notebooks/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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>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('../resources/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": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>home_ownership</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>application_type</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>RENT</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RENT</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68812</th>\n",
       "      <td>RENT</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68813</th>\n",
       "      <td>RENT</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68814</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68815</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68816</th>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Verified</td>\n",
       "      <td>Jan-2019</td>\n",
       "      <td>low_risk</td>\n",
       "      <td>n</td>\n",
       "      <td>w</td>\n",
       "      <td>May-2019</td>\n",
       "      <td>Individual</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68817 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      home_ownership verification_status   issue_d loan_status pymnt_plan  \\\n",
       "0               RENT     Source Verified  Mar-2019    low_risk          n   \n",
       "1           MORTGAGE            Verified  Mar-2019    low_risk          n   \n",
       "2           MORTGAGE            Verified  Mar-2019    low_risk          n   \n",
       "3               RENT            Verified  Mar-2019    low_risk          n   \n",
       "4           MORTGAGE        Not Verified  Mar-2019    low_risk          n   \n",
       "...              ...                 ...       ...         ...        ...   \n",
       "68812           RENT     Source Verified  Jan-2019    low_risk          n   \n",
       "68813           RENT        Not Verified  Jan-2019    low_risk          n   \n",
       "68814       MORTGAGE     Source Verified  Jan-2019    low_risk          n   \n",
       "68815       MORTGAGE            Verified  Jan-2019    low_risk          n   \n",
       "68816       MORTGAGE            Verified  Jan-2019    low_risk          n   \n",
       "\n",
       "      initial_list_status next_pymnt_d application_type hardship_flag  \\\n",
       "0                       w     May-2019       Individual             N   \n",
       "1                       w     May-2019       Individual             N   \n",
       "2                       w     May-2019       Individual             N   \n",
       "3                       w     May-2019       Individual             N   \n",
       "4                       w     May-2019       Individual             N   \n",
       "...                   ...          ...              ...           ...   \n",
       "68812                   w     May-2019       Individual             N   \n",
       "68813                   w     May-2019       Individual             N   \n",
       "68814                   w     May-2019       Individual             N   \n",
       "68815                   f     May-2019       Individual             N   \n",
       "68816                   w     May-2019       Individual             N   \n",
       "\n",
       "      debt_settlement_flag  \n",
       "0                        N  \n",
       "1                        N  \n",
       "2                        N  \n",
       "3                        N  \n",
       "4                        N  \n",
       "...                    ...  \n",
       "68812                    N  \n",
       "68813                    N  \n",
       "68814                    N  \n",
       "68815                    N  \n",
       "68816                    N  \n",
       "\n",
       "[68817 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df, columns=[\"home_ownership\", \"verification_status\",\n",
    "                                \"issue_d\", \"pymnt_plan\", \n",
    "                                \"initial_list_status\", \"next_pymnt_d\",\n",
    "                                \"application_type\", \"hardship_flag\",\n",
    "                                \"debt_settlement_flag\"]).drop('loan_status', axis = 1)\n",
    "\n",
    "\n",
    "# Create our target\n",
    "y = df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>application_type_Individual</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>353.55</td>\n",
       "      <td>92000.0</td>\n",
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       "<p>5 rows × 95 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    10500.0    0.1719       375.35     66000.0  27.24          0.0   \n",
       "1    25000.0    0.2000       929.09    105000.0  20.23          0.0   \n",
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       "3    10000.0    0.1640       353.55     92000.0  31.44          0.0   \n",
       "4    22000.0    0.1474       520.39     52000.0  18.76          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  issue_d_Mar-2019  \\\n",
       "0             0.0       8.0      0.0     1609.0  ...                 1   \n",
       "1             0.0      17.0      1.0    18368.0  ...                 1   \n",
       "2             0.0       8.0      0.0    13247.0  ...                 1   \n",
       "3             1.0      10.0      1.0    17996.0  ...                 1   \n",
       "4             1.0      14.0      0.0     9091.0  ...                 1   \n",
       "\n",
       "   pymnt_plan_n  initial_list_status_f  initial_list_status_w  \\\n",
       "0             1                      0                      1   \n",
       "1             1                      0                      1   \n",
       "2             1                      0                      1   \n",
       "3             1                      0                      1   \n",
       "4             1                      0                      1   \n",
       "\n",
       "   next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  application_type_Individual  \\\n",
       "0                      0                      1                            1   \n",
       "1                      0                      1                            1   \n",
       "2                      0                      1                            1   \n",
       "3                      0                      1                            1   \n",
       "4                      0                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  debt_settlement_flag_N  \n",
       "0                           0                1                       1  \n",
       "1                           0                1                       1  \n",
       "2                           0                1                       1  \n",
       "3                           0                1                       1  \n",
       "4                           0                1                       1  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "0    10500.0    0.1719       375.35     66000.0  27.24          0.0   \n",
       "1    25000.0    0.2000       929.09    105000.0  20.23          0.0   \n",
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       "3    10000.0    0.1640       353.55     92000.0  31.44          0.0   \n",
       "4    22000.0    0.1474       520.39     52000.0  18.76          0.0   \n",
       "\n",
       "   inq_last_6mths  open_acc  pub_rec  revol_bal  ...  issue_d_Mar-2019  \\\n",
       "0             0.0       8.0      0.0     1609.0  ...                 1   \n",
       "1             0.0      17.0      1.0    18368.0  ...                 1   \n",
       "2             0.0       8.0      0.0    13247.0  ...                 1   \n",
       "3             1.0      10.0      1.0    17996.0  ...                 1   \n",
       "4             1.0      14.0      0.0     9091.0  ...                 1   \n",
       "\n",
       "   pymnt_plan_n  initial_list_status_f  initial_list_status_w  \\\n",
       "0             1                      0                      1   \n",
       "1             1                      0                      1   \n",
       "2             1                      0                      1   \n",
       "3             1                      0                      1   \n",
       "4             1                      0                      1   \n",
       "\n",
       "   next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  application_type_Individual  \\\n",
       "0                      0                      1                            1   \n",
       "1                      0                      1                            1   \n",
       "2                      0                      1                            1   \n",
       "3                      0                      1                            1   \n",
       "4                      0                      1                            1   \n",
       "\n",
       "   application_type_Joint App  hardship_flag_N  debt_settlement_flag_N  \n",
       "0                           0                1                       1  \n",
       "1                           0                1                       1  \n",
       "2                           0                1                       1  \n",
       "3                           0                1                       1  \n",
       "4                           0                1                       1  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>68817.0</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>68817.000000</td>\n",
       "      <td>68817.0</td>\n",
       "      <td>68817.0</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>0.177238</td>\n",
       "      <td>1.0</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",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.381873</td>\n",
       "      <td>0.0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</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>1.0</td>\n",
       "      <td>1.0</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>1.0</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",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>1.0</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",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>0.155700</td>\n",
       "      <td>648.100000</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>26.660000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21735.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</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",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40000.000000</td>\n",
       "      <td>0.308400</td>\n",
       "      <td>1676.230000</td>\n",
       "      <td>8.797500e+06</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>587191.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 95 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  ...  issue_d_Mar-2019  pymnt_plan_n  \\\n",
       "count   68817.000000  ...      68817.000000       68817.0   \n",
       "mean    17604.142828  ...          0.177238           1.0   \n",
       "std     21835.880400  ...          0.381873           0.0   \n",
       "min         0.000000  ...          0.000000           1.0   \n",
       "25%      6293.000000  ...          0.000000           1.0   \n",
       "50%     12068.000000  ...          0.000000           1.0   \n",
       "75%     21735.000000  ...          0.000000           1.0   \n",
       "max    587191.000000  ...          1.000000           1.0   \n",
       "\n",
       "       initial_list_status_f  initial_list_status_w  next_pymnt_d_Apr-2019  \\\n",
       "count           68817.000000           68817.000000           68817.000000   \n",
       "mean                0.123879               0.876121               0.383161   \n",
       "std                 0.329446               0.329446               0.486161   \n",
       "min                 0.000000               0.000000               0.000000   \n",
       "25%                 0.000000               1.000000               0.000000   \n",
       "50%                 0.000000               1.000000               0.000000   \n",
       "75%                 0.000000               1.000000               1.000000   \n",
       "max                 1.000000               1.000000               1.000000   \n",
       "\n",
       "       next_pymnt_d_May-2019  application_type_Individual  \\\n",
       "count           68817.000000                 68817.000000   \n",
       "mean                0.616839                     0.860340   \n",
       "std                 0.486161                     0.346637   \n",
       "min                 0.000000                     0.000000   \n",
       "25%                 0.000000                     1.000000   \n",
       "50%                 1.000000                     1.000000   \n",
       "75%                 1.000000                     1.000000   \n",
       "max                 1.000000                     1.000000   \n",
       "\n",
       "       application_type_Joint App  hardship_flag_N  debt_settlement_flag_N  \n",
       "count                68817.000000          68817.0                 68817.0  \n",
       "mean                     0.139660              1.0                     1.0  \n",
       "std                      0.346637              0.0                     0.0  \n",
       "min                      0.000000              1.0                     1.0  \n",
       "25%                      0.000000              1.0                     1.0  \n",
       "50%                      0.000000              1.0                     1.0  \n",
       "75%                      0.000000              1.0                     1.0  \n",
       "max                      1.000000              1.0                     1.0  \n",
       "\n",
       "[8 rows x 95 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'low_risk': 51352, 'high_risk': 260})\n",
      "Counter({'low_risk': 17118, 'high_risk': 87})\n",
      "(51612, 95)\n",
      "(17205, 95)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify= y)\n",
    "\n",
    "print(Counter(y_train))\n",
    "print(Counter(y_test))\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Learners\n",
    "\n",
    "In this section, you will compare two ensemble algorithms to determine which algorithm results in the best performance. You will train a Balanced Random Forest Classifier and an Easy Ensemble AdaBoost classifier . For each algorithm, be sure to complete the folliowing steps:\n",
    "\n",
    "1. Train the model using the training data. \n",
    "2. Calculate the balanced accuracy score from sklearn.metrics.\n",
    "3. Print the confusion matrix from sklearn.metrics.\n",
    "4. Generate a classication report using the `imbalanced_classification_report` from imbalanced-learn.\n",
    "5. For the Balanced Random Forest Classifier onely, print the feature importance sorted in descending order (most important feature to least important) along with the feature score\n",
    "\n",
    "Note: Use a random state of 1 for each algorithm to ensure consistency between tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balanced Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(random_state=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# scaler = StandardScaler()\n",
    "# X_scaler = scaler.fit(X_train)\n",
    "# X_train_scaled = X_scaler.transform(X_train)\n",
    "# X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "# Create a random forest classifier.\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brfc_model = BalancedRandomForestClassifier(n_estimators=100, random_state=1)\n",
    "\n",
    "# Fitting the model\n",
    "brfc_model = brfc_model.fit(X_train, y_train)\n",
    "brfc_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7877672625306695"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "predictions = brfc_model.predict(X_test)\n",
    "brfc_bac = balanced_accuracy_score(y_test, predictions)\n",
    "brfc_bac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 High Risk</th>\n",
       "      <th>Predicted Low Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual High Risk</th>\n",
       "      <td>58</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Low Risk</th>\n",
       "      <td>1560</td>\n",
       "      <td>15558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted High Risk  Predicted Low Risk\n",
       "Actual High Risk                   58                  29\n",
       "Actual Low Risk                  1560               15558"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "    cm, index=[\"Actual High Risk\", \"Actual Low Risk\"], columns=[\"Predicted High Risk\", \"Predicted Low Risk\"])\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.04      0.67      0.91      0.07      0.78      0.59        87\n",
      "   low_risk       1.00      0.91      0.67      0.95      0.78      0.62     17118\n",
      "\n",
      "avg / total       0.99      0.91      0.67      0.95      0.78      0.62     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.07376667607601396, 'total_rec_prncp'),\n",
       " (0.06390324452717588, 'total_rec_int'),\n",
       " (0.06073336071656837, 'total_pymnt_inv'),\n",
       " (0.05811195697921674, 'total_pymnt'),\n",
       " (0.04951778391272079, 'last_pymnt_amnt'),\n",
       " (0.02458051789018817, 'int_rate'),\n",
       " (0.020398879691407974, 'out_prncp'),\n",
       " (0.018625883307901298, 'dti'),\n",
       " (0.018378884967316288, 'max_bal_bc'),\n",
       " (0.017480030880564042, 'issue_d_Jan-2019'),\n",
       " (0.01698703048268034, 'installment'),\n",
       " (0.01661321625916749, 'annual_inc'),\n",
       " (0.01623198470200134, 'bc_util'),\n",
       " (0.015821923028819504, 'mths_since_recent_inq'),\n",
       " (0.015385686591272854, 'total_bc_limit'),\n",
       " (0.015337070167127763, 'out_prncp_inv'),\n",
       " (0.01506649550412446, 'avg_cur_bal'),\n",
       " (0.014926455495755147, 'issue_d_Mar-2019'),\n",
       " (0.014924489307566175, 'revol_bal'),\n",
       " (0.014583210727445232, 'mo_sin_old_rev_tl_op'),\n",
       " (0.01428231366275729, 'total_bal_ex_mort'),\n",
       " (0.014238473969302518, 'tot_cur_bal'),\n",
       " (0.014067369111282934, 'mo_sin_old_il_acct'),\n",
       " (0.01351439379612201, 'total_bal_il'),\n",
       " (0.013343855077273033, 'bc_open_to_buy'),\n",
       " (0.013138998568932102, 'il_util'),\n",
       " (0.013106005248709657, 'tot_hi_cred_lim'),\n",
       " (0.013009300843392654, 'all_util'),\n",
       " (0.012104219411815444, 'total_il_high_credit_limit'),\n",
       " (0.011927748948209041, 'num_rev_accts'),\n",
       " (0.011194718062040365, 'total_rev_hi_lim'),\n",
       " (0.01072808547266412, 'mths_since_recent_bc'),\n",
       " (0.010485766778776592, 'pct_tl_nvr_dlq'),\n",
       " (0.010480039757813501, 'total_acc'),\n",
       " (0.010433852907121713, 'mths_since_rcnt_il'),\n",
       " (0.010171477424921242, 'mo_sin_rcnt_rev_tl_op'),\n",
       " (0.00990081847673844, 'num_rev_tl_bal_gt_0'),\n",
       " (0.00982912014219909, 'loan_amnt'),\n",
       " (0.009709211384642156, 'inq_last_12m'),\n",
       " (0.00939406635676521, 'num_bc_tl'),\n",
       " (0.009390960836832822, 'num_op_rev_tl'),\n",
       " (0.008883434647669785, 'mo_sin_rcnt_tl'),\n",
       " (0.008287412625195438, 'num_actv_bc_tl'),\n",
       " (0.00794880573688952, 'total_cu_tl'),\n",
       " (0.007944596002325902, 'percent_bc_gt_75'),\n",
       " (0.007841727903546316, 'num_sats'),\n",
       " (0.007813875400824535, 'num_actv_rev_tl'),\n",
       " (0.007760323250042998, 'open_acc'),\n",
       " (0.007420667106687749, 'acc_open_past_24mths'),\n",
       " (0.007404967411706728, 'inq_fi'),\n",
       " (0.007339507170175945, 'num_il_tl'),\n",
       " (0.006876404000725424, 'open_il_24m'),\n",
       " (0.00678542812487817, 'num_bc_sats'),\n",
       " (0.006660043732852357, 'open_act_il'),\n",
       " (0.006599809671624734, 'open_rv_24m'),\n",
       " (0.006393774120528892, 'mort_acc'),\n",
       " (0.006391381020548387, 'total_rec_late_fee'),\n",
       " (0.006065825838862652, 'num_tl_op_past_12m'),\n",
       " (0.006004776142662084, 'open_acc_6m'),\n",
       " (0.005920335354841418, 'next_pymnt_d_Apr-2019'),\n",
       " (0.005413306654546768, 'open_rv_12m'),\n",
       " (0.005248840731650208, 'open_il_12m'),\n",
       " (0.00487120814476765, 'issue_d_Feb-2019'),\n",
       " (0.004802823221798599, 'next_pymnt_d_May-2019'),\n",
       " (0.0047562588132075015, 'inq_last_6mths'),\n",
       " (0.0045238834578884635, 'delinq_2yrs'),\n",
       " (0.0039029173953907185, 'num_accts_ever_120_pd'),\n",
       " (0.003789677267237295, 'tot_coll_amt'),\n",
       " (0.0020853638216694215, 'home_ownership_OWN'),\n",
       " (0.0020060097354201176, 'home_ownership_MORTGAGE'),\n",
       " (0.001984067551288744, 'verification_status_Source Verified'),\n",
       " (0.0018789153640443002, 'verification_status_Verified'),\n",
       " (0.0018544555674736187, 'verification_status_Not Verified'),\n",
       " (0.0016995259802440514, 'home_ownership_RENT'),\n",
       " (0.001520907850775936, 'pub_rec_bankruptcies'),\n",
       " (0.0014185754855487824, 'pub_rec'),\n",
       " (0.0013879548937552864, 'initial_list_status_f'),\n",
       " (0.001239058844214324, 'application_type_Individual'),\n",
       " (0.0011046122443753368, 'num_tl_90g_dpd_24m'),\n",
       " (0.000805030988421211, 'initial_list_status_w'),\n",
       " (0.000710297252940571, 'application_type_Joint App'),\n",
       " (0.0006949062181389056, 'collections_12_mths_ex_med'),\n",
       " (0.00013272979926763346, 'home_ownership_ANY'),\n",
       " (0.0, 'tax_liens'),\n",
       " (0.0, 'recoveries'),\n",
       " (0.0, 'pymnt_plan_n'),\n",
       " (0.0, 'policy_code'),\n",
       " (0.0, 'num_tl_30dpd'),\n",
       " (0.0, 'num_tl_120dpd_2m'),\n",
       " (0.0, 'hardship_flag_N'),\n",
       " (0.0, 'delinq_amnt'),\n",
       " (0.0, 'debt_settlement_flag_N'),\n",
       " (0.0, 'collection_recovery_fee'),\n",
       " (0.0, 'chargeoff_within_12_mths'),\n",
       " (0.0, 'acc_now_delinq')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "sorted(zip(brfc_model.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the EasyEnsembleClassifier\n",
    "from imblearn.ensemble import EasyEnsembleClassifier\n",
    "\n",
    "eec_model = EasyEnsembleClassifier(n_estimators=100, random_state=1)\n",
    "\n",
    "eec_model = eec_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925427358175101"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "predictions = eec_model.predict(X_test)\n",
    "ee_bac = balanced_accuracy_score(y_test, predictions)\n",
    "ee_bac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 High Risk</th>\n",
       "      <th>Predicted Low Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual High Risk</th>\n",
       "      <td>79</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual Low Risk</th>\n",
       "      <td>979</td>\n",
       "      <td>16139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted High Risk  Predicted Low Risk\n",
       "Actual High Risk                   79                   8\n",
       "Actual Low Risk                   979               16139"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "y_pred = eec_model.predict(X_test)\n",
    "confusion_matrix(y_test, y_pred)\n",
    "\n",
    "cm_ee = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_ee_df = pd.DataFrame(\n",
    "    cm_ee, index=[\"Actual High Risk\", \"Actual Low Risk\"], columns=[\"Predicted High Risk\", \"Predicted Low Risk\"])\n",
    "cm_ee_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.07      0.91      0.94      0.14      0.93      0.85        87\n",
      "   low_risk       1.00      0.94      0.91      0.97      0.93      0.86     17118\n",
      "\n",
      "avg / total       0.99      0.94      0.91      0.97      0.93      0.86     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report\n",
    "print(classification_report_imbalanced(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculations for table\n",
    "brfc_bac_table = round(brfc_bac, 2)\n",
    "ee_bac_table = round(ee_bac, 2)\n",
    "\n",
    "brfc_precision = round(cm_df.iloc[0,0]/(cm_df.iloc[0,0]+cm_df.iloc[1,0]), 2)\n",
    "ee_precision = round(cm_ee_df.iloc[0,0]/(cm_ee_df.iloc[0,0]+cm_ee_df.iloc[1,0]), 2)\n",
    "brfc_recall = round(cm_df.iloc[0,0]/(cm_df.iloc[0,0]+cm_df.iloc[0,1]), 2)\n",
    "ee_recall = round(cm_ee_df.iloc[0,0]/(cm_ee_df.iloc[0,0]+cm_ee_df.iloc[0,1]), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Balanced Accuracy Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Balanaced Random Forest Classifier</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Easy Ensemble AdaBoost Classifier</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Balanced Accuracy Score  Precision  Recall\n",
       "Balanaced Random Forest Classifier                     0.79       0.04    0.67\n",
       "Easy Ensemble AdaBoost Classifier                      0.93       0.07    0.91"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build the table \n",
    "index = ['Balanaced Random Forest Classifier', 'Easy Ensemble AdaBoost Classifier']\n",
    "\n",
    "data = {'Balanced Accuracy Score': pd.Series([brfc_bac_table, ee_bac_table], index = index),\n",
    "       'Precision': pd.Series([brfc_precision, ee_precision], index = index),\n",
    "        'Recall': pd.Series([brfc_recall, ee_recall], index = index)\n",
    "       }\n",
    "\n",
    "results_df = pd.DataFrame(data)\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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