credit_risk_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://help.lendingclub.com/hc/en-us/articles/215488038-What-do-the-different-Note-statuses-mean-\n",
    "\n",
    "columns = [\n",
    "    \"loan_amnt\", \"int_rate\", \"installment\", \"home_ownership\",\n",
    "    \"annual_inc\", \"verification_status\", \"issue_d\", \"loan_status\",\n",
    "    \"pymnt_plan\", \"dti\", \"delinq_2yrs\", \"inq_last_6mths\",\n",
    "    \"open_acc\", \"pub_rec\", \"revol_bal\", \"total_acc\",\n",
    "    \"initial_list_status\", \"out_prncp\", \"out_prncp_inv\", \"total_pymnt\",\n",
    "    \"total_pymnt_inv\", \"total_rec_prncp\", \"total_rec_int\", \"total_rec_late_fee\",\n",
    "    \"recoveries\", \"collection_recovery_fee\", \"last_pymnt_amnt\", \"next_pymnt_d\",\n",
    "    \"collections_12_mths_ex_med\", \"policy_code\", \"application_type\", \"acc_now_delinq\",\n",
    "    \"tot_coll_amt\", \"tot_cur_bal\", \"open_acc_6m\", \"open_act_il\",\n",
    "    \"open_il_12m\", \"open_il_24m\", \"mths_since_rcnt_il\", \"total_bal_il\",\n",
    "    \"il_util\", \"open_rv_12m\", \"open_rv_24m\", \"max_bal_bc\",\n",
    "    \"all_util\", \"total_rev_hi_lim\", \"inq_fi\", \"total_cu_tl\",\n",
    "    \"inq_last_12m\", \"acc_open_past_24mths\", \"avg_cur_bal\", \"bc_open_to_buy\",\n",
    "    \"bc_util\", \"chargeoff_within_12_mths\", \"delinq_amnt\", \"mo_sin_old_il_acct\",\n",
    "    \"mo_sin_old_rev_tl_op\", \"mo_sin_rcnt_rev_tl_op\", \"mo_sin_rcnt_tl\", \"mort_acc\",\n",
    "    \"mths_since_recent_bc\", \"mths_since_recent_inq\", \"num_accts_ever_120_pd\", \"num_actv_bc_tl\",\n",
    "    \"num_actv_rev_tl\", \"num_bc_sats\", \"num_bc_tl\", \"num_il_tl\",\n",
    "    \"num_op_rev_tl\", \"num_rev_accts\", \"num_rev_tl_bal_gt_0\",\n",
    "    \"num_sats\", \"num_tl_120dpd_2m\", \"num_tl_30dpd\", \"num_tl_90g_dpd_24m\",\n",
    "    \"num_tl_op_past_12m\", \"pct_tl_nvr_dlq\", \"percent_bc_gt_75\", \"pub_rec_bankruptcies\",\n",
    "    \"tax_liens\", \"tot_hi_cred_lim\", \"total_bal_ex_mort\", \"total_bc_limit\",\n",
    "    \"total_il_high_credit_limit\", \"hardship_flag\", \"debt_settlement_flag\"\n",
    "]\n",
    "\n",
    "target = [\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amnt  int_rate  installment home_ownership  annual_inc  \\\n",
       "0    10500.0    0.1719       375.35           RENT     66000.0   \n",
       "1    25000.0    0.2000       929.09       MORTGAGE    105000.0   \n",
       "2    20000.0    0.2000       529.88       MORTGAGE     56000.0   \n",
       "3    10000.0    0.1640       353.55           RENT     92000.0   \n",
       "4    22000.0    0.1474       520.39       MORTGAGE     52000.0   \n",
       "\n",
       "  verification_status   issue_d loan_status pymnt_plan    dti  ...  \\\n",
       "0     Source Verified  Mar-2019    low_risk          n  27.24  ...   \n",
       "1            Verified  Mar-2019    low_risk          n  20.23  ...   \n",
       "2            Verified  Mar-2019    low_risk          n  24.26  ...   \n",
       "3            Verified  Mar-2019    low_risk          n  31.44  ...   \n",
       "4        Not Verified  Mar-2019    low_risk          n  18.76  ...   \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  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "file_path = Path('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",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = df.drop(columns='loan_status', axis=1)\n",
    "\n",
    "# Create our target\n",
    "y = df[\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>...</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>6.881700e+04</td>\n",
       "      <td>68817.000000</td>\n",
       "      <td>6.881700e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16677.594562</td>\n",
       "      <td>0.127718</td>\n",
       "      <td>480.652863</td>\n",
       "      <td>8.821371e+04</td>\n",
       "      <td>21.778153</td>\n",
       "      <td>0.217766</td>\n",
       "      <td>0.497697</td>\n",
       "      <td>12.587340</td>\n",
       "      <td>0.126030</td>\n",
       "      <td>17604.142828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052138</td>\n",
       "      <td>2.219423</td>\n",
       "      <td>95.057627</td>\n",
       "      <td>30.626217</td>\n",
       "      <td>0.125972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.100332e+05</td>\n",
       "      <td>6.133843e+04</td>\n",
       "      <td>29734.128558</td>\n",
       "      <td>5.572240e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10277.348590</td>\n",
       "      <td>0.048130</td>\n",
       "      <td>288.062432</td>\n",
       "      <td>1.155800e+05</td>\n",
       "      <td>20.199244</td>\n",
       "      <td>0.718367</td>\n",
       "      <td>0.758122</td>\n",
       "      <td>6.022869</td>\n",
       "      <td>0.336797</td>\n",
       "      <td>21835.880400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.390633</td>\n",
       "      <td>1.897432</td>\n",
       "      <td>8.326426</td>\n",
       "      <td>33.631463</td>\n",
       "      <td>0.336732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.928088e+05</td>\n",
       "      <td>5.738798e+04</td>\n",
       "      <td>26795.394232</td>\n",
       "      <td>5.095845e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>30.890000</td>\n",
       "      <td>4.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.600000e+03</td>\n",
       "      <td>2.350000e+02</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.270000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>0.088100</td>\n",
       "      <td>265.730000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>13.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6293.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.697700e+04</td>\n",
       "      <td>2.650300e+04</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>2.288000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>404.560000</td>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>19.760000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12068.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.467100e+05</td>\n",
       "      <td>4.535700e+04</td>\n",
       "      <td>22100.000000</td>\n",
       "      <td>4.200000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>0.155700</td>\n",
       "      <td>648.100000</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>26.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.036400e+05</td>\n",
       "      <td>7.657000e+04</td>\n",
       "      <td>39300.000000</td>\n",
       "      <td>7.249900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>8.797500e+06</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>587191.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.292782e+06</td>\n",
       "      <td>1.295455e+06</td>\n",
       "      <td>509400.000000</td>\n",
       "      <td>1.426964e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          loan_amnt      int_rate   installment    annual_inc           dti  \\\n",
       "count  68817.000000  68817.000000  68817.000000  6.881700e+04  68817.000000   \n",
       "mean   16677.594562      0.127718    480.652863  8.821371e+04     21.778153   \n",
       "std    10277.348590      0.048130    288.062432  1.155800e+05     20.199244   \n",
       "min     1000.000000      0.060000     30.890000  4.000000e+01      0.000000   \n",
       "25%     9000.000000      0.088100    265.730000  5.000000e+04     13.890000   \n",
       "50%    15000.000000      0.118000    404.560000  7.300000e+04     19.760000   \n",
       "75%    24000.000000      0.155700    648.100000  1.040000e+05     26.660000   \n",
       "max    40000.000000      0.308400   1676.230000  8.797500e+06    999.000000   \n",
       "\n",
       "        delinq_2yrs  inq_last_6mths      open_acc       pub_rec  \\\n",
       "count  68817.000000    68817.000000  68817.000000  68817.000000   \n",
       "mean       0.217766        0.497697     12.587340      0.126030   \n",
       "std        0.718367        0.758122      6.022869      0.336797   \n",
       "min        0.000000        0.000000      2.000000      0.000000   \n",
       "25%        0.000000        0.000000      8.000000      0.000000   \n",
       "50%        0.000000        0.000000     11.000000      0.000000   \n",
       "75%        0.000000        1.000000     16.000000      0.000000   \n",
       "max       18.000000        5.000000     72.000000      4.000000   \n",
       "\n",
       "           revol_bal  ...  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "count   68817.000000  ...        68817.000000        68817.000000   \n",
       "mean    17604.142828  ...            0.052138            2.219423   \n",
       "std     21835.880400  ...            0.390633            1.897432   \n",
       "min         0.000000  ...            0.000000            0.000000   \n",
       "25%      6293.000000  ...            0.000000            1.000000   \n",
       "50%     12068.000000  ...            0.000000            2.000000   \n",
       "75%     21735.000000  ...            0.000000            3.000000   \n",
       "max    587191.000000  ...           18.000000           19.000000   \n",
       "\n",
       "       pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "count    68817.000000      68817.000000          68817.000000    68817.0   \n",
       "mean        95.057627         30.626217              0.125972        0.0   \n",
       "std          8.326426         33.631463              0.336732        0.0   \n",
       "min         20.000000          0.000000              0.000000        0.0   \n",
       "25%         93.000000          0.000000              0.000000        0.0   \n",
       "50%        100.000000         20.000000              0.000000        0.0   \n",
       "75%        100.000000         50.000000              0.000000        0.0   \n",
       "max        100.000000        100.000000              4.000000        0.0   \n",
       "\n",
       "       tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \\\n",
       "count     6.881700e+04       6.881700e+04    68817.000000   \n",
       "mean      2.100332e+05       6.133843e+04    29734.128558   \n",
       "std       1.928088e+05       5.738798e+04    26795.394232   \n",
       "min       3.600000e+03       2.350000e+02      100.000000   \n",
       "25%       6.697700e+04       2.650300e+04    11600.000000   \n",
       "50%       1.467100e+05       4.535700e+04    22100.000000   \n",
       "75%       3.036400e+05       7.657000e+04    39300.000000   \n",
       "max       3.292782e+06       1.295455e+06   509400.000000   \n",
       "\n",
       "       total_il_high_credit_limit  \n",
       "count                6.881700e+04  \n",
       "mean                 5.572240e+04  \n",
       "std                  5.095845e+04  \n",
       "min                  1.270000e+02  \n",
       "25%                  2.288000e+04  \n",
       "50%                  4.200000e+04  \n",
       "75%                  7.249900e+04  \n",
       "max                  1.426964e+06  \n",
       "\n",
       "[8 rows x 76 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'low_risk': 51366, 'high_risk': 246})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                   y,\n",
    "                                                   random_state=1)\n",
    "\n",
    "Counter(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brfc = BalancedRandomForestClassifier(max_depth=20, random_state=1)\n",
    "brfc.fit(X_train, y_train)\n",
    "y_pred = brfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7891313131547018"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "balanced_accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   71,    30],\n",
       "       [ 2133, 14971]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>71</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>2133</td>\n",
       "      <td>14971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           71           30\n",
       "Actual 1         2133        14971"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a Df from the confusion matrix\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.70      0.88      0.06      0.78      0.60       101\n",
      "   low_risk       1.00      0.88      0.70      0.93      0.78      0.63     17104\n",
      "\n",
      "avg / total       0.99      0.87      0.70      0.93      0.78      0.63     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('verification_status_Verified', 0.0019336813806702417),\n",
       " ('verification_status_Source Verified', 0.0015046400907840706),\n",
       " ('verification_status_Not Verified', 0.0022049463775658125),\n",
       " ('total_rev_hi_lim', 0.013217108173059117),\n",
       " ('total_rec_prncp', 0.07904581127380739),\n",
       " ('total_rec_late_fee', 0.007124789842635381),\n",
       " ('total_rec_int', 0.05321466162712985),\n",
       " ('total_pymnt_inv', 0.056144085150313244),\n",
       " ('total_pymnt', 0.05855965320702644),\n",
       " ('total_il_high_credit_limit', 0.013176911509333821),\n",
       " ('total_cu_tl', 0.006083625031483025),\n",
       " ('total_bc_limit', 0.013046395098921115),\n",
       " ('total_bal_il', 0.011974133145264325),\n",
       " ('total_bal_ex_mort', 0.016474425953168247),\n",
       " ('total_acc', 0.010144142517914843),\n",
       " ('tot_hi_cred_lim', 0.014491146542519942),\n",
       " ('tot_cur_bal', 0.012887822113575585),\n",
       " ('tot_coll_amt', 0.004132237764715283),\n",
       " ('tax_liens', 0.0),\n",
       " ('revol_bal', 0.014927171132797915),\n",
       " ('recoveries', 0.0),\n",
       " ('pymnt_plan_n', 0.0),\n",
       " ('pub_rec_bankruptcies', 0.001372792512078185),\n",
       " ('pub_rec', 0.001849681669827488),\n",
       " ('policy_code', 0.0),\n",
       " ('percent_bc_gt_75', 0.005131343774948076),\n",
       " ('pct_tl_nvr_dlq', 0.013030046723135836),\n",
       " ('out_prncp_inv', 0.016742726217420786),\n",
       " ('out_prncp', 0.014881365808418307),\n",
       " ('open_rv_24m', 0.005536752550947682),\n",
       " ('open_rv_12m', 0.004018916067963883),\n",
       " ('open_il_24m', 0.006561432872333854),\n",
       " ('open_il_12m', 0.004860321582059033),\n",
       " ('open_act_il', 0.0074897174919349606),\n",
       " ('open_acc_6m', 0.00932607028346347),\n",
       " ('open_acc', 0.007193925395791305),\n",
       " ('num_tl_op_past_12m', 0.007480899228759638),\n",
       " ('num_tl_90g_dpd_24m', 0.0009956935704327381),\n",
       " ('num_tl_30dpd', 0.0),\n",
       " ('num_tl_120dpd_2m', 0.0),\n",
       " ('num_sats', 0.008628938824946402),\n",
       " ('num_rev_tl_bal_gt_0', 0.0072726650065980504),\n",
       " ('num_rev_accts', 0.010320342701576599),\n",
       " ('num_op_rev_tl', 0.008330966254402505),\n",
       " ('num_il_tl', 0.009662050208879064),\n",
       " ('num_bc_tl', 0.008724481895503549),\n",
       " ('num_bc_sats', 0.00757084160996214),\n",
       " ('num_actv_rev_tl', 0.009633234584832211),\n",
       " ('num_actv_bc_tl', 0.006825915558666981),\n",
       " ('num_accts_ever_120_pd', 0.0036485323647652335),\n",
       " ('next_pymnt_d_May-2019', 0.005239092557001041),\n",
       " ('next_pymnt_d_Apr-2019', 0.005106000423451098),\n",
       " ('mths_since_recent_inq', 0.015220714904737207),\n",
       " ('mths_since_recent_bc', 0.010575363106694517),\n",
       " ('mths_since_rcnt_il', 0.013661836008764184),\n",
       " ('mort_acc', 0.006187857664251554),\n",
       " ('mo_sin_rcnt_tl', 0.010982948025240224),\n",
       " ('mo_sin_rcnt_rev_tl_op', 0.009393346012674943),\n",
       " ('mo_sin_old_rev_tl_op', 0.014842672510860456),\n",
       " ('mo_sin_old_il_acct', 0.01378319970107616),\n",
       " ('max_bal_bc', 0.014829758385315457),\n",
       " ('loan_amnt', 0.010876945856764495),\n",
       " ('last_pymnt_amnt', 0.0504861815322257),\n",
       " ('issue_d_Mar-2019', 0.015445371293982033),\n",
       " ('issue_d_Jan-2019', 0.02096643264808101),\n",
       " ('issue_d_Feb-2019', 0.005640206440873573),\n",
       " ('int_rate', 0.029673936365075534),\n",
       " ('installment', 0.019857411638928096),\n",
       " ('inq_last_6mths', 0.0062164096332386586),\n",
       " ('inq_last_12m', 0.012683026811452303),\n",
       " ('inq_fi', 0.006240598451492286),\n",
       " ('initial_list_status_w', 0.0011520703643731526),\n",
       " ('initial_list_status_f', 0.001100570416563426),\n",
       " ('il_util', 0.013227364495104435),\n",
       " ('home_ownership_RENT', 0.002913322144317049),\n",
       " ('home_ownership_OWN', 0.0028245236291144686),\n",
       " ('home_ownership_MORTGAGE', 0.001459328475953721),\n",
       " ('home_ownership_ANY', 0.0004955956183545531),\n",
       " ('hardship_flag_N', 0.0),\n",
       " ('dti', 0.017457672826002656),\n",
       " ('delinq_amnt', 0.0),\n",
       " ('delinq_2yrs', 0.003156570308867927),\n",
       " ('debt_settlement_flag_N', 0.0),\n",
       " ('collections_12_mths_ex_med', 0.0007150315534652694),\n",
       " ('collection_recovery_fee', 0.0),\n",
       " ('chargeoff_within_12_mths', 0.0002730803587770787),\n",
       " ('bc_util', 0.014939508171295033),\n",
       " ('bc_open_to_buy', 0.01300689029385484),\n",
       " ('avg_cur_bal', 0.013323315027201297),\n",
       " ('application_type_Joint App', 0.002091238573836157),\n",
       " ('application_type_Individual', 0.002002453127086669),\n",
       " ('annual_inc', 0.014832933450096364),\n",
       " ('all_util', 0.013044606528895199),\n",
       " ('acc_open_past_24mths', 0.006697571972326505),\n",
       " ('acc_now_delinq', 0.0)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(X.columns, brfc.feature_importances_), reverse=True)\n",
    "# print(brfc.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "# YOUR CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train EasyEnsembleClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "eec = EasyEnsembleClassifier(random_state=1)\n",
    "eec.fit(X_train, y_train)\n",
    "\n",
    "y_pred_eec = eec.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9154751595365336"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate balanced accuracy score.\n",
    "balanced_accuracy_score(y_test, y_pred_eec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   94,     7],\n",
       "       [ 1706, 15398]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display confusion matrix\n",
    "confusion_matrix(y_test, y_pred_eec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>1706</td>\n",
       "      <td>15398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           94            7\n",
       "Actual 1         1706        15398"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the confusion matrix\n",
    "cm_eec = confusion_matrix(y_test, y_pred_eec)\n",
    "\n",
    "# Create a Df from the confusion matrix\n",
    "cm_df_eec = pd.DataFrame(\n",
    "    cm_eec, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "cm_df_eec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.05      0.93      0.90      0.10      0.92      0.84       101\n",
      "   low_risk       1.00      0.90      0.93      0.95      0.92      0.84     17104\n",
      "\n",
      "avg / total       0.99      0.90      0.93      0.94      0.92      0.84     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, y_pred_eec))"
   ]
  }
 ],
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