfo3vq1KkRRbWCNBb2q6++cvdrbG3nzp1T3Z6m2FLQQPUCNK+2agOot1+OOOKIUIBBmQKqDaDpvZRNoCDA888/b0OHDnUZAz///LMruqVsAQAAkEcKCOrLfvTo0XbFFVe4eT5VuVPzb2oc4ltvveVOENQLEW7EiBEutTDcGWeckc0tBwAg783RrYKA3jm6gypWrOh+qvq/AviqlK1ptFSBO5ZKlSq51P89e/a4OgEaEnDfffe5AoJ6fCyal7tNmzZWr149FzgYOHCgm6e7Xbt2riAYAADII8EATRmkE4nixYuHlnXq1MlNyaHsgIkTJ7oegnCaeqhy5co50FoAAPw3R3cswbmyVSgwLYULF3Y3DQ9QrQF9jwczA8LNnj3bdQJoGq7gPpQZIHos83MDAJCHhgnUr18/IhAQ1Lp1a/dTYwZj2blzp/33339Z3j4AAPw+R7emG9TQgB9++MGN/9e4/44dO7pif6eddppbZ8OGDS5D4J133gk9bs6cOW52gDVr1rjZAzS9lmYOGDx4cFQ7du3a5ToD1AmgqQxFmQea81tDEp588sl0ByoA5D3KMFJ20ueff255gYZE6Zj53nvv5XRTkIclfTAgnvXr17ufpUuXjrpPJx4KIKhCscYaqicDAABknKYIVO++5sMuW7Zs6Hb11VeH1lEvvtL1GzRo4Kr7K9VfvfsaAhDs4VcPvi7a//7779DjVIiwR48ebliBtle+fHk3na9+eqlwoLZ55plnhpb169fPDRVUx4FqCjzzzDNZ/noASE6qZ1K7dm0755xzsmwfCoxqmvFjjz3WjjnmGLevDz74IGbNs5YtW7qMp+OOO84VXFVA0xto1fFM66i+mZeGP/Xv398NjVJgAMgKKYFAIGC5jD4Qiv4vXbrUZQaoaJAohfH77793AQB9SHXSoWV//PGHqz2gisNp0XRIwUBD0IoVK1wPx4IFC9wBAAAAAEByUMBSWQETJkxwM4tklYsvvtgFLhX4VCBSWVEKmCqzSbOjiC76FeBUYFRZTn/99ZfddtttLmipYVBBd911l5slRdceTzzxhMW6JNPwJ9VS0fNSMBRItFwZDNCHR2mB+oCpnkBq/vzzT/dh1EFCH7aiRYumur6KIykKFwvBAAAAACC5TJs2za6//no3TFgzk4h623Whrs5D3a+s4S5duqR57ZBRp5xyigsS6IJeFCDQtYqGU6lzUjQd6mWXXeZmPlEAINyLL75ot956a8xggNx8881uBrXwQALg22ECffv2dYEApduk58Os1ByNMVSVYl3Mp6V9+/ZuvfDb2LFjE9R6AAAAAImknnlNPR4MBASp2LhqiyxfvtwNX3rwwQddrZIgXSOoozC1W6waJuHZyhruVKRIkdCyL774wqX/BwMBcuGFF7ohU5mpZ6BtaSY1wJezCXh77QcNGuSiZ88991y6HxecWWDbtm1prqsUo+DUSAAAAACSmwqXxqo1olnJOnTo4H7v1q2bjRw50s1K0rx5c7dswIAB1qtXr1S3XaJEibj36bpEwQB1UgZpeHKZMmUi1tPU6NqO7suoChUquIKtqrcSq6g64ItgQDB9X6kyGjcTq9BGPErJiVdsEAAAAEDupVlIjj/++KjlyhYIp/H3mzdvDv1dqlQpd8uMZ5991hUtVLV/XbBnFRVEDz5HggHw5TABRe0UCGjbtq0rBBhr7mFNIxheoThIdQLGjBnjhguosCAAAACAvKNkyZIR052G98iHU2fioUOHDnuYwPDhw92wg/fffz+UZRCk2VZULyCcZlNR+3RfRulxareuZQDfZQaMHj3aTR10wgknWIsWLey1116LuF+9/ZqXWJU7TzzxRFdpUxU8g7MJKItA973++uuu6icAAEj/vN2asUffoVk5XVciaCzuDTfc4L77NVUXAP+oV6+em0EsozIzTODhhx92+9LU5ZrdzEuzCrzxxhuuXpmmHxRNs6ogRGaOo5rVrE6dOlGBDcAXwQBVAJXff//dDRHwatKkiQsG6EL/f//7ny1evNhVDFUAQOlCCiAoctewYcMcaD0AALlXdszbrSrZAwcOtLfeess2btxo1atXd50AGusbpKzAV155xZ0U79+/30466STr0aOH3XjjjaF11EbNHqQeO52sA/APVfPv2bOnqx2gzsH0yugwgXvuuccVFleAVMeqYAaAChcGgwZt2rRxxzT9VFaBggJdu3Z11ynhMwmsXr3aXa/oGkdU5FDUARoegJg3b55deuml6W4jkOenFsxuCxcudEMMmFoQAOAX2TVvty7ogzP3VK1a1WbOnOku9DUVlypwy0033eTm6FaPmzL/3nnnHbv//vvdCXnr1q1D23r77betc+fOboigt6o4gLzt/PPPd52ECiaKUutfeOEFu+WWW0LrNG3a1BUW13R+mRGvZpn2q4v2IGUoaXpBZSxpzL8CASNGjIiY4lxt0SwIXuFt/uWXX1x2ln5WqlQpU20GUkMwIB0IBgAA/CY75u1WwKFYsWLu5FcX/EEKPqg3LdaJctAll1zisgKnTp0asT21Scs0pzcA/9CUfldffbW7cA6/6M7NVNNAl2pMcw5fFxAEAAB5b95uFdXSPN3BatlBushftGiRuz8eBQvC5/YWbUdja5mTG/AfZQ5pqr81a9ZYXqBjo7KzdMwFsgqZAelAZgAAwG9UkDdfvnwRPe/KDLjzzjtdcd8gjZvV+P4hQ4a4v7ds2eKyCVKj8bDBMbEqwKVChVOmTHFpsB9++KHr3VMvv2oIxKq+/dJLL7m5wxUwUOGwcHqsTm00lAAAAOTiAoIAACDvzts9adIku/322129AE0drPGx+nvUqFExpxJ+9913XfbBxIkTowIBweyAWFOMAdmlcu8ZOd0EAAny29C8XbyRYQIAACDH5u1WNoCm3QpW1V65cqUbJnD00Ue7NoTTdF2qY6BAQNu2bWO2W232Pg4AAEQjMwAAAOTovN1SuHBhdztw4IAbMqBhCuGZAePHj3fVuTXF4LXXXht325p+ULUMAABA6ggGAACAHJu3W8UHNSThlFNOcVMCPvzww+7v8OwBTcl177332rPPPutqDATn9lZNg/AsgJ9//tnVGdBMAwAAIHUMEwAAAFFq1qxpzZo1s5dffjlL9/PPP/9Yjx493P5U/K98+fKucK9+Bmk6Q1XW7tixoysoGLw1aNAgYlvKGmjevLkraggAAFLHbALpwGwCAAA/yk3zdiuoUK1aNTeLgL6zgZxCAUEg7/iNAoIAAMCPctO83Wqj5uMmEAAAQPpQMwAAAMTVoUMHyw3q1KnjbgAAIH0IBgAAkgoptkDekddTbAEgN2OYAAAAAAAAPkMwAAAAAAAAnyEYAAAAAACAzxAMAAAAAADAZwgGAAAAAAD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      "text/plain": [
       "<Figure size 1040x585 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Default rate by homeownership\n",
    "if 'homeownership' in df.columns:\n",
    "    own_labels = le_dict['homeownership'].classes_\n",
    "    own_dr = (\n",
    "        df.groupby('homeownership')['target']\n",
    "          .agg(default_rate='mean', count='count')\n",
    "          .reset_index()\n",
    "    )\n",
    "    own_dr['label'] = own_labels[own_dr['homeownership'].astype(int)]\n",
    "\n",
    "    plt.figure(figsize=(8, 4.5))\n",
    "    bars = plt.bar(own_dr['label'], own_dr['default_rate'] * 100)\n",
    "    plt.title('Default/High-Risk Rate by Homeownership')\n",
    "    plt.xlabel('Homeownership')\n",
    "    plt.ylabel('Rate (%)')\n",
    "    for bar, rate, count in zip(bars, own_dr['default_rate'], own_dr['count']):\n",
    "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.2,\n",
    "                 f'{rate*100:.1f}%\\n(n={count:,})', ha='center', fontsize=8)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b549a9a-941c-42e3-827c-606c9bf0a64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1820x1300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Key feature distributions\n",
    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
    "\n",
    "def plot_hist_by_target(ax, col, xlabel, title):\n",
    "    if col not in df.columns:\n",
    "        ax.set_visible(False)\n",
    "        return\n",
    "    for t, color, label in [(0, PALETTE['paid'], 'Fully Paid'),\n",
    "                             (1, PALETTE['default'], 'Late / Charged Off')]:\n",
    "        subset = df.loc[df['target'] == t, col].dropna()\n",
    "        ax.hist(subset, bins=35, alpha=0.6, color=color, label=label, density=True)\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel('Density')\n",
    "    ax.set_title(title)\n",
    "    ax.legend()\n",
    "\n",
    "plot_hist_by_target(axes[0, 0], 'loan_amount', 'Loan Amount ($)', 'Loan Amount Distribution')\n",
    "plot_hist_by_target(axes[0, 1], 'interest_rate', 'Interest Rate (%)', 'Interest Rate Distribution')\n",
    "plot_hist_by_target(axes[1, 0], 'annual_income', 'Annual Income ($)', 'Annual Income Distribution')\n",
    "plot_hist_by_target(axes[1, 1], 'debt_to_income', 'Debt-to-Income Ratio', 'DTI Distribution')\n",
    "\n",
    "plt.suptitle('Key Feature Distributions by Outcome', fontsize=15, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4b594471-bf0b-4c61-bcfd-2d659b5119a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1560x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation heatmap\n",
    "corr_cols = [\n",
    "    'loan_amount', 'interest_rate', 'annual_income', 'debt_to_income',\n",
    "    'total_credit_utilized', 'total_credit_limit', 'open_credit_lines',\n",
    "    'total_credit_lines', 'delinq_2y', 'inquiries_last_12m',\n",
    "    'public_record_bankrupt', 'installment', 'target'\n",
    "]\n",
    "corr_cols = [c for c in corr_cols if c in df.columns]\n",
    "corr_matrix = df[corr_cols].corr()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
    "sns.heatmap(corr_matrix, mask=mask, annot=True, fmt='.2f',\n",
    "            cmap='Reds', center=0, vmin=-1, vmax=1,\n",
    "            linewidths=0.5, ax=ax, annot_kws={'size': 8})\n",
    "ax.set_title('Feature Correlation Heatmap')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f29dd3b-3f53-4904-90a3-97fe3c4afe16",
   "metadata": {},
   "source": [
    "# Preprocessing Pipeline Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75d2598d-7448-40ba-82f0-9ae9aba210cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature engineering complete.\n",
      "Total features (excluding target): 53\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income_to_loan_ratio</th>\n",
       "      <th>interest_burden</th>\n",
       "      <th>loan_to_income</th>\n",
       "      <th>credit_utilization_ratio</th>\n",
       "      <th>open_credit_ratio</th>\n",
       "      <th>total_delinquency</th>\n",
       "      <th>high_interest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "      <td>558.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.5793</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>0.2367</td>\n",
       "      <td>0.3831</td>\n",
       "      <td>0.4852</td>\n",
       "      <td>0.5072</td>\n",
       "      <td>0.2419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.7272</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.1748</td>\n",
       "      <td>0.2780</td>\n",
       "      <td>0.1645</td>\n",
       "      <td>0.8595</td>\n",
       "      <td>0.4286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.7916</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.1111</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.1291</td>\n",
       "      <td>0.0408</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.2026</td>\n",
       "      <td>0.0680</td>\n",
       "      <td>0.1922</td>\n",
       "      <td>0.3104</td>\n",
       "      <td>0.4737</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.9279</td>\n",
       "      <td>0.1116</td>\n",
       "      <td>0.3195</td>\n",
       "      <td>0.6216</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84.9575</td>\n",
       "      <td>0.4106</td>\n",
       "      <td>1.2631</td>\n",
       "      <td>1.1378</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       income_to_loan_ratio  interest_burden  loan_to_income  \\\n",
       "count              558.0000         558.0000        558.0000   \n",
       "mean                 7.5793           0.0845          0.2367   \n",
       "std                  7.7272           0.0610          0.1748   \n",
       "min                  0.7916           0.0049          0.0118   \n",
       "25%                  3.1291           0.0408          0.1120   \n",
       "50%                  5.2026           0.0680          0.1922   \n",
       "75%                  8.9279           0.1116          0.3195   \n",
       "max                 84.9575           0.4106          1.2631   \n",
       "\n",
       "       credit_utilization_ratio  open_credit_ratio  total_delinquency  \\\n",
       "count                  558.0000           558.0000           558.0000   \n",
       "mean                     0.3831             0.4852             0.5072   \n",
       "std                      0.2780             0.1645             0.8595   \n",
       "min                      0.0002             0.1111             0.0000   \n",
       "25%                      0.1429             0.3636             0.0000   \n",
       "50%                      0.3104             0.4737             0.0000   \n",
       "75%                      0.6216             0.6000             1.0000   \n",
       "max                      1.1378             0.9167             4.0000   \n",
       "\n",
       "       high_interest  \n",
       "count       558.0000  \n",
       "mean          0.2419  \n",
       "std           0.4286  \n",
       "min           0.0000  \n",
       "25%           0.0000  \n",
       "50%           0.0000  \n",
       "75%           0.0000  \n",
       "max           1.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Derived features\n",
    "df['income_to_loan_ratio'] = df['annual_income'] / (df['loan_amount'] + 1)\n",
    "df['interest_burden'] = (df['installment'] * 12) / (df['annual_income'] + 1)\n",
    "df['loan_to_income'] = df['loan_amount'] / (df['annual_income'] + 1)\n",
    "df['credit_utilization_ratio'] = (\n",
    "    df['total_credit_utilized'] / (df['total_credit_limit'] + 1)\n",
    ")\n",
    "df['open_credit_ratio'] = (\n",
    "    df['open_credit_lines'] / (df['total_credit_lines'] + 1)\n",
    ")\n",
    "df['total_delinquency'] = (\n",
    "    df['delinq_2y'].fillna(0)\n",
    "    + df['num_historical_failed_to_pay'].fillna(0)\n",
    "    + df['public_record_bankrupt'].fillna(0)\n",
    ")\n",
    "df['high_interest'] = (\n",
    "    df['interest_rate'] > df['interest_rate'].quantile(0.75)\n",
    ").astype(int)\n",
    "\n",
    "print('Feature engineering complete.')\n",
    "print(f'Total features (excluding target): {df.shape[1] - 1}')\n",
    "\n",
    "display(df[[\n",
    "    'income_to_loan_ratio', 'interest_burden', 'loan_to_income',\n",
    "    'credit_utilization_ratio', 'open_credit_ratio',\n",
    "    'total_delinquency', 'high_interest'\n",
    "]].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef532659-1bfc-424f-819d-0e435cc39a05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set: 446 rows, 53 features\n",
      "Test set    : 112 rows, 53 features\n",
      "Train default rate: 0.1996\n",
      "Test default rate : 0.1964\n"
     ]
    }
   ],
   "source": [
    "# Train/Test Split \n",
    "RANDOM_STATE = 42\n",
    "CV_FOLDS = 5\n",
    "FEATURE_COLS = [c for c in df.columns if c != 'target']\n",
    "X = df[FEATURE_COLS].copy()\n",
    "y = df['target'].copy()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.20,\n",
    "    random_state=RANDOM_STATE,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(f\"Training set: {X_train.shape[0]:,} rows, {X_train.shape[1]} features\")\n",
    "print(f\"Test set    : {X_test.shape[0]:,} rows, {X_test.shape[1]} features\")\n",
    "print(f\"Train default rate: {y_train.mean():.4f}\")\n",
    "print(f\"Test default rate : {y_test.mean():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b3da0b8a-afcd-44fd-88ac-dd81b2907404",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling for Logistic Regression only \n",
    "# Logistic Regression is sensitive to feature scale.\n",
    "# XGBoost is tree-based and does not require scaling.\n",
    "\n",
    "scaler = RobustScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9688b4d7-3578-4afe-bf86-c03f610e8a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function create to evaluate model\n",
    "def evaluate_classifier(name, model, X_eval, y_eval, plot=True):\n",
    "    y_pred = model.predict(X_eval)\n",
    "    y_proba = model.predict_proba(X_eval)[:, 1]\n",
    "\n",
    "    result = {\n",
    "        'Model': name,\n",
    "        'Accuracy': accuracy_score(y_eval, y_pred),\n",
    "        'Precision': precision_score(y_eval, y_pred, zero_division=0),\n",
    "        'Recall': recall_score(y_eval, y_pred, zero_division=0),\n",
    "        'F1-Score': f1_score(y_eval, y_pred, zero_division=0),\n",
    "        'ROC-AUC': roc_auc_score(y_eval, y_proba),\n",
    "        'y_pred': y_pred,\n",
    "        'y_proba': y_proba\n",
    "    }\n",
    "\n",
    "    print(f\"===== {name} =====\")\n",
    "    print(classification_report(y_eval, y_pred, target_names=['Not Default', 'Default']))\n",
    "    print(f\"ROC-AUC: {result['ROC-AUC']:.4f}\")\n",
    "\n",
    "    if plot:\n",
    "        ConfusionMatrixDisplay.from_predictions(\n",
    "            y_eval,\n",
    "            y_pred,\n",
    "            display_labels=['Not Default', 'Default'],\n",
    "            cmap='Blues',\n",
    "            values_format='d'\n",
    "        )\n",
    "        plt.title(f'Confusion Matrix: {name}')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        RocCurveDisplay.from_predictions(y_eval, y_proba)\n",
    "        plt.title(f'ROC Curve: {name}')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8857ccfa-845e-442f-a1ac-ace8c62fe47e",
   "metadata": {},
   "source": [
    "# Baseline Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a95ef51-1b86-4f87-a348-d2ec21a89e9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Baseline Logistic Regression =====\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " Not Default       0.84      0.51      0.63        90\n",
      "     Default       0.23      0.59      0.33        22\n",
      "\n",
      "    accuracy                           0.53       112\n",
      "   macro avg       0.53      0.55      0.48       112\n",
      "weighted avg       0.72      0.53      0.57       112\n",
      "\n",
      "ROC-AUC: 0.5141\n"
     ]
    }
   ],
   "source": [
    "# Baseline Logistic Regression (Class Weights are used because classes are imbalanced)\n",
    "\n",
    "lr_baseline = LogisticRegression(\n",
    "    max_iter=2000,\n",
    "    solver='saga',\n",
    "    penalty='l2',\n",
    "    C=1.0,\n",
    "    class_weight='balanced',\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "lr_baseline.fit(X_train_scaled, y_train)\n",
    "lr_baseline_result = evaluate_classifier(\n",
    "    'Baseline Logistic Regression',\n",
    "    lr_baseline,\n",
    "    X_test_scaled,\n",
    "    y_test,\n",
    "    plot=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d0073a-093a-46d1-bd32-f962c7a5d3ba",
   "metadata": {},
   "source": [
    "# Tuned Logistic Regression using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7747cfdb-bb90-463b-91f9-94acd1a2b606",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "Best Logistic Regression parameters:\n",
      "{'C': 0.1, 'penalty': 'l2'}\n",
      "Best CV ROC-AUC: 0.5681\n",
      "===== Tuned Logistic Regression =====\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " Not Default       0.84      0.51      0.63        90\n",
      "     Default       0.23      0.59      0.33        22\n",
      "\n",
      "    accuracy                           0.53       112\n",
      "   macro avg       0.53      0.55      0.48       112\n",
      "weighted avg       0.72      0.53      0.57       112\n",
      "\n",
      "ROC-AUC: 0.5202\n"
     ]
    }
   ],
   "source": [
    "# Tuned Logistic Regression \n",
    "lr_for_tuning = LogisticRegression(\n",
    "    max_iter=3000,\n",
    "    solver='saga',\n",
    "    class_weight='balanced',\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "lr_param_grid = {\n",
    "    'C': [0.1, 1, 10],\n",
    "    'penalty': ['l1', 'l2']\n",
    "}\n",
    "\n",
    "cv_strategy = StratifiedKFold(\n",
    "    n_splits=CV_FOLDS,\n",
    "    shuffle=True,\n",
    "    random_state=RANDOM_STATE\n",
    ")\n",
    "\n",
    "lr_grid = GridSearchCV(\n",
    "    estimator=lr_for_tuning,\n",
    "    param_grid=lr_param_grid,\n",
    "    scoring='roc_auc',\n",
    "    cv=cv_strategy,\n",
    "    n_jobs=-1,\n",
    "    verbose=1,\n",
    "    refit=True\n",
    ")\n",
    "\n",
    "lr_grid.fit(X_train_scaled, y_train)\n",
    "\n",
    "lr_tuned = lr_grid.best_estimator_\n",
    "\n",
    "print('Best Logistic Regression parameters:')\n",
    "print(lr_grid.best_params_)\n",
    "print(f\"Best CV ROC-AUC: {lr_grid.best_score_:.4f}\")\n",
    "\n",
    "lr_tuned_result = evaluate_classifier(\n",
    "    'Tuned Logistic Regression',\n",
    "    lr_tuned,\n",
    "    X_test_scaled,\n",
    "    y_test,\n",
    "    plot=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96fb6564-ada5-4aac-8635-05306925e9b9",
   "metadata": {},
   "source": [
    "# Baseline XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c9c8e600-5cad-4b19-8805-69262e40dd91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scale_pos_weight: 4.011\n",
      "===== Baseline XGBoost =====\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " Not Default       0.83      0.92      0.87        90\n",
      "     Default       0.42      0.23      0.29        22\n",
      "\n",
      "    accuracy                           0.79       112\n",
      "   macro avg       0.62      0.57      0.58       112\n",
      "weighted avg       0.75      0.79      0.76       112\n",
      "\n",
      "ROC-AUC: 0.6369\n"
     ]
    }
   ],
   "source": [
    "# Baseline XGBoost Classifier\n",
    "scale_pos_weight = (y_train == 0).sum() / max((y_train == 1).sum(), 1)\n",
    "print(f\"scale_pos_weight: {scale_pos_weight:.3f}\")\n",
    "\n",
    "xgb_baseline = xgb.XGBClassifier(\n",
    "    n_estimators=250,\n",
    "    max_depth=4,\n",
    "    learning_rate=0.08,\n",
    "    subsample=0.85,\n",
    "    colsample_bytree=0.85,\n",
    "    scale_pos_weight=scale_pos_weight,\n",
    "    objective='binary:logistic',\n",
    "    eval_metric='auc',\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1,\n",
    "    tree_method='hist'\n",
    ")\n",
    "\n",
    "xgb_baseline.fit(X_train, y_train)\n",
    "xgb_baseline_result = evaluate_classifier(\n",
    "    'Baseline XGBoost',\n",
    "    xgb_baseline,\n",
    "    X_test,\n",
    "    y_test,\n",
    "    plot=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9079beeb-fb24-4020-aa89-1699eea40e80",
   "metadata": {},
   "source": [
    "# Tuned XGBoost Classifier using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d6626d1-8c72-4fcc-b6fb-eb51c8fc962f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Best XGBoost parameters:\n",
      "{'subsample': 0.8, 'scale_pos_weight': 1, 'reg_lambda': 5.0, 'reg_alpha': 0, 'n_estimators': 250, 'min_child_weight': 7, 'max_depth': 4, 'learning_rate': 0.03, 'gamma': 0.5, 'colsample_bytree': 0.8}\n",
      "Best CV ROC-AUC: 0.6886\n",
      "===== Tuned XGBoost =====\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " Not Default       0.81      0.96      0.88        90\n",
      "     Default       0.33      0.09      0.14        22\n",
      "\n",
      "    accuracy                           0.79       112\n",
      "   macro avg       0.57      0.52      0.51       112\n",
      "weighted avg       0.72      0.79      0.73       112\n",
      "\n",
      "ROC-AUC: 0.6217\n"
     ]
    }
   ],
   "source": [
    "# Tuned XGBoost using RandomizedSearchCV\n",
    "xgb_for_tuning = xgb.XGBClassifier(\n",
    "    objective=\"binary:logistic\",\n",
    "    eval_metric=\"auc\",\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=1,\n",
    "    tree_method=\"hist\"\n",
    ")\n",
    "\n",
    "xgb_param_dist_fast = {\n",
    "    \"n_estimators\": [150, 200, 250, 300, 400, 500],\n",
    "    \"max_depth\": [2, 3, 4, 5, 6],\n",
    "    \"learning_rate\": [0.01, 0.03, 0.05, 0.08, 0.10],\n",
    "    \"subsample\": [0.70, 0.80, 0.90, 1.00],\n",
    "    \"colsample_bytree\": [0.70, 0.80, 0.90, 1.00],\n",
    "    \"min_child_weight\": [1, 3, 5, 7, 10],\n",
    "    \"gamma\": [0, 0.05, 0.10, 0.25, 0.50],\n",
    "    \"reg_alpha\": [0, 0.01, 0.10, 0.50, 1.00],\n",
    "    \"reg_lambda\": [0.50, 1.0, 2.0, 5.0, 10.0],\n",
    "\n",
    "    # Let tuning test both weighted and unweighted versions\n",
    "    \"scale_pos_weight\": [1, scale_pos_weight]\n",
    "}\n",
    "\n",
    "xgb_random = RandomizedSearchCV(\n",
    "    estimator=xgb_for_tuning,\n",
    "    param_distributions=xgb_param_dist_fast,\n",
    "    n_iter=40,\n",
    "    scoring=\"roc_auc\",\n",
    "    cv=5,\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1,\n",
    "    verbose=1,\n",
    "    refit=True,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "xgb_random.fit(X_train, y_train)\n",
    "\n",
    "xgb_tuned = xgb_random.best_estimator_\n",
    "\n",
    "print(\"Best XGBoost parameters:\")\n",
    "print(xgb_random.best_params_)\n",
    "print(f\"Best CV ROC-AUC: {xgb_random.best_score_:.4f}\")\n",
    "\n",
    "xgb_tuned_result = evaluate_classifier(\n",
    "    \"Tuned XGBoost\",\n",
    "    xgb_tuned,\n",
    "    X_test,\n",
    "    y_test,\n",
    "    plot=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e68374d-1f63-4c0d-b7e5-1aa12900d36f",
   "metadata": {},
   "source": [
    "# Cross-Validation Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8c55b1d9-7bb8-4530-9f20-6a7cb444d4ee",
   "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>Model</th>\n",
       "      <th>Train ROC-AUC</th>\n",
       "      <th>CV Accuracy</th>\n",
       "      <th>CV Precision</th>\n",
       "      <th>CV Recall</th>\n",
       "      <th>CV F1</th>\n",
       "      <th>CV ROC-AUC</th>\n",
       "      <th>CV ROC-AUC Std</th>\n",
       "      <th>Overfit Gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuned XGBoost</td>\n",
       "      <td>0.9494</td>\n",
       "      <td>0.8094</td>\n",
       "      <td>0.4810</td>\n",
       "      <td>0.1333</td>\n",
       "      <td>0.2064</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baseline XGBoost</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7937</td>\n",
       "      <td>0.4744</td>\n",
       "      <td>0.3255</td>\n",
       "      <td>0.3769</td>\n",
       "      <td>0.6727</td>\n",
       "      <td>0.0567</td>\n",
       "      <td>0.3273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuned Logistic Regression</td>\n",
       "      <td>0.6414</td>\n",
       "      <td>0.5761</td>\n",
       "      <td>0.2508</td>\n",
       "      <td>0.5719</td>\n",
       "      <td>0.3475</td>\n",
       "      <td>0.5681</td>\n",
       "      <td>0.0679</td>\n",
       "      <td>0.0733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline Logistic Regression</td>\n",
       "      <td>0.6359</td>\n",
       "      <td>0.5784</td>\n",
       "      <td>0.2535</td>\n",
       "      <td>0.5830</td>\n",
       "      <td>0.3520</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.0761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Train ROC-AUC  CV Accuracy  CV Precision  \\\n",
       "3                 Tuned XGBoost         0.9494       0.8094        0.4810   \n",
       "2              Baseline XGBoost         1.0000       0.7937        0.4744   \n",
       "1     Tuned Logistic Regression         0.6414       0.5761        0.2508   \n",
       "0  Baseline Logistic Regression         0.6359       0.5784        0.2535   \n",
       "\n",
       "   CV Recall  CV F1  CV ROC-AUC  CV ROC-AUC Std  Overfit Gap  \n",
       "3     0.1333 0.2064      0.7143          0.0350       0.2350  \n",
       "2     0.3255 0.3769      0.6727          0.0567       0.3273  \n",
       "1     0.5719 0.3475      0.5681          0.0679       0.0733  \n",
       "0     0.5830 0.3520      0.5598          0.0633       0.0761  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cross-validation comparison \n",
    "# Using multiple metrics to show generalisation, not only one train/test split.\n",
    "\n",
    "cv_scoring = {\n",
    "    'accuracy': 'accuracy',\n",
    "    'precision': 'precision',\n",
    "    'recall': 'recall',\n",
    "    'f1': 'f1',\n",
    "    'roc_auc': 'roc_auc'\n",
    "}\n",
    "\n",
    "cv_models = {\n",
    "    'Baseline Logistic Regression': (lr_baseline, X_train_scaled),\n",
    "    'Tuned Logistic Regression': (lr_tuned, X_train_scaled),\n",
    "    'Baseline XGBoost': (xgb_baseline, X_train),\n",
    "    'Tuned XGBoost': (xgb_tuned, X_train)\n",
    "}\n",
    "\n",
    "cv_rows = []\n",
    "\n",
    "for model_name, (model, X_cv_data) in cv_models.items():\n",
    "    scores = cross_validate(\n",
    "        estimator=clone(model),\n",
    "        X=X_cv_data,\n",
    "        y=y_train,\n",
    "        scoring=cv_scoring,\n",
    "        cv=cv_strategy,\n",
    "        n_jobs=-1,\n",
    "        return_train_score=True\n",
    "    )\n",
    "\n",
    "    cv_rows.append({\n",
    "        'Model': model_name,\n",
    "        'Train ROC-AUC': scores['train_roc_auc'].mean(),\n",
    "        'CV Accuracy': scores['test_accuracy'].mean(),\n",
    "        'CV Precision': scores['test_precision'].mean(),\n",
    "        'CV Recall': scores['test_recall'].mean(),\n",
    "        'CV F1': scores['test_f1'].mean(),\n",
    "        'CV ROC-AUC': scores['test_roc_auc'].mean(),\n",
    "        'CV ROC-AUC Std': scores['test_roc_auc'].std(),\n",
    "        'Overfit Gap': scores['train_roc_auc'].mean() - scores['test_roc_auc'].mean()\n",
    "    })\n",
    "\n",
    "cv_results_df = pd.DataFrame(cv_rows).sort_values('CV ROC-AUC', ascending=False)\n",
    "display(cv_results_df.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d758dd99-5290-4ae2-8eec-133a1a6647b8",
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>ROC-AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baseline XGBoost</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>0.4167</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.2941</td>\n",
       "      <td>0.6369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tuned XGBoost</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0909</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.6217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tuned Logistic Regression</td>\n",
       "      <td>0.5268</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>0.3291</td>\n",
       "      <td>0.5202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline Logistic Regression</td>\n",
       "      <td>0.5268</td>\n",
       "      <td>0.2281</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>0.3291</td>\n",
       "      <td>0.5141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model  Accuracy  Precision  Recall  F1-Score  \\\n",
       "2              Baseline XGBoost    0.7857     0.4167  0.2273    0.2941   \n",
       "3                 Tuned XGBoost    0.7857     0.3333  0.0909    0.1429   \n",
       "1     Tuned Logistic Regression    0.5268     0.2281  0.5909    0.3291   \n",
       "0  Baseline Logistic Regression    0.5268     0.2281  0.5909    0.3291   \n",
       "\n",
       "   ROC-AUC  \n",
       "2   0.6369  \n",
       "3   0.6217  \n",
       "1   0.5202  \n",
       "0   0.5141  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Final test-set comparison \n",
    "all_results = [\n",
    "    lr_baseline_result,\n",
    "    lr_tuned_result,\n",
    "    xgb_baseline_result,\n",
    "    xgb_tuned_result\n",
    "]\n",
    "\n",
    "metrics_df = pd.DataFrame([{\n",
    "    'Model': r['Model'],\n",
    "    'Accuracy': r['Accuracy'],\n",
    "    'Precision': r['Precision'],\n",
    "    'Recall': r['Recall'],\n",
    "    'F1-Score': r['F1-Score'],\n",
    "    'ROC-AUC': r['ROC-AUC']\n",
    "} for r in all_results]).sort_values('ROC-AUC', ascending=False)\n",
    "\n",
    "display(metrics_df.round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2fd7b4bd-be7a-476f-a9f8-83b173c6ef42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1690x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual comparison of final test metrics \n",
    "metric_names = ['Accuracy', 'Precision', 'Recall', 'F1-Score', 'ROC-AUC']\n",
    "plot_df = metrics_df.set_index('Model')[metric_names]\n",
    "\n",
    "ax = plot_df.plot(kind='bar', figsize=(13, 6), edgecolor='white')\n",
    "ax.set_ylim(0, 1.05)\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_title('Final Test-Set Model Comparison')\n",
    "ax.legend(loc='lower right')\n",
    "plt.xticks(rotation=25, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4069f484-4951-4a94-b3f7-271e61cf3a08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1040x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  ROC curves for all four model versions \n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "for result in all_results:\n",
    "    fpr, tpr, _ = roc_curve(y_test, result['y_proba'])\n",
    "    ax.plot(fpr, tpr, linewidth=2, label=f\"{result['Model']} AUC={result['ROC-AUC']:.3f}\")\n",
    "\n",
    "ax.plot([0, 1], [0, 1], 'k--', linewidth=1, label='Random classifier')\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_title('ROC Curves: Logistic Regression vs XGBoost')\n",
    "ax.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69e9867e-582c-4fa1-b747-e96d894b3eba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model by test ROC-AUC: Baseline XGBoost\n",
      "ROC-AUC: 0.6369\n",
      "===== Baseline XGBoost =====\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " Not Default       0.83      0.92      0.87        90\n",
      "     Default       0.42      0.23      0.29        22\n",
      "\n",
      "    accuracy                           0.79       112\n",
      "   macro avg       0.62      0.57      0.58       112\n",
      "weighted avg       0.75      0.79      0.76       112\n",
      "\n",
      "ROC-AUC: 0.6369\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Model': 'Baseline XGBoost',\n",
       " 'Accuracy': 0.7857142857142857,\n",
       " 'Precision': 0.4166666666666667,\n",
       " 'Recall': 0.22727272727272727,\n",
       " 'F1-Score': 0.29411764705882354,\n",
       " 'ROC-AUC': 0.6368686868686869,\n",
       " 'y_pred': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "        0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "        0, 0]),\n",
       " 'y_proba': array([1.51971817e-01, 1.08001500e-01, 1.96283922e-01, 3.47232781e-02,\n",
       "        2.48112362e-02, 4.05586213e-02, 2.23992951e-02, 1.04398094e-02,\n",
       "        7.17805550e-02, 4.10296433e-02, 8.90773535e-02, 1.72425550e-03,\n",
       "        7.77189359e-02, 8.20181668e-01, 8.31530243e-03, 3.43103372e-02,\n",
       "        1.95285589e-01, 5.49489520e-02, 6.37547255e-01, 3.55371356e-01,\n",
       "        3.55335742e-01, 1.00039437e-01, 7.30327144e-02, 6.13051414e-01,\n",
       "        7.14184821e-01, 3.30155268e-02, 2.31975570e-01, 2.63509005e-02,\n",
       "        1.63803965e-01, 8.13483633e-03, 3.36017867e-04, 6.05346961e-03,\n",
       "        9.22989994e-02, 1.00499049e-01, 4.40449528e-02, 5.28448939e-01,\n",
       "        7.20391870e-02, 8.71382892e-01, 2.65002400e-02, 2.13602209e-03,\n",
       "        2.68478900e-01, 4.89844615e-03, 8.36601760e-03, 2.71714502e-03,\n",
       "        3.65012959e-02, 3.58765870e-02, 1.17542133e-01, 5.96217401e-02,\n",
       "        1.16977822e-02, 7.15648413e-01, 2.28783473e-01, 3.37743089e-02,\n",
       "        6.00177571e-02, 1.20993637e-01, 2.51356382e-02, 3.51131529e-01,\n",
       "        8.85925665e-02, 3.77491750e-02, 3.31446156e-02, 4.35098540e-03,\n",
       "        4.05978352e-01, 6.79727793e-02, 1.67132937e-03, 4.29487601e-02,\n",
       "        8.17697570e-02, 6.87378109e-01, 9.64682102e-02, 1.78680703e-01,\n",
       "        4.17063851e-03, 2.97483951e-01, 1.16046660e-01, 8.73563588e-02,\n",
       "        7.24959493e-01, 1.61694083e-02, 3.72452429e-03, 1.50405094e-01,\n",
       "        1.25647485e-01, 1.50675848e-01, 4.86562513e-02, 1.58757403e-01,\n",
       "        1.95206910e-01, 9.54849496e-02, 2.28399813e-01, 9.59599297e-03,\n",
       "        2.67605856e-02, 1.00645185e-01, 1.03265628e-01, 2.37297684e-01,\n",
       "        3.44163999e-02, 9.34037983e-01, 9.65279937e-02, 2.03690641e-02,\n",
       "        8.31894111e-03, 1.82214659e-02, 7.17732549e-01, 1.02902547e-01,\n",
       "        4.19045627e-01, 1.47265822e-01, 6.09994270e-02, 3.50684941e-01,\n",
       "        4.06100526e-02, 6.70672506e-02, 1.51151430e-03, 2.20611952e-02,\n",
       "        9.10019875e-01, 2.40699485e-01, 4.56351601e-03, 8.22997373e-03,\n",
       "        9.76750255e-02, 5.55674657e-02, 4.34562981e-01, 2.98577026e-02],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select best model based on test ROC-AUC \n",
    "best_result = max(all_results, key=lambda r: r['ROC-AUC'])\n",
    "best_model_name = best_result['Model']\n",
    "\n",
    "if best_model_name == 'Baseline Logistic Regression':\n",
    "    best_model = lr_baseline\n",
    "    best_X_test = X_test_scaled\n",
    "    best_X_train = X_train_scaled\n",
    "    best_is_scaled = True\n",
    "elif best_model_name == 'Tuned Logistic Regression':\n",
    "    best_model = lr_tuned\n",
    "    best_X_test = X_test_scaled\n",
    "    best_X_train = X_train_scaled\n",
    "    best_is_scaled = True\n",
    "elif best_model_name == 'Baseline XGBoost':\n",
    "    best_model = xgb_baseline\n",
    "    best_X_test = X_test\n",
    "    best_X_train = X_train\n",
    "    best_is_scaled = False\n",
    "else:\n",
    "    best_model = xgb_tuned\n",
    "    best_X_test = X_test\n",
    "    best_X_train = X_train\n",
    "    best_is_scaled = False\n",
    "\n",
    "print(f\"Best model by test ROC-AUC: {best_model_name}\")\n",
    "print(f\"ROC-AUC: {best_result['ROC-AUC']:.4f}\")\n",
    "\n",
    "# Draw final confusion matrix and classification report for best model\n",
    "evaluate_classifier(best_model_name, best_model, best_X_test, y_test, plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfdcf882-6975-470e-a466-7aea6c4398d5",
   "metadata": {},
   "source": [
    "# Model Interpretation and Insight Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c45c97fd-f8c5-4442-b241-3fa4a4051757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Coefficient</th>\n",
       "      <th>AbsCoefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>months_since_last_delinq</td>\n",
       "      <td>-0.0064</td>\n",
       "      <td>0.0064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>interest_rate</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>num_total_cc_accounts</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>grade</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_credit_lines</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>num_open_cc_accounts</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>income_to_loan_ratio</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>0.0035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>num_mort_accounts</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>num_satisfactory_accounts</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>open_credit_lines</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>accounts_opened_24m</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>installment</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>open_credit_ratio</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>homeownership</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>interest_burden</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>high_interest</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>months_since_90d_late</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>loan_to_income</td>\n",
       "      <td>0.0016</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>inquiries_last_12m</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Feature  Coefficient  AbsCoefficient\n",
       "8    months_since_last_delinq      -0.0064          0.0064\n",
       "37              interest_rate       0.0053          0.0053\n",
       "40                  sub_grade       0.0049          0.0049\n",
       "27      num_total_cc_accounts      -0.0042          0.0042\n",
       "39                      grade       0.0042          0.0042\n",
       "10         total_credit_lines      -0.0042          0.0042\n",
       "28       num_open_cc_accounts      -0.0041          0.0041\n",
       "46       income_to_loan_ratio      -0.0035          0.0035\n",
       "30          num_mort_accounts      -0.0033          0.0033\n",
       "22  num_satisfactory_accounts      -0.0033          0.0033\n",
       "11          open_credit_lines      -0.0033          0.0033\n",
       "20        accounts_opened_24m      -0.0022          0.0022\n",
       "38                installment       0.0019          0.0019\n",
       "50          open_credit_ratio       0.0019          0.0019\n",
       "2               homeownership       0.0018          0.0018\n",
       "47            interest_burden       0.0017          0.0017\n",
       "52              high_interest       0.0017          0.0017\n",
       "16      months_since_90d_late       0.0017          0.0017\n",
       "48             loan_to_income       0.0016          0.0016\n",
       "9          inquiries_last_12m       0.0015          0.0015"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x910 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression coefficient interpretation \n",
    "# Positive coefficients increase predicted default risk.\n",
    "# Negative coefficients decrease predicted default risk.\n",
    "\n",
    "coef_df = pd.DataFrame({\n",
    "    'Feature': FEATURE_COLS,\n",
    "    'Coefficient': lr_tuned.coef_[0]\n",
    "})\n",
    "coef_df['AbsCoefficient'] = coef_df['Coefficient'].abs()\n",
    "coef_df = coef_df.sort_values('AbsCoefficient', ascending=False)\n",
    "\n",
    "display(coef_df.head(20).round(4))\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.barplot(\n",
    "    data=coef_df.head(20),\n",
    "    x='Coefficient',\n",
    "    y='Feature'\n",
    ")\n",
    "plt.axvline(0, color='black', linewidth=1)\n",
    "plt.title('Top Logistic Regression Coefficients')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61a06c88-9e9e-422c-a08a-49e2ee2449a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0.0687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>interest_rate</td>\n",
       "      <td>0.0414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>months_since_last_credit_inquiry</td>\n",
       "      <td>0.0407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>initial_listing_status</td>\n",
       "      <td>0.0348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>num_open_cc_accounts</td>\n",
       "      <td>0.0346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_credit_lines</td>\n",
       "      <td>0.0336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>homeownership</td>\n",
       "      <td>0.0330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>state</td>\n",
       "      <td>0.0321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>accounts_opened_24m</td>\n",
       "      <td>0.0314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>num_cc_carrying_balance</td>\n",
       "      <td>0.0293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>num_mort_accounts</td>\n",
       "      <td>0.0291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>total_delinquency</td>\n",
       "      <td>0.0290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>num_total_cc_accounts</td>\n",
       "      <td>0.0289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified_income</td>\n",
       "      <td>0.0277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>annual_income</td>\n",
       "      <td>0.0273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>income_to_loan_ratio</td>\n",
       "      <td>0.0261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>installment</td>\n",
       "      <td>0.0249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>credit_utilization_ratio</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>inquiries_last_12m</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>term</td>\n",
       "      <td>0.0234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Feature  Importance\n",
       "40                         sub_grade      0.0687\n",
       "37                     interest_rate      0.0414\n",
       "21  months_since_last_credit_inquiry      0.0407\n",
       "41            initial_listing_status      0.0348\n",
       "28              num_open_cc_accounts      0.0346\n",
       "10                total_credit_lines      0.0336\n",
       "2                      homeownership      0.0330\n",
       "1                              state      0.0321\n",
       "20               accounts_opened_24m      0.0314\n",
       "29           num_cc_carrying_balance      0.0293\n",
       "30                 num_mort_accounts      0.0291\n",
       "51                 total_delinquency      0.0290\n",
       "27             num_total_cc_accounts      0.0289\n",
       "4                    verified_income      0.0277\n",
       "3                      annual_income      0.0273\n",
       "46              income_to_loan_ratio      0.0261\n",
       "38                       installment      0.0249\n",
       "49          credit_utilization_ratio      0.0237\n",
       "9                 inquiries_last_12m      0.0237\n",
       "36                              term      0.0234"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x910 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost feature importance \n",
    "xgb_importance = pd.DataFrame({\n",
    "    'Feature': FEATURE_COLS,\n",
    "    'Importance': xgb_tuned.feature_importances_\n",
    "}).sort_values('Importance', ascending=False)\n",
    "\n",
    "display(xgb_importance.head(20).round(4))\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.barplot(\n",
    "    data=xgb_importance.head(20),\n",
    "    x='Importance',\n",
    "    y='Feature'\n",
    ")\n",
    "plt.title('Top XGBoost Feature Importances')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e2901ba1-adf2-44d7-a7d7-3f4c29dc1ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Importance Mean</th>\n",
       "      <th>Importance Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>interest_rate</td>\n",
       "      <td>0.0597</td>\n",
       "      <td>0.0525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>months_since_last_credit_inquiry</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>0.0186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>debt_to_income</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>credit_utilization_ratio</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>total_credit_lines</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>verified_income</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_credit_utilized</td>\n",
       "      <td>0.0097</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>num_open_cc_accounts</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.0046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>num_mort_accounts</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0.0061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>annual_income</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>income_to_loan_ratio</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>accounts_opened_24m</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>0.0142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>interest_burden</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>term</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>initial_listing_status</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Feature  Importance Mean  Importance Std\n",
       "37                     interest_rate           0.0597          0.0525\n",
       "21  months_since_last_credit_inquiry           0.0348          0.0186\n",
       "5                     debt_to_income           0.0329          0.0094\n",
       "49          credit_utilization_ratio           0.0274          0.0138\n",
       "10                total_credit_lines           0.0156          0.0080\n",
       "4                    verified_income           0.0107          0.0057\n",
       "13             total_credit_utilized           0.0097          0.0021\n",
       "28              num_open_cc_accounts           0.0096          0.0046\n",
       "30                 num_mort_accounts           0.0060          0.0061\n",
       "3                      annual_income           0.0053          0.0254\n",
       "46              income_to_loan_ratio           0.0039          0.0085\n",
       "20               accounts_opened_24m           0.0037          0.0142\n",
       "47                   interest_burden           0.0034          0.0053\n",
       "36                              term           0.0033          0.0016\n",
       "41            initial_listing_status           0.0020          0.0008"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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23sXYe1edLDr2NJoGAAAAAADwIEIhAAAAAAAADyIUAgAAAAAA8CBCIQDIJGXLlpURI0bYR3gLY+9NjLt3Mfbexdh7F2PvXWWz+NiHOY7jBPskAAAAAAAAkLmoFAIAAAAAAPAgQiEAAAAAAAAPIhQCAAAAAADwIEIhAAAAAAAADyIUAgAAAAAA8CBCIQAAAAAAAA8iFAKAVLh8+bJMmjRJqlevLpGRkVKuXDkZPHiwnD59OkNev3z5cmnatKnky5dPihYtKnfddZfs3bs3na8KoTb2CxYskB49ekjdunUlV65cEhYWJvv27cuAq0Iojf2JEydkypQpcsstt9g+efLkkWrVqknv3r3l4MGDGXR1CIWxv3Dhgjz88MNSv359KVasmOTOnVsqVaokd999t2zevDmDrg6h8r3en465/p1fu3btdLgShPLYt2zZ0sY6scfGjRsz4OoQSl/3Fy9elKlTp8r1119vP+cXKlTIns+cOVOCxgEApOjRRx919K/M9u3bO7NmzXIGDhzohIeHO9HR0c6lS5fS9fXvv/++ExYW5tSrV8+ZPn26M3r0aKdEiRJO6dKlnV9++SUDrxLBHvsWLVo4kZGRTqNGjZxq1arZ6/bu3ZuBV4dQGPsVK1Y4OXPmdG655RZn7NixzmuvveY89thjTp48eZxChQo527Zty+ArRbDGPjY21qlfv77zyCOPOJMmTbKxf/bZZ51y5co5uXLlcj755JMMvlIE6+97f0uWLHFy5MhhX/O1atVK56tCKH6vL1asmDN37twEj2PHjmXgVSLYY3/u3DmnTZs2TkREhNOjRw9n5syZ9rO+fs9/+umnnWAhFAKAFPz4448W0nTo0CHO9qlTp9o3gfnz56fb68+fP++UKVPGKV++vBMTE+PbvnnzZvuBsVevXul2XQitsVf79+93Lly4YM/79etHKOSRsdcx3rNnT4JjrF692vbt2LHjFV8PQvfrPjG//vqr/VLx97//PY1Xgawy7vq9XkNADQYrVKhAKOSBsddQSMca3hv74cOH2z8Cffrpp04oIRQCgBQMGzbM/mJft25dnO1nz5518ubNm+IP7YG83v0l8Pnnn09wnFatWjkFCxa04AjZb+zjIxTy7tj7K1q0qFWNwVtjf/HiRSd//vxO48aN03gVyCrjrlUG+o9BJ0+eJBTyyNi7oZBWkei4X758OR2vBqE69rGxsU6BAgV8AZKO+6lTp5xQQE8hAEjBhg0bJEeOHNKwYcM423XecL169ezz6fV693mTJk0SHKdx48Zy6tQp+emnn67wihCKY4/QEgpjf/LkSYmJiZGSJUum8SqQVcb+0qVLcvToUfntt9/s8126dJHY2Fi57bbb0umqEIrj/u2338q0adOsH0nBggXT6UqQFcb+l19+kfz581s/Gf3YoUMH2blzZzpdEUJx7D///HP7nq495AYMGGBf8/ooXry4DB061HoNBQuhEACk4Ndff/U1AI3v6quvth/kz58/ny6v133d7Ynt6/4ggew39ggtoTD2o0aNskbE3bp1S+NVIKuM/Y4dO+wXg9KlS9svFx999JE8/fTT9kD2HHf9BfDBBx+0BvOdO3dOxytBqI+9NpN/8sknZc6cObJw4ULp27evrFixQho1aiRbt25NxytDKI39rl277OPkyZPl/ffflxdffFHeffddW1hmzJgx0rNnTwmW8KC9MwBkEWfOnEn0L3v3XwLcfSIiIq749fpRJba//77IfmOP0BLssX/vvfdkwoQJcuutt9qKdMjeY6+/JK5evdp+edizZ4/MmzfPKsXOnTsn4eH8uJ4dx338+PE21h988EE6XQGyythrGOSvU6dOcuedd9qqZIMGDbK/C5D9xj4mJsb+//jx47Jt2zZbZVRpKBwdHS1vvfWWDBkyRGrUqCGZjUohAEhB3rx57QfzxPz111++fdLj9e7HxPZPzXsh6449Qkswx3758uVy7733Wom5/iuiLlOM7D32uizxzTffbNPFHn30Ufn000/tF8OOHTtewZUgVMddw6Dnn39ehg0bJpUrV06Hs0dW/17fvHlzuemmm2TNmjVy9uzZAM4eWWXs8+TJ42sH4QZCrq5du9rHtWvXSjAQCgFACsqUKWPln4n9pa9TubRsNLlKj0Ber/u62xPbN6mpZcj6Y4/QEqyxX7lypfWWqFWrlqxatYo+Ix79und7jOg0sv/+979pvBKE6rgPHjxYihYtKu3bt7eAyH3olDK3Wuzw4cPpfIUI5a95VbFiResvduLEiTRcBUJ97MuWLWsfS5UqlWBfnTqsgjX2hEIAkIIGDRrI5cuXrSFk/H8B2LJli9xwww3p9nrdV61fvz7Bcb7++mv7BTEqKuoKrwihOPYILcEYew2E2rVrJ9WrV5ePP/5YihQpkk5Xg6z4de9WC+hUA2Svcd+/f7/1ItHwt2rVqr6H/hK5e/due96rV690vkKE+te8jr1OF9XAENlv7Bv+XzPqQ4cOJTiOu61EiRJXdD1pFuzlzwAg1P3www9OWFiYbwlJ19SpU20Zyrlz5/q27dmzx9mxY0eaX6/LzZcuXdopX768ExMT49u+ZcsWJ0eOHE7Pnj0z4AoRCmMfH0vSe2vsP/roIycyMtK59tprnaNHj2bINSH0xv7IkSO2LHV8hw8ftu8Fuiz96dOn0/HqEArjvnr1amfhwoUJHsWLF3fKlStnz7/44osMu1YEb+z//PNP5+LFiwnOYenSpbZvSkugI2t/r2/WrJntv2nTJt82/fPQsGFDJzw83Nm/f78TDIRCAJAK/fv3t7/c27dv77z66qvOoEGD7C/vFi1axPmBvkKFCrZfWl+vFixYYN8w6tWr50yfPt0ZM2aMU6JECadkyZLOoUOHMuV6EZyx/+yzz5wXXnjBHo0aNbLXDR482LcN2XPsN2zYYIFQ7ty5nUmTJtkPkfEfyJ5jr+Otx3jsscecKVOmOK+88oozcOBAp2jRovZ9YPbs2Zl2zcjcv+8To8etVatWul8XQmfsFy9e7FSqVMl59NFHncmTJzvTpk1zunbtav/wV6xYMWfXrl2Zds3I/K/77777zsmXL59TpEgRZ8SIERYeaVCkr3/22WedYCEUAoBU0BR/woQJTlRUlBMREeGUKVPGfnD3r+ZJ7htGal/vWrJkiYUCefLkcQoXLux07NjR/oUC2Xvs9QcEPUZSD2TPsZ8zZ06y487YZ9+x37hxo3Pvvfc6VapUsV8UcuXK5ZQtW9bp3Lmz8+WXX2b4dSK43+vjIxTK/mO/fft256677nIqV65sX/O6rz7v27cv//Dnka/777//3mnbtq1TqFAh+8cg/Udg/TkgmML0P8GZuAYAAAAAAIBgodE0AAAAAACABxEKAQAAAAAAeBChEAAAAAAAgAcRCgEAAAAAAHgQoRAAAAAAAIAHEQoBAAAAAAB4EKEQAAAAAACABxEKAQAAAAAAeBChEAAAAAAAgAcRCgEAAAAAAHgQoRAAAAAAAIAHEQoBAAAgQ61du1bCwsJkwoQJkl188MEH8txzz0l2HzN99O/fP9F9jhw5IhEREbZPy5YtM/0cAQBXjlAIAAAASEMoNHLkSMnuIiMj5T//+Y+cO3cuwefmzp0rjuNIeHh4UM4NAHDlCIUAAACAVIqJiREvad++vZw4cUI+/PDDBJ+bM2eO3HbbbZI7d+6gnBsA4MoRCgEAACDT7du3z6Yd6RSsBQsWSL169SRPnjxSpUoVCxvUgQMHpFOnTlK0aFEpUKCA3HfffQlCme7du9tx/vjjD+natatcddVVki9fPvnb3/4m3333XYL3vXjxoowbN05q1qxpVTC6vwYfW7duTfL83n33Xalfv76d3yOPPGJTpd58803bz51ipY833njDtu3cuVP69u0rtWrVsvPOmzevvf61115LcD56fH3trl27ZOjQoVK2bFkLWerWrSvLly9P9N69//77dg6FCxe2Y1erVk0effRROX/+vG8freB55ZVX7H11n/z580t0dLSsWbMmoHG6/vrr5dprr/WNievbb7+Vbdu2SY8ePZJ87caNG+3eFitWzK5Jz3PUqFE2BvGPpeMYFRVl56r3rFmzZrJ48eIEx3TH++TJk9KnTx8pUaKEjaPu/8033wR0bQAAEWo9AQAAEDRLly6VGTNmWIii4c/s2bPlgQcesF41GpK0atVKRo8eLRs2bJDXX3/dAoDEwpVbb73VXq8hy2+//SbTpk2TFi1ayPr166V27dq+/e69914LoVq3bm2hgu47ffp0adKkiXz++edy3XXXJZgmNnXqVNv34YcfloIFC9r7XL582fbXKVSupk2b+vrxrFu3Tu644w6pVKmSnD59WhYuXCi9evWy8Orpp59OcP7dunWTXLlyyeOPP27hzuTJk6Vdu3by008/ScWKFX37DRs2zO6HhloDBw6U0qVLy3//+18Lip5//nm7b+r++++Xt99+20I1DW50+tf8+fPtuhctWiR33nlnqsdIx2PQoEHyyy+/yNVXX23bdCw0kNFrTMyyZcukQ4cOFvINHjzY7pmOxbPPPitbtmyx++HS8EeDtM6dO0uFChXk2LFjFrrp6/Wcu3TpkuD4bdq0keLFi9vxdP+JEyfK7bffLnv37rVQCQCQSg4AAACQgdasWePoj53jx4/3bdu7d69ty5s3r7Nv3z7f9iNHjji5c+d2wsLCnJdeeinOcdq3b+/kypXLiYmJ8W3r1q2bHUc/d/nyZd/2jRs32jHatGnj27Zq1Srbt3PnznH23bJli5MzZ07nxhtvTHB+4eHhzvbt2xNck/u+iYmNjU2w7dKlS06LFi2cggULOufPn/dtHzFihB3n9ttvj3NO3377rW0fMmSIb9s333xj26Kjo52zZ8/GOb6+1n39okWLbL+ZM2fG2efChQtO/fr1nYoVK8Z5r5TG7OjRo05ERIQzatQo+9yZM2ecQoUKOYMHD7b/z5cvn12bS8+tZMmSTvPmze09/U2cONGOq8dP7n6dPn3aiYqKcmrUqJHofe/Tp0+c7QsWLLDtM2bMSPa6AABxMX0MAAAAQaPVMFod4tLqD51mlCNHDunXr1+cfZs3by4XLlywqV3xPfnkkzatyKXTprQq5uOPP5bY2Fjb5k5H0mob/311qlbbtm3liy++sEoef1p9UqNGjYCuSaevuf766y+rZDl+/LjccsstcurUKauKiW/AgAFxzqlBgwY25Wv37t2+bVo1o8aMGWMVU/7cKWxq3rx5Vi2j9/bo0aO+x59//mnXqffP/7gp0Sl2WlnkTo/TSiOdvqUVRIlZvXq1/P7771ahpO/pfw7ag0itWrUq0ft15swZu1/6UavEduzYYfcsPq2S8qf7qkCuCwDA9DEAAAAEUeXKlRNsK1KkiE2Lit/AWLcrDQ3iSyy40SlWGj7s37/f+vvo1CINmxLbVz+vU8V0Hw2mXNrnJlAaQrm9kg4ePJjg89q4OTX3QcMY/2vVwEODHw2xkqNBivZeKlmyZJL7aGgTyLVpwKMBmQZnOnWsYcOGdn+Ten+VVGjkvr//0vbDhw+3Ztb6PD4NlnTaXnL3S+9VUn82AABJIxQCAABA0OTMmTOg7W4T5cyijY8DpT1wtFdS79695aabbrLAQq9HG0dPmjTJ+hGl9nrjX6t/RVBS9DUabOlS8knx77OUGtrDR/sJjRw50ppVaxPr5N5fjR8/3hqIJ6ZMmTK+fbWCSoMkrZa64YYbpFChQnY/tLm1XsOV3C8AQPIIhQAAAJDlaajQuHHjONu2b99u4YE7PU2rSzRg0H11Ra34+yptDJ0aSQUzWtWigZA2etYG2v50KtuV0MqeFStWyPfff2+VOkmpWrWqNajW+6FT0NKD3kdd3U2nrukqbPfcc0+y7+9OC7v55puTPe4PP/xg16MNozVw8pdYQ3EAQPqipxAAAACyvBdffDFOlYguR68hjC5N7wYj2mNHabDhv++PP/4o//M//yM33nhjnKljyXGPqb2CEqtgiV+xcvjw4SsOOdxVuHRVNv/l513ue2p4o+FXYqucxZ+6FQhdfW3EiBEWdsWfzhW/qkhXJhs7dmyC+6POnj1r09uSu186JoktSQ8ASF9UCgEAACDL075BGkZoQ2QNYHRJeq1o0SlMLm08rcuev/POO9bXR5dTd5ek18bNuvR8amkVjr5H3759rdeOLiffqFEjqzTS6VDa7FnfXxtG67nNnDnTPnclPW+0Ouipp56ScePGyfXXXy933323lCpVyvogvffee/Ltt99K4cKFfcvQ6/lpOKbXWaxYMTl06JAtC79nzx75+eefA37/8uXLW6+klGiF0FtvvWUhnDYN195CujS9VlFpk21tVK2BT8uWLa2/k/Zz0lBPm0vr/lrlpPerTp06smnTpjTeLQBAahAKAQAAIMtbuXKlDBo0yCpZtBJFQxsNhOJPE9MVvDRQ0ZW0Bg8ebAFGixYt5IUXXrAQIrV0+tTmzZstYFq4cKFV5mgPHA1+NBAaMmSILFmyRN58802bTjVq1CgLjjSsuRJafaONpjXw0SBF37dcuXK2qpd//yNtBh0dHS2zZs2yyiitLNIASa9d/z+jaUC3YcMGO1+9H7qqmzYKv+aaa2yc3HHRSqFly5bJ448/bvfq9OnT1u9In+u0MkIhAMhYYboufQa/BwAAAJAhunfvbgECP9ICABA4egoBAAAAAAB4EKEQAAAAAACABxEKAQAAAAAAeBA9hQAAAAAAADyISiEAAAAAAAAPIhQCAAAAAADwIEIhAAAAAAAADyIUAgAAAAAA8CBCIQAAAAAAAA8iFAIAAAAAAPAgQiEAAAAAAAAPIhQCAAAAAADwIEIhAAAAAAAADyIUAgAAAAAA8CBCIQAAAAAAAA8iFAIAAAAAABDv+V8D0X7rOUlY4gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1170x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Model-agnostic permutation importance \n",
    "# This measures how much model performance drops when a feature is shuffled.\n",
    "# It is slower than built-in importance, so use a test sample if needed.\n",
    "\n",
    "PERM_SAMPLE_SIZE = min(5000, len(X_test))\n",
    "X_perm = X_test.sample(PERM_SAMPLE_SIZE, random_state=RANDOM_STATE)\n",
    "y_perm = y_test.loc[X_perm.index]\n",
    "\n",
    "perm = permutation_importance(\n",
    "    estimator=xgb_tuned,\n",
    "    X=X_perm,\n",
    "    y=y_perm,\n",
    "    scoring='roc_auc',\n",
    "    n_repeats=5,\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "perm_df = pd.DataFrame({\n",
    "    'Feature': FEATURE_COLS,\n",
    "    'Importance Mean': perm.importances_mean,\n",
    "    'Importance Std': perm.importances_std\n",
    "}).sort_values('Importance Mean', ascending=False)\n",
    "\n",
    "display(perm_df.head(15).round(4))\n",
    "\n",
    "plt.figure(figsize=(9, 6))\n",
    "sns.barplot(\n",
    "    data=perm_df.head(15),\n",
    "    x='Importance Mean',\n",
    "    y='Feature'\n",
    ")\n",
    "plt.title('Permutation Importance: Tuned XGBoost')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cf230752-f8cb-4bf3-befc-875b3a1b50a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 832x624 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Partial dependence plots\n",
    "pdp_features = [\n",
    "    c for c in [\n",
    "        'interest_rate', 'annual_income', 'debt_to_income',\n",
    "        'loan_amount', 'credit_utilization_ratio', 'income_to_loan_ratio'\n",
    "    ] if c in X_train.columns\n",
    "]\n",
    "\n",
    "for feature in pdp_features[:4]:\n",
    "    PartialDependenceDisplay.from_estimator(\n",
    "        xgb_tuned,\n",
    "        X_train.sample(min(10000, len(X_train)), random_state=RANDOM_STATE),\n",
    "        features=[feature],\n",
    "        grid_resolution=20\n",
    "    )\n",
    "    plt.title(f'Partial Dependence Plot: {feature}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d89eb16-bd4d-4513-8849-41e68ab371f9",
   "metadata": {},
   "source": [
    "# Fairness and Ethical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfd0704e-ee22-4c46-9f68-30b9d7bd32f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      actual_default  predicted_default  predicted_pd\n",
      "1971               1                  0        0.2507\n",
      "1545               0                  0        0.0906\n",
      "2206               1                  0        0.1436\n",
      "9498               0                  0        0.1282\n",
      "1399               0                  0        0.1961\n"
     ]
    }
   ],
   "source": [
    "# Fairness / subgroup dataset\n",
    "fairness_df = X_test.copy()\n",
    "fairness_df['actual_default'] = y_test.values\n",
    "fairness_df['predicted_default'] = xgb_tuned.predict(X_test)\n",
    "fairness_df['predicted_pd'] = xgb_tuned.predict_proba(X_test)[:, 1]\n",
    "\n",
    "if 'annual_income' in fairness_df.columns:\n",
    "    fairness_df['income_group'] = pd.qcut(\n",
    "        fairness_df['annual_income'],\n",
    "        q=4,\n",
    "        labels=['Low Income', 'Lower-Middle Income', 'Upper-Middle Income', 'High Income'],\n",
    "        duplicates='drop'\n",
    "    )\n",
    "\n",
    "print(fairness_df[['actual_default', 'predicted_default', 'predicted_pd']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "824e8c1c-d3eb-41e9-8b8c-0936a8a1b95d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Fairness table by income_group =====\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Actual Default Rate</th>\n",
       "      <th>Predicted Default Rate</th>\n",
       "      <th>Average Predicted PD</th>\n",
       "      <th>False Negative Rate</th>\n",
       "      <th>False Positive Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lower-Middle Income</td>\n",
       "      <td>30</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.1825</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low Income</td>\n",
       "      <td>28</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>0.1851</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Upper-Middle Income</td>\n",
       "      <td>27</td>\n",
       "      <td>0.1852</td>\n",
       "      <td>0.0741</td>\n",
       "      <td>0.1991</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.0455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High Income</td>\n",
       "      <td>27</td>\n",
       "      <td>0.1111</td>\n",
       "      <td>0.0370</td>\n",
       "      <td>0.1909</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Group  Count  Actual Default Rate  Predicted Default Rate  \\\n",
       "1  Lower-Middle Income     30               0.2000                  0.0667   \n",
       "0           Low Income     28               0.2857                  0.0357   \n",
       "2  Upper-Middle Income     27               0.1852                  0.0741   \n",
       "3          High Income     27               0.1111                  0.0370   \n",
       "\n",
       "   Average Predicted PD  False Negative Rate  False Positive Rate  \n",
       "1                0.1825               1.0000               0.0833  \n",
       "0                0.1851               0.8750               0.0000  \n",
       "2                0.1991               0.8000               0.0455  \n",
       "3                0.1909               1.0000               0.0417  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Fairness table by emp_length =====\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Actual Default Rate</th>\n",
       "      <th>Predicted Default Rate</th>\n",
       "      <th>Average Predicted PD</th>\n",
       "      <th>False Negative Rate</th>\n",
       "      <th>False Positive Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0000</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0556</td>\n",
       "      <td>0.0278</td>\n",
       "      <td>0.1862</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>17</td>\n",
       "      <td>0.2941</td>\n",
       "      <td>0.0588</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>0.0909</td>\n",
       "      <td>0.2260</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1976</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.2018</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.2222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1429</td>\n",
       "      <td>0.1903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1585</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1356</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2041</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2279</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1892</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Count  Actual Default Rate  Predicted Default Rate  \\\n",
       "10 10.0000     36               0.0556                  0.0278   \n",
       "1   1.0000     17               0.2941                  0.0588   \n",
       "2   2.0000     11               0.3636                  0.0909   \n",
       "3   3.0000     11               0.1818                  0.0000   \n",
       "6   6.0000     10               0.1000                  0.2000   \n",
       "7   7.0000      7               0.0000                  0.1429   \n",
       "0   0.0000      5               0.6000                  0.0000   \n",
       "5   5.0000      5               0.2000                  0.0000   \n",
       "4   4.0000      5               0.4000                  0.0000   \n",
       "9   9.0000      3               0.3333                  0.0000   \n",
       "8   8.0000      2               0.5000                  0.0000   \n",
       "\n",
       "    Average Predicted PD  False Negative Rate  False Positive Rate  \n",
       "10                0.1862               1.0000               0.0294  \n",
       "1                 0.1719               0.8000               0.0000  \n",
       "2                 0.2260               0.7500               0.0000  \n",
       "3                 0.1976               1.0000               0.0000  \n",
       "6                 0.2018               1.0000               0.2222  \n",
       "7                 0.1903                  NaN               0.1429  \n",
       "0                 0.1585               1.0000               0.0000  \n",
       "5                 0.1356               1.0000               0.0000  \n",
       "4                 0.2041               1.0000               0.0000  \n",
       "9                 0.2279               1.0000               0.0000  \n",
       "8                 0.1892               1.0000               0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Fairness table by grade =====\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Actual Default Rate</th>\n",
       "      <th>Predicted Default Rate</th>\n",
       "      <th>Average Predicted PD</th>\n",
       "      <th>False Negative Rate</th>\n",
       "      <th>False Positive Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.0312</td>\n",
       "      <td>0.1932</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1113</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.2819</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1017</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.2143</td>\n",
       "      <td>0.3168</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1072</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Group  Count  Actual Default Rate  Predicted Default Rate  \\\n",
       "2      2     32               0.0625                  0.0312   \n",
       "1      1     25               0.1600                  0.0000   \n",
       "3      3     20               0.3000                  0.1000   \n",
       "0      0     19               0.2632                  0.0000   \n",
       "4      4     14               0.2857                  0.2143   \n",
       "5      5      2               0.5000                  0.0000   \n",
       "\n",
       "   Average Predicted PD  False Negative Rate  False Positive Rate  \n",
       "2                0.1932               1.0000               0.0333  \n",
       "1                0.1113               1.0000               0.0000  \n",
       "3                0.2819               0.6667               0.0000  \n",
       "0                0.1017               1.0000               0.0000  \n",
       "4                0.3168               1.0000               0.3000  \n",
       "5                0.1072               1.0000               0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B5XlvQHneGx1DVXVxmR0AAAAAkBvK5gAAAAAgp4SDAAAAAJBTwkEAAAAAyCnhIAAAAADklHAQAAAAAHJKOAgAAAAAOSUcBAAAAICcEg4CAACQG/PmzWvrIUBFW7RoUVsPgWVMOAgAAEAu/P73v0+jRo1KL7zwQlsPBSrOvffem/3buXPnth4Ky1iXZf2AUJ+ZM2em1157LU2bNi0NGTIkrb/++qlXr15tPSyoqG/w/KKG/++pp55Kzz77bHrkkUfS7rvvnrbYYovUu3fvth4WVMz7Y/bs2enll19OO+20U+rZs2dabbXV2npY0Kb+93//N+23336pe/fuaYUVVkhjxoxJ6623XlsPCyrCr3/96/Sd73wnTZw4MX3ve99r6+GwjAkHqQi33npruuiii9Jzzz2Xlfn36NEjHXzwwen000/3C5vci/Bj0003zYLBxYsXp06dFH3Db3/723Tuuedm4cdHH32UJkyYkEaOHJnOPvvstNJKK7X18KBN3XjjjemMM85Ib7/9dvrkk0+y98TQoUPTqaeemrbbbru2Hh60mQjJV1xxxbTGGmuk8ePHp88++yz98Ic/TH379m3roUGbuu6669J3v/vddNJJJ6Wvfe1rNedXV1enqqqqNh0by4ZPmFTEB7zDDjssCz/iw91tt92WvvzlL6err7463X333TUHJcjr+2OzzTZL5513XvZzBIMREEKe3XTTTdnvje233z5df/312RdLEXxcddVV6fXXX8+u4/cGeRV/Ox1xxBHp61//epo8eXJ66KGH0vDhw9OUKVPSrrvumv7whz+09RChTcTvhQgG11577eyLpKiQuvLKK9OPf/zj9Morr7T18KDNXHvttVkweOKJJ2anddZZp+YywWB+VFX765k2FC3EI0aMSLvttlv2rV20Eof4cBcBYbQVR/l/ly6KXMmf+++/P+29995p7ty5qVu3bumss87K3idBBSF5ngvn29/+dvrGN76RTjnllNSvX7/s/AsuuCBdeuml2eXbbLONb7rJndjnFy5cmAWDUXH+u9/9LvXv37/Wh794n7z44ovplltuSfvvv3+bjhfayl577ZW12McXSvE55K677krHHntsVlkbHUuffvpp9tnD7xDyIL5kjb+romIw/q4qTOsVX8TGvJwvvfRS+spXvpK++MUvpg033LCth0sr8smSNhN/wN55553ZL+BDDz20JhiM0CO+0Rs0aFA2T867777b1kOFZW7WrFlZ0BHtLxdeeGH2x2q0iMW320EFIXn0wQcfZAFHBILxQa4QDIY33ngjde3aNas6j/bik08+Of3jH/9o0/HCshRBxnLLLZdV0kYbcSEYjL+zQlRJ/exnP0ubbLJJOvDAA9PUqVPbeMSwbBVqYjbeeOP0zDPPZPMO3nzzzdkchFFBeMkll6Snn346C0huv/32th4utLqYluWoo47K/h+t9YVgML48ii+aYtqvCAm/9a1vpYMOOsjfVR2ccJA2/QUdFYLxCzlaw4rPDwMHDswqpuIEeRPvg2gNO+SQQ9Jpp52WBSLxbZ2AkDyLwGOXXXZJRx55ZPY7ouCXv/xldhowYED25dKbb76ZVYTE4lYPPPBAm44ZlqX58+en5ZdfPr366qtZYB6/I6ICqvC7Yp999smmqYj51o4//nirtZJLUXkeBQj/+c9/skVJYq61CEN+8YtfZFWF48aNS6uvvno2HyF0ZCuvvHIW+MX+ftlll2UV5/vuu2/2t1NUmj/88MPpySefTKNHj06PP/54FhjOmDGjrYdNa4m2Ymgrs2fPrp41a1b2/0WLFtW67Cc/+Un1CiusUHN5wfz585fpGKGtPPXUU9Wffvppzc8PPvhg9SabbFLdqVOn6osvvrjm/IULF7bRCGHZK93f77///uqqqqrqMWPGVL/00ks150+aNCk7f6eddqqeM2dOG4wU2sbEiROzfX/ChAk15y1evLjW31nnnXdedp1rr7225nLI099X3bp1q/79739fc97rr79e3adPn+rOnTtX77bbbtXPPvtsm44RlqV///vf1SuvvHL2eyE+a0yZMqX6k08+qXWdM844I7v8wgsvzH72e6PjUTlIm4oKj8JqxIX50wrfbkd7WOFb8IL4piImEJ4+fXqbjBeWpc0337xWxccOO+yQfvWrX6WNNtqoVgVhtJFFpeHMmTPbeMTQ+mJ/LxZtYfFNd8zJWZieIhQWZIi51wptlZAHUR245ZZbZpWBv//972tajuNU+H0Sv0Oivbj4csiD+Hsp/r6K90jMax7ef//9dMwxx2TdSl/4whfSX//613TxxRdnFbiQB/F+ePDBB9Oqq66arWi/8847Z/Odh0IFbfzeiIVK/va3v2U/+73R8QgHqViFUKRz587ZzzEHSEyUGq1j0QIAeVG88EhpQBjz44T4Rf29730vC0ggTwYPHpy1g0VrTChutY8PejFvZ+EyyIOYM+qGG27I2vBjnsH/+Z//qfNBLt4n8bfUggUL2nCksOwV3gfxZVK0TMbviVilNdooJ06cmL1fhg4dmu644w4LIpIrMV1LtBjHwjzxxWtB4bN44b0TU1fQMQkHqdggJL6liP/HASkqouJAFd/wxQquEYxAXkVAOGnSpGxC7Vi9+Oijj87eH//85z/T8OHD23p4sMwV/mAtXsX7L3/5S7awz2677VZrPlvIgy222CKrfpo3b142iXyEhaHw/og5pCIY3GyzzbL3hvcHeVHY1/fcc8/sd0TMYxvvlSuuuCKbb3DNNdfMFiOJ90jv3r3beriwTMVni6isLbxP4t/C31h//OMf0yeffFKzVoDfGx2Pr0OoWBEKxge9mCw4KqUiFIxy56222qqthwZtatGiRWnHHXfM3hexeliszhptAH//+9/T5z73ubYeHrSJ4mDwsccey6pq471ywgkn1Kq+hbzYZptt0iOPPJL23nvvdNhhh2WLXEUQsnDhwnTbbbelt99+O2ul1BpGnhT29y9+8YvptddeS3PmzMkqBmORkkJn0oorrpidIK8K01AU/n7617/+lQXo8XljxIgRNdehYxEOUnEK31DEwSiqB0855ZT0yiuvZMGHYBD+f3l/lPXH/wvBYFSAQF4V/oCNqtqbbrop/fvf/05Tp05N/fv3b+uhQZsZNGhQNu3ET3/603T99denyZMnZ6tSRktlVEtFlQjkUfzNFB0XL774Ytpjjz1MWQT1/F0V4Xn87oiCnfi7qrBeAB1PVaxK0taDgHKuu+66bA6QHj16ZBWDMQ8C8H8iDDzxxBOzRXpizhzvD/IuqqGiSnDKlCnZhNlRWRutMcD/ef7557NpWtZYY43Ur1+/bD5OyLvitkng/4vF3C688MLs76mYyza+YPJ3VccmHKRivfzyy+m4447Lvu0eMGBAWw8HKu5D3gEHHJD9oo4VxoCUnnvuuayNMuYZXHvttdt6OAAA7frzeBTpxOrFffr0aevh0MqEg1R8JYgVkaC8+fPnp27durX1MKCiqAIBAGgZ/q7KD+EgAAAAAOSU5fsAAAAAIKeEgwAAAACQU8JBAAAAAMgp4SAAAAAA5JRwEAAAAABySjgIAAAAADklHAQAAACAnBIOAgAAAEBOCQcBAAAAIKeEgwAANGj+/PnpiiuuSF/5ylfSmmuumZZbbrm06qqrpsGDB6dTTz01zZgxo62HCADAEqqqrq6uXtIbAwDQsb3wwgtpr732Ss8880waMmRI2n333dPaa6+d5syZk5544ol01113pffeey+9/PLLaZ111mnr4QIA0ExdmnsDAADy4ZNPPklf//rX08yZM9Ptt9+e9ttvv7JVhWPHjk1VVVUN3tenn36aFi1alLp169aKIwYAoLm0FQMAUNakSZOyluEf/OAHZYPBEGHfaaedlvr06VNz3jnnnJOFhU899VQ66aST0rrrrptd75FHHskuf+edd9LIkSNT37590/LLL5/9Gz+/++67te67cD8vvfRSncft169f2nnnnWudF9f99re/ne6999603Xbbpe7du6fevXunE088Mat0BACgLpWDAACUdeutt2b/fu9731ui2x9yyCFphRVWSGPGjMmCu2hH/vDDD9P222+fnn/++fTd7343bb311unxxx9PV155ZfrrX/+apk2blnr06LHEY37ssceycR955JHpsMMOS1OnTk3jxo1LTz75ZLrnnntSp06+GwcAKCYcBACgrAjUVl555dS/f/9a50d78Pvvv1/rvBVXXDELAovFoiVRxdely///k/OMM85I//3vf9MvfvGL9P3vf7/m/EGDBqXjjjsu/fSnP03nn3/+Eo95+vTp6Y477kj77rtv9nM8RlQORkD4u9/9Ln3zm99c4vsGAOiIfHUKAEBZH330URYOlorFSWLV4uJThH2lRo0aVSsYDBHcxfWPOuqoWucfffTR2flx+dLYdNNNa4LBgh/+8Ic1jw0AQG3CQQAAyopgMALCUlFJGC26cbr00kvrvf0mm2xS57wXX3wxC/BKQ8P4Oa4fqyMvjc0226zOedHOHFWMS3vfAAAdkbZiAADK2mKLLdLf/va3LNArbi2OFuJdd901+39pyFcsFgRZGg2tgPzZZ58t1X0DAPB/VA4CAFDWAQccULNqcUvZYIMN0rPPPlsn3Iufn3vuuezygtVXXz3797333qt13fnz56fXX3+97P1Hy3OpuO4HH3xQ674BAPg/wkEAAMqKVYoHDBiQLrnkknrn66uurm7WfcZ8gG+//XadwPHqq6/Ozt9vv/3qtCXHoibFxo4dmxYvXlz2/iN4vPPOO2ud95Of/KTmsQEAqE1bMQAAZcXqw1OmTEl77bVXGjZsWNp5553T7rvvnnr37p3NRThjxoz029/+NnXu3Dn17du3Sfd5yimnpFtuuSWNHDkyPfbYY+nzn/98evzxx9M111yTzUUYlxdE63Kcd9ZZZ6V33303a21+8MEH0yOPPJJ69uxZ9v4HDhyYvvWtb6Ujjzwybbzxxmnq1Knp1ltvTUOGDEnDhw9vsW0DANBRqBwEAKBe0Yr7r3/9K02YMCH7+bLLLstWGj777LPTo48+mlUXPvXUU00O3lZZZZX097//PVud+O67704nnHBC9u8xxxyTBX89evSouW6EjnfddVcWSo4fPz5bdXjhwoXpgQceyOY9LGfrrbfOKgcfeuihNGbMmGzOxOOOOy794Q9/SJ06+dMXAKBUVXVze0EAAKACxQImhx9+eLruuuvaeigAAO2Gr08BAAAAIKeEgwAAAACQU8JBAAAAAMgpqxUDANAhmEobAKD5VA4CAAAAQE4JBwEAAAAgp4SDAAAAAJBTwkEAAAAAyCnhIAAAAADklHAQAAAAAHJKOAgAAAAAOSUcBAAAAICcEg4CAAAAQE4JBwEAAAAgp4SDAAAAAJBTwkEAAAAASPn0/wD/EP6Rj0if8wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1300x520 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def subgroup_fairness_table(data, group_col):\n",
    "    \"\"\"\n",
    "    Build subgroup-level fairness summary.\n",
    "\n",
    "    Important columns:\n",
    "    - actual_default_rate: observed default rate in group\n",
    "    - predicted_default_rate: share classified as default\n",
    "    - avg_predicted_pd: average predicted probability of default\n",
    "    - false_negative_rate: missed-default rate among actual defaulters\n",
    "    - false_positive_rate: false alarm rate among actual non-defaulters\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "\n",
    "    for group_value, g in data.groupby(group_col, dropna=False):\n",
    "        tn, fp, fn, tp = confusion_matrix(\n",
    "            g['actual_default'],\n",
    "            g['predicted_default'],\n",
    "            labels=[0, 1]\n",
    "        ).ravel()\n",
    "\n",
    "        rows.append({\n",
    "            'Group': group_value,\n",
    "            'Count': len(g),\n",
    "            'Actual Default Rate': g['actual_default'].mean(),\n",
    "            'Predicted Default Rate': g['predicted_default'].mean(),\n",
    "            'Average Predicted PD': g['predicted_pd'].mean(),\n",
    "            'False Negative Rate': fn / (fn + tp) if (fn + tp) > 0 else np.nan,\n",
    "            'False Positive Rate': fp / (fp + tn) if (fp + tn) > 0 else np.nan\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(rows).sort_values('Count', ascending=False)\n",
    "\n",
    "fairness_group_cols = [\n",
    "    c for c in ['income_group', 'home_ownership', 'purpose', 'emp_length', 'grade']\n",
    "    if c in fairness_df.columns\n",
    "]\n",
    "\n",
    "for group_col in fairness_group_cols:\n",
    "    print(f\"===== Fairness table by {group_col} =====\")\n",
    "    table = subgroup_fairness_table(fairness_df, group_col)\n",
    "    display(table.round(4).head(15))\n",
    "\n",
    "    table.head(15).plot(\n",
    "        x='Group',\n",
    "        y=['Actual Default Rate', 'Predicted Default Rate'],\n",
    "        kind='bar',\n",
    "        figsize=(10, 4),\n",
    "        edgecolor='white'\n",
    "    )\n",
    "    plt.title(f'Actual vs Predicted Default Rate by {group_col}')\n",
    "    plt.ylabel('Rate')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}