credit_risk_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from imblearn.metrics import classification_report_imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional preprocessing Libraries\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\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",
       "\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",
       "\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",
       "\n",
       "   total_il_high_credit_limit  hardship_flag  debt_settlement_flag  \n",
       "0                     61987.0              N                     N  \n",
       "1                     49197.0              N                     N  \n",
       "\n",
       "[2 rows x 86 columns]"
      ]
     },
     "execution_count": 142,
     "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(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the Data into Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create our features\n",
    "X = pd.get_dummies(df.drop(columns='loan_status'))\n",
    "\n",
    "# Create our target\n",
    "y = df['loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>issue_d_Mar-2019</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_Apr-2019</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>debt_settlement_flag_N</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>68817.0</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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float64    76\n",
       "uint8      19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify the types of data in the X df\n",
    "X.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low_risk     68470\n",
       "high_risk      347\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the balance of our target values\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51612, 95)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check X_train shape\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51612,)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check y_train shape\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>issue_d_Mar-2019</th>\n",
       "      <th>pymnt_plan_n</th>\n",
       "      <th>initial_list_status_f</th>\n",
       "      <th>initial_list_status_w</th>\n",
       "      <th>next_pymnt_d_Apr-2019</th>\n",
       "      <th>next_pymnt_d_May-2019</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "      <th>hardship_flag_N</th>\n",
       "      <th>debt_settlement_flag_N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3903</th>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0881</td>\n",
       "      <td>50.74</td>\n",
       "      <td>35964.0</td>\n",
       "      <td>24.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28390</th>\n",
       "      <td>9000.0</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>280.21</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>16.89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11157.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15470</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.1033</td>\n",
       "      <td>214.10</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>17.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16904.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4279</th>\n",
       "      <td>36000.0</td>\n",
       "      <td>0.1033</td>\n",
       "      <td>1167.21</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>19.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57514</th>\n",
       "      <td>18000.0</td>\n",
       "      <td>0.0881</td>\n",
       "      <td>570.81</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>19.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17586.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49100</th>\n",
       "      <td>8400.0</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>257.30</td>\n",
       "      <td>37992.0</td>\n",
       "      <td>33.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7316.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20609</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.0646</td>\n",
       "      <td>918.93</td>\n",
       "      <td>217000.0</td>\n",
       "      <td>19.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142377.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21440</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>442.88</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>16.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50057</th>\n",
       "      <td>11625.0</td>\n",
       "      <td>0.1447</td>\n",
       "      <td>273.34</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>18.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8237.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5192</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.1033</td>\n",
       "      <td>428.20</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>59.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49907.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51612 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "3903      1600.0    0.0881        50.74     35964.0  24.12          0.0   \n",
       "28390     9000.0    0.0756       280.21     41000.0  16.89          1.0   \n",
       "15470    10000.0    0.1033       214.10    112000.0  17.75          0.0   \n",
       "4279     36000.0    0.1033      1167.21    120000.0  19.95          0.0   \n",
       "57514    18000.0    0.0881       570.81     51000.0  19.11          1.0   \n",
       "...          ...       ...          ...         ...    ...          ...   \n",
       "49100     8400.0    0.0646       257.30     37992.0  33.16          0.0   \n",
       "20609    30000.0    0.0646       918.93    217000.0  19.56          0.0   \n",
       "21440    20000.0    0.1180       442.88     35000.0  16.19          0.0   \n",
       "50057    11625.0    0.1447       273.34     30000.0  18.96          0.0   \n",
       "5192     20000.0    0.1033       428.20     60000.0  59.56          0.0   \n",
       "\n",
       "       inq_last_6mths  open_acc  pub_rec  revol_bal  ...  issue_d_Mar-2019  \\\n",
       "3903              0.0      20.0      0.0     1156.0  ...                 1   \n",
       "28390             0.0      16.0      0.0    11157.0  ...                 0   \n",
       "15470             0.0      14.0      0.0    16904.0  ...                 0   \n",
       "4279              1.0      13.0      0.0    63248.0  ...                 1   \n",
       "57514             0.0      16.0      0.0    17586.0  ...                 0   \n",
       "...               ...       ...      ...        ...  ...               ...   \n",
       "49100             0.0       9.0      0.0     7316.0  ...                 0   \n",
       "20609             2.0      25.0      0.0   142377.0  ...                 0   \n",
       "21440             0.0       6.0      0.0      273.0  ...                 0   \n",
       "50057             2.0      10.0      0.0     8237.0  ...                 0   \n",
       "5192              0.0      15.0      0.0    49907.0  ...                 1   \n",
       "\n",
       "       pymnt_plan_n  initial_list_status_f  initial_list_status_w  \\\n",
       "3903              1                      1                      0   \n",
       "28390             1                      0                      1   \n",
       "15470             1                      0                      1   \n",
       "4279              1                      0                      1   \n",
       "57514             1                      0                      1   \n",
       "...             ...                    ...                    ...   \n",
       "49100             1                      0                      1   \n",
       "20609             1                      0                      1   \n",
       "21440             1                      0                      1   \n",
       "50057             1                      0                      1   \n",
       "5192              1                      0                      1   \n",
       "\n",
       "       next_pymnt_d_Apr-2019  next_pymnt_d_May-2019  \\\n",
       "3903                       0                      1   \n",
       "28390                      0                      1   \n",
       "15470                      0                      1   \n",
       "4279                       0                      1   \n",
       "57514                      0                      1   \n",
       "...                      ...                    ...   \n",
       "49100                      1                      0   \n",
       "20609                      1                      0   \n",
       "21440                      1                      0   \n",
       "50057                      1                      0   \n",
       "5192                       0                      1   \n",
       "\n",
       "       application_type_Individual  application_type_Joint App  \\\n",
       "3903                             1                           0   \n",
       "28390                            1                           0   \n",
       "15470                            1                           0   \n",
       "4279                             1                           0   \n",
       "57514                            1                           0   \n",
       "...                            ...                         ...   \n",
       "49100                            1                           0   \n",
       "20609                            1                           0   \n",
       "21440                            0                           1   \n",
       "50057                            1                           0   \n",
       "5192                             0                           1   \n",
       "\n",
       "       hardship_flag_N  debt_settlement_flag_N  \n",
       "3903                 1                       1  \n",
       "28390                1                       1  \n",
       "15470                1                       1  \n",
       "4279                 1                       1  \n",
       "57514                1                       1  \n",
       "...                ...                     ...  \n",
       "49100                1                       1  \n",
       "20609                1                       1  \n",
       "21440                1                       1  \n",
       "50057                1                       1  \n",
       "5192                 1                       1  \n",
       "\n",
       "[51612 rows x 95 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check X-train\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3903     low_risk\n",
       "28390    low_risk\n",
       "15470    low_risk\n",
       "4279     low_risk\n",
       "57514    low_risk\n",
       "           ...   \n",
       "49100    low_risk\n",
       "20609    low_risk\n",
       "21440    low_risk\n",
       "50057    low_risk\n",
       "5192     low_risk\n",
       "Name: loan_status, Length: 51612, dtype: object"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check y_train\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'low_risk': 51366, 'high_risk': 246})\n",
      "Counter({'low_risk': 17104, 'high_risk': 101})\n"
     ]
    }
   ],
   "source": [
    "# Check balances\n",
    "print(Counter(y_train))\n",
    "print(Counter(y_test))"
   ]
  },
  {
   "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": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BalancedRandomForestClassifier(random_state=1)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resample the training data with the BalancedRandomForestClassifier\n",
    "\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier\n",
    "brfc = BalancedRandomForestClassifier(n_estimators = 100, random_state=1)\n",
    "brfc = brfc.fit(X_train, y_train)\n",
    "brfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7885466545953005"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = brfc.predict(X_test)\n",
    "brfc_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "brfc_accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>71</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual low_risk</th>\n",
       "      <td>2153</td>\n",
       "      <td>14951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted high_risk  Predicted low_risk\n",
       "Actual high_risk                   71                  30\n",
       "Actual low_risk                  2153               14951"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the confusion matrix.\n",
    "cm1 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm1_df = pd.DataFrame(\n",
    "    cm1, index=[\"Actual high_risk\", \"Actual low_risk\"], columns=[\"Predicted high_risk\", \"Predicted low_risk\"])\n",
    "\n",
    "cm1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.03      0.70      0.87      0.06      0.78      0.60       101\n",
      "   low_risk       1.00      0.87      0.70      0.93      0.78      0.62     17104\n",
      "\n",
      "avg / total       0.99      0.87      0.70      0.93      0.78      0.62     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report for the balanced Random Forest Classifier \n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix results, the precision for the bad loan applications is low, indicating a large number of false positives, which indicates an unreliable positive classification. The recall is not too low for the bad loan applications, but it still indicates a large number of false negatives. The F1 score is really low (0.06).\n",
    "\n",
    "Model accuracy is 0.78.\n",
    "\n",
    "\n",
    "In summary, this algorith is not good at classifying fraudulent loan applications because F1 score are low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01057991, 0.0296696 , 0.01980243, 0.01483256, 0.01747063,\n",
       "       0.00308285, 0.00621641, 0.00738223, 0.00201526, 0.01492666,\n",
       "       0.01020921, 0.01488107, 0.01685829, 0.05883807, 0.05625614,\n",
       "       0.07876809, 0.05355513, 0.00699883, 0.        , 0.        ,\n",
       "       0.05003318, 0.00071503, 0.        , 0.        , 0.00419858,\n",
       "       0.0128559 , 0.00975384, 0.00748972, 0.00486002, 0.00656143,\n",
       "       0.01353413, 0.01196899, 0.01311545, 0.00401892, 0.00563455,\n",
       "       0.01489935, 0.01304461, 0.01336476, 0.0062406 , 0.00608322,\n",
       "       0.01277358, 0.0067145 , 0.0133229 , 0.01326593, 0.01461382,\n",
       "       0.00027308, 0.        , 0.01392109, 0.01485945, 0.00939335,\n",
       "       0.01098295, 0.00617089, 0.01057536, 0.01522071, 0.00375719,\n",
       "       0.00686666, 0.00963247, 0.00757054, 0.00872448, 0.00966205,\n",
       "       0.00833097, 0.01032007, 0.00727267, 0.00862894, 0.        ,\n",
       "       0.        , 0.00099569, 0.00754881, 0.01303005, 0.00513105,\n",
       "       0.00137279, 0.        , 0.01448769, 0.01641297, 0.0130453 ,\n",
       "       0.01279909, 0.0004956 , 0.00145897, 0.00282452, 0.00291332,\n",
       "       0.00220495, 0.00150464, 0.00193258, 0.00564021, 0.02112913,\n",
       "       0.01511524, 0.        , 0.00110057, 0.00115207, 0.005106  ,\n",
       "       0.00503665, 0.0019016 , 0.00209124, 0.        , 0.        ])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate feature importance in the Random Forest model.\n",
    "importances1 = brfc.feature_importances_\n",
    "importances1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.07876809003486353, 'total_rec_prncp'),\n",
       " (0.05883806887524815, 'total_pymnt'),\n",
       " (0.05625613759225244, 'total_pymnt_inv'),\n",
       " (0.05355513093134745, 'total_rec_int'),\n",
       " (0.0500331813446525, 'last_pymnt_amnt'),\n",
       " (0.02966959508700077, 'int_rate'),\n",
       " (0.021129125328012987, 'issue_d_Jan-2019'),\n",
       " (0.01980242888931366, 'installment'),\n",
       " (0.01747062730041245, 'dti'),\n",
       " (0.016858293184471483, 'out_prncp_inv'),\n",
       " (0.01641297102011915, 'total_bal_ex_mort'),\n",
       " (0.015220714904737209, 'mths_since_recent_inq'),\n",
       " (0.015115240704562424, 'issue_d_Mar-2019'),\n",
       " (0.014926655663448373, 'revol_bal'),\n",
       " (0.014899352873994727, 'max_bal_bc'),\n",
       " (0.014881069023035237, 'out_prncp'),\n",
       " (0.014859446582326507, 'mo_sin_old_rev_tl_op'),\n",
       " (0.014832564501144122, 'annual_inc'),\n",
       " (0.014613819728800227, 'bc_util'),\n",
       " (0.014487685026878092, 'tot_hi_cred_lim'),\n",
       " (0.013921085423763812, 'mo_sin_old_il_acct'),\n",
       " (0.013534131593418711, 'mths_since_rcnt_il'),\n",
       " (0.013364759441576994, 'total_rev_hi_lim'),\n",
       " (0.01332289882475225, 'avg_cur_bal'),\n",
       " (0.013265926832893358, 'bc_open_to_buy'),\n",
       " (0.01311545089813887, 'il_util'),\n",
       " (0.01304530062898567, 'total_bc_limit'),\n",
       " (0.0130446065288952, 'all_util'),\n",
       " (0.013030046723135838, 'pct_tl_nvr_dlq'),\n",
       " (0.012855901280381887, 'tot_cur_bal'),\n",
       " (0.01279908506759016, 'total_il_high_credit_limit'),\n",
       " (0.012773576514405109, 'inq_last_12m'),\n",
       " (0.011968994260747247, 'total_bal_il'),\n",
       " (0.010982948025240226, 'mo_sin_rcnt_tl'),\n",
       " (0.010579906006851516, 'loan_amnt'),\n",
       " (0.010575363106694519, 'mths_since_recent_bc'),\n",
       " (0.010320067009550682, 'num_rev_accts'),\n",
       " (0.010209212170253059, 'total_acc'),\n",
       " (0.009753839399393215, 'open_acc_6m'),\n",
       " (0.009662050208879065, 'num_il_tl'),\n",
       " (0.009632472481996241, 'num_actv_rev_tl'),\n",
       " (0.009393346012674945, 'mo_sin_rcnt_rev_tl_op'),\n",
       " (0.00872448189550355, 'num_bc_tl'),\n",
       " (0.008628938824946404, 'num_sats'),\n",
       " (0.008330966254402506, 'num_op_rev_tl'),\n",
       " (0.007570544824579072, 'num_bc_sats'),\n",
       " (0.007548811505974241, 'num_tl_op_past_12m'),\n",
       " (0.007489717491934961, 'open_act_il'),\n",
       " (0.007382231721841728, 'open_acc'),\n",
       " (0.007272665006598051, 'num_rev_tl_bal_gt_0'),\n",
       " (0.006998827313196186, 'total_rec_late_fee'),\n",
       " (0.006866662924995743, 'num_actv_bc_tl'),\n",
       " (0.006714495620628373, 'acc_open_past_24mths'),\n",
       " (0.006561432872333855, 'open_il_24m'),\n",
       " (0.006240598451492287, 'inq_fi'),\n",
       " (0.006216409633238659, 'inq_last_6mths'),\n",
       " (0.0061708920490257954, 'mort_acc'),\n",
       " (0.006083218608279307, 'total_cu_tl'),\n",
       " (0.005640206440873574, 'issue_d_Feb-2019'),\n",
       " (0.005634546230136711, 'open_rv_24m'),\n",
       " (0.005131046989565006, 'percent_bc_gt_75'),\n",
       " (0.005106000423451099, 'next_pymnt_d_Apr-2019'),\n",
       " (0.005036652777545191, 'next_pymnt_d_May-2019'),\n",
       " (0.004860024796675963, 'open_il_12m'),\n",
       " (0.004198582835532627, 'tot_coll_amt'),\n",
       " (0.004018916067963884, 'open_rv_12m'),\n",
       " (0.0037571920083085985, 'num_accts_ever_120_pd'),\n",
       " (0.003082852259926947, 'delinq_2yrs'),\n",
       " (0.0029133221443170495, 'home_ownership_RENT'),\n",
       " (0.002824523629114469, 'home_ownership_OWN'),\n",
       " (0.002204946377565813, 'verification_status_Not Verified'),\n",
       " (0.0020912385738361574, 'application_type_Joint App'),\n",
       " (0.002015258269512615, 'pub_rec'),\n",
       " (0.0019325773153555006, 'verification_status_Verified'),\n",
       " (0.001901604006185586, 'application_type_Individual'),\n",
       " (0.0015046400907840708, 'verification_status_Source Verified'),\n",
       " (0.0014589723334940362, 'home_ownership_MORTGAGE'),\n",
       " (0.0013727925120781853, 'pub_rec_bankruptcies'),\n",
       " (0.0011520703643731528, 'initial_list_status_w'),\n",
       " (0.0011005704165634263, 'initial_list_status_f'),\n",
       " (0.0009956935704327383, 'num_tl_90g_dpd_24m'),\n",
       " (0.0007150315534652695, 'collections_12_mths_ex_med'),\n",
       " (0.0004955956183545533, 'home_ownership_ANY'),\n",
       " (0.0002730803587770788, 'chargeoff_within_12_mths'),\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, 'acc_now_delinq')]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "sorted(zip(brfc.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Easy Ensemble AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import EasyEnsembleClassifier \n",
    "from imblearn.ensemble import EasyEnsembleClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EasyEnsembleClassifier(n_estimators=100, random_state=1)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and train the EasyEnsembleClassifier\n",
    "eec_model = EasyEnsembleClassifier(n_estimators=100, random_state=1) \n",
    "eec_model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9316600714093861"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculated the balanced accuracy score\n",
    "y_pred = eec_model.predict(X_test)\n",
    "eec_accuracy = balanced_accuracy_score(y_test, y_pred)\n",
    "\n",
    "eec_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>93</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual low_risk</th>\n",
       "      <td>983</td>\n",
       "      <td>16121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Predicted high_risk  Predicted low_risk\n",
       "Actual high_risk                   93                   8\n",
       "Actual low_risk                   983               16121"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the confusion matrix\n",
    "cm2 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm2_df = pd.DataFrame(\n",
    "    cm2, index=[\"Actual high_risk\", \"Actual low_risk\"], columns=[\"Predicted high_risk\", \"Predicted low_risk\"])\n",
    "\n",
    "cm2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   pre       rec       spe        f1       geo       iba       sup\n",
      "\n",
      "  high_risk       0.09      0.92      0.94      0.16      0.93      0.87       101\n",
      "   low_risk       1.00      0.94      0.92      0.97      0.93      0.87     17104\n",
      "\n",
      "avg / total       0.99      0.94      0.92      0.97      0.93      0.87     17205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the imbalanced classification report for the Easy Ensemble Ada Boost Classifier\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "print(classification_report_imbalanced(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Score Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Model</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Balanced Random Forest Classifier</td>\n",
       "      <td>0.788547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Easy Ensemble Classifier</td>\n",
       "      <td>0.931660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Model  Balanced Accuracy\n",
       "0  Balanced Random Forest Classifier           0.788547\n",
       "1           Easy Ensemble Classifier           0.931660"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Model\":[\"Balanced Random Forest Classifier\", \"Easy Ensemble Classifier\"],\n",
    "            \"Balanced Accuracy\":[brfc_accuracy, eec_accuracy]})"
   ]
  }
 ],
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