credit_risk_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn==0.9.0 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (0.9.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from imbalanced-learn==0.9.0) (1.1.0)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from imbalanced-learn==0.9.0) (1.7.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from imbalanced-learn==0.9.0) (2.2.0)\n",
      "Collecting scikit-learn>=1.0.1\n",
      "  Using cached scikit_learn-1.0.2-cp37-cp37m-win_amd64.whl (7.1 MB)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from imbalanced-learn==0.9.0) (1.21.5)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 1.0\n",
      "    Uninstalling scikit-learn-1.0:\n",
      "      Successfully uninstalled scikit-learn-1.0\n",
      "Successfully installed scikit-learn-1.0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install imbalanced-learn==0.9.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn==1.0\n",
      "  Using cached scikit_learn-1.0-cp37-cp37m-win_amd64.whl (7.1 MB)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from scikit-learn==1.0) (1.1.0)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from scikit-learn==1.0) (1.21.5)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from scikit-learn==1.0) (2.2.0)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\dee\\anaconda3\\envs\\mlenv\\lib\\site-packages (from scikit-learn==1.0) (1.7.3)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 1.0.2\n",
      "    Uninstalling scikit-learn-1.0.2:\n",
      "      Successfully uninstalled scikit-learn-1.0.2\n",
      "Successfully installed scikit-learn-1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "imbalanced-learn 0.9.0 requires scikit-learn>=1.0.1, but you have scikit-learn 1.0 which is incompatible.\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn==1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://help.lendingclub.com/hc/en-us/articles/215488038-What-do-the-different-Note-statuses-mean-\n",
    "\n",
    "columns = [\n",
    "    \"loan_amnt\", \"int_rate\", \"installment\", \"home_ownership\",\n",
    "    \"annual_inc\", \"verification_status\", \"issue_d\", \"loan_status\",\n",
    "    \"pymnt_plan\", \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
    "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
    "    \"initial_list_status\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\",\n",
    "    \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
    "    \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"next_pymnt_d\",\n",
    "    \"collections_12_mths_ex_med\", \"policy_code\", \"application_type\", \"acc_now_delinq\",\n",
    "    \"tot_coll_amt\", \"tot_cur_bal\", \"open_acc_6m\", \"open_act_il\",\n",
    "    \"open_il_12m\", \"open_il_24m\", \"mths_since_rcnt_il\", \"total_bal_il\",\n",
    "    \"il_util\", \"open_rv_12m\", \"open_rv_24m\", \"max_bal_bc\",\n",
    "    \"all_util\", \"total_rev_hi_lim\", \"inq_fi\", \"total_cu_tl\",\n",
    "    \"inq_last_12m\", \"acc_open_past_24mths\", \"avg_cur_bal\", \"bc_open_to_buy\",\n",
    "    \"bc_util\", \"chargeoff_within_12_mths\", \"delinq_amnt\", \"mo_sin_old_il_acct\",\n",
    "    \"mo_sin_old_rev_tl_op\", \"mo_sin_rcnt_rev_tl_op\", \"mo_sin_rcnt_tl\", \"mort_acc\",\n",
    "    \"mths_since_recent_bc\", \"mths_since_recent_inq\", \"num_accts_ever_120_pd\", \"num_actv_bc_tl\",\n",
    "    \"num_actv_rev_tl\", \"num_bc_sats\", \"num_bc_tl\", \"num_il_tl\",\n",
    "    \"num_op_rev_tl\", \"num_rev_accts\", \"num_rev_tl_bal_gt_0\",\n",
    "    \"num_sats\", \"num_tl_120dpd_2m\", \"num_tl_30dpd\", \"num_tl_90g_dpd_24m\",\n",
    "    \"num_tl_op_past_12m\", \"pct_tl_nvr_dlq\", \"percent_bc_gt_75\", \"pub_rec_bankruptcies\",\n",
    "    \"tax_liens\", \"tot_hi_cred_lim\", \"total_bal_ex_mort\", \"total_bc_limit\",\n",
    "    \"total_il_high_credit_limit\", \"hardship_flag\", \"debt_settlement_flag\"\n",
    "]\n",
    "\n",
    "target = [\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>375.35</td>\n",
       "      <td>RENT</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65687.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>61987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>929.09</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>20.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271427.0</td>\n",
       "      <td>60641.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>49197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>24.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60644.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>43144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</td>\n",
       "      <td>RENT</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>31.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99506.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>19700.0</td>\n",
       "      <td>76506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>520.39</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>18.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219750.0</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>27600.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68812</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1502</td>\n",
       "      <td>346.76</td>\n",
       "      <td>RENT</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>9.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20625.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>11300.0</td>\n",
       "      <td>5425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68813</th>\n",
       "      <td>12000.0</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>368.37</td>\n",
       "      <td>RENT</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>29.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>96.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87939.0</td>\n",
       "      <td>60350.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>62939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68814</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>185.62</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>14.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30592.0</td>\n",
       "      <td>18611.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>18492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68815</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>1225.24</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>520000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>9.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>98.2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1033574.0</td>\n",
       "      <td>95958.0</td>\n",
       "      <td>100800.0</td>\n",
       "      <td>78634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68816</th>\n",
       "      <td>16000.0</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>350.36</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>7.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>251486.0</td>\n",
       "      <td>74835.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>63090.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68817 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  int_rate  installment home_ownership  annual_inc  \\\n",
       "0        10500.0    0.1719       375.35           RENT     66000.0   \n",
       "1        25000.0    0.2000       929.09       MORTGAGE    105000.0   \n",
       "2        20000.0    0.2000       529.88       MORTGAGE     56000.0   \n",
       "3        10000.0    0.1640       353.55           RENT     92000.0   \n",
       "4        22000.0    0.1474       520.39       MORTGAGE     52000.0   \n",
       "...          ...       ...          ...            ...         ...   \n",
       "68812    10000.0    0.1502       346.76           RENT     26000.0   \n",
       "68813    12000.0    0.2727       368.37           RENT     63000.0   \n",
       "68814     5000.0    0.1992       185.62       MORTGAGE     52000.0   \n",
       "68815    40000.0    0.0646      1225.24       MORTGAGE    520000.0   \n",
       "68816    16000.0    0.1131       350.36       MORTGAGE     72000.0   \n",
       "\n",
       "      verification_status   issue_d loan_status    dti  delinq_2yrs  ...  \\\n",
       "0         Source Verified  Mar-2019    low_risk  27.24          0.0  ...   \n",
       "1                Verified  Mar-2019    low_risk  20.23          0.0  ...   \n",
       "2                Verified  Mar-2019    low_risk  24.26          0.0  ...   \n",
       "3                Verified  Mar-2019    low_risk  31.44          0.0  ...   \n",
       "4            Not Verified  Mar-2019    low_risk  18.76          0.0  ...   \n",
       "...                   ...       ...         ...    ...          ...  ...   \n",
       "68812     Source Verified  Jan-2019    low_risk   9.60          0.0  ...   \n",
       "68813        Not Verified  Jan-2019    low_risk  29.07          0.0  ...   \n",
       "68814     Source Verified  Jan-2019    low_risk  14.86          0.0  ...   \n",
       "68815            Verified  Jan-2019    low_risk   9.96          0.0  ...   \n",
       "68816            Verified  Jan-2019    low_risk   7.02          2.0  ...   \n",
       "\n",
       "       num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  \\\n",
       "0                     0.0                 3.0            85.7   \n",
       "1                     0.0                 6.0            91.2   \n",
       "2                     0.0                 4.0            66.7   \n",
       "3                     0.0                 1.0           100.0   \n",
       "4                     0.0                 1.0           100.0   \n",
       "...                   ...                 ...             ...   \n",
       "68812                 0.0                 3.0            80.0   \n",
       "68813                 0.0                 5.0            96.2   \n",
       "68814                 0.0                 3.0           100.0   \n",
       "68815                 0.0                 3.0            98.2   \n",
       "68816                 2.0                 0.0            94.3   \n",
       "\n",
       "       percent_bc_gt_75  pub_rec_bankruptcies tax_liens  tot_hi_cred_lim  \\\n",
       "0                 100.0                   0.0       0.0          65687.0   \n",
       "1                  50.0                   1.0       0.0         271427.0   \n",
       "2                  50.0                   0.0       0.0          60644.0   \n",
       "3                  50.0                   1.0       0.0          99506.0   \n",
       "4                   0.0                   0.0       0.0         219750.0   \n",
       "...                 ...                   ...       ...              ...   \n",
       "68812               0.0                   0.0       0.0          20625.0   \n",
       "68813               0.0                   0.0       0.0          87939.0   \n",
       "68814               0.0                   1.0       0.0          30592.0   \n",
       "68815              12.5                   0.0       0.0        1033574.0   \n",
       "68816               0.0                   1.0       0.0         251486.0   \n",
       "\n",
       "       total_bal_ex_mort  total_bc_limit  total_il_high_credit_limit  \n",
       "0                38199.0          2000.0                     61987.0  \n",
       "1                60641.0         41200.0                     49197.0  \n",
       "2                45684.0          7500.0                     43144.0  \n",
       "3                68784.0         19700.0                     76506.0  \n",
       "4                25919.0         27600.0                     20000.0  \n",
       "...                  ...             ...                         ...  \n",
       "68812             6798.0         11300.0                      5425.0  \n",
       "68813            60350.0         13500.0                     62939.0  \n",
       "68814            18611.0          3600.0                     18492.0  \n",
       "68815            95958.0        100800.0                     78634.0  \n",
       "68816            74835.0         23000.0                     63090.0  \n",
       "\n",
       "[68817 rows x 83 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('./Data/clean_loans.csv')\n",
    "df = pd.read_csv(file_path)\n",
    "df\n",
    "# df = df.loc[:, columns].copy()\n",
    "\n",
    "# # Drop the null columns where all values are null\n",
    "# df = df.dropna(axis='columns', how='all')\n",
    "\n",
    "# # Drop the null rows\n",
    "# df = df.dropna()\n",
    "\n",
    "# # Remove the `Issued` loan status\n",
    "# issued_mask = df['loan_status'] != 'Issued'\n",
    "# df = df.loc[issued_mask]\n",
    "\n",
    "# # convert interest rate to numerical\n",
    "# df['int_rate'] = df['int_rate'].str.replace('%', '')\n",
    "# df['int_rate'] = df['int_rate'].astype('float') / 100\n",
    "\n",
    "\n",
    "# # Convert the target column values to low_risk and high_risk based on their values\n",
    "# x = {'Current': 'low_risk'}   \n",
    "# df = df.replace(x)\n",
    "\n",
    "# x = dict.fromkeys(['Late (31-120 days)', 'Late (16-30 days)', 'Default', 'In Grace Period'], 'high_risk')    \n",
    "# df = df.replace(x)\n",
    "\n",
    "# df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.1719</td>\n",
       "      <td>375.35</td>\n",
       "      <td>RENT</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>85.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65687.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>61987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>929.09</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>20.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>91.2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>271427.0</td>\n",
       "      <td>60641.0</td>\n",
       "      <td>41200.0</td>\n",
       "      <td>49197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>529.88</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>24.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60644.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>43144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</td>\n",
       "      <td>RENT</td>\n",
       "      <td>92000.0</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>31.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99506.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>19700.0</td>\n",
       "      <td>76506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.1474</td>\n",
       "      <td>520.39</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>18.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219750.0</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>27600.0</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment home_ownership  annual_inc  \\\n",
       "0    10500.0    0.1719       375.35           RENT     66000.0   \n",
       "1    25000.0    0.2000       929.09       MORTGAGE    105000.0   \n",
       "2    20000.0    0.2000       529.88       MORTGAGE     56000.0   \n",
       "3    10000.0    0.1640       353.55           RENT     92000.0   \n",
       "4    22000.0    0.1474       520.39       MORTGAGE     52000.0   \n",
       "\n",
       "  verification_status   issue_d    dti  delinq_2yrs  inq_last_6mths  ...  \\\n",
       "0     Source Verified  Mar-2019  27.24          0.0             0.0  ...   \n",
       "1            Verified  Mar-2019  20.23          0.0             0.0  ...   \n",
       "2            Verified  Mar-2019  24.26          0.0             0.0  ...   \n",
       "3            Verified  Mar-2019  31.44          0.0             1.0  ...   \n",
       "4        Not Verified  Mar-2019  18.76          0.0             1.0  ...   \n",
       "\n",
       "   num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "0                 0.0                 3.0            85.7             100.0   \n",
       "1                 0.0                 6.0            91.2              50.0   \n",
       "2                 0.0                 4.0            66.7              50.0   \n",
       "3                 0.0                 1.0           100.0              50.0   \n",
       "4                 0.0                 1.0           100.0               0.0   \n",
       "\n",
       "  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "0                  0.0        0.0          65687.0            38199.0   \n",
       "1                  1.0        0.0         271427.0            60641.0   \n",
       "2                  0.0        0.0          60644.0            45684.0   \n",
       "3                  1.0        0.0          99506.0            68784.0   \n",
       "4                  0.0        0.0         219750.0            25919.0   \n",
       "\n",
       "   total_bc_limit  total_il_high_credit_limit  \n",
       "0          2000.0                     61987.0  \n",
       "1         41200.0                     49197.0  \n",
       "2          7500.0                     43144.0  \n",
       "3         19700.0                     76506.0  \n",
       "4         27600.0                     20000.0  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create our features\n",
    "X = df.drop('loan_status', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create our target\n",
    "y = df['loan_status']\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16677.594562</td>\n",
       "      <td>0.127718</td>\n",
       "      <td>480.652863</td>\n",
       "      <td>8.821371e+04</td>\n",
       "      <td>21.778153</td>\n",
       "      <td>0.217766</td>\n",
       "      <td>0.497697</td>\n",
       "      <td>12.587340</td>\n",
       "      <td>0.126030</td>\n",
       "      <td>17604.142828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052138</td>\n",
       "      <td>2.219423</td>\n",
       "      <td>95.057627</td>\n",
       "      <td>30.626217</td>\n",
       "      <td>0.125972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.100332e+05</td>\n",
       "      <td>6.133843e+04</td>\n",
       "      <td>29734.128558</td>\n",
       "      <td>5.572240e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10277.348590</td>\n",
       "      <td>0.048130</td>\n",
       "      <td>288.062432</td>\n",
       "      <td>1.155800e+05</td>\n",
       "      <td>20.199244</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.758122</td>\n",
       "      <td>6.022869</td>\n",
       "      <td>0.336797</td>\n",
       "      <td>21835.880400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390633</td>\n",
       "      <td>1.897432</td>\n",
       "      <td>8.326426</td>\n",
       "      <td>33.631463</td>\n",
       "      <td>0.336732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.928088e+05</td>\n",
       "      <td>5.738798e+04</td>\n",
       "      <td>26795.394232</td>\n",
       "      <td>5.095845e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>30.890000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.600000e+03</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.270000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>0.088100</td>\n",
       "      <td>265.730000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>13.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6293.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.697700e+04</td>\n",
       "      <td>2.650300e+04</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>2.288000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>404.560000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12068.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.467100e+05</td>\n",
       "      <td>4.535700e+04</td>\n",
       "      <td>22100.000000</td>\n",
       "      <td>4.200000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>0.155700</td>\n",
       "      <td>648.100000</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>26.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.036400e+05</td>\n",
       "      <td>7.657000e+04</td>\n",
       "      <td>39300.000000</td>\n",
       "      <td>7.249900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>8.797500e+06</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>587191.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.292782e+06</td>\n",
       "      <td>1.295455e+06</td>\n",
       "      <td>509400.000000</td>\n",
       "      <td>1.426964e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate   installment    annual_inc           dti  \\\n",
       "count  68817.000000  68817.000000  68817.000000  6.881700e+04  68817.000000   \n",
       "mean   16677.594562      0.127718    480.652863  8.821371e+04     21.778153   \n",
       "std    10277.348590      0.048130    288.062432  1.155800e+05     20.199244   \n",
       "min     1000.000000      0.060000     30.890000  4.000000e+01      0.000000   \n",
       "25%     9000.000000      0.088100    265.730000  5.000000e+04     13.890000   \n",
       "50%    15000.000000      0.118000    404.560000  7.300000e+04     19.760000   \n",
       "75%    24000.000000      0.155700    648.100000  1.040000e+05     26.660000   \n",
       "max    40000.000000      0.308400   1676.230000  8.797500e+06    999.000000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths      open_acc       pub_rec  \\\n",
       "count  68817.000000    68817.000000  68817.000000  68817.000000   \n",
       "mean       0.217766        0.497697     12.587340      0.126030   \n",
       "std        0.718367        0.758122      6.022869      0.336797   \n",
       "min        0.000000        0.000000      2.000000      0.000000   \n",
       "25%        0.000000        0.000000      8.000000      0.000000   \n",
       "50%        0.000000        0.000000     11.000000      0.000000   \n",
       "75%        0.000000        1.000000     16.000000      0.000000   \n",
       "max       18.000000        5.000000     72.000000      4.000000   \n",
       "\n",
       "           revol_bal  ...  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "count   68817.000000  ...        68817.000000        68817.000000   \n",
       "mean    17604.142828  ...            0.052138            2.219423   \n",
       "std     21835.880400  ...            0.390633            1.897432   \n",
       "min         0.000000  ...            0.000000            0.000000   \n",
       "25%      6293.000000  ...            0.000000            1.000000   \n",
       "50%     12068.000000  ...            0.000000            2.000000   \n",
       "75%     21735.000000  ...            0.000000            3.000000   \n",
       "max    587191.000000  ...           18.000000           19.000000   \n",
       "\n",
       "       pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "count    68817.000000      68817.000000          68817.000000    68817.0   \n",
       "mean        95.057627         30.626217              0.125972        0.0   \n",
       "std          8.326426         33.631463              0.336732        0.0   \n",
       "min         20.000000          0.000000              0.000000        0.0   \n",
       "25%         93.000000          0.000000              0.000000        0.0   \n",
       "50%        100.000000         20.000000              0.000000        0.0   \n",
       "75%        100.000000         50.000000              0.000000        0.0   \n",
       "max        100.000000        100.000000              4.000000        0.0   \n",
       "\n",
       "       tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "count     6.881700e+04       6.881700e+04    68817.000000   \n",
       "mean      2.100332e+05       6.133843e+04    29734.128558   \n",
       "std       1.928088e+05       5.738798e+04    26795.394232   \n",
       "min       3.600000e+03       2.350000e+02      100.000000   \n",
       "25%       6.697700e+04       2.650300e+04    11600.000000   \n",
       "50%       1.467100e+05       4.535700e+04    22100.000000   \n",
       "75%       3.036400e+05       7.657000e+04    39300.000000   \n",
       "max       3.292782e+06       1.295455e+06   509400.000000   \n",
       "\n",
       "       total_il_high_credit_limit  \n",
       "count                6.881700e+04  \n",
       "mean                 5.572240e+04  \n",
       "std                  5.095845e+04  \n",
       "min                  1.270000e+02  \n",
       "25%                  2.288000e+04  \n",
       "50%                  4.200000e+04  \n",
       "75%                  7.249900e+04  \n",
       "max                  1.426964e+06  \n",
       "\n",
       "[8 rows x 76 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>verification_status_Verified</th>\n",
       "      <th>issue_d_Feb-2019</th>\n",
       "      <th>issue_d_Jan-2019</th>\n",
       "      <th>issue_d_Mar-2019</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_Apr-2019</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16677.594562</td>\n",
       "      <td>0.127718</td>\n",
       "      <td>480.652863</td>\n",
       "      <td>8.821371e+04</td>\n",
       "      <td>21.778153</td>\n",
       "      <td>0.217766</td>\n",
       "      <td>0.497697</td>\n",
       "      <td>12.587340</td>\n",
       "      <td>0.126030</td>\n",
       "      <td>17604.142828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148001</td>\n",
       "      <td>0.371696</td>\n",
       "      <td>0.451066</td>\n",
       "      <td>0.177238</td>\n",
       "      <td>0.123879</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.383161</td>\n",
       "      <td>0.616839</td>\n",
       "      <td>0.860340</td>\n",
       "      <td>0.139660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10277.348590</td>\n",
       "      <td>0.048130</td>\n",
       "      <td>288.062432</td>\n",
       "      <td>1.155800e+05</td>\n",
       "      <td>20.199244</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.758122</td>\n",
       "      <td>6.022869</td>\n",
       "      <td>0.336797</td>\n",
       "      <td>21835.880400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.355104</td>\n",
       "      <td>0.483261</td>\n",
       "      <td>0.497603</td>\n",
       "      <td>0.381873</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>0.329446</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.486161</td>\n",
       "      <td>0.346637</td>\n",
       "      <td>0.346637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>30.890000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>0.088100</td>\n",
       "      <td>265.730000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>13.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6293.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>404.560000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12068.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>0.155700</td>\n",
       "      <td>648.100000</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>26.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>8.797500e+06</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>587191.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate   installment    annual_inc           dti  \\\n",
       "count  68817.000000  68817.000000  68817.000000  6.881700e+04  68817.000000   \n",
       "mean   16677.594562      0.127718    480.652863  8.821371e+04     21.778153   \n",
       "std    10277.348590      0.048130    288.062432  1.155800e+05     20.199244   \n",
       "min     1000.000000      0.060000     30.890000  4.000000e+01      0.000000   \n",
       "25%     9000.000000      0.088100    265.730000  5.000000e+04     13.890000   \n",
       "50%    15000.000000      0.118000    404.560000  7.300000e+04     19.760000   \n",
       "75%    24000.000000      0.155700    648.100000  1.040000e+05     26.660000   \n",
       "max    40000.000000      0.308400   1676.230000  8.797500e+06    999.000000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths      open_acc       pub_rec  \\\n",
       "count  68817.000000    68817.000000  68817.000000  68817.000000   \n",
       "mean       0.217766        0.497697     12.587340      0.126030   \n",
       "std        0.718367        0.758122      6.022869      0.336797   \n",
       "min        0.000000        0.000000      2.000000      0.000000   \n",
       "25%        0.000000        0.000000      8.000000      0.000000   \n",
       "50%        0.000000        0.000000     11.000000      0.000000   \n",
       "75%        0.000000        1.000000     16.000000      0.000000   \n",
       "max       18.000000        5.000000     72.000000      4.000000   \n",
       "\n",
       "           revol_bal  ...  verification_status_Verified  issue_d_Feb-2019  \\\n",
       "count   68817.000000  ...                  68817.000000      68817.000000   \n",
       "mean    17604.142828  ...                      0.148001          0.371696   \n",
       "std     21835.880400  ...                      0.355104          0.483261   \n",
       "min         0.000000  ...                      0.000000          0.000000   \n",
       "25%      6293.000000  ...                      0.000000          0.000000   \n",
       "50%     12068.000000  ...                      0.000000          0.000000   \n",
       "75%     21735.000000  ...                      0.000000          1.000000   \n",
       "max    587191.000000  ...                      1.000000          1.000000   \n",
       "\n",
       "       issue_d_Jan-2019  issue_d_Mar-2019  initial_list_status_f  \\\n",
       "count      68817.000000      68817.000000           68817.000000   \n",
       "mean           0.451066          0.177238               0.123879   \n",
       "std            0.497603          0.381873               0.329446   \n",
       "min            0.000000          0.000000               0.000000   \n",
       "25%            0.000000          0.000000               0.000000   \n",
       "50%            0.000000          0.000000               0.000000   \n",
       "75%            1.000000          0.000000               0.000000   \n",
       "max            1.000000          1.000000               1.000000   \n",
       "\n",
       "       initial_list_status_w  next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  \\\n",
       "count           68817.000000           68817.000000           68817.000000   \n",
       "mean                0.876121               0.383161               0.616839   \n",
       "std                 0.329446               0.486161               0.486161   \n",
       "min                 0.000000               0.000000               0.000000   \n",
       "25%                 1.000000               0.000000               0.000000   \n",
       "50%                 1.000000               0.000000               1.000000   \n",
       "75%                 1.000000               1.000000               1.000000   \n",
       "max                 1.000000               1.000000               1.000000   \n",
       "\n",
       "       application_type_Individual  application_type_Joint App  \n",
       "count                 68817.000000                68817.000000  \n",
       "mean                      0.860340                    0.139660  \n",
       "std                       0.346637                    0.346637  \n",
       "min                       0.000000                    0.000000  \n",
       "25%                       1.000000                    0.000000  \n",
       "50%                       1.000000                    0.000000  \n",
       "75%                       1.000000                    0.000000  \n",
       "max                       1.000000                    1.000000  \n",
       "\n",
       "[8 rows x 92 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use get_dummies to transform object dtypes into int\n",
    "X_encoded = pd.get_dummies(X)\n",
    "X_encoded.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 68817 entries, 0 to 68816\n",
      "Data columns (total 92 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   loan_amnt                            68817 non-null  float64\n",
      " 1   int_rate                             68817 non-null  float64\n",
      " 2   installment                          68817 non-null  float64\n",
      " 3   annual_inc                           68817 non-null  float64\n",
      " 4   dti                                  68817 non-null  float64\n",
      " 5   delinq_2yrs                          68817 non-null  float64\n",
      " 6   inq_last_6mths                       68817 non-null  float64\n",
      " 7   open_acc                             68817 non-null  float64\n",
      " 8   pub_rec                              68817 non-null  float64\n",
      " 9   revol_bal                            68817 non-null  float64\n",
      " 10  total_acc                            68817 non-null  float64\n",
      " 11  out_prncp                            68817 non-null  float64\n",
      " 12  out_prncp_inv                        68817 non-null  float64\n",
      " 13  total_pymnt                          68817 non-null  float64\n",
      " 14  total_pymnt_inv                      68817 non-null  float64\n",
      " 15  total_rec_prncp                      68817 non-null  float64\n",
      " 16  total_rec_int                        68817 non-null  float64\n",
      " 17  total_rec_late_fee                   68817 non-null  float64\n",
      " 18  recoveries                           68817 non-null  float64\n",
      " 19  collection_recovery_fee              68817 non-null  float64\n",
      " 20  last_pymnt_amnt                      68817 non-null  float64\n",
      " 21  collections_12_mths_ex_med           68817 non-null  float64\n",
      " 22  policy_code                          68817 non-null  float64\n",
      " 23  acc_now_delinq                       68817 non-null  float64\n",
      " 24  tot_coll_amt                         68817 non-null  float64\n",
      " 25  tot_cur_bal                          68817 non-null  float64\n",
      " 26  open_acc_6m                          68817 non-null  float64\n",
      " 27  open_act_il                          68817 non-null  float64\n",
      " 28  open_il_12m                          68817 non-null  float64\n",
      " 29  open_il_24m                          68817 non-null  float64\n",
      " 30  mths_since_rcnt_il                   68817 non-null  float64\n",
      " 31  total_bal_il                         68817 non-null  float64\n",
      " 32  il_util                              68817 non-null  float64\n",
      " 33  open_rv_12m                          68817 non-null  float64\n",
      " 34  open_rv_24m                          68817 non-null  float64\n",
      " 35  max_bal_bc                           68817 non-null  float64\n",
      " 36  all_util                             68817 non-null  float64\n",
      " 37  total_rev_hi_lim                     68817 non-null  float64\n",
      " 38  inq_fi                               68817 non-null  float64\n",
      " 39  total_cu_tl                          68817 non-null  float64\n",
      " 40  inq_last_12m                         68817 non-null  float64\n",
      " 41  acc_open_past_24mths                 68817 non-null  float64\n",
      " 42  avg_cur_bal                          68817 non-null  float64\n",
      " 43  bc_open_to_buy                       68817 non-null  float64\n",
      " 44  bc_util                              68817 non-null  float64\n",
      " 45  chargeoff_within_12_mths             68817 non-null  float64\n",
      " 46  delinq_amnt                          68817 non-null  float64\n",
      " 47  mo_sin_old_il_acct                   68817 non-null  float64\n",
      " 48  mo_sin_old_rev_tl_op                 68817 non-null  float64\n",
      " 49  mo_sin_rcnt_rev_tl_op                68817 non-null  float64\n",
      " 50  mo_sin_rcnt_tl                       68817 non-null  float64\n",
      " 51  mort_acc                             68817 non-null  float64\n",
      " 52  mths_since_recent_bc                 68817 non-null  float64\n",
      " 53  mths_since_recent_inq                68817 non-null  float64\n",
      " 54  num_accts_ever_120_pd                68817 non-null  float64\n",
      " 55  num_actv_bc_tl                       68817 non-null  float64\n",
      " 56  num_actv_rev_tl                      68817 non-null  float64\n",
      " 57  num_bc_sats                          68817 non-null  float64\n",
      " 58  num_bc_tl                            68817 non-null  float64\n",
      " 59  num_il_tl                            68817 non-null  float64\n",
      " 60  num_op_rev_tl                        68817 non-null  float64\n",
      " 61  num_rev_accts                        68817 non-null  float64\n",
      " 62  num_rev_tl_bal_gt_0                  68817 non-null  float64\n",
      " 63  num_sats                             68817 non-null  float64\n",
      " 64  num_tl_120dpd_2m                     68817 non-null  float64\n",
      " 65  num_tl_30dpd                         68817 non-null  float64\n",
      " 66  num_tl_90g_dpd_24m                   68817 non-null  float64\n",
      " 67  num_tl_op_past_12m                   68817 non-null  float64\n",
      " 68  pct_tl_nvr_dlq                       68817 non-null  float64\n",
      " 69  percent_bc_gt_75                     68817 non-null  float64\n",
      " 70  pub_rec_bankruptcies                 68817 non-null  float64\n",
      " 71  tax_liens                            68817 non-null  float64\n",
      " 72  tot_hi_cred_lim                      68817 non-null  float64\n",
      " 73  total_bal_ex_mort                    68817 non-null  float64\n",
      " 74  total_bc_limit                       68817 non-null  float64\n",
      " 75  total_il_high_credit_limit           68817 non-null  float64\n",
      " 76  home_ownership_ANY                   68817 non-null  uint8  \n",
      " 77  home_ownership_MORTGAGE              68817 non-null  uint8  \n",
      " 78  home_ownership_OWN                   68817 non-null  uint8  \n",
      " 79  home_ownership_RENT                  68817 non-null  uint8  \n",
      " 80  verification_status_Not Verified     68817 non-null  uint8  \n",
      " 81  verification_status_Source Verified  68817 non-null  uint8  \n",
      " 82  verification_status_Verified         68817 non-null  uint8  \n",
      " 83  issue_d_Feb-2019                     68817 non-null  uint8  \n",
      " 84  issue_d_Jan-2019                     68817 non-null  uint8  \n",
      " 85  issue_d_Mar-2019                     68817 non-null  uint8  \n",
      " 86  initial_list_status_f                68817 non-null  uint8  \n",
      " 87  initial_list_status_w                68817 non-null  uint8  \n",
      " 88  next_pymnt_d_Apr-2019                68817 non-null  uint8  \n",
      " 89  next_pymnt_d_May-2019                68817 non-null  uint8  \n",
      " 90  application_type_Individual          68817 non-null  uint8  \n",
      " 91  application_type_Joint App           68817 non-null  uint8  \n",
      "dtypes: float64(76), uint8(16)\n",
      "memory usage: 41.0 MB\n"
     ]
    }
   ],
   "source": [
    "X_encoded.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68817, 92)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68817,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.60109272,  0.91798436, -0.36555835, -0.1921948 ,  0.27040056,\n",
       "        -0.30314253, -0.65649151, -0.76165917, -0.37420432, -0.73252183,\n",
       "        -0.8658573 , -0.58124513, -0.58081958, -0.50046544, -0.50029335,\n",
       "        -0.3228639 , -0.89960419, -0.03227363,  0.        ,  0.        ,\n",
       "        -0.17383528, -0.12011247,  0.        ,  0.        , -0.00589695,\n",
       "        -0.72692228,  1.67997825,  0.2392533 ,  1.19137226,  1.35299632,\n",
       "        -0.80895603, -0.13418715, -0.45717571, -0.16310175, -0.24864022,\n",
       "        -0.83203994,  0.12422197, -1.04213361, -0.24758239, -0.59166015,\n",
       "         0.75578041,  0.37274923, -0.59052419, -0.80915288,  1.04494582,\n",
       "        -0.07820908, -0.00778953, -0.56994022, -0.7981628 , -0.64845959,\n",
       "        -0.66313947, -0.8249868 , -0.14674317, -1.10824038, -0.33490355,\n",
       "        -1.13862411, -1.05103022, -1.27998521, -1.40011594,  0.06599699,\n",
       "        -1.11347944, -1.31043257, -1.0519437 , -0.75930693,  0.        ,\n",
       "         0.        , -0.13347217,  0.41138923, -1.12385485,  2.06277933,\n",
       "        -0.37410344,  0.        , -0.74865495, -0.40321335, -1.03504094,\n",
       "         0.12293632, -0.0968118 , -1.054078  , -0.34569279,  1.34013488,\n",
       "        -0.95693996,  1.29377539, -0.41678604, -0.76914635, -0.90648342,\n",
       "         2.15455915, -0.37602554,  0.37602554, -0.78814271,  0.78814271,\n",
       "         0.40290386, -0.40290386]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the scaler instance\n",
    "data_scaler = StandardScaler()\n",
    "X_scaled = data_scaler.fit_transform(X_encoded)\n",
    "X_scaled[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     51366\n",
       "high_risk      246\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learners\n",
    "\n",
    "In this section, you will compare two ensemble algorithms to determine which algorithm results in the best performance. You will train a Balanced Random Forest Classifier and an Easy Ensemble AdaBoost classifier . For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. Train the model using the training data. \n",
    "2. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "3. Print the confusion matrix from sklearn.metrics.\n",
    "4. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "5. For the Balanced Random Forest Classifier onely, print the feature importance sorted in descending order (most important feature to least important) along with the feature score\n",
    "\n",
    "Note: Use a random state of 1 for each algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "# model\n",
    "brf_model = BalancedRandomForestClassifier(n_estimators=100,random_state=1) \n",
    "\n",
    "# fit\n",
    "brf_model = brf_model.fit(X_train, y_train)\n",
    "\n",
    "# # predict\n",
    "predictions = brf_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The balanced accuracy score is: 0.759\n"
     ]
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "print(f\"The balanced accuracy score is: {balanced_accuracy_score(y_test,predictions):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7589504857875872"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balanced_accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   64    37]\n",
      " [ 1980 15124]]\n"
     ]
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "matrix = confusion_matrix(y_test, predictions)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=matrix,display_labels=brf_model.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/BalancedRandomForest_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.63      0.88      0.06      0.75      0.55       101\n",
      "   low_risk       1.00      0.88      0.63      0.94      0.75      0.57     17104\n",
      "\n",
      "avg / total       0.99      0.88      0.64      0.93      0.75      0.57     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "report_imbalanced = classification_report_imbalanced(y_test, predictions) \n",
    "print(report_imbalanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0.011204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.029096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>installment</td>\n",
       "      <td>0.018744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0.015412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dti</td>\n",
       "      <td>0.017872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>initial_list_status_w</td>\n",
       "      <td>0.001988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>next_pymnt_d_Apr-2019</td>\n",
       "      <td>0.004364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>next_pymnt_d_May-2019</td>\n",
       "      <td>0.004253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>application_type_Individual</td>\n",
       "      <td>0.001842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>application_type_Joint App</td>\n",
       "      <td>0.002701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        feature  importance\n",
       "0                     loan_amnt    0.011204\n",
       "1                      int_rate    0.029096\n",
       "2                   installment    0.018744\n",
       "3                    annual_inc    0.015412\n",
       "4                           dti    0.017872\n",
       "..                          ...         ...\n",
       "87        initial_list_status_w    0.001988\n",
       "88        next_pymnt_d_Apr-2019    0.004364\n",
       "89        next_pymnt_d_May-2019    0.004253\n",
       "90  application_type_Individual    0.001842\n",
       "91   application_type_Joint App    0.002701\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "importances = brf_model.feature_importances_\n",
    "\n",
    "# Put in a dataframe\n",
    "cols = X_encoded.columns\n",
    "\n",
    "# Create a dataframe\n",
    "important_features_df = pd.DataFrame({'feature':cols,'importance':importances})\n",
    "important_features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>16</th>\n",
       "      <td>total_rec_int</td>\n",
       "      <td>0.067485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>last_pymnt_amnt</td>\n",
       "      <td>0.065452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>total_pymnt</td>\n",
       "      <td>0.060943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>total_rec_prncp</td>\n",
       "      <td>0.053099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>total_pymnt_inv</td>\n",
       "      <td>0.048026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0.029096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>mths_since_recent_inq</td>\n",
       "      <td>0.020169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>issue_d_Jan-2019</td>\n",
       "      <td>0.019739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>installment</td>\n",
       "      <td>0.018744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>out_prncp_inv</td>\n",
       "      <td>0.018143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature  importance\n",
       "16          total_rec_int    0.067485\n",
       "20        last_pymnt_amnt    0.065452\n",
       "13            total_pymnt    0.060943\n",
       "15        total_rec_prncp    0.053099\n",
       "14        total_pymnt_inv    0.048026\n",
       "1                int_rate    0.029096\n",
       "53  mths_since_recent_inq    0.020169\n",
       "84       issue_d_Jan-2019    0.019739\n",
       "2             installment    0.018744\n",
       "12          out_prncp_inv    0.018143"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort in descending order\n",
    "important_features_df.sort_values('importance',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the EasyEnsembleClassifier\n",
    "# model\n",
    "ee_clf = EasyEnsembleClassifier(n_estimators=100,random_state=1)\n",
    "\n",
    "# fit\n",
    "ee_clf.fit(X_train, y_train)\n",
    "\n",
    "# predict\n",
    "y_pred = ee_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9319231677611166"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "balanced_accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   93     8]\n",
      " [  974 16130]]\n"
     ]
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "matrix = confusion_matrix(y_test, y_pred)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=matrix,display_labels=ee_clf.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/EasyEnsemble_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.09      0.92      0.94      0.16      0.93      0.87       101\n",
      "   low_risk       1.00      0.94      0.92      0.97      0.93      0.87     17104\n",
      "\n",
      "avg / total       0.99      0.94      0.92      0.97      0.93      0.87     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mlenv",
   "language": "python",
   "name": "mlenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}