credit_risk_resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Risk Resampling Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import sklearn dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imbalanced learn dependendencies\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import ClusterCentroids\n",
    "from imblearn.combine import SMOTEENN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>pymnt_plan</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>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</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",
       "      <th>hardship_flag</th>\n",
       "      <th>debt_settlement_flag</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>n</td>\n",
       "      <td>27.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>w</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>355.29</td>\n",
       "      <td>355.29</td>\n",
       "      <td>295.13</td>\n",
       "      <td>60.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.35</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36590.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>N</td>\n",
       "      <td>N</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>n</td>\n",
       "      <td>20.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18368.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>w</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>873.53</td>\n",
       "      <td>873.53</td>\n",
       "      <td>706.87</td>\n",
       "      <td>166.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>929.09</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>228119.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42273.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13419.0</td>\n",
       "      <td>29502.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>N</td>\n",
       "      <td>N</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>n</td>\n",
       "      <td>24.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13247.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>w</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>485.44</td>\n",
       "      <td>485.44</td>\n",
       "      <td>363.21</td>\n",
       "      <td>122.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.88</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32437.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5711.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>N</td>\n",
       "      <td>N</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>n</td>\n",
       "      <td>31.44</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>w</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>335.33</td>\n",
       "      <td>335.33</td>\n",
       "      <td>280.66</td>\n",
       "      <td>54.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.55</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50788.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>N</td>\n",
       "      <td>N</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>n</td>\n",
       "      <td>18.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9091.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>w</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>484.36</td>\n",
       "      <td>484.36</td>\n",
       "      <td>385.27</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.39</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>19699.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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 loan_status pymnt_plan    dti  delinq_2yrs  \\\n",
       "0     Source Verified  Mar-2019    low_risk          n  27.24          0.0   \n",
       "1            Verified  Mar-2019    low_risk          n  20.23          0.0   \n",
       "2            Verified  Mar-2019    low_risk          n  24.26          0.0   \n",
       "3            Verified  Mar-2019    low_risk          n  31.44          0.0   \n",
       "4        Not Verified  Mar-2019    low_risk          n  18.76          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  total_acc  \\\n",
       "0             0.0       8.0      0.0     1609.0       14.0   \n",
       "1             0.0      17.0      1.0    18368.0       34.0   \n",
       "2             0.0       8.0      0.0    13247.0       21.0   \n",
       "3             1.0      10.0      1.0    17996.0       23.0   \n",
       "4             1.0      14.0      0.0     9091.0       28.0   \n",
       "\n",
       "  initial_list_status  out_prncp  out_prncp_inv  total_pymnt  total_pymnt_inv  \\\n",
       "0                   w   10204.87       10204.87       355.29           355.29   \n",
       "1                   w   24293.13       24293.13       873.53           873.53   \n",
       "2                   w   19636.79       19636.79       485.44           485.44   \n",
       "3                   w    9719.34        9719.34       335.33           335.33   \n",
       "4                   w   21614.73       21614.73       484.36           484.36   \n",
       "\n",
       "   total_rec_prncp  total_rec_int  total_rec_late_fee  recoveries  \\\n",
       "0           295.13          60.16                 0.0         0.0   \n",
       "1           706.87         166.66                 0.0         0.0   \n",
       "2           363.21         122.23                 0.0         0.0   \n",
       "3           280.66          54.67                 0.0         0.0   \n",
       "4           385.27          99.09                 0.0         0.0   \n",
       "\n",
       "   collection_recovery_fee  last_pymnt_amnt next_pymnt_d  \\\n",
       "0                      0.0           375.35     May-2019   \n",
       "1                      0.0           929.09     May-2019   \n",
       "2                      0.0           529.88     May-2019   \n",
       "3                      0.0           353.55     May-2019   \n",
       "4                      0.0           520.39     May-2019   \n",
       "\n",
       "   collections_12_mths_ex_med  policy_code application_type  acc_now_delinq  \\\n",
       "0                         0.0          1.0       Individual             0.0   \n",
       "1                         0.0          1.0       Individual             0.0   \n",
       "2                         0.0          1.0       Individual             0.0   \n",
       "3                         0.0          1.0       Individual             0.0   \n",
       "4                         0.0          1.0       Individual             0.0   \n",
       "\n",
       "   tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  open_il_12m  \\\n",
       "0         178.0      38199.0          3.0          4.0          2.0   \n",
       "1         165.0     228119.0          3.0          3.0          2.0   \n",
       "2           0.0      45684.0          2.0          3.0          2.0   \n",
       "3           0.0      68784.0          0.0          4.0          1.0   \n",
       "4           0.0     177745.0          0.0          1.0          0.0   \n",
       "\n",
       "   open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  \\\n",
       "0          4.0                 3.0       36590.0     59.0          1.0   \n",
       "1          3.0                 4.0       42273.0     86.0          4.0   \n",
       "2          3.0                 4.0       32437.0     75.0          2.0   \n",
       "3          3.0                 9.0       50788.0     66.0          0.0   \n",
       "4          2.0                14.0       16828.0     84.0          1.0   \n",
       "\n",
       "   open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  total_cu_tl  \\\n",
       "0          2.0      1553.0      58.0            3700.0     1.0          0.0   \n",
       "1          8.0      5197.0      61.0           50400.0     4.0          9.0   \n",
       "2          3.0      2810.0      75.0           17500.0     0.0          4.0   \n",
       "3          1.0      6360.0      69.0           23000.0     3.0          5.0   \n",
       "4          1.0      3600.0      41.0           43100.0     2.0          0.0   \n",
       "\n",
       "   inq_last_12m  acc_open_past_24mths  avg_cur_bal  bc_open_to_buy  bc_util  \\\n",
       "0           4.0                   6.0       4775.0           447.0     77.7   \n",
       "1           3.0                  13.0      13419.0         29502.0     28.4   \n",
       "2           1.0                   6.0       5711.0          2839.0     62.1   \n",
       "3           4.0                   4.0       6878.0          4899.0     75.1   \n",
       "4           2.0                   4.0      13673.0         19699.0     28.6   \n",
       "\n",
       "   chargeoff_within_12_mths  delinq_amnt  mo_sin_old_il_acct  \\\n",
       "0                       0.0          0.0                93.0   \n",
       "1                       0.0          0.0                87.0   \n",
       "2                       0.0          0.0               159.0   \n",
       "3                       0.0          0.0               100.0   \n",
       "4                       0.0          0.0                67.0   \n",
       "\n",
       "   mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl  mort_acc  \\\n",
       "0                  96.0                    3.0             3.0       0.0   \n",
       "1                 286.0                    1.0             1.0       2.0   \n",
       "2                 156.0                    7.0             4.0       0.0   \n",
       "3                  89.0                   24.0             9.0       0.0   \n",
       "4                 138.0                   11.0            11.0       4.0   \n",
       "\n",
       "   mths_since_recent_bc  mths_since_recent_inq  num_accts_ever_120_pd  \\\n",
       "0                  20.0                    1.0                    0.0   \n",
       "1                   1.0                   11.0                    3.0   \n",
       "2                   7.0                    7.0                    6.0   \n",
       "3                  34.0                    0.0                    0.0   \n",
       "4                  11.0                    0.0                    0.0   \n",
       "\n",
       "   num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  num_bc_tl  num_il_tl  \\\n",
       "0             1.0              2.0          1.0        1.0       10.0   \n",
       "1             7.0             11.0          8.0       13.0       14.0   \n",
       "2             2.0              5.0          2.0        7.0        8.0   \n",
       "3             4.0              6.0          4.0        4.0       17.0   \n",
       "4             4.0              6.0          7.0       11.0        6.0   \n",
       "\n",
       "   num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  num_sats  \\\n",
       "0            3.0            3.0                  2.0       8.0   \n",
       "1           13.0           18.0                 11.0      17.0   \n",
       "2            5.0           13.0                  5.0       8.0   \n",
       "3            6.0            6.0                  6.0      10.0   \n",
       "4           12.0           18.0                  6.0      14.0   \n",
       "\n",
       "   num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0               0.0           0.0                 0.0                 3.0   \n",
       "1               0.0           0.0                 0.0                 6.0   \n",
       "2               0.0           0.0                 0.0                 4.0   \n",
       "3               0.0           0.0                 0.0                 1.0   \n",
       "4               0.0           0.0                 0.0                 1.0   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "0            85.7             100.0                   0.0        0.0   \n",
       "1            91.2              50.0                   1.0        0.0   \n",
       "2            66.7              50.0                   0.0        0.0   \n",
       "3           100.0              50.0                   1.0        0.0   \n",
       "4           100.0               0.0                   0.0        0.0   \n",
       "\n",
       "   tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0          65687.0            38199.0          2000.0   \n",
       "1         271427.0            60641.0         41200.0   \n",
       "2          60644.0            45684.0          7500.0   \n",
       "3          99506.0            68784.0         19700.0   \n",
       "4         219750.0            25919.0         27600.0   \n",
       "\n",
       "   total_il_high_credit_limit hardship_flag debt_settlement_flag  \n",
       "0                     61987.0             N                    N  \n",
       "1                     49197.0             N                    N  \n",
       "2                     43144.0             N                    N  \n",
       "3                     76506.0             N                    N  \n",
       "4                     20000.0             N                    N  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('./Data/LoanStats_2019Q1.csv')\n",
    "df = pd.read_csv(file_path, skiprows=1)[:-2]\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",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# Categorical columns\n",
    "columns_obj = df.select_dtypes(include='object').columns\n",
    "print(len(columns_obj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['home_ownership', 'verification_status', 'issue_d', 'loan_status',\n",
       "       'pymnt_plan', 'initial_list_status', 'next_pymnt_d', 'application_type',\n",
       "       'hardship_flag', 'debt_settlement_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "home_ownership \n",
      " MORTGAGE    36219\n",
      "RENT        24613\n",
      "OWN          7346\n",
      "ANY           639\n",
      "Name: home_ownership, dtype: int64\n",
      "--------------------------\n",
      "verification_status \n",
      " Not Verified       32895\n",
      "Source Verified    25737\n",
      "Verified           10185\n",
      "Name: verification_status, dtype: int64\n",
      "--------------------------\n",
      "issue_d \n",
      " Jan-2019    31041\n",
      "Feb-2019    25579\n",
      "Mar-2019    12197\n",
      "Name: issue_d, dtype: int64\n",
      "--------------------------\n",
      "loan_status \n",
      " low_risk     68470\n",
      "high_risk      347\n",
      "Name: loan_status, dtype: int64\n",
      "--------------------------\n",
      "pymnt_plan \n",
      " n    68817\n",
      "Name: pymnt_plan, dtype: int64\n",
      "--------------------------\n",
      "initial_list_status \n",
      " w    60292\n",
      "f     8525\n",
      "Name: initial_list_status, dtype: int64\n",
      "--------------------------\n",
      "next_pymnt_d \n",
      " May-2019    42449\n",
      "Apr-2019    26368\n",
      "Name: next_pymnt_d, dtype: int64\n",
      "--------------------------\n",
      "application_type \n",
      " Individual    59206\n",
      "Joint App      9611\n",
      "Name: application_type, dtype: int64\n",
      "--------------------------\n",
      "hardship_flag \n",
      " N    68817\n",
      "Name: hardship_flag, dtype: int64\n",
      "--------------------------\n",
      "debt_settlement_flag \n",
      " N    68817\n",
      "Name: debt_settlement_flag, dtype: int64\n",
      "--------------------------\n"
     ]
    }
   ],
   "source": [
    "for column in df[columns_obj]:\n",
    "    print(column, \"\\n\", df[column].value_counts())\n",
    "    print(\"--------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68817, 83)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop columns with one value in value_counts()\n",
    "df = df.drop(columns=['debt_settlement_flag','hardship_flag','pymnt_plan'])\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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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>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</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>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>w</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>355.29</td>\n",
       "      <td>355.29</td>\n",
       "      <td>295.13</td>\n",
       "      <td>60.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.35</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36590.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18368.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>w</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>873.53</td>\n",
       "      <td>873.53</td>\n",
       "      <td>706.87</td>\n",
       "      <td>166.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>929.09</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>228119.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42273.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13419.0</td>\n",
       "      <td>29502.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13247.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>w</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>485.44</td>\n",
       "      <td>485.44</td>\n",
       "      <td>363.21</td>\n",
       "      <td>122.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.88</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32437.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5711.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>w</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>335.33</td>\n",
       "      <td>335.33</td>\n",
       "      <td>280.66</td>\n",
       "      <td>54.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.55</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50788.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9091.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>w</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>484.36</td>\n",
       "      <td>484.36</td>\n",
       "      <td>385.27</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.39</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>19699.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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",
       "      <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",
       "      <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>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2684.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>w</td>\n",
       "      <td>9326.86</td>\n",
       "      <td>9326.86</td>\n",
       "      <td>1019.42</td>\n",
       "      <td>1019.42</td>\n",
       "      <td>673.14</td>\n",
       "      <td>346.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>346.76</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>6798.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>4114.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>9677.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13314.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>w</td>\n",
       "      <td>11706.42</td>\n",
       "      <td>11706.42</td>\n",
       "      <td>1086.93</td>\n",
       "      <td>1086.93</td>\n",
       "      <td>293.58</td>\n",
       "      <td>793.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.37</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60350.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47036.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7544.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>15.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>w</td>\n",
       "      <td>4666.78</td>\n",
       "      <td>4666.78</td>\n",
       "      <td>571.33</td>\n",
       "      <td>571.33</td>\n",
       "      <td>333.22</td>\n",
       "      <td>238.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.62</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18611.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14896.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3722.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59529.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>f</td>\n",
       "      <td>36953.94</td>\n",
       "      <td>36953.94</td>\n",
       "      <td>3661.36</td>\n",
       "      <td>3661.36</td>\n",
       "      <td>3046.06</td>\n",
       "      <td>615.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1225.24</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>864480.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27151.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38479.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>111100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>41166.0</td>\n",
       "      <td>41467.0</td>\n",
       "      <td>34.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11882.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>w</td>\n",
       "      <td>15395.66</td>\n",
       "      <td>15395.66</td>\n",
       "      <td>1041.03</td>\n",
       "      <td>1041.03</td>\n",
       "      <td>604.34</td>\n",
       "      <td>436.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.36</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>225413.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>62953.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5568.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18784.0</td>\n",
       "      <td>11705.0</td>\n",
       "      <td>49.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "       inq_last_6mths  open_acc  pub_rec  revol_bal  total_acc  \\\n",
       "0                 0.0       8.0      0.0     1609.0       14.0   \n",
       "1                 0.0      17.0      1.0    18368.0       34.0   \n",
       "2                 0.0       8.0      0.0    13247.0       21.0   \n",
       "3                 1.0      10.0      1.0    17996.0       23.0   \n",
       "4                 1.0      14.0      0.0     9091.0       28.0   \n",
       "...               ...       ...      ...        ...        ...   \n",
       "68812             0.0       9.0      0.0     2684.0       20.0   \n",
       "68813             0.0       8.0      0.0    13314.0       26.0   \n",
       "68814             0.0       5.0      1.0     3715.0       14.0   \n",
       "68815             1.0      21.0      0.0    59529.0       57.0   \n",
       "68816             0.0      12.0      1.0    11882.0       39.0   \n",
       "\n",
       "      initial_list_status  out_prncp  out_prncp_inv  total_pymnt  \\\n",
       "0                       w   10204.87       10204.87       355.29   \n",
       "1                       w   24293.13       24293.13       873.53   \n",
       "2                       w   19636.79       19636.79       485.44   \n",
       "3                       w    9719.34        9719.34       335.33   \n",
       "4                       w   21614.73       21614.73       484.36   \n",
       "...                   ...        ...            ...          ...   \n",
       "68812                   w    9326.86        9326.86      1019.42   \n",
       "68813                   w   11706.42       11706.42      1086.93   \n",
       "68814                   w    4666.78        4666.78       571.33   \n",
       "68815                   f   36953.94       36953.94      3661.36   \n",
       "68816                   w   15395.66       15395.66      1041.03   \n",
       "\n",
       "       total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "0               355.29           295.13          60.16                 0.0   \n",
       "1               873.53           706.87         166.66                 0.0   \n",
       "2               485.44           363.21         122.23                 0.0   \n",
       "3               335.33           280.66          54.67                 0.0   \n",
       "4               484.36           385.27          99.09                 0.0   \n",
       "...                ...              ...            ...                 ...   \n",
       "68812          1019.42           673.14         346.28                 0.0   \n",
       "68813          1086.93           293.58         793.35                 0.0   \n",
       "68814           571.33           333.22         238.11                 0.0   \n",
       "68815          3661.36          3046.06         615.30                 0.0   \n",
       "68816          1041.03           604.34         436.69                 0.0   \n",
       "\n",
       "       recoveries  collection_recovery_fee  last_pymnt_amnt next_pymnt_d  \\\n",
       "0             0.0                      0.0           375.35     May-2019   \n",
       "1             0.0                      0.0           929.09     May-2019   \n",
       "2             0.0                      0.0           529.88     May-2019   \n",
       "3             0.0                      0.0           353.55     May-2019   \n",
       "4             0.0                      0.0           520.39     May-2019   \n",
       "...           ...                      ...              ...          ...   \n",
       "68812         0.0                      0.0           346.76     May-2019   \n",
       "68813         0.0                      0.0           368.37     May-2019   \n",
       "68814         0.0                      0.0           185.62     May-2019   \n",
       "68815         0.0                      0.0          1225.24     May-2019   \n",
       "68816         0.0                      0.0           350.36     May-2019   \n",
       "\n",
       "       collections_12_mths_ex_med  policy_code application_type  \\\n",
       "0                             0.0          1.0       Individual   \n",
       "1                             0.0          1.0       Individual   \n",
       "2                             0.0          1.0       Individual   \n",
       "3                             0.0          1.0       Individual   \n",
       "4                             0.0          1.0       Individual   \n",
       "...                           ...          ...              ...   \n",
       "68812                         0.0          1.0       Individual   \n",
       "68813                         0.0          1.0       Individual   \n",
       "68814                         0.0          1.0       Individual   \n",
       "68815                         0.0          1.0       Individual   \n",
       "68816                         0.0          1.0       Individual   \n",
       "\n",
       "       acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  \\\n",
       "0                 0.0         178.0      38199.0          3.0          4.0   \n",
       "1                 0.0         165.0     228119.0          3.0          3.0   \n",
       "2                 0.0           0.0      45684.0          2.0          3.0   \n",
       "3                 0.0           0.0      68784.0          0.0          4.0   \n",
       "4                 0.0           0.0     177745.0          0.0          1.0   \n",
       "...               ...           ...          ...          ...          ...   \n",
       "68812             0.0         242.0       6798.0          1.0          3.0   \n",
       "68813             0.0           0.0      60350.0          3.0          4.0   \n",
       "68814             0.0           0.0      18611.0          2.0          1.0   \n",
       "68815             0.0           0.0     864480.0          2.0          3.0   \n",
       "68816             0.0           0.0     225413.0          0.0          2.0   \n",
       "\n",
       "       open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  \\\n",
       "0              2.0          4.0                 3.0       36590.0     59.0   \n",
       "1              2.0          3.0                 4.0       42273.0     86.0   \n",
       "2              2.0          3.0                 4.0       32437.0     75.0   \n",
       "3              1.0          3.0                 9.0       50788.0     66.0   \n",
       "4              0.0          2.0                14.0       16828.0     84.0   \n",
       "...            ...          ...                 ...           ...      ...   \n",
       "68812          0.0          0.0               160.0        4114.0     76.0   \n",
       "68813          2.0          2.0                 2.0       47036.0     75.0   \n",
       "68814          1.0          1.0                 8.0       14896.0     81.0   \n",
       "68815          0.0          0.0                28.0       27151.0     35.0   \n",
       "68816          0.0          0.0                28.0       62953.0     79.0   \n",
       "\n",
       "       open_rv_12m  open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  \\\n",
       "0              1.0          2.0      1553.0      58.0            3700.0   \n",
       "1              4.0          8.0      5197.0      61.0           50400.0   \n",
       "2              2.0          3.0      2810.0      75.0           17500.0   \n",
       "3              0.0          1.0      6360.0      69.0           23000.0   \n",
       "4              1.0          1.0      3600.0      41.0           43100.0   \n",
       "...            ...          ...         ...       ...               ...   \n",
       "68812          3.0          6.0       977.0      33.0           15200.0   \n",
       "68813          3.0          5.0      1177.0      69.0           25000.0   \n",
       "68814          2.0          3.0         0.0      61.0           12100.0   \n",
       "68815          3.0          5.0     38479.0      34.0          111100.0   \n",
       "68816          0.0          0.0      5568.0      45.0           32000.0   \n",
       "\n",
       "       inq_fi  total_cu_tl  inq_last_12m  acc_open_past_24mths  avg_cur_bal  \\\n",
       "0         1.0          0.0           4.0                   6.0       4775.0   \n",
       "1         4.0          9.0           3.0                  13.0      13419.0   \n",
       "2         0.0          4.0           1.0                   6.0       5711.0   \n",
       "3         3.0          5.0           4.0                   4.0       6878.0   \n",
       "4         2.0          0.0           2.0                   4.0      13673.0   \n",
       "...       ...          ...           ...                   ...          ...   \n",
       "68812     0.0          1.0           1.0                   6.0        755.0   \n",
       "68813     1.0          0.0           4.0                   7.0       7544.0   \n",
       "68814     0.0          2.0           1.0                   4.0       3722.0   \n",
       "68815     1.0          0.0           3.0                   7.0      41166.0   \n",
       "68816     2.0          2.0           4.0                   1.0      18784.0   \n",
       "\n",
       "       bc_open_to_buy  bc_util  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "0               447.0     77.7                       0.0          0.0   \n",
       "1             29502.0     28.4                       0.0          0.0   \n",
       "2              2839.0     62.1                       0.0          0.0   \n",
       "3              4899.0     75.1                       0.0          0.0   \n",
       "4             19699.0     28.6                       0.0          0.0   \n",
       "...               ...      ...                       ...          ...   \n",
       "68812          9677.0     14.4                       0.0          0.0   \n",
       "68813         11387.0     15.7                       0.0          0.0   \n",
       "68814          3600.0      0.0                       0.0          0.0   \n",
       "68815         41467.0     34.3                       0.0          0.0   \n",
       "68816         11705.0     49.1                       2.0          0.0   \n",
       "\n",
       "       mo_sin_old_il_acct  mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  \\\n",
       "0                    93.0                  96.0                    3.0   \n",
       "1                    87.0                 286.0                    1.0   \n",
       "2                   159.0                 156.0                    7.0   \n",
       "3                   100.0                  89.0                   24.0   \n",
       "4                    67.0                 138.0                   11.0   \n",
       "...                   ...                   ...                    ...   \n",
       "68812               185.0                  17.0                    4.0   \n",
       "68813               164.0                 107.0                    1.0   \n",
       "68814               197.0                  91.0                    0.0   \n",
       "68815               147.0                 146.0                    2.0   \n",
       "68816               159.0                 129.0                   25.0   \n",
       "\n",
       "       mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  mths_since_recent_inq  \\\n",
       "0                 3.0       0.0                  20.0                    1.0   \n",
       "1                 1.0       2.0                   1.0                   11.0   \n",
       "2                 4.0       0.0                   7.0                    7.0   \n",
       "3                 9.0       0.0                  34.0                    0.0   \n",
       "4                11.0       4.0                  11.0                    0.0   \n",
       "...               ...       ...                   ...                    ...   \n",
       "68812             4.0       0.0                   4.0                    3.0   \n",
       "68813             1.0       0.0                   1.0                    2.0   \n",
       "68814             0.0       1.0                   0.0                   10.0   \n",
       "68815             2.0       9.0                   3.0                    3.0   \n",
       "68816            24.0       3.0                  25.0                    1.0   \n",
       "\n",
       "       num_accts_ever_120_pd  num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  \\\n",
       "0                        0.0             1.0              2.0          1.0   \n",
       "1                        3.0             7.0             11.0          8.0   \n",
       "2                        6.0             2.0              5.0          2.0   \n",
       "3                        0.0             4.0              6.0          4.0   \n",
       "4                        0.0             4.0              6.0          7.0   \n",
       "...                      ...             ...              ...          ...   \n",
       "68812                    4.0             3.0              5.0          3.0   \n",
       "68813                    0.0             3.0              4.0          3.0   \n",
       "68814                    0.0             0.0              2.0          2.0   \n",
       "68815                    0.0             4.0              5.0          8.0   \n",
       "68816                    2.0             4.0              5.0          5.0   \n",
       "\n",
       "       num_bc_tl  num_il_tl  num_op_rev_tl  num_rev_accts  \\\n",
       "0            1.0       10.0            3.0            3.0   \n",
       "1           13.0       14.0           13.0           18.0   \n",
       "2            7.0        8.0            5.0           13.0   \n",
       "3            4.0       17.0            6.0            6.0   \n",
       "4           11.0        6.0           12.0           18.0   \n",
       "...          ...        ...            ...            ...   \n",
       "68812        3.0       14.0            6.0            6.0   \n",
       "68813        4.0       18.0            4.0            8.0   \n",
       "68814        2.0        8.0            4.0            5.0   \n",
       "68815       16.0       10.0           14.0           36.0   \n",
       "68816        9.0       20.0            9.0           16.0   \n",
       "\n",
       "       num_rev_tl_bal_gt_0  num_sats  num_tl_120dpd_2m  num_tl_30dpd  \\\n",
       "0                      2.0       8.0               0.0           0.0   \n",
       "1                     11.0      17.0               0.0           0.0   \n",
       "2                      5.0       8.0               0.0           0.0   \n",
       "3                      6.0      10.0               0.0           0.0   \n",
       "4                      6.0      14.0               0.0           0.0   \n",
       "...                    ...       ...               ...           ...   \n",
       "68812                  5.0       9.0               0.0           0.0   \n",
       "68813                  4.0       8.0               0.0           0.0   \n",
       "68814                  2.0       5.0               0.0           0.0   \n",
       "68815                  5.0      21.0               0.0           0.0   \n",
       "68816                  5.0      12.0               0.0           0.0   \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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save cleaned data set\n",
    "clean_loans_df = df\n",
    "clean_loans_df.to_csv(\"./Data/clean_loans.csv\",index=False)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</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>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>w</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>10204.87</td>\n",
       "      <td>355.29</td>\n",
       "      <td>355.29</td>\n",
       "      <td>295.13</td>\n",
       "      <td>60.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.35</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>38199.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36590.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1553.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4775.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>77.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18368.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>w</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>24293.13</td>\n",
       "      <td>873.53</td>\n",
       "      <td>873.53</td>\n",
       "      <td>706.87</td>\n",
       "      <td>166.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>929.09</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>228119.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>42273.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5197.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>50400.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13419.0</td>\n",
       "      <td>29502.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13247.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>w</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>19636.79</td>\n",
       "      <td>485.44</td>\n",
       "      <td>485.44</td>\n",
       "      <td>363.21</td>\n",
       "      <td>122.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>529.88</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32437.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5711.0</td>\n",
       "      <td>2839.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>w</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>9719.34</td>\n",
       "      <td>335.33</td>\n",
       "      <td>335.33</td>\n",
       "      <td>280.66</td>\n",
       "      <td>54.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>353.55</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68784.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>50788.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6878.0</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>75.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9091.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>w</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>21614.73</td>\n",
       "      <td>484.36</td>\n",
       "      <td>484.36</td>\n",
       "      <td>385.27</td>\n",
       "      <td>99.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.39</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16828.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>43100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>19699.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "</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  open_acc  \\\n",
       "0     Source Verified  Mar-2019  27.24          0.0             0.0       8.0   \n",
       "1            Verified  Mar-2019  20.23          0.0             0.0      17.0   \n",
       "2            Verified  Mar-2019  24.26          0.0             0.0       8.0   \n",
       "3            Verified  Mar-2019  31.44          0.0             1.0      10.0   \n",
       "4        Not Verified  Mar-2019  18.76          0.0             1.0      14.0   \n",
       "\n",
       "   pub_rec  revol_bal  total_acc initial_list_status  out_prncp  \\\n",
       "0      0.0     1609.0       14.0                   w   10204.87   \n",
       "1      1.0    18368.0       34.0                   w   24293.13   \n",
       "2      0.0    13247.0       21.0                   w   19636.79   \n",
       "3      1.0    17996.0       23.0                   w    9719.34   \n",
       "4      0.0     9091.0       28.0                   w   21614.73   \n",
       "\n",
       "   out_prncp_inv  total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "0       10204.87       355.29           355.29           295.13   \n",
       "1       24293.13       873.53           873.53           706.87   \n",
       "2       19636.79       485.44           485.44           363.21   \n",
       "3        9719.34       335.33           335.33           280.66   \n",
       "4       21614.73       484.36           484.36           385.27   \n",
       "\n",
       "   total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "0          60.16                 0.0         0.0                      0.0   \n",
       "1         166.66                 0.0         0.0                      0.0   \n",
       "2         122.23                 0.0         0.0                      0.0   \n",
       "3          54.67                 0.0         0.0                      0.0   \n",
       "4          99.09                 0.0         0.0                      0.0   \n",
       "\n",
       "   last_pymnt_amnt next_pymnt_d  collections_12_mths_ex_med  policy_code  \\\n",
       "0           375.35     May-2019                         0.0          1.0   \n",
       "1           929.09     May-2019                         0.0          1.0   \n",
       "2           529.88     May-2019                         0.0          1.0   \n",
       "3           353.55     May-2019                         0.0          1.0   \n",
       "4           520.39     May-2019                         0.0          1.0   \n",
       "\n",
       "  application_type  acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  \\\n",
       "0       Individual             0.0         178.0      38199.0          3.0   \n",
       "1       Individual             0.0         165.0     228119.0          3.0   \n",
       "2       Individual             0.0           0.0      45684.0          2.0   \n",
       "3       Individual             0.0           0.0      68784.0          0.0   \n",
       "4       Individual             0.0           0.0     177745.0          0.0   \n",
       "\n",
       "   open_act_il  open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  \\\n",
       "0          4.0          2.0          4.0                 3.0       36590.0   \n",
       "1          3.0          2.0          3.0                 4.0       42273.0   \n",
       "2          3.0          2.0          3.0                 4.0       32437.0   \n",
       "3          4.0          1.0          3.0                 9.0       50788.0   \n",
       "4          1.0          0.0          2.0                14.0       16828.0   \n",
       "\n",
       "   il_util  open_rv_12m  open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  \\\n",
       "0     59.0          1.0          2.0      1553.0      58.0            3700.0   \n",
       "1     86.0          4.0          8.0      5197.0      61.0           50400.0   \n",
       "2     75.0          2.0          3.0      2810.0      75.0           17500.0   \n",
       "3     66.0          0.0          1.0      6360.0      69.0           23000.0   \n",
       "4     84.0          1.0          1.0      3600.0      41.0           43100.0   \n",
       "\n",
       "   inq_fi  total_cu_tl  inq_last_12m  acc_open_past_24mths  avg_cur_bal  \\\n",
       "0     1.0          0.0           4.0                   6.0       4775.0   \n",
       "1     4.0          9.0           3.0                  13.0      13419.0   \n",
       "2     0.0          4.0           1.0                   6.0       5711.0   \n",
       "3     3.0          5.0           4.0                   4.0       6878.0   \n",
       "4     2.0          0.0           2.0                   4.0      13673.0   \n",
       "\n",
       "   bc_open_to_buy  bc_util  chargeoff_within_12_mths  delinq_amnt  \\\n",
       "0           447.0     77.7                       0.0          0.0   \n",
       "1         29502.0     28.4                       0.0          0.0   \n",
       "2          2839.0     62.1                       0.0          0.0   \n",
       "3          4899.0     75.1                       0.0          0.0   \n",
       "4         19699.0     28.6                       0.0          0.0   \n",
       "\n",
       "   mo_sin_old_il_acct  mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  \\\n",
       "0                93.0                  96.0                    3.0   \n",
       "1                87.0                 286.0                    1.0   \n",
       "2               159.0                 156.0                    7.0   \n",
       "3               100.0                  89.0                   24.0   \n",
       "4                67.0                 138.0                   11.0   \n",
       "\n",
       "   mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  mths_since_recent_inq  \\\n",
       "0             3.0       0.0                  20.0                    1.0   \n",
       "1             1.0       2.0                   1.0                   11.0   \n",
       "2             4.0       0.0                   7.0                    7.0   \n",
       "3             9.0       0.0                  34.0                    0.0   \n",
       "4            11.0       4.0                  11.0                    0.0   \n",
       "\n",
       "   num_accts_ever_120_pd  num_actv_bc_tl  num_actv_rev_tl  num_bc_sats  \\\n",
       "0                    0.0             1.0              2.0          1.0   \n",
       "1                    3.0             7.0             11.0          8.0   \n",
       "2                    6.0             2.0              5.0          2.0   \n",
       "3                    0.0             4.0              6.0          4.0   \n",
       "4                    0.0             4.0              6.0          7.0   \n",
       "\n",
       "   num_bc_tl  num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "0        1.0       10.0            3.0            3.0                  2.0   \n",
       "1       13.0       14.0           13.0           18.0                 11.0   \n",
       "2        7.0        8.0            5.0           13.0                  5.0   \n",
       "3        4.0       17.0            6.0            6.0                  6.0   \n",
       "4       11.0        6.0           12.0           18.0                  6.0   \n",
       "\n",
       "   num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "0       8.0               0.0           0.0                 0.0   \n",
       "1      17.0               0.0           0.0                 0.0   \n",
       "2       8.0               0.0           0.0                 0.0   \n",
       "3      10.0               0.0           0.0                 0.0   \n",
       "4      14.0               0.0           0.0                 0.0   \n",
       "\n",
       "   num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  \\\n",
       "0                 3.0            85.7             100.0                   0.0   \n",
       "1                 6.0            91.2              50.0                   1.0   \n",
       "2                 4.0            66.7              50.0                   0.0   \n",
       "3                 1.0           100.0              50.0                   1.0   \n",
       "4                 1.0           100.0               0.0                   0.0   \n",
       "\n",
       "   tax_liens  tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "0        0.0          65687.0            38199.0          2000.0   \n",
       "1        0.0         271427.0            60641.0         41200.0   \n",
       "2        0.0          60644.0            45684.0          7500.0   \n",
       "3        0.0          99506.0            68784.0         19700.0   \n",
       "4        0.0         219750.0            25919.0         27600.0   \n",
       "\n",
       "   total_il_high_credit_limit  \n",
       "0                     61987.0  \n",
       "1                     49197.0  \n",
       "2                     43144.0  \n",
       "3                     76506.0  \n",
       "4                     20000.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create our features\n",
    "X = df.copy()\n",
    "X = X.drop('loan_status', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our target\n",
    "y = df[\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>total_acc</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</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>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>68817.0</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</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>6.881700e+04</td>\n",
       "      <td>68817.00000</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",
       "      <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",
       "      <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",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</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>24.666347</td>\n",
       "      <td>16018.845833</td>\n",
       "      <td>16015.919519</td>\n",
       "      <td>970.639473</td>\n",
       "      <td>970.431151</td>\n",
       "      <td>658.748750</td>\n",
       "      <td>311.855687</td>\n",
       "      <td>0.035072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517.202218</td>\n",
       "      <td>0.016188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188.664298</td>\n",
       "      <td>1.634976e+05</td>\n",
       "      <td>1.023047</td>\n",
       "      <td>3.290481</td>\n",
       "      <td>0.843730</td>\n",
       "      <td>1.909644</td>\n",
       "      <td>14.303152</td>\n",
       "      <td>4.324044e+04</td>\n",
       "      <td>69.21191</td>\n",
       "      <td>1.240042</td>\n",
       "      <td>2.622245</td>\n",
       "      <td>6297.491710</td>\n",
       "      <td>55.702472</td>\n",
       "      <td>42431.895360</td>\n",
       "      <td>1.382071</td>\n",
       "      <td>1.687795</td>\n",
       "      <td>2.189023</td>\n",
       "      <td>4.827572</td>\n",
       "      <td>14142.936193</td>\n",
       "      <td>16991.852667</td>\n",
       "      <td>48.330667</td>\n",
       "      <td>0.006830</td>\n",
       "      <td>2.347065</td>\n",
       "      <td>125.468605</td>\n",
       "      <td>175.136841</td>\n",
       "      <td>14.294215</td>\n",
       "      <td>7.179781</td>\n",
       "      <td>1.441882</td>\n",
       "      <td>24.714954</td>\n",
       "      <td>7.688028</td>\n",
       "      <td>0.472485</td>\n",
       "      <td>3.806123</td>\n",
       "      <td>5.648038</td>\n",
       "      <td>5.135156</td>\n",
       "      <td>7.414403</td>\n",
       "      <td>9.500153</td>\n",
       "      <td>8.630818</td>\n",
       "      <td>13.517663</td>\n",
       "      <td>5.653036</td>\n",
       "      <td>12.571995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>12.318917</td>\n",
       "      <td>10002.695495</td>\n",
       "      <td>10004.985940</td>\n",
       "      <td>1229.563301</td>\n",
       "      <td>1229.569864</td>\n",
       "      <td>1126.237377</td>\n",
       "      <td>279.786954</td>\n",
       "      <td>1.086720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>816.021053</td>\n",
       "      <td>0.134774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1808.457195</td>\n",
       "      <td>1.723699e+05</td>\n",
       "      <td>1.176782</td>\n",
       "      <td>2.965579</td>\n",
       "      <td>0.970543</td>\n",
       "      <td>1.544994</td>\n",
       "      <td>13.972619</td>\n",
       "      <td>4.956130e+04</td>\n",
       "      <td>22.33711</td>\n",
       "      <td>1.471745</td>\n",
       "      <td>2.502608</td>\n",
       "      <td>5702.281757</td>\n",
       "      <td>18.495480</td>\n",
       "      <td>37166.229316</td>\n",
       "      <td>1.543220</td>\n",
       "      <td>2.852664</td>\n",
       "      <td>2.396186</td>\n",
       "      <td>3.145377</td>\n",
       "      <td>15863.878993</td>\n",
       "      <td>20447.276820</td>\n",
       "      <td>28.106287</td>\n",
       "      <td>0.087327</td>\n",
       "      <td>301.312407</td>\n",
       "      <td>56.968853</td>\n",
       "      <td>99.149467</td>\n",
       "      <td>17.417118</td>\n",
       "      <td>6.303066</td>\n",
       "      <td>1.747777</td>\n",
       "      <td>32.130889</td>\n",
       "      <td>6.034861</td>\n",
       "      <td>1.410819</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>3.470941</td>\n",
       "      <td>3.230651</td>\n",
       "      <td>4.581371</td>\n",
       "      <td>7.573846</td>\n",
       "      <td>5.056994</td>\n",
       "      <td>8.026158</td>\n",
       "      <td>3.472679</td>\n",
       "      <td>6.021318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>3.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.350000e+02</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>1.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>16.000000</td>\n",
       "      <td>8578.800000</td>\n",
       "      <td>8576.500000</td>\n",
       "      <td>403.760000</td>\n",
       "      <td>403.550000</td>\n",
       "      <td>250.370000</td>\n",
       "      <td>115.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.110000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.570000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.422300e+04</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2558.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>19200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3505.000000</td>\n",
       "      <td>3644.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>23.000000</td>\n",
       "      <td>14067.750000</td>\n",
       "      <td>14061.030000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>449.890000</td>\n",
       "      <td>225.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.960600e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.862600e+04</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4874.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>32800.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8261.000000</td>\n",
       "      <td>10239.000000</td>\n",
       "      <td>46.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>31.000000</td>\n",
       "      <td>22860.700000</td>\n",
       "      <td>22860.490000</td>\n",
       "      <td>1214.600000</td>\n",
       "      <td>1214.590000</td>\n",
       "      <td>762.170000</td>\n",
       "      <td>415.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>652.580000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.472060e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.406500e+04</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8321.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>54100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19859.000000</td>\n",
       "      <td>22806.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>164.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>41418.981165</td>\n",
       "      <td>41418.980000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>2735.400000</td>\n",
       "      <td>75.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40500.340000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159199.000000</td>\n",
       "      <td>3.140082e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>1.260281e+06</td>\n",
       "      <td>313.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>207484.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>759500.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>448583.000000</td>\n",
       "      <td>506507.000000</td>\n",
       "      <td>169.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "</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     total_acc     out_prncp  out_prncp_inv   total_pymnt  \\\n",
       "count   68817.000000  68817.000000  68817.000000   68817.000000  68817.000000   \n",
       "mean    17604.142828     24.666347  16018.845833   16015.919519    970.639473   \n",
       "std     21835.880400     12.318917  10002.695495   10004.985940   1229.563301   \n",
       "min         0.000000      3.000000      0.000000       0.000000      0.000000   \n",
       "25%      6293.000000     16.000000   8578.800000    8576.500000    403.760000   \n",
       "50%     12068.000000     23.000000  14067.750000   14061.030000    710.790000   \n",
       "75%     21735.000000     31.000000  22860.700000   22860.490000   1214.600000   \n",
       "max    587191.000000    164.000000  40000.000000   40000.000000  41418.981165   \n",
       "\n",
       "       total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "count     68817.000000     68817.000000   68817.000000        68817.000000   \n",
       "mean        970.431151       658.748750     311.855687            0.035072   \n",
       "std        1229.569864      1126.237377     279.786954            1.086720   \n",
       "min           0.000000         0.000000       0.000000            0.000000   \n",
       "25%         403.550000       250.370000     115.440000            0.000000   \n",
       "50%         710.790000       449.890000     225.250000            0.000000   \n",
       "75%        1214.590000       762.170000     415.710000            0.000000   \n",
       "max       41418.980000     40000.000000    2735.400000           75.240000   \n",
       "\n",
       "       recoveries  collection_recovery_fee  last_pymnt_amnt  \\\n",
       "count     68817.0                  68817.0     68817.000000   \n",
       "mean          0.0                      0.0       517.202218   \n",
       "std           0.0                      0.0       816.021053   \n",
       "min           0.0                      0.0         0.000000   \n",
       "25%           0.0                      0.0       267.110000   \n",
       "50%           0.0                      0.0       408.520000   \n",
       "75%           0.0                      0.0       652.580000   \n",
       "max           0.0                      0.0     40500.340000   \n",
       "\n",
       "       collections_12_mths_ex_med  policy_code  acc_now_delinq   tot_coll_amt  \\\n",
       "count                68817.000000      68817.0         68817.0   68817.000000   \n",
       "mean                     0.016188          1.0             0.0     188.664298   \n",
       "std                      0.134774          0.0             0.0    1808.457195   \n",
       "min                      0.000000          1.0             0.0       0.000000   \n",
       "25%                      0.000000          1.0             0.0       0.000000   \n",
       "50%                      0.000000          1.0             0.0       0.000000   \n",
       "75%                      0.000000          1.0             0.0       0.000000   \n",
       "max                      4.000000          1.0             0.0  159199.000000   \n",
       "\n",
       "        tot_cur_bal   open_acc_6m   open_act_il   open_il_12m   open_il_24m  \\\n",
       "count  6.881700e+04  68817.000000  68817.000000  68817.000000  68817.000000   \n",
       "mean   1.634976e+05      1.023047      3.290481      0.843730      1.909644   \n",
       "std    1.723699e+05      1.176782      2.965579      0.970543      1.544994   \n",
       "min    2.350000e+02      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    3.570000e+04      0.000000      2.000000      0.000000      1.000000   \n",
       "50%    9.960600e+04      1.000000      2.000000      1.000000      2.000000   \n",
       "75%    2.472060e+05      2.000000      4.000000      1.000000      3.000000   \n",
       "max    3.140082e+06     14.000000     63.000000      6.000000     22.000000   \n",
       "\n",
       "       mths_since_rcnt_il  total_bal_il      il_util   open_rv_12m  \\\n",
       "count        68817.000000  6.881700e+04  68817.00000  68817.000000   \n",
       "mean            14.303152  4.324044e+04     69.21191      1.240042   \n",
       "std             13.972619  4.956130e+04     22.33711      1.471745   \n",
       "min              0.000000  1.000000e+00      0.00000      0.000000   \n",
       "25%              6.000000  1.422300e+04     56.00000      0.000000   \n",
       "50%             11.000000  2.862600e+04     71.00000      1.000000   \n",
       "75%             18.000000  5.406500e+04     85.00000      2.000000   \n",
       "max            389.000000  1.260281e+06    313.00000     18.000000   \n",
       "\n",
       "        open_rv_24m     max_bal_bc      all_util  total_rev_hi_lim  \\\n",
       "count  68817.000000   68817.000000  68817.000000      68817.000000   \n",
       "mean       2.622245    6297.491710     55.702472      42431.895360   \n",
       "std        2.502608    5702.281757     18.495480      37166.229316   \n",
       "min        0.000000       0.000000      1.000000        200.000000   \n",
       "25%        1.000000    2558.000000     43.000000      19200.000000   \n",
       "50%        2.000000    4874.000000     56.000000      32800.000000   \n",
       "75%        4.000000    8321.000000     68.000000      54100.000000   \n",
       "max       44.000000  207484.000000    216.000000     759500.000000   \n",
       "\n",
       "             inq_fi   total_cu_tl  inq_last_12m  acc_open_past_24mths  \\\n",
       "count  68817.000000  68817.000000  68817.000000          68817.000000   \n",
       "mean       1.382071      1.687795      2.189023              4.827572   \n",
       "std        1.543220      2.852664      2.396186              3.145377   \n",
       "min        0.000000      0.000000      0.000000              0.000000   \n",
       "25%        0.000000      0.000000      1.000000              3.000000   \n",
       "50%        1.000000      1.000000      2.000000              4.000000   \n",
       "75%        2.000000      2.000000      3.000000              6.000000   \n",
       "max       29.000000     70.000000     42.000000             45.000000   \n",
       "\n",
       "         avg_cur_bal  bc_open_to_buy       bc_util  chargeoff_within_12_mths  \\\n",
       "count   68817.000000    68817.000000  68817.000000              68817.000000   \n",
       "mean    14142.936193    16991.852667     48.330667                  0.006830   \n",
       "std     15863.878993    20447.276820     28.106287                  0.087327   \n",
       "min        20.000000        0.000000      0.000000                  0.000000   \n",
       "25%      3505.000000     3644.000000     25.400000                  0.000000   \n",
       "50%      8261.000000    10239.000000     46.400000                  0.000000   \n",
       "75%     19859.000000    22806.000000     71.100000                  0.000000   \n",
       "max    448583.000000   506507.000000    169.500000                  2.000000   \n",
       "\n",
       "        delinq_amnt  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  \\\n",
       "count  68817.000000        68817.000000          68817.000000   \n",
       "mean       2.347065          125.468605            175.136841   \n",
       "std      301.312407           56.968853             99.149467   \n",
       "min        0.000000            0.000000              5.000000   \n",
       "25%        0.000000           87.000000            101.000000   \n",
       "50%        0.000000          132.000000            157.000000   \n",
       "75%        0.000000          159.000000            226.000000   \n",
       "max    65000.000000          606.000000            902.000000   \n",
       "\n",
       "       mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl      mort_acc  \\\n",
       "count           68817.000000    68817.000000  68817.000000   \n",
       "mean               14.294215        7.179781      1.441882   \n",
       "std                17.417118        6.303066      1.747777   \n",
       "min                 0.000000        0.000000      0.000000   \n",
       "25%                 4.000000        3.000000      0.000000   \n",
       "50%                 9.000000        5.000000      1.000000   \n",
       "75%                19.000000       10.000000      2.000000   \n",
       "max               564.000000      170.000000     20.000000   \n",
       "\n",
       "       mths_since_recent_bc  mths_since_recent_inq  num_accts_ever_120_pd  \\\n",
       "count          68817.000000           68817.000000           68817.000000   \n",
       "mean              24.714954               7.688028               0.472485   \n",
       "std               32.130889               6.034861               1.410819   \n",
       "min                0.000000               0.000000               0.000000   \n",
       "25%                6.000000               3.000000               0.000000   \n",
       "50%               15.000000               6.000000               0.000000   \n",
       "75%               31.000000              12.000000               0.000000   \n",
       "max              564.000000              24.000000              34.000000   \n",
       "\n",
       "       num_actv_bc_tl  num_actv_rev_tl   num_bc_sats     num_bc_tl  \\\n",
       "count    68817.000000     68817.000000  68817.000000  68817.000000   \n",
       "mean         3.806123         5.648038      5.135156      7.414403   \n",
       "std          2.464504         3.470941      3.230651      4.581371   \n",
       "min          0.000000         0.000000      0.000000      1.000000   \n",
       "25%          2.000000         3.000000      3.000000      4.000000   \n",
       "50%          3.000000         5.000000      4.000000      6.000000   \n",
       "75%          5.000000         7.000000      7.000000     10.000000   \n",
       "max         26.000000        43.000000     43.000000     60.000000   \n",
       "\n",
       "          num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "count  68817.000000   68817.000000   68817.000000         68817.000000   \n",
       "mean       9.500153       8.630818      13.517663             5.653036   \n",
       "std        7.573846       5.056994       8.026158             3.472679   \n",
       "min        1.000000       1.000000       2.000000             0.000000   \n",
       "25%        4.000000       5.000000       8.000000             3.000000   \n",
       "50%        7.000000       8.000000      12.000000             5.000000   \n",
       "75%       12.000000      11.000000      17.000000             7.000000   \n",
       "max      149.000000      70.000000      98.000000            43.000000   \n",
       "\n",
       "           num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "count  68817.000000           68817.0       68817.0        68817.000000   \n",
       "mean      12.571995               0.0           0.0            0.052138   \n",
       "std        6.021318               0.0           0.0            0.390633   \n",
       "min        2.000000               0.0           0.0            0.000000   \n",
       "25%        8.000000               0.0           0.0            0.000000   \n",
       "50%       11.000000               0.0           0.0            0.000000   \n",
       "75%       16.000000               0.0           0.0            0.000000   \n",
       "max       72.000000               0.0           0.0           18.000000   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "count        68817.000000    68817.000000      68817.000000   \n",
       "mean             2.219423       95.057627         30.626217   \n",
       "std              1.897432        8.326426         33.631463   \n",
       "min              0.000000       20.000000          0.000000   \n",
       "25%              1.000000       93.000000          0.000000   \n",
       "50%              2.000000      100.000000         20.000000   \n",
       "75%              3.000000      100.000000         50.000000   \n",
       "max             19.000000      100.000000        100.000000   \n",
       "\n",
       "       pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "count          68817.000000    68817.0     6.881700e+04       6.881700e+04   \n",
       "mean               0.125972        0.0     2.100332e+05       6.133843e+04   \n",
       "std                0.336732        0.0     1.928088e+05       5.738798e+04   \n",
       "min                0.000000        0.0     3.600000e+03       2.350000e+02   \n",
       "25%                0.000000        0.0     6.697700e+04       2.650300e+04   \n",
       "50%                0.000000        0.0     1.467100e+05       4.535700e+04   \n",
       "75%                0.000000        0.0     3.036400e+05       7.657000e+04   \n",
       "max                4.000000        0.0     3.292782e+06       1.295455e+06   \n",
       "\n",
       "       total_bc_limit  total_il_high_credit_limit  \n",
       "count    68817.000000                6.881700e+04  \n",
       "mean     29734.128558                5.572240e+04  \n",
       "std      26795.394232                5.095845e+04  \n",
       "min        100.000000                1.270000e+02  \n",
       "25%      11600.000000                2.288000e+04  \n",
       "50%      22100.000000                4.200000e+04  \n",
       "75%      39300.000000                7.249900e+04  \n",
       "max     509400.000000                1.426964e+06  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y = df['loan_status']\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>total_acc</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</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",
       "      <th>home_ownership_ANY</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>verification_status_Not Verified</th>\n",
       "      <th>verification_status_Source Verified</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>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>68817.0</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</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>6.881700e+04</td>\n",
       "      <td>68817.00000</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",
       "      <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",
       "      <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",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</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",
       "      <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",
       "      <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>24.666347</td>\n",
       "      <td>16018.845833</td>\n",
       "      <td>16015.919519</td>\n",
       "      <td>970.639473</td>\n",
       "      <td>970.431151</td>\n",
       "      <td>658.748750</td>\n",
       "      <td>311.855687</td>\n",
       "      <td>0.035072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>517.202218</td>\n",
       "      <td>0.016188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188.664298</td>\n",
       "      <td>1.634976e+05</td>\n",
       "      <td>1.023047</td>\n",
       "      <td>3.290481</td>\n",
       "      <td>0.843730</td>\n",
       "      <td>1.909644</td>\n",
       "      <td>14.303152</td>\n",
       "      <td>4.324044e+04</td>\n",
       "      <td>69.21191</td>\n",
       "      <td>1.240042</td>\n",
       "      <td>2.622245</td>\n",
       "      <td>6297.491710</td>\n",
       "      <td>55.702472</td>\n",
       "      <td>42431.895360</td>\n",
       "      <td>1.382071</td>\n",
       "      <td>1.687795</td>\n",
       "      <td>2.189023</td>\n",
       "      <td>4.827572</td>\n",
       "      <td>14142.936193</td>\n",
       "      <td>16991.852667</td>\n",
       "      <td>48.330667</td>\n",
       "      <td>0.006830</td>\n",
       "      <td>2.347065</td>\n",
       "      <td>125.468605</td>\n",
       "      <td>175.136841</td>\n",
       "      <td>14.294215</td>\n",
       "      <td>7.179781</td>\n",
       "      <td>1.441882</td>\n",
       "      <td>24.714954</td>\n",
       "      <td>7.688028</td>\n",
       "      <td>0.472485</td>\n",
       "      <td>3.806123</td>\n",
       "      <td>5.648038</td>\n",
       "      <td>5.135156</td>\n",
       "      <td>7.414403</td>\n",
       "      <td>9.500153</td>\n",
       "      <td>8.630818</td>\n",
       "      <td>13.517663</td>\n",
       "      <td>5.653036</td>\n",
       "      <td>12.571995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.009285</td>\n",
       "      <td>0.526309</td>\n",
       "      <td>0.106747</td>\n",
       "      <td>0.357659</td>\n",
       "      <td>0.478007</td>\n",
       "      <td>0.373992</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>12.318917</td>\n",
       "      <td>10002.695495</td>\n",
       "      <td>10004.985940</td>\n",
       "      <td>1229.563301</td>\n",
       "      <td>1229.569864</td>\n",
       "      <td>1126.237377</td>\n",
       "      <td>279.786954</td>\n",
       "      <td>1.086720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>816.021053</td>\n",
       "      <td>0.134774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1808.457195</td>\n",
       "      <td>1.723699e+05</td>\n",
       "      <td>1.176782</td>\n",
       "      <td>2.965579</td>\n",
       "      <td>0.970543</td>\n",
       "      <td>1.544994</td>\n",
       "      <td>13.972619</td>\n",
       "      <td>4.956130e+04</td>\n",
       "      <td>22.33711</td>\n",
       "      <td>1.471745</td>\n",
       "      <td>2.502608</td>\n",
       "      <td>5702.281757</td>\n",
       "      <td>18.495480</td>\n",
       "      <td>37166.229316</td>\n",
       "      <td>1.543220</td>\n",
       "      <td>2.852664</td>\n",
       "      <td>2.396186</td>\n",
       "      <td>3.145377</td>\n",
       "      <td>15863.878993</td>\n",
       "      <td>20447.276820</td>\n",
       "      <td>28.106287</td>\n",
       "      <td>0.087327</td>\n",
       "      <td>301.312407</td>\n",
       "      <td>56.968853</td>\n",
       "      <td>99.149467</td>\n",
       "      <td>17.417118</td>\n",
       "      <td>6.303066</td>\n",
       "      <td>1.747777</td>\n",
       "      <td>32.130889</td>\n",
       "      <td>6.034861</td>\n",
       "      <td>1.410819</td>\n",
       "      <td>2.464504</td>\n",
       "      <td>3.470941</td>\n",
       "      <td>3.230651</td>\n",
       "      <td>4.581371</td>\n",
       "      <td>7.573846</td>\n",
       "      <td>5.056994</td>\n",
       "      <td>8.026158</td>\n",
       "      <td>3.472679</td>\n",
       "      <td>6.021318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.095914</td>\n",
       "      <td>0.499311</td>\n",
       "      <td>0.308793</td>\n",
       "      <td>0.479314</td>\n",
       "      <td>0.499520</td>\n",
       "      <td>0.483865</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>3.000000</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.350000e+02</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>1.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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",
       "      <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>16.000000</td>\n",
       "      <td>8578.800000</td>\n",
       "      <td>8576.500000</td>\n",
       "      <td>403.760000</td>\n",
       "      <td>403.550000</td>\n",
       "      <td>250.370000</td>\n",
       "      <td>115.440000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267.110000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.570000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.422300e+04</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2558.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>19200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3505.000000</td>\n",
       "      <td>3644.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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",
       "      <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>23.000000</td>\n",
       "      <td>14067.750000</td>\n",
       "      <td>14061.030000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>710.790000</td>\n",
       "      <td>449.890000</td>\n",
       "      <td>225.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.520000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.960600e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.862600e+04</td>\n",
       "      <td>71.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4874.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>32800.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8261.000000</td>\n",
       "      <td>10239.000000</td>\n",
       "      <td>46.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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",
       "      <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>31.000000</td>\n",
       "      <td>22860.700000</td>\n",
       "      <td>22860.490000</td>\n",
       "      <td>1214.600000</td>\n",
       "      <td>1214.590000</td>\n",
       "      <td>762.170000</td>\n",
       "      <td>415.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>652.580000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.472060e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.406500e+04</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8321.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>54100.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19859.000000</td>\n",
       "      <td>22806.000000</td>\n",
       "      <td>71.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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>1.000000</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>164.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>41418.981165</td>\n",
       "      <td>41418.980000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>2735.400000</td>\n",
       "      <td>75.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40500.340000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159199.000000</td>\n",
       "      <td>3.140082e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>389.000000</td>\n",
       "      <td>1.260281e+06</td>\n",
       "      <td>313.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>207484.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>759500.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>448583.000000</td>\n",
       "      <td>506507.000000</td>\n",
       "      <td>169.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>65000.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <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",
       "      <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",
       "</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     total_acc     out_prncp  out_prncp_inv   total_pymnt  \\\n",
       "count   68817.000000  68817.000000  68817.000000   68817.000000  68817.000000   \n",
       "mean    17604.142828     24.666347  16018.845833   16015.919519    970.639473   \n",
       "std     21835.880400     12.318917  10002.695495   10004.985940   1229.563301   \n",
       "min         0.000000      3.000000      0.000000       0.000000      0.000000   \n",
       "25%      6293.000000     16.000000   8578.800000    8576.500000    403.760000   \n",
       "50%     12068.000000     23.000000  14067.750000   14061.030000    710.790000   \n",
       "75%     21735.000000     31.000000  22860.700000   22860.490000   1214.600000   \n",
       "max    587191.000000    164.000000  40000.000000   40000.000000  41418.981165   \n",
       "\n",
       "       total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "count     68817.000000     68817.000000   68817.000000        68817.000000   \n",
       "mean        970.431151       658.748750     311.855687            0.035072   \n",
       "std        1229.569864      1126.237377     279.786954            1.086720   \n",
       "min           0.000000         0.000000       0.000000            0.000000   \n",
       "25%         403.550000       250.370000     115.440000            0.000000   \n",
       "50%         710.790000       449.890000     225.250000            0.000000   \n",
       "75%        1214.590000       762.170000     415.710000            0.000000   \n",
       "max       41418.980000     40000.000000    2735.400000           75.240000   \n",
       "\n",
       "       recoveries  collection_recovery_fee  last_pymnt_amnt  \\\n",
       "count     68817.0                  68817.0     68817.000000   \n",
       "mean          0.0                      0.0       517.202218   \n",
       "std           0.0                      0.0       816.021053   \n",
       "min           0.0                      0.0         0.000000   \n",
       "25%           0.0                      0.0       267.110000   \n",
       "50%           0.0                      0.0       408.520000   \n",
       "75%           0.0                      0.0       652.580000   \n",
       "max           0.0                      0.0     40500.340000   \n",
       "\n",
       "       collections_12_mths_ex_med  policy_code  acc_now_delinq   tot_coll_amt  \\\n",
       "count                68817.000000      68817.0         68817.0   68817.000000   \n",
       "mean                     0.016188          1.0             0.0     188.664298   \n",
       "std                      0.134774          0.0             0.0    1808.457195   \n",
       "min                      0.000000          1.0             0.0       0.000000   \n",
       "25%                      0.000000          1.0             0.0       0.000000   \n",
       "50%                      0.000000          1.0             0.0       0.000000   \n",
       "75%                      0.000000          1.0             0.0       0.000000   \n",
       "max                      4.000000          1.0             0.0  159199.000000   \n",
       "\n",
       "        tot_cur_bal   open_acc_6m   open_act_il   open_il_12m   open_il_24m  \\\n",
       "count  6.881700e+04  68817.000000  68817.000000  68817.000000  68817.000000   \n",
       "mean   1.634976e+05      1.023047      3.290481      0.843730      1.909644   \n",
       "std    1.723699e+05      1.176782      2.965579      0.970543      1.544994   \n",
       "min    2.350000e+02      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    3.570000e+04      0.000000      2.000000      0.000000      1.000000   \n",
       "50%    9.960600e+04      1.000000      2.000000      1.000000      2.000000   \n",
       "75%    2.472060e+05      2.000000      4.000000      1.000000      3.000000   \n",
       "max    3.140082e+06     14.000000     63.000000      6.000000     22.000000   \n",
       "\n",
       "       mths_since_rcnt_il  total_bal_il      il_util   open_rv_12m  \\\n",
       "count        68817.000000  6.881700e+04  68817.00000  68817.000000   \n",
       "mean            14.303152  4.324044e+04     69.21191      1.240042   \n",
       "std             13.972619  4.956130e+04     22.33711      1.471745   \n",
       "min              0.000000  1.000000e+00      0.00000      0.000000   \n",
       "25%              6.000000  1.422300e+04     56.00000      0.000000   \n",
       "50%             11.000000  2.862600e+04     71.00000      1.000000   \n",
       "75%             18.000000  5.406500e+04     85.00000      2.000000   \n",
       "max            389.000000  1.260281e+06    313.00000     18.000000   \n",
       "\n",
       "        open_rv_24m     max_bal_bc      all_util  total_rev_hi_lim  \\\n",
       "count  68817.000000   68817.000000  68817.000000      68817.000000   \n",
       "mean       2.622245    6297.491710     55.702472      42431.895360   \n",
       "std        2.502608    5702.281757     18.495480      37166.229316   \n",
       "min        0.000000       0.000000      1.000000        200.000000   \n",
       "25%        1.000000    2558.000000     43.000000      19200.000000   \n",
       "50%        2.000000    4874.000000     56.000000      32800.000000   \n",
       "75%        4.000000    8321.000000     68.000000      54100.000000   \n",
       "max       44.000000  207484.000000    216.000000     759500.000000   \n",
       "\n",
       "             inq_fi   total_cu_tl  inq_last_12m  acc_open_past_24mths  \\\n",
       "count  68817.000000  68817.000000  68817.000000          68817.000000   \n",
       "mean       1.382071      1.687795      2.189023              4.827572   \n",
       "std        1.543220      2.852664      2.396186              3.145377   \n",
       "min        0.000000      0.000000      0.000000              0.000000   \n",
       "25%        0.000000      0.000000      1.000000              3.000000   \n",
       "50%        1.000000      1.000000      2.000000              4.000000   \n",
       "75%        2.000000      2.000000      3.000000              6.000000   \n",
       "max       29.000000     70.000000     42.000000             45.000000   \n",
       "\n",
       "         avg_cur_bal  bc_open_to_buy       bc_util  chargeoff_within_12_mths  \\\n",
       "count   68817.000000    68817.000000  68817.000000              68817.000000   \n",
       "mean    14142.936193    16991.852667     48.330667                  0.006830   \n",
       "std     15863.878993    20447.276820     28.106287                  0.087327   \n",
       "min        20.000000        0.000000      0.000000                  0.000000   \n",
       "25%      3505.000000     3644.000000     25.400000                  0.000000   \n",
       "50%      8261.000000    10239.000000     46.400000                  0.000000   \n",
       "75%     19859.000000    22806.000000     71.100000                  0.000000   \n",
       "max    448583.000000   506507.000000    169.500000                  2.000000   \n",
       "\n",
       "        delinq_amnt  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  \\\n",
       "count  68817.000000        68817.000000          68817.000000   \n",
       "mean       2.347065          125.468605            175.136841   \n",
       "std      301.312407           56.968853             99.149467   \n",
       "min        0.000000            0.000000              5.000000   \n",
       "25%        0.000000           87.000000            101.000000   \n",
       "50%        0.000000          132.000000            157.000000   \n",
       "75%        0.000000          159.000000            226.000000   \n",
       "max    65000.000000          606.000000            902.000000   \n",
       "\n",
       "       mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl      mort_acc  \\\n",
       "count           68817.000000    68817.000000  68817.000000   \n",
       "mean               14.294215        7.179781      1.441882   \n",
       "std                17.417118        6.303066      1.747777   \n",
       "min                 0.000000        0.000000      0.000000   \n",
       "25%                 4.000000        3.000000      0.000000   \n",
       "50%                 9.000000        5.000000      1.000000   \n",
       "75%                19.000000       10.000000      2.000000   \n",
       "max               564.000000      170.000000     20.000000   \n",
       "\n",
       "       mths_since_recent_bc  mths_since_recent_inq  num_accts_ever_120_pd  \\\n",
       "count          68817.000000           68817.000000           68817.000000   \n",
       "mean              24.714954               7.688028               0.472485   \n",
       "std               32.130889               6.034861               1.410819   \n",
       "min                0.000000               0.000000               0.000000   \n",
       "25%                6.000000               3.000000               0.000000   \n",
       "50%               15.000000               6.000000               0.000000   \n",
       "75%               31.000000              12.000000               0.000000   \n",
       "max              564.000000              24.000000              34.000000   \n",
       "\n",
       "       num_actv_bc_tl  num_actv_rev_tl   num_bc_sats     num_bc_tl  \\\n",
       "count    68817.000000     68817.000000  68817.000000  68817.000000   \n",
       "mean         3.806123         5.648038      5.135156      7.414403   \n",
       "std          2.464504         3.470941      3.230651      4.581371   \n",
       "min          0.000000         0.000000      0.000000      1.000000   \n",
       "25%          2.000000         3.000000      3.000000      4.000000   \n",
       "50%          3.000000         5.000000      4.000000      6.000000   \n",
       "75%          5.000000         7.000000      7.000000     10.000000   \n",
       "max         26.000000        43.000000     43.000000     60.000000   \n",
       "\n",
       "          num_il_tl  num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  \\\n",
       "count  68817.000000   68817.000000   68817.000000         68817.000000   \n",
       "mean       9.500153       8.630818      13.517663             5.653036   \n",
       "std        7.573846       5.056994       8.026158             3.472679   \n",
       "min        1.000000       1.000000       2.000000             0.000000   \n",
       "25%        4.000000       5.000000       8.000000             3.000000   \n",
       "50%        7.000000       8.000000      12.000000             5.000000   \n",
       "75%       12.000000      11.000000      17.000000             7.000000   \n",
       "max      149.000000      70.000000      98.000000            43.000000   \n",
       "\n",
       "           num_sats  num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  \\\n",
       "count  68817.000000           68817.0       68817.0        68817.000000   \n",
       "mean      12.571995               0.0           0.0            0.052138   \n",
       "std        6.021318               0.0           0.0            0.390633   \n",
       "min        2.000000               0.0           0.0            0.000000   \n",
       "25%        8.000000               0.0           0.0            0.000000   \n",
       "50%       11.000000               0.0           0.0            0.000000   \n",
       "75%       16.000000               0.0           0.0            0.000000   \n",
       "max       72.000000               0.0           0.0           18.000000   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "count        68817.000000    68817.000000      68817.000000   \n",
       "mean             2.219423       95.057627         30.626217   \n",
       "std              1.897432        8.326426         33.631463   \n",
       "min              0.000000       20.000000          0.000000   \n",
       "25%              1.000000       93.000000          0.000000   \n",
       "50%              2.000000      100.000000         20.000000   \n",
       "75%              3.000000      100.000000         50.000000   \n",
       "max             19.000000      100.000000        100.000000   \n",
       "\n",
       "       pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "count          68817.000000    68817.0     6.881700e+04       6.881700e+04   \n",
       "mean               0.125972        0.0     2.100332e+05       6.133843e+04   \n",
       "std                0.336732        0.0     1.928088e+05       5.738798e+04   \n",
       "min                0.000000        0.0     3.600000e+03       2.350000e+02   \n",
       "25%                0.000000        0.0     6.697700e+04       2.650300e+04   \n",
       "50%                0.000000        0.0     1.467100e+05       4.535700e+04   \n",
       "75%                0.000000        0.0     3.036400e+05       7.657000e+04   \n",
       "max                4.000000        0.0     3.292782e+06       1.295455e+06   \n",
       "\n",
       "       total_bc_limit  total_il_high_credit_limit  home_ownership_ANY  \\\n",
       "count    68817.000000                6.881700e+04        68817.000000   \n",
       "mean     29734.128558                5.572240e+04            0.009285   \n",
       "std      26795.394232                5.095845e+04            0.095914   \n",
       "min        100.000000                1.270000e+02            0.000000   \n",
       "25%      11600.000000                2.288000e+04            0.000000   \n",
       "50%      22100.000000                4.200000e+04            0.000000   \n",
       "75%      39300.000000                7.249900e+04            0.000000   \n",
       "max     509400.000000                1.426964e+06            1.000000   \n",
       "\n",
       "       home_ownership_MORTGAGE  home_ownership_OWN  home_ownership_RENT  \\\n",
       "count             68817.000000        68817.000000         68817.000000   \n",
       "mean                  0.526309            0.106747             0.357659   \n",
       "std                   0.499311            0.308793             0.479314   \n",
       "min                   0.000000            0.000000             0.000000   \n",
       "25%                   0.000000            0.000000             0.000000   \n",
       "50%                   1.000000            0.000000             0.000000   \n",
       "75%                   1.000000            0.000000             1.000000   \n",
       "max                   1.000000            1.000000             1.000000   \n",
       "\n",
       "       verification_status_Not Verified  verification_status_Source Verified  \\\n",
       "count                      68817.000000                         68817.000000   \n",
       "mean                           0.478007                             0.373992   \n",
       "std                            0.499520                             0.483865   \n",
       "min                            0.000000                             0.000000   \n",
       "25%                            0.000000                             0.000000   \n",
       "50%                            0.000000                             0.000000   \n",
       "75%                            1.000000                             1.000000   \n",
       "max                            1.000000                             1.000000   \n",
       "\n",
       "       verification_status_Verified  issue_d_Feb-2019  issue_d_Jan-2019  \\\n",
       "count                  68817.000000      68817.000000      68817.000000   \n",
       "mean                       0.148001          0.371696          0.451066   \n",
       "std                        0.355104          0.483261          0.497603   \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%                        0.000000          1.000000          1.000000   \n",
       "max                        1.000000          1.000000          1.000000   \n",
       "\n",
       "       issue_d_Mar-2019  initial_list_status_f  initial_list_status_w  \\\n",
       "count      68817.000000           68817.000000           68817.000000   \n",
       "mean           0.177238               0.123879               0.876121   \n",
       "std            0.381873               0.329446               0.329446   \n",
       "min            0.000000               0.000000               0.000000   \n",
       "25%            0.000000               0.000000               1.000000   \n",
       "50%            0.000000               0.000000               1.000000   \n",
       "75%            0.000000               0.000000               1.000000   \n",
       "max            1.000000               1.000000               1.000000   \n",
       "\n",
       "       next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  \\\n",
       "count           68817.000000           68817.000000   \n",
       "mean                0.383161               0.616839   \n",
       "std                 0.486161               0.486161   \n",
       "min                 0.000000               0.000000   \n",
       "25%                 0.000000               0.000000   \n",
       "50%                 0.000000               1.000000   \n",
       "75%                 1.000000               1.000000   \n",
       "max                 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  "
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68817, 92)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_encoded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create the scaler instance\n",
    "data_scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.01092723e-01,  9.17984364e-01, -3.65558353e-01,\n",
       "        -1.92194797e-01,  2.70400563e-01, -3.03142532e-01,\n",
       "        -6.56491511e-01, -7.61659174e-01, -3.74204323e-01,\n",
       "        -7.32521829e-01, -8.65857295e-01, -5.81245133e-01,\n",
       "        -5.80819581e-01, -5.00465445e-01, -5.00293345e-01,\n",
       "        -3.22863900e-01, -8.99604190e-01, -3.22736265e-02,\n",
       "         0.00000000e+00,  0.00000000e+00, -1.73835281e-01,\n",
       "        -1.20112470e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -5.89694662e-03, -7.26922276e-01,  1.67997825e+00,\n",
       "         2.39253304e-01,  1.19137226e+00,  1.35299632e+00,\n",
       "        -8.08956027e-01, -1.34187152e-01, -4.57175710e-01,\n",
       "        -1.63101750e-01, -2.48640217e-01, -8.32039942e-01,\n",
       "         1.24221966e-01, -1.04213361e+00, -2.47582393e-01,\n",
       "        -5.91660154e-01,  7.55780406e-01,  3.72749228e-01,\n",
       "        -5.90524188e-01, -8.09152878e-01,  1.04494582e+00,\n",
       "        -7.82090839e-02, -7.78953140e-03, -5.69940224e-01,\n",
       "        -7.98162796e-01, -6.48459594e-01, -6.63139469e-01,\n",
       "        -8.24986795e-01, -1.46743168e-01, -1.10824038e+00,\n",
       "        -3.34903552e-01, -1.13862411e+00, -1.05103022e+00,\n",
       "        -1.27998521e+00, -1.40011594e+00,  6.59969904e-02,\n",
       "        -1.11347944e+00, -1.31043257e+00, -1.05194370e+00,\n",
       "        -7.59306928e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.33472172e-01,  4.11389226e-01, -1.12385485e+00,\n",
       "         2.06277933e+00, -3.74103443e-01,  0.00000000e+00,\n",
       "        -7.48654947e-01, -4.03213352e-01, -1.03504094e+00,\n",
       "         1.22936320e-01, -9.68118013e-02, -1.05407800e+00,\n",
       "        -3.45692791e-01,  1.34013488e+00, -9.56939962e-01,\n",
       "         1.29377539e+00, -4.16786038e-01, -7.69146348e-01,\n",
       "        -9.06483424e-01,  2.15455915e+00, -3.76025544e-01,\n",
       "         3.76025544e-01, -7.88142711e-01,  7.88142711e-01,\n",
       "         4.02903860e-01, -4.02903860e-01],\n",
       "       [ 8.09787255e-01,  1.50182932e+00,  1.55674724e+00,\n",
       "         1.45236397e-01, -7.66446528e-02, -3.03142532e-01,\n",
       "        -6.56491511e-01,  7.32656063e-01,  2.59496654e+00,\n",
       "         3.49819979e-02,  7.57673792e-01,  8.27211454e-01,\n",
       "         8.27314568e-01, -7.89794059e-02, -7.88095562e-02,\n",
       "         4.27277597e-02, -5.18954654e-01, -3.22736265e-02,\n",
       "         0.00000000e+00,  0.00000000e+00,  5.04755083e-01,\n",
       "        -1.20112470e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.30854466e-02,  3.74902281e-01,  1.67997825e+00,\n",
       "        -9.79514110e-02,  1.19137226e+00,  7.05739781e-01,\n",
       "        -7.37386960e-01, -1.95202319e-02,  7.51583900e-01,\n",
       "         1.87530974e+00,  2.14887602e+00, -1.92992867e-01,\n",
       "         2.86424935e-01,  2.14392546e-01,  1.69641915e+00,\n",
       "         2.56330882e+00,  3.38447424e-01,  2.59825380e+00,\n",
       "        -4.56345799e-02,  6.11829064e-01, -7.09122909e-01,\n",
       "        -7.82090839e-02, -7.78953140e-03, -6.75261698e-01,\n",
       "         1.11814987e+00, -7.63289990e-01, -9.80447694e-01,\n",
       "         3.19332562e-01, -7.38078757e-01,  5.48810751e-01,\n",
       "         1.79153573e+00,  1.29596036e+00,  1.54194528e+00,\n",
       "         8.86776436e-01,  1.21920657e+00,  5.94134078e-01,\n",
       "         8.63994250e-01,  5.58470148e-01,  1.53973414e+00,\n",
       "         7.35393288e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.33472172e-01,  1.99248491e+00, -4.63302569e-01,\n",
       "         5.76065437e-01,  2.59563662e+00,  0.00000000e+00,\n",
       "         3.18420236e-01, -1.21529903e-02,  4.27907671e-01,\n",
       "        -1.28054315e-01, -9.68118013e-02,  9.48696394e-01,\n",
       "        -3.45692791e-01, -7.46193550e-01, -9.56939962e-01,\n",
       "        -7.72931691e-01,  2.39931262e+00, -7.69146348e-01,\n",
       "        -9.06483424e-01,  2.15455915e+00, -3.76025544e-01,\n",
       "         3.76025544e-01, -7.88142711e-01,  7.88142711e-01,\n",
       "         4.02903860e-01, -4.02903860e-01],\n",
       "       [ 3.23276918e-01,  1.50182932e+00,  1.70891756e-01,\n",
       "        -2.78715616e-01,  1.22869216e-01, -3.03142532e-01,\n",
       "        -6.56491511e-01, -7.61659174e-01, -3.74204323e-01,\n",
       "        -1.99541965e-01, -2.97621415e-01,  3.61699549e-01,\n",
       "         3.61909233e-01, -3.94614087e-01, -3.94442552e-01,\n",
       "        -2.62414392e-01, -6.77755207e-01, -3.22736265e-02,\n",
       "         0.00000000e+00,  0.00000000e+00,  1.55362092e-02,\n",
       "        -1.20112470e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.04324100e-01, -6.83497904e-01,  8.30196824e-01,\n",
       "        -9.79514110e-02,  1.19137226e+00,  7.05739781e-01,\n",
       "        -7.37386960e-01, -2.17982985e-01,  2.59126281e-01,\n",
       "         5.16368746e-01,  1.50945823e-01, -6.11600268e-01,\n",
       "         1.04337212e+00, -6.70826096e-01, -8.95582908e-01,\n",
       "         8.10548278e-01, -4.96218540e-01,  3.72749228e-01,\n",
       "        -5.31521796e-01, -6.92168235e-01,  4.89905815e-01,\n",
       "        -7.82090839e-02, -7.78953140e-03,  5.88595988e-01,\n",
       "        -1.93011427e-01, -4.18798803e-01, -5.04485357e-01,\n",
       "        -8.24986795e-01, -5.51341202e-01, -1.14009703e-01,\n",
       "         3.91797501e+00, -7.32860034e-01, -1.86705054e-01,\n",
       "        -9.70447829e-01, -9.04546835e-02, -1.98071554e-01,\n",
       "        -7.17984698e-01, -6.44974261e-02, -1.88051082e-01,\n",
       "        -7.59306928e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.33472172e-01,  9.38421122e-01, -3.40576271e+00,\n",
       "         5.76065437e-01, -3.74103443e-01,  0.00000000e+00,\n",
       "        -7.74810581e-01, -2.72784388e-01, -8.29780294e-01,\n",
       "        -2.46838233e-01, -9.68118013e-02,  9.48696394e-01,\n",
       "        -3.45692791e-01, -7.46193550e-01, -9.56939962e-01,\n",
       "        -7.72931691e-01,  2.39931262e+00, -7.69146348e-01,\n",
       "        -9.06483424e-01,  2.15455915e+00, -3.76025544e-01,\n",
       "         3.76025544e-01, -7.88142711e-01,  7.88142711e-01,\n",
       "         4.02903860e-01, -4.02903860e-01],\n",
       "       [-6.49743757e-01,  7.53842900e-01, -4.41236942e-01,\n",
       "         3.27593326e-02,  4.78330649e-01, -3.03142532e-01,\n",
       "         6.62567652e-01, -4.29589121e-01,  2.59496654e+00,\n",
       "         1.79456936e-02, -1.35268306e-01, -6.29785402e-01,\n",
       "        -6.29348737e-01, -5.16698969e-01, -5.16526782e-01,\n",
       "        -3.35712084e-01, -9.19226405e-01, -3.22736265e-02,\n",
       "         0.00000000e+00,  0.00000000e+00, -2.00550472e-01,\n",
       "        -1.20112470e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.04324100e-01, -5.49482806e-01, -8.69366021e-01,\n",
       "         2.39253304e-01,  1.61013652e-01,  7.05739781e-01,\n",
       "        -3.79541627e-01,  1.52288465e-01, -1.43793589e-01,\n",
       "        -8.42572245e-01, -6.48226257e-01,  1.09620582e-02,\n",
       "         7.18966185e-01, -5.22841216e-01,  1.04841864e+00,\n",
       "         1.16110039e+00,  7.55780406e-01, -2.63109221e-01,\n",
       "        -4.57957917e-01, -5.91420590e-01,  9.52439150e-01,\n",
       "        -7.82090839e-02, -7.78953140e-03, -4.47065172e-01,\n",
       "        -8.68763789e-01,  5.57259561e-01,  2.88785204e-01,\n",
       "        -8.24986795e-01,  2.88977792e-01, -1.27394550e+00,\n",
       "        -3.34903552e-01,  7.86681249e-02,  1.01403335e-01,\n",
       "        -3.51373074e-01, -7.45285312e-01,  9.90236894e-01,\n",
       "        -5.20237330e-01, -9.36652029e-01,  9.99131218e-02,\n",
       "        -4.27151324e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.33472172e-01, -6.42674566e-01,  5.93581073e-01,\n",
       "         5.76065437e-01,  2.59563662e+00,  0.00000000e+00,\n",
       "        -5.73251936e-01,  1.29741873e-01, -3.74474859e-01,\n",
       "         4.07856808e-01, -9.68118013e-02, -1.05407800e+00,\n",
       "        -3.45692791e-01,  1.34013488e+00, -9.56939962e-01,\n",
       "        -7.72931691e-01,  2.39931262e+00, -7.69146348e-01,\n",
       "        -9.06483424e-01,  2.15455915e+00, -3.76025544e-01,\n",
       "         3.76025544e-01, -7.88142711e-01,  7.88142711e-01,\n",
       "         4.02903860e-01, -4.02903860e-01],\n",
       "       [ 5.17881053e-01,  4.08938051e-01,  1.37947269e-01,\n",
       "        -3.13323943e-01, -1.49420183e-01, -3.03142532e-01,\n",
       "         6.62567652e-01,  2.34550984e-01, -3.74204323e-01,\n",
       "        -3.89872289e-01,  2.70614466e-01,  5.59441685e-01,\n",
       "         5.59606100e-01, -3.95492454e-01, -3.95320914e-01,\n",
       "        -2.42826905e-01, -7.60461594e-01, -3.22736265e-02,\n",
       "         0.00000000e+00,  0.00000000e+00,  3.90652276e-03,\n",
       "        -1.20112470e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.04324100e-01,  8.26565448e-02, -8.69366021e-01,\n",
       "        -7.72360841e-01, -8.69344959e-01,  5.84832406e-02,\n",
       "        -2.16962941e-02, -5.32928606e-01,  6.62046151e-01,\n",
       "        -1.63101750e-01, -6.48226257e-01, -4.73058229e-01,\n",
       "        -7.94928190e-01,  1.79762517e-02,  4.00418122e-01,\n",
       "        -5.91660154e-01, -7.88855578e-02, -2.63109221e-01,\n",
       "        -2.96232471e-02,  1.32397435e-01, -7.02007011e-01,\n",
       "        -7.82090839e-02, -7.78953140e-03, -1.02633328e+00,\n",
       "        -3.74556838e-01, -1.89138011e-01,  6.06093429e-01,\n",
       "         1.46365192e+00, -4.26849500e-01, -1.27394550e+00,\n",
       "        -3.34903552e-01,  7.86681249e-02,  1.01403335e-01,\n",
       "         5.77239058e-01,  7.82652821e-01, -4.62140097e-01,\n",
       "         6.66246881e-01,  5.58470148e-01,  9.99131218e-02,\n",
       "         2.37159883e-01,  0.00000000e+00,  0.00000000e+00,\n",
       "        -1.33472172e-01, -6.42674566e-01,  5.93581073e-01,\n",
       "        -9.10648458e-01, -3.74103443e-01,  0.00000000e+00,\n",
       "         5.03963016e-02, -6.17197009e-01, -7.96459289e-02,\n",
       "        -7.01015501e-01, -9.68118013e-02,  9.48696394e-01,\n",
       "        -3.45692791e-01, -7.46193550e-01,  1.04499764e+00,\n",
       "        -7.72931691e-01, -4.16786038e-01, -7.69146348e-01,\n",
       "        -9.06483424e-01,  2.15455915e+00, -3.76025544e-01,\n",
       "         3.76025544e-01, -7.88142711e-01,  7.88142711e-01,\n",
       "         4.02903860e-01, -4.02903860e-01]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled = data_scaler.fit_transform(X_encoded)\n",
    "X_scaled[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68817,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     51366\n",
       "high_risk      246\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the LogisticRegression classifier to make predictions and evaluate the model’s performance.\n",
    "classifier = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the data\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "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>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7034</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38804</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65533</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37736</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6390</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31854</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27835</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43343</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27206</th>\n",
       "      <td>low_risk</td>\n",
       "      <td>low_risk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17205 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prediction    Actual\n",
       "7034    low_risk  low_risk\n",
       "38804   low_risk  low_risk\n",
       "65533   low_risk  low_risk\n",
       "37736   low_risk  low_risk\n",
       "6390    low_risk  low_risk\n",
       "...          ...       ...\n",
       "31854   low_risk  low_risk\n",
       "27835   low_risk  low_risk\n",
       "3089    low_risk  low_risk\n",
       "43343   low_risk  low_risk\n",
       "27206   low_risk  low_risk\n",
       "\n",
       "[17205 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict outcomes for test data set\n",
    "y_pred = classifier.predict(X_test)\n",
    "pd.DataFrame({\"Prediction\": y_pred, \"Actual\": y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.995059575704737"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the accuracy score of the model.\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   21    80]\n",
      " [    5 17099]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a confusion matrix.\n",
    "matrix = confusion_matrix(y_test, y_pred)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "# Confusion matrix using ConfusionMatrixDisplay\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=matrix,display_labels=classifier.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/LogisticRegression_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   high_risk       0.81      0.21      0.33       101\n",
      "    low_risk       1.00      1.00      1.00     17104\n",
      "\n",
      "    accuracy                           1.00     17205\n",
      "   macro avg       0.90      0.60      0.66     17205\n",
      "weighted avg       0.99      1.00      0.99     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print out the classification report.\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "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       0.81      0.21      1.00      0.33      0.46      0.19       101\n",
      "   low_risk       1.00      1.00      0.21      1.00      0.46      0.22     17104\n",
      "\n",
      "avg / total       0.99      1.00      0.21      0.99      0.46      0.22     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "report_imbalanced = classification_report_imbalanced(y_test, y_pred) \n",
    "print(report_imbalanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oversampling\n",
    "\n",
    "In this section, you will compare two oversampling algorithms to determine which algorithm results in the best performance. You will oversample the data using the naive random oversampling algorithm and the SMOTE algorithm. For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Random Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 51366, 'high_risk': 51366})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the RandomOversampler\n",
    "ros = RandomOverSampler(random_state=1)\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "oversampling_model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "oversampling_model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict\n",
    "y_pred = oversampling_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8325468421491353"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OSevjgZsjYkNELAIWAvtJGgD0jIjHIyKAqQV1mqyYlMgfgGnAAOAjwK3ATaVe0MyspUQUtwB9Jc0uWE7Z/DzxOnAJsBhYBqyJiPuBHSJiWTpmGdA/VRkIvFZwiiWpbGBar11ekmJuOioiri/YvkHSN0q9oJlZiyn+puPKiBhT386Umx4PDAXeBm6VdHwD56ur2x4NlJekoblEeqfVhyWdB9ycLnQccHepFzQzazHNN6zvMGBRRKwAkPQn4JPAm5IGRMSylO5Yno5fAgwuqD+ILIWyJK3XLi9JQz3sp9n8N8SpBfsC+EmpFzUzawlqvmF9i4GxkrYB3gMOJZtf6V3gRODn6ecd6fg7gT9I+iVZ6ngYMCsiqiStlTQWeBI4Afh1qY1qaC6RoaWe1Mxsq2vGpxgj4klJtwHPAJXAs8AUoAcwTdJEsqB+bDp+rqRpwLx0/JkRUfOA4enAtUA3YHpaSlLUk46SRgEjgK4FH2hqqRc1M2t+TRoB0qiIuBC4sFbxBrLedl3HTwIm1VE+GxjVHG1qNGBLuhA4mCxg3wMcCcwkG55iZtZ2+ElHvkj2G+WNiDgJ2BPo0qKtMjMrhSd/4r2IqJZUKakn2V1RPzhjZm1Le36BQYHZkrYDfkc2cmQdMKslG2VmVopmHCXSJjUasCPijLT6W0n3kj1m+ULLNsvMrATtNWBL2ruhfRHxTMs0ycysNO25h31pA/sCOKSZ29J29ehG9d6jW7sV1gQP3PL71m6CNVHFgGY4SXvNYUfEv2zNhpiZbZGcjwApRlEPzpiZ5YIDtplZPrTnHLaZWb5Ut3YDWlYxb5yRpOMlXZC2d5K0X8s3zcyseIril7wq5tH0ycD+wJfT9lrgNy3WIjOzUoWKW3KqmJTIJyJib0nPAkTEW5I6t3C7zMyaLse952IUE7A3prf/BoCkfpR9psjM8ijP6Y5iFJMSuRy4HegvaRLZ1Kr/2aKtMjMrRXufrS8ibpT0NNkUqwKOiYj5Ld4yM7OmCFCZ/+1fzAsMdgLWA3cVlkXE4pZsmJlZk+W491yMYnLYd/Phy3i7kr32fQEwsgXbZWbWZOWewy4mJbJH4Xaaxe/Ueg43M7MW0uQnHSPiGUn7tkRjzMy2SHvvYUv6bsFmB2BvYEWLtcjMrBTNfNMxvWnrKrI3ngfwNbJ08C3AEOAV4EsR8VY6/nxgIlAFnBUR96XyfYBrgW5kLzL/VkSU9KulmGF92xYsXchy2uNLuZiZWYtq3mF9/wXcGxHDyV4+Ph84D3gwIoYBD6ZtJI0AJpDd2xsHTE7PrwBcAZwCDEvLuFI/XoM97HTBHhFxTqkXMDPbGkTz3XRMLxw/CPgqQER8AHwgaTxwcDrsOuAR4PtkndibI2IDsEjSQmA/Sa+QvVbx8XTeqcAxwPRS2lVvD1tSx4ioIkuBmJm1fc3Xw96ZLPX7e0nPSrpKUndgh4hYBpB+9k/HDwReK6i/JJUNTOu1y0vSUA97Flmwfk7SncCtwLs1OyPiT6Ve1Mys2TVtJr6+kmYXbE+JiCkF2x3J4t83I+JJSf9FSn/Uo64ZpaKB8pIUM0qkN7CK7B2ONQ0IwAHbzNqW4kPhyogY08D+JcCSiHgybd9GFrDflDQgIpZJGgAsLzh+cEH9QcDSVD6ojvKSNHTTsX8aIfIiMCf9nJt+vljqBc3MWoqqi1saExFvAK9J2j0VHQrMA+4ETkxlJwJ3pPU7gQmSukgaSnZzcVZKm6yVNFaSgBMK6jRZQz3sCqAHzdylNzNrMc0bmb4J3Jimk/4HcBJZJ3eapInAYuBYgIiYK2kaWVCvBM5M9wABTufDYX3TKfGGIzQcsJdFxI9LPbGZ2VbVzDPxRcRzQF1pk0PrOX4SMKmO8tlkY7m3WEMBO7+vZTCzdqk9zyVS528RM7M2q70G7IhYvTUbYma2pdpzD9vMLD+Csn95oQO2mZUFUf433hywzax8OCViZpYPzmGbmeWFA7aZWQ74relmZjniHraZWT44h21mlhcO2GZm+eAetplZHjTzbH1tkQO2mZUF4VEiZmb54R62mVk+KMo7Yjtgm1l5cA7bzCw/PErEzCwnfNPRzCwv3MM2M8uBKP+USIfWboCZWbOJIpciSaqQ9Kykv6Tt3pIekPRy+rl9wbHnS1ooaYGkIwrK95E0J+27XFLJL8ZxwDazsiCyHnYxSxN8C5hfsH0e8GBEDAMeTNtIGgFMAEYC44DJkipSnSuAU4BhaRlX6md0wDaz8hFR3FIESYOAzwBXFRSPB65L69cBxxSU3xwRGyJiEbAQ2E/SAKBnRDweEQFMLajTZM5hm1l5aNoLDPpKml2wPSUiptQ65lfAucC2BWU7RMQygIhYJql/Kh8IPFFw3JJUtjGt1y4viQN2menX513OPfNRem/3HtXV4p4Hd+P26SMAGD9uPuOPmE9VVQeefHYQV904hoqKar576mMMG7qKiorggRm7cPOfPw7AsKErOeeMmXTuXMWsZwcx+dr9KP/3Ure8S78zmCf/2pPt+lYy5eEFm+279Yp+XPWTgUybM4defao2lS9f0omTDx7O8We/wbGnr+D99WLSqUNY+koXOlQEYw9/h4k/WLbZuR79Sy9+espQfj19Abvt+d5W+WytrQkBe2VEjKn3PNJngeUR8bSkg4u5dB1l0UB5SRywy0xVlbjy+n1ZuKgP3bpuZPLP7uLpFz7C9tu9xyfHLObUc8azsbKC7Xpm/wEfNPYVOnWq4pRzjqFL50quuvR2Hn5sKG+u2Jazvv4El035JPNf7sek8/7KvqNf56nnBrXyJ8y/Tx+3mqNPWsnF39pps/Llr3fi2Rnb0n/gB/9U57c/Gsi+h6zdrOwLp61g9KfWsfED8f0v7cJTD2276Zj16zrw56v7MXzvd1vug7RFzTdK5FPA0ZKOAroCPSXdALwpaUDqXQ8AlqfjlwCDC+oPApam8kF1lJdkq+awJa3bSte5R9J2Dex/RVLfrdGWrW3129uwcFEfAN57vxOLX+9F397r+b+HL+DmO/ZgY2V2H+Ttd7plFQK6dqmkQ4dqOneupLKygvXrO9N7u/Vs0+0D5r/cHxB/nbELn9x3cSt9qvKyx9h32Xb7qn8qv/JHA5n4w6XUHkPwv9N7MWCnD/jobu9vKuu6TTD6U9l/Tp06B8P2eI8Vyzpt2n/dLwZw7BnL6dylzMe51dJcNx0j4vyIGBQRQ8huJj4UEccDdwInpsNOBO5I63cCEyR1kTSU7ObirJQ+WStpbBodckJBnSYrq5uOynSIiKMi4u3Wbk9r26HfWnYdupqXFvZl0IA17DH8TS7/6V+49MLp7LbLSgBmPDmE9zd05JYrb+HG39zGrX8Zydp3u9C393pWru6+6VwrVnen7/brW+ujlL3H7+tJ3x03ssvI9zcrf399B6ZN7s/xZ79Rb911ayp44oGe7HVAFsAXzunGiqWdGHv4Oy3a5jYnaNabjvX4OXC4pJeBw9M2ETEXmAbMA+4FzoyImt/Kp5PduFwI/B2YXurFWyVgp8B6saQX0/jE41L5ZElHp/XbJV2T1idK+mk95xoiab6kycAzwOCaHrSk7pLulvR8utZxtep2k3SvpJNb9hNvfV27bOSC7z7CFdftx/r3OtOhIujR/QPO+uFnmHLDGH747UeAYPiuK6iu7sCE047jhG9+gS9+di479l9bZ+atffXVtp7314ubLt+BE85Z9k/7pl68I587eQXdutednK2qhJ+d8VHGT1zJgI9+QHV11lM/5cKS/+rOtRYY1kdEPBIRn03rqyLi0IgYln6uLjhuUkTsEhG7R8T0gvLZETEq7ftGGi1SktbKYX8eGA3sCfQFnpI0A5gBHEj258VAYEA6/gDg5gbOtztwUkScAVAwLn0csDQiPpPKexXU6ZHOOTUiptY+oaRTyMZO0qVLr9q727SKimouPPthHpq5MzNnfRSAlau2YeasnQCx4O/9iGrRa9sNHPKpRcx+biBVVR14+51uzF3Qn912XsmLL+1A394f5j/79X6XVW9t00qfqLwte7ULbyzuzOmHDQdgxbJOnHnE7lx+z9946dltmHn3dlz904+w7p0K1CHo3CUY/7XsL6RfnTOYgUM38PmTVwDw3roOvPJSV879wq4ArF7RkQu/ujP/ce0/yv7Go19g0HIOAG5KfzK8Kel/gH2BR4Fvp0Ho84DtU2J/f+CsBs73akQ8UUf5HOASSRcBf4mIRwv23QH8IiJurOuEaYjPFICe2w7MUecyOPu0x1j8ei/+ePfITaX/+9RO7DXyDV6YN4CBA9bQsWMVa9Z2YfnK7owetYy/ProzXbtU8rFhK/jTPSNY/fY2vPd+Jz42bDnzX+7HYQf9nTvu/Vgrfq7yNfRj7zNtztxN2yfsN4JfT19Arz5V/PLPCzeVX3/JjnTtXrUpWF970Y68u7aC71z62qZjuves5ta5L27aPucLu3LyBa+XfbAGmiPd0ea1VsCuc2xYRLyeHvUcR9bb7g18CVgXEWvrqpPUeSs8Iv4maR/gKOBnku6PiB+n3Y8BR0r6w5b8idLWjNx9OYcf9Hf+8er2/Pai7N7GNTftw70PD+Ps0x9jyiV/prKyAxdPPhAQd9w3nHPOmMnvLrkDKbjvkWEsWtwbgMuv2p/vnTGTLp2qeOq5gcx6ruTho1bgZ6d/lBce78Ga1R35yj4j+Lez32Dcv65uvGKBFUs7cdN/7cjgXd/nzE/vDsDRJ63gyK807TzlptznEtHWjFWS1kVED0mfB04lC6S9gdnAJyLiDUnXAoekpQ9wG3BbRHynnnMOIes9jyooewUYA3QGVkfE+5KOAb4aEccU7P93oHNEnN5Qu3tuOzD23fvMkj+3bX0P3PL71m6CNVHFgIVPNzQ2ujHbbjco9jroW0Ud++hd527RtVpLa40SuR14AXgeeAg4NyJqboM/CnSMiIVkNxF7p7JS7AHMkvQc8AOg9o3LbwNdJf2ixPObWRvSEjcd25KtmhKJiB7pZwDnpKX2MVcDV6f1jUD32sfUOv4VYFStsiFp9b601K4zpGDzpCKbb2ZtWQBVOY7GRfCTjmZWNvLcey5GbgK2pD5k0xnWdmhErNra7TGzNqh8xg/UKTcBOwXl0a3dDjNru9zDNjPLgya+TSaPHLDNrCxkb5wp74jtgG1mZUMeJWJmlgNOiZiZ5YXnEjEzyw2PEjEzywv3sM3McqBpb03PJQdsMysf1e5hm5nlgsdhm5nlhQO2mVkOBFDmOezWeoGBmVmzEoGiuKXRc0mDJT0sab6kuZK+lcp7S3pA0svp5/YFdc6XtFDSAklHFJTvI2lO2ne5Ct4S3lQO2GZWPqqri1saVwmcHREfA8YCZ6aXg58HPBgRw8imez4PIO2bAIwkeyftZEkV6VxXAKcAw9IyrtSP54BtZuWhJiVSzNLYqSKWRcQzaX0tMB8YCIwHrkuHXQcck9bHAzdHxIaIWAQsBPaTNADoGRGPpzdtTS2o02TOYZtZ2WjCKJG+kmYXbE+JiCl1njN70fdewJPADhGxDLKgLql/Omwg8ERBtSWpbGNar11eEgdsMysfxQfslcW8NV1SD+CPwLcj4p0G0s917YgGykvilIiZlYk0+VMxSxEkdSIL1jdGxJ9S8ZspzUH6uTyVLwEGF1QfBCxN5YPqKC+JA7aZlYeg2QJ2GslxNTA/In5ZsOtO4MS0fiJwR0H5BEldJA0lu7k4K6VP1koam855QkGdJnNKxMzKRjO+wOBTwL8BcyQ9l8r+H/BzYJqkicBi4FiAiJgraRowj2yEyZkRUZXqnQ5cC3QDpqelJA7YZlY+mulJx4iYSd35Z4BD66kzCZhUR/lsYFRztMsB28zKQ+DJn8zM8sFvnDEzyw8HbDOznHDANjPLgQioqmr8uBxzwDaz8uEetplZDniUiJlZjriHbWaWEw7YZmY54JuOZmY54h62mVlOOGCbmeVBeJSImVkuBEQU9YLd3HLANrPy4R62mVkOeJSImVmO+KajmVk+RLVz2GZmOeAXGJiZ5YMnfzIzy4cAwjcdzcxyIAI8DtvMLB/CKREzs5wo8x62oszvqjYHSSuAV1u7HS2kL7CytRthRSvn7+ujEdGv1MqS7iX79ynGyogYV+q1WosDdjsnaXZEjGntdlhx/H21bx1auwFmZlYcB2wzs5xwwLYprd0AaxJ/X+2Yc9hmZjnhHraZWU44YJuZ5YQDtplZTjhg54SkIZJerKP8x5IOa6TujyR9bwuufY+k7RrY/4qkYh9YaHckrdtK1/H3VOb8aHrORcQFLXVuSSK7MX1US13Dtpy/p/bDPex8qZD0O0lzJd0vqZukayV9EUDSUZJekjRT0uWS/lJQd4SkRyT9Q9JZ9V0g9eTnS5oMPAMMrumZSeou6W5Jz0t6UdJxtep2k3SvpJNb5NPnnDIXp3+7OTX/fpImSzo6rd8u6Zq0PlHST+s5l7+ndsgBO1+GAb+JiJHA28AXanZI6gpcCRwZEQcAtedkGA4cAewHXCipUwPX2R2YGhF7RUThHCrjgKURsWdEjALuLdjXA7gL+ENE/K6kT1f+Pg+MBvYEDgMuljQAmAEcmI4ZCIxI6wcAjzZwPn9P7YwDdr4siojn0vrTwJCCfcOBf0TEorR9U626d0fEhohYCSwHdmjgOq9GxBN1lM8BDpN0kaQDI2JNwb47gN9HxNQiP0t7dABwU0RURcSbwP8A+5IF5QMljQDmAW+mQL4/8L8NnM/fUzvjgJ0vGwrWq9j8HoS2oG5t79ZVGBF/A/YhCwg/k1SYP38MODLlU61udf7bRMTrwPZkPeMZZAH8S8C6iFjbwPn8PbUzDtjl4yVgZ0lD0vZxDRxbEkkfAdZHxA3AJcDeBbsvAFYBk5v7umVkBnCcpApJ/YCDgFlp3+PAt/kwYH+PhtMh9fL3VL4csMtERLwHnAHcK2km8CawpuFaTbYHMEvSc8APgNo3xL4NdJX0i2a+brm4HXgBeB54CDg3It5I+x4FOkbEQrKbiL0pMWDj76lseS6RMiKpR0SsS3/u/gZ4OSIua+12mVnzcA+7vJycelVzgV5ko0bMrEy4h91OSeoDPFjHrkMjYtXWbo/Vzd+TFXLANjPLCadEzMxywgHbzCwnHLCtWUiqkvRcmrviVknbbMG5CudHuSo9AVjfsQdL+mQJ16hz5rpiZrRr6ux7WzpbolkNB2xrLu9FxOg0d8UHwGmFOyVVlHLSiPh6RMxr4JCDgSYHbLM8csC2lvAosGvq/T4s6Q/AnPSE38WSnpL0gqRTYdMsdv8taZ6ku4H+NSdKMwyOSevjJD2TZqF7MD3VeRrwndS7P1BSP0l/TNd4StKnUt0+ymY4fFbSlTT+KD+S/izpaWWzI55Sa9+lqS0PpqcWkbRLmgXvaUmPShreLP+aZonnw7ZmJakjcCQfzhC3HzAqIhaloLcmIvaV1AV4TNL9wF5kM8/tQTYp1Tzgmlrn7Qf8Djgonat3RKyW9FuyOTcuScf9AbgsImZK2gm4D/gYcCEwMyJ+LOkzwGYBuB5fS9foBjwl6Y9pKF134JmIODvN03Eh8A2yN5qfFhEvS/oE2ePfh5Twz2hWJwdsay7d0kM7kPWwryZLVcwqmEHw08DHa/LTZA/3DCObU+OmiKgClkp6qI7zjwVm1JwrIlbX047DyOb+rtnuKWnbdI3Pp7p3S3qriM90lqTPpfXBqa2rgGrgllR+A/AnST3S57214NpdiriGWdEcsK25vBcRowsLUuAqnFFOwDcj4r5axx0FNPZAgIo4BrI03/5pbpXabSn6oQNJB5MF//0jYr2kR4Cu9Rwe6bpv1/43MGtOzmHb1nQfcHrNyxMk7SapO9kMdRNSjnsA8C911H0c+D+Shqa6vVP5WmDbguPuJ0tPkI4bnVZnAF9JZUeSTWfakF7AWylYDyfr4dfoANT8lfCvZKmWd4BFko5N15CkPRu5hlmTOGDb1nQVWX76GWUvFL6S7K+824GXyeZvvoJsYv/NRMQKsrzznyQ9z4cpibuAz9XcdATOAsakm5rz+HC0yn8AB0l6hiw1s7iRtt4LdJT0AvAToPBFAe8CIyU9TZaj/nEq/wowMbVvLjC+iH8Ts6L50XQzs5xwD9vMLCccsM3McsIB28wsJxywzcxywgHbzCwnHLDNzHLCAdvMLCf+P6AFfiK0hsMUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "oversampling_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=oversampling_matrix,display_labels=oversampling_model.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/Oversampling_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.82      0.84      0.06      0.83      0.69       101\n",
      "   low_risk       1.00      0.84      0.82      0.91      0.83      0.69     17104\n",
      "\n",
      "avg / total       0.99      0.84      0.82      0.91      0.83      0.69     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 51366, 'high_risk': 51366})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTE\n",
    "X_resampled, y_resampled = SMOTE(random_state=1, sampling_strategy='auto').fit_resample(\n",
    "    X_train, y_train\n",
    ")\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "smote_model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "smote_model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8440938486973113"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = smote_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
    "smote_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=smote_matrix,display_labels=smote_model.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/SMOTE_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.04      0.82      0.87      0.07      0.84      0.71       101\n",
      "   low_risk       1.00      0.87      0.82      0.93      0.84      0.72     17104\n",
      "\n",
      "avg / total       0.99      0.87      0.82      0.92      0.84      0.72     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undersampling\n",
    "\n",
    "In this section, you will test an undersampling algorithms to determine which algorithm results in the best performance compared to the oversampling algorithms above. You will undersample the data using the Cluster Centroids algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 246, 'low_risk': 246})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the data using the ClusterCentroids resampler\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "cc = ClusterCentroids(random_state=1)\n",
    "X_resampled, y_resampled = cc.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "cc_model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "cc_model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8203882595930314"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = cc_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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",
    "cc_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cc_matrix,display_labels=cc_model.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/ClusterCentroids_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.02      0.88      0.76      0.04      0.82      0.68       101\n",
      "   low_risk       1.00      0.76      0.88      0.86      0.82      0.66     17104\n",
      "\n",
      "avg / total       0.99      0.76      0.88      0.86      0.82      0.66     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combination (Over and Under) Sampling\n",
    "\n",
    "In this section, you will test a combination over- and under-sampling algorithm to determine if the algorithm results in the best performance compared to the other sampling algorithms above. You will resample the data using the SMOTEENN algorithm and complete the folliowing steps:\n",
    "\n",
    "1. View the count of the target classes using `Counter` from the collections library. \n",
    "3. Use the resampled data to train a logistic regression model.\n",
    "3. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "4. Print the confusion matrix from sklearn.metrics.\n",
    "5. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "\n",
    "Note: Use a random state of 1 for each sampling algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'high_risk': 51366, 'low_risk': 47640})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with SMOTEENN\n",
    "# Warning: This is a large dataset, and this step may take some time to complete\n",
    "smote_enn = SMOTEENN(random_state=0)\n",
    "X_resampled, y_resampled = smote_enn.fit_resample(X_train, y_train)\n",
    "Counter(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Logistic Regression model using the resampled data\n",
    "smote_enn_model = LogisticRegression(solver='lbfgs', random_state=1)\n",
    "smote_enn_model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.844016280135965"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = smote_enn_model.predict(X_test)\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
    "smote_enn_matrix = confusion_matrix(y_test, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=smote_enn_matrix,display_labels=smote_enn_model.classes_)\n",
    "disp.plot()\n",
    "\n",
    "# Save the image\n",
    "plt.savefig(\"./Images/SMOTEEN_confusion_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.83      0.86      0.06      0.84      0.71       101\n",
      "   low_risk       1.00      0.86      0.83      0.92      0.84      0.71     17104\n",
      "\n",
      "avg / total       0.99      0.86      0.83      0.92      0.84      0.71     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}