data_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('./data/LoanStats_securev1_2017Q1.csv', skiprows=[0])\n",
    "df2 = pd.read_csv('./data/LoanStats_securev1_2017Q2.csv', skiprows=[0])\n",
    "df3 = pd.read_csv('./data/LoanStats_securev1_2017Q3.csv', skiprows=[0])\n",
    "df4 = pd.read_csv('./data/LoanStats3c_securev1_2014.csv', skiprows=[0])\n",
    "df5 = pd.read_csv('./data/LoanStats3d_securev1_2015.csv', skiprows=[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if all the datasets have same column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = np.dstack((list(df1.columns), list(df2.columns), list(df3.columns), list(df4.columns), list(df5.columns))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "coldf = pd.DataFrame(columns[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coldf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df1, df2, df3, df4, df5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get familiar with data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(981665, 151)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['id', 'member_id', 'loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate', 'installment', 'grade', 'sub_grade', 'emp_title', 'emp_length', 'home_ownership', 'annual_inc', 'verification_status', 'issue_d', 'loan_status', 'pymnt_plan', 'url', 'desc', 'purpose', 'title', 'zip_code', 'addr_state', 'dti', 'delinq_2yrs', 'earliest_cr_line', 'fico_range_low', 'fico_range_high', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc', 'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'policy_code', 'application_type', 'annual_inc_joint', 'dti_joint', 'verification_status_joint', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_act_il', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'max_bal_bc', 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m', 'acc_open_past_24mths', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mort_acc', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'pub_rec_bankruptcies', 'tax_liens', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'total_il_high_credit_limit', 'revol_bal_joint', 'sec_app_fico_range_low', 'sec_app_fico_range_high', 'sec_app_earliest_cr_line', 'sec_app_inq_last_6mths', 'sec_app_mort_acc', 'sec_app_open_acc', 'sec_app_revol_util', 'sec_app_open_act_il', 'sec_app_num_rev_accts', 'sec_app_chargeoff_within_12_mths', 'sec_app_collections_12_mths_ex_med', 'sec_app_mths_since_last_major_derog', 'hardship_flag', 'hardship_type', 'hardship_reason', 'hardship_status', 'deferral_term', 'hardship_amount', 'hardship_start_date', 'hardship_end_date', 'payment_plan_start_date', 'hardship_length', 'hardship_dpd', 'hardship_loan_status', 'orig_projected_additional_accrued_interest', 'hardship_payoff_balance_amount', 'hardship_last_payment_amount', 'disbursement_method', 'debt_settlement_flag', 'debt_settlement_flag_date', 'settlement_status', 'settlement_date', 'settlement_amount', 'settlement_percentage', 'settlement_term']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</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>url</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>last_fico_range_high</th>\n",
       "      <th>last_fico_range_low</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>verification_status_joint</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_bc_dlq</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>mths_since_recent_revol_delinq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>total_il_high_credit_limit</th>\n",
       "      <th>revol_bal_joint</th>\n",
       "      <th>sec_app_fico_range_low</th>\n",
       "      <th>sec_app_fico_range_high</th>\n",
       "      <th>sec_app_earliest_cr_line</th>\n",
       "      <th>sec_app_inq_last_6mths</th>\n",
       "      <th>sec_app_mort_acc</th>\n",
       "      <th>sec_app_open_acc</th>\n",
       "      <th>sec_app_revol_util</th>\n",
       "      <th>sec_app_open_act_il</th>\n",
       "      <th>sec_app_num_rev_accts</th>\n",
       "      <th>sec_app_chargeoff_within_12_mths</th>\n",
       "      <th>sec_app_collections_12_mths_ex_med</th>\n",
       "      <th>sec_app_mths_since_last_major_derog</th>\n",
       "      <th>hardship_flag</th>\n",
       "      <th>hardship_type</th>\n",
       "      <th>hardship_reason</th>\n",
       "      <th>hardship_status</th>\n",
       "      <th>deferral_term</th>\n",
       "      <th>hardship_amount</th>\n",
       "      <th>hardship_start_date</th>\n",
       "      <th>hardship_end_date</th>\n",
       "      <th>payment_plan_start_date</th>\n",
       "      <th>hardship_length</th>\n",
       "      <th>hardship_dpd</th>\n",
       "      <th>hardship_loan_status</th>\n",
       "      <th>orig_projected_additional_accrued_interest</th>\n",
       "      <th>hardship_payoff_balance_amount</th>\n",
       "      <th>hardship_last_payment_amount</th>\n",
       "      <th>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>debt_settlement_flag_date</th>\n",
       "      <th>settlement_status</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>settlement_amount</th>\n",
       "      <th>settlement_percentage</th>\n",
       "      <th>settlement_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104046719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.99%</td>\n",
       "      <td>492.13</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Project Leader</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Credit card refinancing</td>\n",
       "      <td>672xx</td>\n",
       "      <td>KS</td>\n",
       "      <td>31.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>May-1996</td>\n",
       "      <td>725.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69578.0</td>\n",
       "      <td>63.2%</td>\n",
       "      <td>31.0</td>\n",
       "      <td>w</td>\n",
       "      <td>11438.26</td>\n",
       "      <td>11438.26</td>\n",
       "      <td>3912.17</td>\n",
       "      <td>3912.17</td>\n",
       "      <td>2561.74</td>\n",
       "      <td>1350.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>492.13</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>714.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202617.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34430.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13686.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>110037.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11257.0</td>\n",
       "      <td>29762.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>286204.0</td>\n",
       "      <td>104008.0</td>\n",
       "      <td>81400.0</td>\n",
       "      <td>60167.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104048967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>25.49%</td>\n",
       "      <td>200.10</td>\n",
       "      <td>E</td>\n",
       "      <td>E4</td>\n",
       "      <td>Pilot</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>067xx</td>\n",
       "      <td>CT</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jan-2004</td>\n",
       "      <td>765.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8678.0</td>\n",
       "      <td>18.8%</td>\n",
       "      <td>16.0</td>\n",
       "      <td>f</td>\n",
       "      <td>4190.59</td>\n",
       "      <td>4190.59</td>\n",
       "      <td>1586.64</td>\n",
       "      <td>1586.64</td>\n",
       "      <td>809.41</td>\n",
       "      <td>777.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>200.10</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>779.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44754.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35832.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8659.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>46200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4069.0</td>\n",
       "      <td>37522.0</td>\n",
       "      <td>18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100763.0</td>\n",
       "      <td>44754.0</td>\n",
       "      <td>46200.0</td>\n",
       "      <td>49814.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104028593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.39%</td>\n",
       "      <td>151.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>cable technician</td>\n",
       "      <td>4 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>44470.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Credit card refinancing</td>\n",
       "      <td>066xx</td>\n",
       "      <td>CT</td>\n",
       "      <td>13.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2003</td>\n",
       "      <td>770.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>19.1%</td>\n",
       "      <td>11.0</td>\n",
       "      <td>w</td>\n",
       "      <td>3708.50</td>\n",
       "      <td>3708.50</td>\n",
       "      <td>1205.78</td>\n",
       "      <td>1205.78</td>\n",
       "      <td>891.50</td>\n",
       "      <td>314.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>151.45</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>769.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9625.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4225.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4670.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>9610.0</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>9625.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>11700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104046702</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.74%</td>\n",
       "      <td>316.69</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>Specialty Sales</td>\n",
       "      <td>4 years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Credit card refinancing</td>\n",
       "      <td>926xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>17.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Feb-1974</td>\n",
       "      <td>715.0</td>\n",
       "      <td>719.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25787.0</td>\n",
       "      <td>46.2%</td>\n",
       "      <td>23.0</td>\n",
       "      <td>w</td>\n",
       "      <td>12604.51</td>\n",
       "      <td>12604.51</td>\n",
       "      <td>2585.32</td>\n",
       "      <td>2585.32</td>\n",
       "      <td>1395.49</td>\n",
       "      <td>1189.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>316.69</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>714.0</td>\n",
       "      <td>710.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25787.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8549.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>55800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2579.0</td>\n",
       "      <td>27153.0</td>\n",
       "      <td>48.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55800.0</td>\n",
       "      <td>25787.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104280113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>5.32%</td>\n",
       "      <td>451.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>Executive Account Manager</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>182000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>751xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>9.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-1974</td>\n",
       "      <td>740.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34623.0</td>\n",
       "      <td>33.7%</td>\n",
       "      <td>32.0</td>\n",
       "      <td>w</td>\n",
       "      <td>11869.91</td>\n",
       "      <td>11869.91</td>\n",
       "      <td>3604.97</td>\n",
       "      <td>3604.97</td>\n",
       "      <td>3130.09</td>\n",
       "      <td>474.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>451.73</td>\n",
       "      <td>Jan-2018</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>734.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233521.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12503.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>102600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17963.0</td>\n",
       "      <td>55203.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>325464.0</td>\n",
       "      <td>50766.0</td>\n",
       "      <td>74600.0</td>\n",
       "      <td>4508.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  104046719        NaN    14000.0      14000.0          14000.0   36 months   \n",
       "1  104048967        NaN     5000.0       5000.0           5000.0   36 months   \n",
       "2  104028593        NaN     4600.0       4600.0           4600.0   36 months   \n",
       "3  104046702        NaN    14000.0      14000.0          14000.0   60 months   \n",
       "4  104280113        NaN    15000.0      15000.0          15000.0   36 months   \n",
       "\n",
       "  int_rate  installment grade sub_grade                  emp_title emp_length  \\\n",
       "0   15.99%       492.13     C        C5             Project Leader  10+ years   \n",
       "1   25.49%       200.10     E        E4                      Pilot  10+ years   \n",
       "2   11.39%       151.45     B        B3           cable technician    4 years   \n",
       "3   12.74%       316.69     C        C1            Specialty Sales    4 years   \n",
       "4    5.32%       451.73     A        A1  Executive Account Manager  10+ years   \n",
       "\n",
       "  home_ownership  annual_inc verification_status   issue_d loan_status  \\\n",
       "0       MORTGAGE     82000.0     Source Verified  Mar-2017     Current   \n",
       "1       MORTGAGE    215000.0     Source Verified  Mar-2017     Current   \n",
       "2           RENT     44470.0        Not Verified  Mar-2017     Current   \n",
       "3            OWN     40000.0        Not Verified  Mar-2017     Current   \n",
       "4       MORTGAGE    182000.0        Not Verified  Mar-2017     Current   \n",
       "\n",
       "  pymnt_plan                                                url desc  \\\n",
       "0          n  https://lendingclub.com/browse/loanDetail.acti...  NaN   \n",
       "1          n  https://lendingclub.com/browse/loanDetail.acti...  NaN   \n",
       "2          n  https://lendingclub.com/browse/loanDetail.acti...  NaN   \n",
       "3          n  https://lendingclub.com/browse/loanDetail.acti...  NaN   \n",
       "4          n  https://lendingclub.com/browse/loanDetail.acti...  NaN   \n",
       "\n",
       "              purpose                    title zip_code addr_state    dti  \\\n",
       "0         credit_card  Credit card refinancing    672xx         KS  31.07   \n",
       "1  debt_consolidation       Debt consolidation    067xx         CT   6.06   \n",
       "2         credit_card  Credit card refinancing    066xx         CT  13.43   \n",
       "3         credit_card  Credit card refinancing    926xx         CA  17.48   \n",
       "4  debt_consolidation       Debt consolidation    751xx         TX   9.35   \n",
       "\n",
       "   delinq_2yrs earliest_cr_line  fico_range_low  fico_range_high  \\\n",
       "0          0.0         May-1996           725.0            729.0   \n",
       "1          0.0         Jan-2004           765.0            769.0   \n",
       "2          0.0         Dec-2003           770.0            774.0   \n",
       "3          0.0         Feb-1974           715.0            719.0   \n",
       "4          0.0         Nov-1974           740.0            744.0   \n",
       "\n",
       "   inq_last_6mths  mths_since_last_delinq  mths_since_last_record  open_acc  \\\n",
       "0             0.0                     NaN                     NaN      18.0   \n",
       "1             4.0                     NaN                     NaN      11.0   \n",
       "2             1.0                     NaN                     NaN       7.0   \n",
       "3             0.0                     NaN                     NaN      10.0   \n",
       "4             0.0                     NaN                     NaN      13.0   \n",
       "\n",
       "   pub_rec  revol_bal revol_util  total_acc initial_list_status  out_prncp  \\\n",
       "0      0.0    69578.0      63.2%       31.0                   w   11438.26   \n",
       "1      0.0     8678.0      18.8%       16.0                   f    4190.59   \n",
       "2      0.0     5400.0      19.1%       11.0                   w    3708.50   \n",
       "3      0.0    25787.0      46.2%       23.0                   w   12604.51   \n",
       "4      0.0    34623.0      33.7%       32.0                   w   11869.91   \n",
       "\n",
       "   out_prncp_inv  total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "0       11438.26      3912.17          3912.17          2561.74   \n",
       "1        4190.59      1586.64          1586.64           809.41   \n",
       "2        3708.50      1205.78          1205.78           891.50   \n",
       "3       12604.51      2585.32          2585.32          1395.49   \n",
       "4       11869.91      3604.97          3604.97          3130.09   \n",
       "\n",
       "   total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "0        1350.43                 0.0         0.0                      0.0   \n",
       "1         777.23                 0.0         0.0                      0.0   \n",
       "2         314.28                 0.0         0.0                      0.0   \n",
       "3        1189.83                 0.0         0.0                      0.0   \n",
       "4         474.88                 0.0         0.0                      0.0   \n",
       "\n",
       "  last_pymnt_d  last_pymnt_amnt next_pymnt_d last_credit_pull_d  \\\n",
       "0     Dec-2017           492.13     Jan-2018           Dec-2017   \n",
       "1     Dec-2017           200.10     Jan-2018           Dec-2017   \n",
       "2     Dec-2017           151.45     Jan-2018           Dec-2017   \n",
       "3     Dec-2017           316.69     Jan-2018           Dec-2017   \n",
       "4     Dec-2017           451.73     Jan-2018           Dec-2017   \n",
       "\n",
       "   last_fico_range_high  last_fico_range_low  collections_12_mths_ex_med  \\\n",
       "0                 714.0                710.0                         0.0   \n",
       "1                 779.0                775.0                         0.0   \n",
       "2                 769.0                765.0                         0.0   \n",
       "3                 714.0                710.0                         0.0   \n",
       "4                 734.0                730.0                         0.0   \n",
       "\n",
       "   mths_since_last_major_derog  policy_code application_type  \\\n",
       "0                          NaN          1.0       Individual   \n",
       "1                          NaN          1.0       Individual   \n",
       "2                          NaN          1.0       Individual   \n",
       "3                          NaN          1.0       Individual   \n",
       "4                          NaN          1.0       Individual   \n",
       "\n",
       "   annual_inc_joint  dti_joint verification_status_joint  acc_now_delinq  \\\n",
       "0               NaN        NaN                       NaN             0.0   \n",
       "1               NaN        NaN                       NaN             0.0   \n",
       "2               NaN        NaN                       NaN             0.0   \n",
       "3               NaN        NaN                       NaN             0.0   \n",
       "4               NaN        NaN                       NaN             0.0   \n",
       "\n",
       "   tot_coll_amt  tot_cur_bal  open_acc_6m  open_act_il  open_il_12m  \\\n",
       "0           0.0     202617.0          0.0          3.0          0.0   \n",
       "1           0.0      44754.0          1.0          3.0          2.0   \n",
       "2           0.0       9625.0          1.0          1.0          0.0   \n",
       "3           0.0      25787.0          2.0          0.0          0.0   \n",
       "4           0.0     233521.0          0.0          1.0          0.0   \n",
       "\n",
       "   open_il_24m  mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  \\\n",
       "0          1.0                22.0       34430.0     57.0          0.0   \n",
       "1          4.0                 7.0       35832.0     68.0          2.0   \n",
       "2          1.0                22.0        4225.0     36.0          2.0   \n",
       "3          0.0               129.0           0.0      NaN          5.0   \n",
       "4          1.0                24.0        2667.0     59.0          2.0   \n",
       "\n",
       "   open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim  inq_fi  total_cu_tl  \\\n",
       "0          2.0     13686.0      61.0          110037.0     0.0          9.0   \n",
       "1          2.0      8659.0      27.0           46200.0     2.0          0.0   \n",
       "2          4.0      4670.0      24.0           28300.0     0.0          0.0   \n",
       "3          7.0      8549.0      46.0           55800.0     0.0          3.0   \n",
       "4          2.0     12503.0      42.0          102600.0     1.0          6.0   \n",
       "\n",
       "   inq_last_12m  acc_open_past_24mths  avg_cur_bal  bc_open_to_buy  bc_util  \\\n",
       "0           0.0                   3.0      11257.0         29762.0     63.4   \n",
       "1           6.0                   6.0       4069.0         37522.0     18.8   \n",
       "2           1.0                   5.0       1375.0          9610.0     33.7   \n",
       "3           0.0                   7.0       2579.0         27153.0     48.3   \n",
       "4           0.0                   4.0      17963.0         55203.0     26.0   \n",
       "\n",
       "   chargeoff_within_12_mths  delinq_amnt  mo_sin_old_il_acct  \\\n",
       "0                       0.0          0.0               140.0   \n",
       "1                       0.0          0.0                83.0   \n",
       "2                       0.0          0.0                77.0   \n",
       "3                       0.0          0.0               129.0   \n",
       "4                       0.0          0.0                92.0   \n",
       "\n",
       "   mo_sin_old_rev_tl_op  mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl  mort_acc  \\\n",
       "0                 250.0                   13.0            13.0       6.0   \n",
       "1                  87.0                    5.0             5.0       0.0   \n",
       "2                 159.0                    1.0             1.0       0.0   \n",
       "3                 517.0                    1.0             1.0       1.0   \n",
       "4                 508.0                    8.0             8.0       5.0   \n",
       "\n",
       "   mths_since_recent_bc  mths_since_recent_bc_dlq  mths_since_recent_inq  \\\n",
       "0                  13.0                       NaN                   13.0   \n",
       "1                   5.0                       NaN                    0.0   \n",
       "2                   7.0                       NaN                    1.0   \n",
       "3                   2.0                       NaN                   23.0   \n",
       "4                   8.0                       NaN                   17.0   \n",
       "\n",
       "   mths_since_recent_revol_delinq  num_accts_ever_120_pd  num_actv_bc_tl  \\\n",
       "0                             NaN                    0.0             7.0   \n",
       "1                             NaN                    0.0             2.0   \n",
       "2                             NaN                    0.0             2.0   \n",
       "3                             NaN                    0.0             7.0   \n",
       "4                             NaN                    0.0             3.0   \n",
       "\n",
       "   num_actv_rev_tl  num_bc_sats  num_bc_tl  num_il_tl  num_op_rev_tl  \\\n",
       "0              9.0          8.0       13.0        4.0           14.0   \n",
       "1              2.0          7.0        8.0        7.0            7.0   \n",
       "2              3.0          3.0        4.0        2.0            6.0   \n",
       "3              9.0          8.0       13.0        1.0           10.0   \n",
       "4              6.0          7.0       11.0        9.0           10.0   \n",
       "\n",
       "   num_rev_accts  num_rev_tl_bal_gt_0  num_sats  num_tl_120dpd_2m  \\\n",
       "0           21.0                  9.0      18.0               0.0   \n",
       "1            8.0                  2.0      11.0               0.0   \n",
       "2            9.0                  3.0       7.0               0.0   \n",
       "3           21.0                  9.0      10.0               0.0   \n",
       "4           16.0                  6.0      13.0               0.0   \n",
       "\n",
       "   num_tl_30dpd  num_tl_90g_dpd_24m  num_tl_op_past_12m  pct_tl_nvr_dlq  \\\n",
       "0           0.0                 0.0                 0.0           100.0   \n",
       "1           0.0                 0.0                 4.0           100.0   \n",
       "2           0.0                 0.0                 2.0           100.0   \n",
       "3           0.0                 0.0                 5.0           100.0   \n",
       "4           0.0                 0.0                 2.0           100.0   \n",
       "\n",
       "   percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  \\\n",
       "0              25.0                   0.0        0.0         286204.0   \n",
       "1               0.0                   0.0        0.0         100763.0   \n",
       "2               0.0                   0.0        0.0          40000.0   \n",
       "3              37.5                   0.0        0.0          55800.0   \n",
       "4               0.0                   0.0        0.0         325464.0   \n",
       "\n",
       "   total_bal_ex_mort  total_bc_limit  total_il_high_credit_limit  \\\n",
       "0           104008.0         81400.0                     60167.0   \n",
       "1            44754.0         46200.0                     49814.0   \n",
       "2             9625.0         14500.0                     11700.0   \n",
       "3            25787.0         52500.0                         0.0   \n",
       "4            50766.0         74600.0                      4508.0   \n",
       "\n",
       "   revol_bal_joint  sec_app_fico_range_low  sec_app_fico_range_high  \\\n",
       "0              NaN                     NaN                      NaN   \n",
       "1              NaN                     NaN                      NaN   \n",
       "2              NaN                     NaN                      NaN   \n",
       "3              NaN                     NaN                      NaN   \n",
       "4              NaN                     NaN                      NaN   \n",
       "\n",
       "  sec_app_earliest_cr_line  sec_app_inq_last_6mths  sec_app_mort_acc  \\\n",
       "0                      NaN                     NaN               NaN   \n",
       "1                      NaN                     NaN               NaN   \n",
       "2                      NaN                     NaN               NaN   \n",
       "3                      NaN                     NaN               NaN   \n",
       "4                      NaN                     NaN               NaN   \n",
       "\n",
       "   sec_app_open_acc  sec_app_revol_util  sec_app_open_act_il  \\\n",
       "0               NaN                 NaN                  NaN   \n",
       "1               NaN                 NaN                  NaN   \n",
       "2               NaN                 NaN                  NaN   \n",
       "3               NaN                 NaN                  NaN   \n",
       "4               NaN                 NaN                  NaN   \n",
       "\n",
       "   sec_app_num_rev_accts  sec_app_chargeoff_within_12_mths  \\\n",
       "0                    NaN                               NaN   \n",
       "1                    NaN                               NaN   \n",
       "2                    NaN                               NaN   \n",
       "3                    NaN                               NaN   \n",
       "4                    NaN                               NaN   \n",
       "\n",
       "   sec_app_collections_12_mths_ex_med  sec_app_mths_since_last_major_derog  \\\n",
       "0                                 NaN                                  NaN   \n",
       "1                                 NaN                                  NaN   \n",
       "2                                 NaN                                  NaN   \n",
       "3                                 NaN                                  NaN   \n",
       "4                                 NaN                                  NaN   \n",
       "\n",
       "  hardship_flag hardship_type hardship_reason hardship_status  deferral_term  \\\n",
       "0             N           NaN             NaN             NaN            NaN   \n",
       "1             N           NaN             NaN             NaN            NaN   \n",
       "2             N           NaN             NaN             NaN            NaN   \n",
       "3             N           NaN             NaN             NaN            NaN   \n",
       "4             N           NaN             NaN             NaN            NaN   \n",
       "\n",
       "   hardship_amount hardship_start_date hardship_end_date  \\\n",
       "0              NaN                 NaN               NaN   \n",
       "1              NaN                 NaN               NaN   \n",
       "2              NaN                 NaN               NaN   \n",
       "3              NaN                 NaN               NaN   \n",
       "4              NaN                 NaN               NaN   \n",
       "\n",
       "  payment_plan_start_date  hardship_length  hardship_dpd hardship_loan_status  \\\n",
       "0                     NaN              NaN           NaN                  NaN   \n",
       "1                     NaN              NaN           NaN                  NaN   \n",
       "2                     NaN              NaN           NaN                  NaN   \n",
       "3                     NaN              NaN           NaN                  NaN   \n",
       "4                     NaN              NaN           NaN                  NaN   \n",
       "\n",
       "   orig_projected_additional_accrued_interest  hardship_payoff_balance_amount  \\\n",
       "0                                         NaN                             NaN   \n",
       "1                                         NaN                             NaN   \n",
       "2                                         NaN                             NaN   \n",
       "3                                         NaN                             NaN   \n",
       "4                                         NaN                             NaN   \n",
       "\n",
       "   hardship_last_payment_amount disbursement_method debt_settlement_flag  \\\n",
       "0                           NaN                Cash                    N   \n",
       "1                           NaN                Cash                    N   \n",
       "2                           NaN                Cash                    N   \n",
       "3                           NaN                Cash                    N   \n",
       "4                           NaN                Cash                    N   \n",
       "\n",
       "  debt_settlement_flag_date settlement_status settlement_date  \\\n",
       "0                       NaN               NaN             NaN   \n",
       "1                       NaN               NaN             NaN   \n",
       "2                       NaN               NaN             NaN   \n",
       "3                       NaN               NaN             NaN   \n",
       "4                       NaN               NaN             NaN   \n",
       "\n",
       "   settlement_amount  settlement_percentage  settlement_term  \n",
       "0                NaN                    NaN              NaN  \n",
       "1                NaN                    NaN              NaN  \n",
       "2                NaN                    NaN              NaN  \n",
       "3                NaN                    NaN              NaN  \n",
       "4                NaN                    NaN              NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_type</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>float64</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>object</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature_type  count\n",
       "0      float64    111\n",
       "1       object     40"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.sort_values().to_frame('feature_type').groupby(by = 'feature_type').size().to_frame('count').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select data with loan_status either Fully Paid or Charged Off "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current               500937\n",
       "Fully Paid            358629\n",
       "Charged Off            99099\n",
       "Late (31-120 days)     13203\n",
       "In Grace Period         6337\n",
       "Late (16-30 days)       3414\n",
       "Default                   36\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[(df['loan_status'].isin(['Fully Paid', 'Charged Off']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457728, 151)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selections and clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the missing columns and their types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dtypes = pd.merge(df.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index(),\n",
    "         df.dtypes.to_frame('feature_type').reset_index(),\n",
    "         on = 'index',\n",
    "         how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>index</th>\n",
       "      <th>missing_value</th>\n",
       "      <th>feature_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tot_hi_cred_lim</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tot_cur_bal</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tot_coll_amt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acc_now_delinq</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_bal_ex_mort</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>total_bc_limit</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_il_high_credit_limit</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>policy_code</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>collections_12_mths_ex_med</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>last_fico_range_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>last_fico_range_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>num_bc_sats</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>collection_recovery_fee</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>recoveries</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>total_rec_late_fee</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>tax_liens</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_rec_int</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>pub_rec_bankruptcies</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>num_tl_op_past_12m</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>num_actv_rev_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>num_actv_bc_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>num_accts_ever_120_pd</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>mort_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>mo_sin_rcnt_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>mo_sin_rcnt_rev_tl_op</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>mo_sin_old_rev_tl_op</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>delinq_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>chargeoff_within_12_mths</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>acc_open_past_24mths</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>num_il_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>num_op_rev_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>total_rev_hi_lim</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>num_rev_tl_bal_gt_0</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>num_sats</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>num_tl_30dpd</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>num_tl_90g_dpd_24m</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>pct_tl_nvr_dlq</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>total_rec_prncp</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>last_pymnt_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>total_pymnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>total_pymnt_inv</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>installment</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>funded_amnt_inv</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>num_bc_tl</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>out_prncp_inv</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>delinq_2yrs</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>out_prncp</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>inq_last_6mths</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>pub_rec</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>open_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>application_type</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hardship_flag</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>disbursement_method</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>url</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>loan_status</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>issue_d</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>verification_status</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>home_ownership</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>term</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>debt_settlement_flag</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>purpose</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>zip_code</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>pymnt_plan</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>earliest_cr_line</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>initial_list_status</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>addr_state</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>num_rev_accts</td>\n",
       "      <td>1</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>avg_cur_bal</td>\n",
       "      <td>6</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>dti</td>\n",
       "      <td>21</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>last_credit_pull_d</td>\n",
       "      <td>26</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>title</td>\n",
       "      <td>62</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>223</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>last_pymnt_d</td>\n",
       "      <td>731</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>mths_since_recent_bc</td>\n",
       "      <td>4477</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>bc_open_to_buy</td>\n",
       "      <td>4772</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>percent_bc_gt_75</td>\n",
       "      <td>5036</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>bc_util</td>\n",
       "      <td>5093</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>mo_sin_old_il_acct</td>\n",
       "      <td>13020</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>num_tl_120dpd_2m</td>\n",
       "      <td>18945</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>emp_length</td>\n",
       "      <td>24885</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>emp_title</td>\n",
       "      <td>26043</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>mths_since_recent_inq</td>\n",
       "      <td>40177</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>mths_since_last_delinq</td>\n",
       "      <td>223121</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>mths_since_recent_revol_delinq</td>\n",
       "      <td>293406</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>mths_since_last_major_derog</td>\n",
       "      <td>323754</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>mths_since_recent_bc_dlq</td>\n",
       "      <td>338157</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>mths_since_last_record</td>\n",
       "      <td>371421</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>inq_fi</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>total_cu_tl</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>open_acc_6m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>open_act_il</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>open_il_12m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>open_il_24m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>total_bal_il</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>inq_last_12m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>open_rv_12m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>open_rv_24m</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>max_bal_bc</td>\n",
       "      <td>417783</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>all_util</td>\n",
       "      <td>417786</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>mths_since_rcnt_il</td>\n",
       "      <td>418684</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>il_util</td>\n",
       "      <td>423107</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>desc</td>\n",
       "      <td>443599</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>settlement_amount</td>\n",
       "      <td>449510</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>settlement_term</td>\n",
       "      <td>449510</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>settlement_percentage</td>\n",
       "      <td>449510</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>settlement_date</td>\n",
       "      <td>449510</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>settlement_status</td>\n",
       "      <td>449510</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>debt_settlement_flag_date</td>\n",
       "      <td>449510</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>dti_joint</td>\n",
       "      <td>456197</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>annual_inc_joint</td>\n",
       "      <td>456197</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>verification_status_joint</td>\n",
       "      <td>456197</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>revol_bal_joint</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>sec_app_fico_range_high</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>sec_app_collections_12_mths_ex_med</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>sec_app_num_rev_accts</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>sec_app_inq_last_6mths</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>sec_app_mort_acc</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>sec_app_open_acc</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>sec_app_open_act_il</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>sec_app_fico_range_low</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>sec_app_chargeoff_within_12_mths</td>\n",
       "      <td>456830</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>sec_app_earliest_cr_line</td>\n",
       "      <td>456830</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>sec_app_revol_util</td>\n",
       "      <td>456847</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>sec_app_mths_since_last_major_derog</td>\n",
       "      <td>457425</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>hardship_last_payment_amount</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>hardship_payoff_balance_amount</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>hardship_dpd</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>hardship_amount</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>deferral_term</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>hardship_length</td>\n",
       "      <td>457455</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>hardship_loan_status</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>payment_plan_start_date</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>hardship_end_date</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>hardship_status</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>hardship_reason</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>hardship_type</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>hardship_start_date</td>\n",
       "      <td>457455</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>orig_projected_additional_accrued_interest</td>\n",
       "      <td>457677</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>member_id</td>\n",
       "      <td>457728</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>next_pymnt_d</td>\n",
       "      <td>457728</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          index  missing_value feature_type\n",
       "1                               tot_hi_cred_lim              0      float64\n",
       "2                                   tot_cur_bal              0      float64\n",
       "3                                  tot_coll_amt              0      float64\n",
       "4                                acc_now_delinq              0      float64\n",
       "5                             total_bal_ex_mort              0      float64\n",
       "6                                total_bc_limit              0      float64\n",
       "7                    total_il_high_credit_limit              0      float64\n",
       "9                                   policy_code              0      float64\n",
       "10                   collections_12_mths_ex_med              0      float64\n",
       "11                          last_fico_range_low              0      float64\n",
       "12                         last_fico_range_high              0      float64\n",
       "14                                  num_bc_sats              0      float64\n",
       "15                      collection_recovery_fee              0      float64\n",
       "16                                   recoveries              0      float64\n",
       "17                           total_rec_late_fee              0      float64\n",
       "18                                    tax_liens              0      float64\n",
       "19                                total_rec_int              0      float64\n",
       "20                         pub_rec_bankruptcies              0      float64\n",
       "21                           num_tl_op_past_12m              0      float64\n",
       "22                              num_actv_rev_tl              0      float64\n",
       "23                               num_actv_bc_tl              0      float64\n",
       "24                        num_accts_ever_120_pd              0      float64\n",
       "25                                     mort_acc              0      float64\n",
       "26                               mo_sin_rcnt_tl              0      float64\n",
       "27                        mo_sin_rcnt_rev_tl_op              0      float64\n",
       "28                         mo_sin_old_rev_tl_op              0      float64\n",
       "29                                  delinq_amnt              0      float64\n",
       "30                     chargeoff_within_12_mths              0      float64\n",
       "31                         acc_open_past_24mths              0      float64\n",
       "32                                    num_il_tl              0      float64\n",
       "33                                num_op_rev_tl              0      float64\n",
       "34                             total_rev_hi_lim              0      float64\n",
       "35                          num_rev_tl_bal_gt_0              0      float64\n",
       "36                                     num_sats              0      float64\n",
       "37                                 num_tl_30dpd              0      float64\n",
       "38                           num_tl_90g_dpd_24m              0      float64\n",
       "39                               pct_tl_nvr_dlq              0      float64\n",
       "40                              total_rec_prncp              0      float64\n",
       "41                              last_pymnt_amnt              0      float64\n",
       "42                                  total_pymnt              0      float64\n",
       "45                              total_pymnt_inv              0      float64\n",
       "49                                   annual_inc              0      float64\n",
       "53                                  installment              0      float64\n",
       "56                              funded_amnt_inv              0      float64\n",
       "57                                  funded_amnt              0      float64\n",
       "58                                    loan_amnt              0      float64\n",
       "63                                    num_bc_tl              0      float64\n",
       "64                                out_prncp_inv              0      float64\n",
       "65                                  delinq_2yrs              0      float64\n",
       "66                                    out_prncp              0      float64\n",
       "68                               fico_range_low              0      float64\n",
       "69                              fico_range_high              0      float64\n",
       "70                               inq_last_6mths              0      float64\n",
       "72                                    total_acc              0      float64\n",
       "73                                    revol_bal              0      float64\n",
       "74                                      pub_rec              0      float64\n",
       "75                                     open_acc              0      float64\n",
       "0                                            id              0       object\n",
       "8                              application_type              0       object\n",
       "13                                hardship_flag              0       object\n",
       "43                          disbursement_method              0       object\n",
       "44                                          url              0       object\n",
       "46                                  loan_status              0       object\n",
       "47                                      issue_d              0       object\n",
       "48                          verification_status              0       object\n",
       "50                               home_ownership              0       object\n",
       "51                                    sub_grade              0       object\n",
       "52                                        grade              0       object\n",
       "54                                     int_rate              0       object\n",
       "55                                         term              0       object\n",
       "59                         debt_settlement_flag              0       object\n",
       "60                                      purpose              0       object\n",
       "61                                     zip_code              0       object\n",
       "62                                   pymnt_plan              0       object\n",
       "67                             earliest_cr_line              0       object\n",
       "71                          initial_list_status              0       object\n",
       "76                                   addr_state              0       object\n",
       "77                                num_rev_accts              1      float64\n",
       "78                                  avg_cur_bal              6      float64\n",
       "79                                          dti             21      float64\n",
       "80                           last_credit_pull_d             26       object\n",
       "81                                        title             62       object\n",
       "82                                   revol_util            223       object\n",
       "83                                 last_pymnt_d            731       object\n",
       "84                         mths_since_recent_bc           4477      float64\n",
       "85                               bc_open_to_buy           4772      float64\n",
       "86                             percent_bc_gt_75           5036      float64\n",
       "87                                      bc_util           5093      float64\n",
       "88                           mo_sin_old_il_acct          13020      float64\n",
       "89                             num_tl_120dpd_2m          18945      float64\n",
       "90                                   emp_length          24885       object\n",
       "91                                    emp_title          26043       object\n",
       "92                        mths_since_recent_inq          40177      float64\n",
       "93                       mths_since_last_delinq         223121      float64\n",
       "94               mths_since_recent_revol_delinq         293406      float64\n",
       "95                  mths_since_last_major_derog         323754      float64\n",
       "96                     mths_since_recent_bc_dlq         338157      float64\n",
       "97                       mths_since_last_record         371421      float64\n",
       "98                                       inq_fi         417783      float64\n",
       "99                                  total_cu_tl         417783      float64\n",
       "100                                 open_acc_6m         417783      float64\n",
       "101                                 open_act_il         417783      float64\n",
       "102                                 open_il_12m         417783      float64\n",
       "103                                 open_il_24m         417783      float64\n",
       "104                                total_bal_il         417783      float64\n",
       "105                                inq_last_12m         417783      float64\n",
       "106                                 open_rv_12m         417783      float64\n",
       "107                                 open_rv_24m         417783      float64\n",
       "108                                  max_bal_bc         417783      float64\n",
       "109                                    all_util         417786      float64\n",
       "110                          mths_since_rcnt_il         418684      float64\n",
       "111                                     il_util         423107      float64\n",
       "112                                        desc         443599       object\n",
       "113                           settlement_amount         449510      float64\n",
       "117                             settlement_term         449510      float64\n",
       "118                       settlement_percentage         449510      float64\n",
       "114                             settlement_date         449510       object\n",
       "115                           settlement_status         449510       object\n",
       "116                   debt_settlement_flag_date         449510       object\n",
       "120                                   dti_joint         456197      float64\n",
       "121                            annual_inc_joint         456197      float64\n",
       "119                   verification_status_joint         456197       object\n",
       "123                             revol_bal_joint         456830      float64\n",
       "124                     sec_app_fico_range_high         456830      float64\n",
       "125          sec_app_collections_12_mths_ex_med         456830      float64\n",
       "126                       sec_app_num_rev_accts         456830      float64\n",
       "127                      sec_app_inq_last_6mths         456830      float64\n",
       "128                            sec_app_mort_acc         456830      float64\n",
       "129                            sec_app_open_acc         456830      float64\n",
       "130                         sec_app_open_act_il         456830      float64\n",
       "131                      sec_app_fico_range_low         456830      float64\n",
       "132            sec_app_chargeoff_within_12_mths         456830      float64\n",
       "122                    sec_app_earliest_cr_line         456830       object\n",
       "133                          sec_app_revol_util         456847      float64\n",
       "134         sec_app_mths_since_last_major_derog         457425      float64\n",
       "135                hardship_last_payment_amount         457455      float64\n",
       "136              hardship_payoff_balance_amount         457455      float64\n",
       "138                                hardship_dpd         457455      float64\n",
       "141                             hardship_amount         457455      float64\n",
       "142                               deferral_term         457455      float64\n",
       "146                             hardship_length         457455      float64\n",
       "137                        hardship_loan_status         457455       object\n",
       "139                     payment_plan_start_date         457455       object\n",
       "140                           hardship_end_date         457455       object\n",
       "143                             hardship_status         457455       object\n",
       "144                             hardship_reason         457455       object\n",
       "145                               hardship_type         457455       object\n",
       "147                         hardship_start_date         457455       object\n",
       "148  orig_projected_additional_accrued_interest         457677      float64\n",
       "149                                   member_id         457728      float64\n",
       "150                                next_pymnt_d         457728       object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtypes.sort_values(['missing_value', 'feature_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Check columns have more than $400000$ missing values ($\\approx90\\%$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_df = df.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "miss_4000 = list(missing_df[missing_df.missing_value >= 400000]['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n"
     ]
    }
   ],
   "source": [
    "print(len(miss_4000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['all_util', 'annual_inc_joint', 'debt_settlement_flag_date', 'deferral_term', 'desc', 'dti_joint', 'hardship_amount', 'hardship_dpd', 'hardship_end_date', 'hardship_last_payment_amount', 'hardship_length', 'hardship_loan_status', 'hardship_payoff_balance_amount', 'hardship_reason', 'hardship_start_date', 'hardship_status', 'hardship_type', 'il_util', 'inq_fi', 'inq_last_12m', 'max_bal_bc', 'member_id', 'mths_since_rcnt_il', 'next_pymnt_d', 'open_acc_6m', 'open_act_il', 'open_il_12m', 'open_il_24m', 'open_rv_12m', 'open_rv_24m', 'orig_projected_additional_accrued_interest', 'payment_plan_start_date', 'revol_bal_joint', 'sec_app_chargeoff_within_12_mths', 'sec_app_collections_12_mths_ex_med', 'sec_app_earliest_cr_line', 'sec_app_fico_range_high', 'sec_app_fico_range_low', 'sec_app_inq_last_6mths', 'sec_app_mort_acc', 'sec_app_mths_since_last_major_derog', 'sec_app_num_rev_accts', 'sec_app_open_acc', 'sec_app_open_act_il', 'sec_app_revol_util', 'settlement_amount', 'settlement_date', 'settlement_percentage', 'settlement_status', 'settlement_term', 'total_bal_il', 'total_cu_tl', 'verification_status_joint']\n"
     ]
    }
   ],
   "source": [
    "print(sorted(miss_4000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(miss_4000, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Remove constant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_constant_features(dataFrame):\n",
    "    const_features = []\n",
    "    for column in list(dataFrame.columns):\n",
    "        if dataFrame[column].unique().size < 2:\n",
    "            const_features.append(column)\n",
    "    return const_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "const_features = find_constant_features(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pymnt_plan', 'out_prncp', 'out_prncp_inv', 'policy_code', 'hardship_flag']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "const_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    457728\n",
       "Name: hardship_flag, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.hardship_flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(const_features, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Remove Duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457728, 93)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457728, 93)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Remove duplicate columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def duplicate_columns(frame):\n",
    "    groups = frame.columns.to_series().groupby(frame.dtypes).groups\n",
    "    dups = []\n",
    "\n",
    "    for t, v in groups.items():\n",
    "\n",
    "        cs = frame[v].columns\n",
    "        vs = frame[v]\n",
    "        lcs = len(cs)\n",
    "\n",
    "        for i in range(lcs):\n",
    "            ia = vs.iloc[:,i].values\n",
    "            for j in range(i+1, lcs):\n",
    "                ja = vs.iloc[:,j].values\n",
    "                if np.array_equal(ia, ja):\n",
    "                    dups.append(cs[i])\n",
    "                    break\n",
    "    return dups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicate_cols = duplicate_columns(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loan_amnt']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457728, 93)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Remove/process features manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_feature(col_name, isContinuous):\n",
    "    \"\"\"\n",
    "    Visualize a variable with and without faceting on the loan status.\n",
    "    - col_name is the variable name in the dataframe\n",
    "    - full_name is the full variable name\n",
    "    - continuous is True if the variable is continuous, False otherwise\n",
    "    \"\"\"\n",
    "    f, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12,3), dpi=90)\n",
    "    \n",
    "    # Plot without loan status\n",
    "    if isContinuous:\n",
    "        sns.distplot(df.loc[df[col_name].notnull(), col_name], kde=False, ax=ax1)\n",
    "    else:\n",
    "        sns.countplot(df[col_name], order=sorted(df[col_name].unique()), color='#5975A4', saturation=1, ax=ax1)\n",
    "    ax1.set_xlabel(col_name)\n",
    "    ax1.set_ylabel('Count')\n",
    "    ax1.set_title(col_name)\n",
    "    plt.xticks(rotation = 90)\n",
    "\n",
    "    # Plot with loan status\n",
    "    if isContinuous:\n",
    "        sns.boxplot(x=col_name, y='loan_status', data=df, ax=ax2)\n",
    "        ax2.set_ylabel('')\n",
    "        ax2.set_title(col_name + ' by Loan Status')\n",
    "    else:\n",
    "        data = df.groupby(col_name)['loan_status'].value_counts(normalize=True).to_frame('proportion').reset_index()        \n",
    "        sns.barplot(x = col_name, y = 'proportion', hue= \"loan_status\", data = data, saturation=1, ax=ax2)\n",
    "        ax2.set_ylabel('Loan fraction')\n",
    "        ax2.set_title('Loan status')\n",
    "        plt.xticks(rotation = 90)\n",
    "    ax2.set_xlabel(col_name)\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0-10 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104190249</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.49%</td>\n",
       "      <td>111.97</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Code/Compliance Inspector</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>104190255</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>25.49%</td>\n",
       "      <td>1037.38</td>\n",
       "      <td>E</td>\n",
       "      <td>E4</td>\n",
       "      <td>Human Resource Specialist LER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>104170260</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.49%</td>\n",
       "      <td>528.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Air Traffic Controller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>104046830</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.49%</td>\n",
       "      <td>329.72</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>General manager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>104220223</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.99%</td>\n",
       "      <td>173.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Lead Supervisor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  loan_amnt  funded_amnt  funded_amnt_inv        term int_rate  \\\n",
       "16  104190249     3600.0       3600.0           3600.0   36 months    7.49%   \n",
       "38  104190255    35000.0      35000.0          35000.0   60 months   25.49%   \n",
       "40  104170260    17000.0      17000.0          17000.0   36 months    7.49%   \n",
       "51  104046830    10000.0      10000.0          10000.0   36 months   11.49%   \n",
       "56  104220223     5000.0       5000.0           5000.0   36 months   14.99%   \n",
       "\n",
       "    installment grade sub_grade                      emp_title  \n",
       "16       111.97     A        A4      Code/Compliance Inspector  \n",
       "38      1037.38     E        E4  Human Resource Specialist LER  \n",
       "40       528.73     A        A4         Air Traffic Controller  \n",
       "51       329.72     B        B5                General manager  \n",
       "56       173.31     C        C4                Lead Supervisor  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 0: 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1415"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loan_amnt.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('loan_amnt', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like all loans are not unique. Certain amount appear several times. It may be the reason, company has some range or certain amount to lend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Term feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.term = df.term.str.replace('months', '').astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36    341042\n",
       "60    116686\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.term.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('term', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### interest rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.int_rate = df.int_rate.str.replace('%', '').astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.int_rate.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mfKhIVW0E3tCz6ZvtzcR+wBfGOzbJcuC5wBOqaj2wPskpNN+EfHSm5X19o5IkqSs+BpyQ5Heq6qfttlXARyY6MEmA/YGHAxe2m/8HeAdwPfDnwD8m+VZP3b3eDewM7AksBz4z7XcxQ5s2baKqOPDAAwcVwpyazT+UNIo7b2PjxtsWzfWlyZuL/4vPf363voe+8847+dGPfsSdd9455j6bN2/myCOP5JJLLpnWOT74wQ/yvOc9jw0bNkw3zHll4KuKJFkGPAb4Ts/m1UluSPLtEb0hHgJsB1zUs+0i4JF9KpckSYP3traHwvDjnjOprKqup0kYHAaQ5P40yYh/HOewR7Q9J66nST4cWVVfaes7o6quq6o7q+oTwCXA40dWkGQJsBJYXVXr2x6kfz9erEnWJKnhx1TfqyQtRjfccENf6+tHj4LxkhbDppu0GDaoYRuDOPdAJ+dsv8U4Dfgh8Kl282uBi4FNwJOATya5parOAoaAjVV1R08164Ed2uczLR8txjXAG6f+7iRJ0jS9tqpO7nOdHwb+IcmbgOcDX6iqa8bZ/7tjrUiS5Cjgr2gm4Sya+4t7j7LrvWm+MLmiZ9sVo+x3t6paA6zpOVffkhfLly8H4Nxzz+1XlZ124IEHsmHz7RPvqP5Ysh33XLbtorm+NHkHHnjgrPe62HXXXfuavJhoUtDJWLJkyYTJi4c+9KEzSl70I86ZnHsuDazHRZu0+AeaXhAHV9VdAFX1jaq6qapur6rPA++n+bYCYAOwPElvwmUn4JY+lW+lqtZUVYYf03qzkiSpHzbQDLcYdt8pHPtFYAnwRzQ9Lz48nQCS7EeTWHg+sEtV7Qx8DxjtHuFXNKujPLBnm+tcS1KfffSj3Rr1v2TJEvbee29WrBh7Re5ly5Zx8skn87SnPW1a53jxi18MwNDQ0LSOn28GkrhokxbvpRki8tSqummc3e/qeX4pzQ3Ao3q2rQC+26dySZLUXRcChyRZlmQv4BWTPbD9gmQtcDJwL+CcacawI3AH8Etgm3ZltN8d45x3Ap8E3txO0nk/4NXTPK8kzVv3vOeMRvyN6/jjj2doaKhvPQCG65movuOPP37MsmXLlvHmN7+Zk08eu/PgiSeeyLJly3jta1875RiXLFnC8573PADOOWe6H2fTN4glUQfV4+I9wBOAp1TVjcMb2w/1pydZnmRJkicDLwX+GaCqNgHrgOOS7JRkb+AImuEmMy6XJEmdtppmostfAmcy9Ym1P0Izr9XHqmq6Ywg+R3Nf8l3gappJO/9jnP2PoOkpcgXwZcafV0OSFqyJ/thdtmzZVvtMdMwBBxzAfvvtN+n9JzLR+Q844IAtzjuy/IQTTuBe97oXxx57LLvtttuodSRh1apV7LPPPpM+78jXX/rSl8YtH1k2k/LR9h+EVM3tvE9JHghcDvya5huLYR8DjqX5BuRh7bbLgZOr6sM9x+9IM3zkIOBW4D1V9eZ+lU8i/prrNhukMy/42bjlh+7b7R6vE8U/kYne30zbZ763r6SFIwkLfUhku7rYdcDjq+o7E+3fBf287xhe7WGxzEEwPMfFhkf/xaBD6YuhCz8G0Nn3M3ThxxhyjguNYrH97tHkTeXeYxDLoV7B6ONAh+07wfE3A4fMVrkkSVp42mGqRwAXzZekhSRJagx0VRFJkqTZ1i5Lup5mssw/G3A4kiRpikxcSJKkBa2dJHPMpc8lSVK3DWw5VEmSJEmSpImYuJAkSZIkSZ1l4kKSJEmSJHWWc1xI45jpcqqSJEmSpJmxx4UkSZIkSeosExeSJEmSJKmzTFxIkiRJkqTOMnEhSZIkSZI6y8SFJEmSJEnqLFcVkSRJWgSGhoYGHYKkRcjfPeoHExfSIjbRcq+H7rvHHEUiSZpt69atG3QIkhYhf/eoH0xcSPOYiQdJkiRJC51zXEiSJEmSpM4ycSFJkiRJkjrLxIUkSZIkSeqsOU9cJNk+yQeT/DTJLUkuSfLCnvIdk5yZ5OYk1yY5dsTxs1ouSZIkSZK6YxCTcy4FfgEcAPwE2Bf4bJKrquoLwCnArsAewG7AeUmuqKqPtsfPdrkkSZIkSeqIOU9cVNVG4A09m76Z5CvAfkm+BjwXeEJVrQfWJzkFeBHw0STLZ7N8Dt6+FpmJVv2QJEmSJI1v4HNcJFkGPAb4DvAQYDvgop5dLgIe2T6f7XJJkiRJktQhU05cJDl3jO2fmUZdAU4Dfgh8ChgCNlbVHT27rQd2aJ/PdvloMa5JUsOPKb1BSZIkSZI0I9PpcfHEMbbvN5VK2qTFP9D0gji4qu4CNgDLk/QOYdkJuKV9PtvlW6mqNVWV4cdU3qMkSZIkSZqZSc9xkeRv2qfb9jwf9r+Aa6ZQV4D30gwReXJV3dQWXQrcDjwK+O922wrgu3NULkmSJEmSOmQqPS6e1T627Xn+LOCZwH2AVVOo6z3AE4CnVNWNwxurahOwDjguyU5J9gaOoBlOMuvlkiRJkiSpWybd46Kq/hggyTuq6v9O94RJHgi8HPg1cEXT+QKAj1XVy4DDgfcDVwG3Au8ZsVTpbJdLkiRJkqSOmPJyqMNJiyS7MGJSy6qacO3HqroCGHOuiKq6GThkUOWSJEmSJKk7ppy4SPKHwOnAHmyZgChgSZ/ikiRJkiRJmnrigmaYxWnAR4CN/Q1HkiRJ6o/ceRtDF35s0GH0x523AXT2/eTO22imwpOk/ptO4uL+wFurqvodjCRJktQPQ0NDgw6hrzZtuh2A5cu6mhzYdsG1uaTumE7i4lzgD4F/63MskiRJUl+sW7du0CFIkvpkOomLW4DPJDkXuKa3oKqO7ktUkiRJkiRJTC9xsS3wqfb5Ln2MRZIkSZIkaQvTWQ71BbMRiCRJkiRJ0kjTWQ51j7HKqupnMwtHkiRJkiTpN6YzVORyoIC0r3tXF1ky04AkSZIkSZKGTSdxMXJei/sBbwDOmXk4kiRJkiRJvzGdOS5uGrHppiQvAS4EzuhLVJIkSZIkScA2farnnsB9+lSXJEmSJEkSML3JOU8csWk58DTg7L5EJEmSJEmS1OrHHBcbgDcDH5t5OJIkSZIkSb8xnTkuXjAbgWh2nHnB+CvUHrrvmKvbSpIkSZI0cNPpcUGSxwGHAQ8ArgJOr6pv9DMwSZIkSZKkKU/OmeS5wBeAAP/ebv5ckkP6GZgkSZIkSdJ0elysBg6sqq8Ob0hyBvA+4OMTHZzkcGAV8Ajgs1V1cE/Z+cDjgNt7DnlwVV3dlu/Ynucg4FbgPVV1XM/xMypX9zjURZIkSZIWt+kkLu4P/MeIbV8H7jfJ468GjgcOoBlqMtLfVtXJYxx7CrArsAewG3Bekiuq6qN9KpckSZIkSR0yncTF94GXAqf2bHsxcPFkDq6qTwEkWcHoiYtRJVkOPBd4QlWtB9YnOQV4EfDRmZZPNg5JkiRprq1cuZINGzYMOoxRbdq0CYDly5cPOJL+GBoaYt26dYMOQ1KP6SQujgQ+m+QVwOXAnjS9F/60TzGtTvIG4ArgpJ7eEA8BtgMu6tn3IuB1fSqXNIJDdSRJ6oYNGzawceNGli+9a9ChbKUqzc9f3zLgSGZu0x1TngJQ0hyYdOIiyVLgvlX1rSQPAg6k6TFxBvA1miEgM/Vamp4bm4AnAZ9McktVnQUMARur6o6e/dcDO7TPZ1o+qiRrgDdO7+1IMzNR4kCSJC0ey5fexfv+8MZBh7GVl311F4BOxjZVw+9FUrdMpcfFUcDDgBdW1U3AmcMFSU4DfgC8cybBjFhS9fNJ3g+sBM4CNgDLkyztST7sBAyndmdaPlZMa4A1w6+T1HTe22LlN/aSJEmSpJmYSl+oQ4ETxih7O/CXMw9nK7194S6lWW3kUT3bVgDf7VO5JEmSJEnqmKkkLh5YVZeNVtBun9RX50mWJllG09tjmyTLkmyXZOckT0+yPMmSJE+mmQT0n9tzbALWAccl2SnJ3sARwGn9KJckSZIkSd0zpdlnkuw6le1jWA3cCrweeEb7/AvAtjRzSVwD3AicBLyqqv6p59jDgZuAq2iWZP3QiKVMZ1ouSZIkSZI6ZCpzXHwTeB5wyihlhwAXTKaSkXNGjLDvBMfe3J5rVsolSZIkSVK3TCVx8VbgX5MMAZ8Afg7cH3gucAz9Ww5VkgAnd5UkSZI0hcRFVX0tyaHAu4Dje4ouBw6tqq/3OTZJkiRJkrTITaXHBVV1DnBOO7HlfYBfjTVhpyTZY0KSJEnSTE0pcTGsqn4I/LDPsUiSJEmSJG1hSquKSJIkSZIkzSUTF5IkSZIkqbNMXEiSJEmSpM4ycSFJkiRJkjrLxIUkSZIkSeosExeSJEmSJKmzTFxIkiQtAitXrmTlypWDDkOS5hV/d3bD0kEHIEmSpNm3YcOGQYcgSfOOvzu7wcSFpGk784KfDToESZIkSQucQ0UkSZIkSVJnmbiQJEmSJEmdZeJCkiRJkiR1lokLSZIkSZLUWXOeuEhyeJJvJfl1krNHlO2Y5MwkNye5Nsmxc1kuSZIkSZK6ZRCrilwNHA8cADxgRNkpwK7AHsBuwHlJrqiqj85RuSRJkiRJ6pA573FRVZ+qqrOBX/VuT7IceC6wuqrWV9VlNImGF81FuSRJkiRJ6p5B9LgYy0OA7YCLerZdBLxujso76cwLfjZu+aH77jFHkUiSJEmSNPe6NDnnELCxqu7o2bYe2GGOykeVZE2SGn5M6R1JkiRJkqQZ6VKPiw3A8iRLe5ILOwG3zFH5qKpqDbBm+PVCS15M1KNDkiRJkqRB6lKPi0uB24FH9WxbAXx3jsolSZIkSVLHDGI51KVJltH09tgmybIk21XVJmAdcFySnZLsDRwBnAYw2+WSJGn2JdmzHX6586BjGUuSndsY95xhPcuSnJVkfZL/bLc9I8nlSTYkObgf8UqStNANYqjIauCNPa9vBf4N2B84HHg/cFW7/T0jliqd7XJJkjRDSfYDXg88FghwBXAGcPIg4+qnJA8C3gY8GdgeuAR4e1Wt69ntz2gmB/+tqvp1u+1E4A3ef0jS/LN27VpOP/10DjvsMFatWgXA/vvvf3f57rvvzjXXXMPuu+/OJz7xibu39+6TZIvjR9vn/PPPn/D1SL3lu+yyCzfeeCO77LILZ5111lble+21Fz/5yU/Ya6+9+PCHP7xV+XhGO/dcGMRyqGuqKiMe+7dlN1fVIVW1Q1XtVlVvHnHsrJZLkqSZSXIQ8Fng88DeVbUzsBLYB7jvLJxvzr+ESfJA4Js0X4Q8DLgXcBzw3iSv6Nn1d4DLepIWw9u+M1exSpL64+KLL2bt2rVUFWvXruXiiy/e6o/9a6655u6fH/jAB4CtEwK9xw8buc9MX9944413/zzppJO2Kv/JT35y988zzzxz0kmL0c41V7o0x4Wm4cwLfjbuQ5KkuZIkwLuBv6uqk6vqVwBVdUlVraqqK3p2f0aSH7XDKNYm2batYyjJp5Ncl+SmJF9N8qiec6xJck6Sf0hyA/B37fYjklyZ5Pokxye5KMmqnuMOSPKf7fm+n+T/6ynbfri+JD8F/s8Eb/VNwLer6qiquraqNlfV2cCRwNuS7JDkncAbgIPaYSGnJNkALAG+3m7bfrptLUmaO3fddRdHH330Ftte/vKXj3vMRAmBo48+ms2bN09Yz3jHA5x00knj7vfpT3963PLhBMtUvOtd75ryMTPVpVVFJEnS/LY3TY+Cj09i3wOBR9MsV/6fwPOAtTRfqpwJHArcSZOY+GSSh1bV8MpefwL8Fc1cVdsleTLwZuBpwEU0w1IfPnyiJI8E/olm6Mb5wOOBc5M8pqoupRnW8jjgd4FN7fnH87T2mJHWAacDj6uqVyW5BVhRVcNzWRzRrk72+Kq6aKIG6rdNmzZRVRx44IFzfWr1wcaNGwkZdBgL3q13hLpjo/9PdLeNGzfOSr2bN2/myCOP5JJLLpnW8RdeeCEwcWJiNpx11lm88pWvnNNzmrjQQNkrRJIWlPu0P38+iX3XVNXNwM1JPgv8PrC23Xb3PBFJ3gj8DXC/nnq/V1Vr2+d3JDkUOKOqhifAPK49ZthL27q/3L7+WpJzgOfQDPF4HvDaqrq6Pf5NwJ+OE/u9gatHbqyq25P8it+0w5QlWcOWc4FJkhao6SaqwA/dAAAR7ElEQVQthg1q2MbwuedyvgsTF5IkqV9+1f68P/DjCfa9puf5RmBngCT3AN4JPB3YFbir3efe/CZxMTLrfT+anhTA3QmEX/SU7wk8KckLerYtBW7uOb53GEvv89H8qj1mC+1wl3sDv5zg+DFV1RpgTU+dNebOU7R8+XIAzj333H5VqTl04IEHUr++ZdBhLHj3WFpk+yH/n+huBx544N091vrtoQ996IySFyMn7ZxLcz1Jp4kLSQvWRD16Dt13jzmKRFo0LgMuB54LvGWadbyKpvfFflV1Vbts6o2wRR/5u0YcczXw28Mv2gk7eycCvRJ4V1UdM8Y5rwYeCFzQvp7ol8MXgUOAD4/YvpJmqMk3JzhekjSPLFu2jKpi8+bNfa3z5JNP5uijj95ios7JevSjHw3AM5/5zDkfLvKsZz1rTs8HTs4pSZL6pJ2D4gjgmHayzHsBJHlwkg+1q3FMZEdgM3BjkiHgrZM45uPAoUn+oO31sBq4Z0/5+4EXJPnjJEvayTgfl+RhPccfk+R+baLkDROc743Ao5OclGS3JMuSHEyz3OvqdriLJGmB2GabbTjxxBO32HbqqaeOe8yhhx46bq+EE088kWXLlk1Yz3jHAxx11FHj7vfMZz5z3PKXvOQlUz73XM9vASYuJElSH1XVOTTzQxwI/DjJeuD/By4BfjHesa0TaSblvBb4HvCNSZzzPJqVPs6mGYKylKb3x6/b8m/T9JA4nmYYx89p5rYYXtXjeOBb7fkuausZ73w/pZnMcw/gUuB6mmTH31TV3E+1Lkmadfvssw+rVq0iCatWrWKfffbZKjGx++673/1zOCEwcp/e44eN3Gemr3fZZZe7fx511FFble+11153/5wowTLSXA8RGeZQEUmS1FdV9TWalT9GczlbDvugqo7seX4N8KQRx/xjT/maMc75LuBdAEm2A15JM0RkuPzLwJfHOHYz8JL2Mey0MeIfPuYymlVKxttnq1irymUhJGmeWrVqFatWrdpi22T+kJ/OPhO9nuo5Zlo+aCYuJA2Mq8pI6pckzwY+S9Ob9HjgBpplViVJ0jznUBFJkrQQ/CXNUJSraSb3fGZV3TbYkCRJUj/Y40KSJM17VTX3U5xLkqQ5YeJC0rw106EmLpcqSZIkdZ9DRSRJkiRJUmeZuJAkSZIkSZ1l4kKSJEmSJHWWc1xoRlzOUpIkSZI0m+xxIUmSJEmSOqtziYska5PclmRDz+NxPeXbJnlPkhvaxylJlvarXJIkSZIkdUfnEhetU6tqqOfxjZ6y1cB+wMPbxxOB1/WxXJIkSZIkdcR87GnwQuCoqvoFQJK3AO8A3tyncs0jzrEhSZIkSQtbV3tcPL8dxvH9JK9Ksg1Akl2ABwAX9ex7EbBHkp1mWj6bb0iSJGmQhoaGGBoaGnQYkjSv+LuzG7rY4+LdwKuBG4D/DXwSuAs4CRi+Ytb37D/8fAcgMyy/aWQwSdYAb5zie5g0ewxIkqS5sG7dukGHIEnzjr87u6FzPS6q6sKq+mVV3VlV3wROAFa2xRvan729I4af39KH8tHiWVNVGX5M7d1IkiRJkqSZ6GKPi5HuGn5SVTcmuQpYAfy43bwCuLKqbgKYabkkTdZEPaYO3XePOYpEkiRJWrg61+MiyXOS7JjGHwDHAP/cs8tHgNcn2T3J7jQrgpzWx3JJkiRJktQRXexxcTjwAZrYfg6cCryzp/w44F7AD9rXZwBv7WO5JEmSJEnqiM4lLqrqDycovx14Rfvoe7kkSZIkSeqOzg0VkSRJkiRJGta5HheS+sfldrvNyT0lSZKkidnjQpIkSZIkdZaJC0mSJEmS1FkmLiRJkiRJUmc5x4UkLVLOsSFJkqT5wMSFJGlWmBiRJElSPzhURJIkSZIkdZaJC0mSJEmS1FkOFZGkMUw01GHQHIohSZKkxcDEhSRpWrqe2JGkhWbTHdvwsq/uMugwtrLpjgB0Mrap2nTHNtxz+0FHIWkkExeSpFGZmJCk7hgaGhp0CGPKnZuan9svH3AkM3fP7bvd1tJiZeJCkhYoEw+StHCsW7du0CFI0sCYuJCkWWLiYHzO0SFJkqTJMHEhSR1l4kOSJElyOVRJkiRJktRhi7LHRZJtgZOAQ9tNZwBHVdUdg4tKkqTfcCiNJElSY7H2uFgN7Ac8vH08EXjdQCOSJEmSJElbWZQ9LoAX0vSw+AVAkrcA7wDePNCoJEmdYY8HSZKkblh0iYskuwAPAC7q2XwRsEeSnarqpsFEJknqNdPJSQedWHByVUmSpP5IVQ06hjmV5LeBnwH3qapftdvuA1wH/HZVXTVi/zXAG+c6TkmS5lpVZdAxaEtJFteNmiRpUZnsvcdiTFzsAtwAPKiqftxuexDwQ2Dn2exxkaS8KZw62216bLfpsd2mx3abHttNs8nra3pst+mx3abHdpse22165nO7LbrJOavqRuAqYEXP5hXAlQ4TkSRJkiSpWxZd4qL1EeD1SXZPsjvNiiKnDTgmSZIkSZI0wqKbnLN1HHAv4Aft6zOAt87Bed80B+dYiGy36bHdpsd2mx7bbXpsN80mr6/psd2mx3abHtttemy36Zm37bbo5riQJEmSJEnzx2IdKiJJkiRJkuYBExeSJEmSJKmzTFxIkiRJkqTOMnEhSZIkSZI6y8TFLEuyNsltSTb0PB436Li6JsnhSb6V5NdJzh5RtmOSM5PcnOTaJMcOKs6umaDdzm+391579xtUrF2SZPskH0zy0yS3JLkkyQt7yr3mRjGJdvOaG0OSU5Jc2V5TP09ycpLt2jKvN0mSpHGYuJgbp1bVUM/jG4MOqIOuBo4HPjhK2SnArsAewBOBFyd5/hzG1mXjtRvA34649q6ew9i6bCnwC+AAYEdgFfDOJE9ty73mRjdRu4HX3FhOBR5aVTsCK4BHAa9py7zeJEmSxmHiQp1QVZ+qqrOBX/VuT7IceC6wuqrWV9VlNDf5LxpAmJ0zVrtpfFW1sareUFU/rsY3ga8A+3nNjW28dht0bF1XVT+oqo09m+4C9vZ6U78k2XfE61cm+Xr7OHpQcUn6jSR/keQrSa5ve2Rf3/ZWfN6gY+uqJLuPeP3MtpfimUmePai45oOFdr2ZuJgbz09yQ5LvJ3lVEtt98h4CbAdc1LPtIuCRgwln3lndXnvf9hvcsSVZBjwG+A5ec5M2ot2Gec2NIckxSW4BrqPpcXEKXm/qny8OP2kTFUcD/wh8FDgiyasGFVjXLbSb+7ngH5NTl2Q18BbgXGAl8HjgOcA5wFuSvH6A4XXZZcNPkvwlcDpwJXAFcFqSwwYVWJctxOtt6aADWATeDbwauAH438Anab5pO2mQQc0jQ8DGqrqjZ9t6YIcBxTOfvBa4GNgEPAn4ZJJbquqswYbVLUkCnAb8EPgU8AS85iY0SruB19y4quoE4IQkDwOeB1wD/A5eb+qP9Dx/AfCcqroAIMm3aJIY7xxEYF3W3ty/mCaR+Baa/387Ab9Hc3O/Z1W9ZYAhdtVlNEMGh/+YPAV4P8097mlJdqiq0wcYXxe9AvjDqvrhiO1fSvJp4N9prkFtqfd325E0v9u+AJDkPJrfa15rW1tw15uJi1lWVRf2vPxmkhOA52PiYrI2AMuTLO25sd8JuGWAMc0LI+ZS+XyS99NkXP0jstX+8f0PNN96H1BVdyXxmpvAaO0GXnOTVVU/SPI/wFqaxLbXm/qhep7fF/jPuwuqvpXk/nMf0ryw4G7u54h/TE7dcpqE9Wiubcu1td7fbQ8EvtTz+ivAnnMazfyx4K43hyzMvbsGHcA8cylwO0236mErgO8OJpx5zWuvR/vH93tphjo8tapuaou85sYxTruNxmtubNsCe+P1pv7ZPsmJSU5sX+86XJBkB+CO0Q9b9Bbczf0c8Y/JqTsXWJfk94aHjSfZJsnvAWfSdOHX1rZLckSSv6G5r+j9P7n9gGKaDxbc9WbiYpYleU671F2S/AFwDPDPg46ra5IsbcfLLwW2SbIsyXZVtQlYBxyXZKckewNH0HRRX/TGarckOyd5epLlSZYkeTLwUrz2er2HZljIU6rqxuGNXnMTGrXdvObGlmQoyQvaNkqSRwCrgc97vamPzgR2aR//Avx2T9mfYDJsLAvu5n6O+Mfk1L0U+CXwDeD2JDcDtwFfp5lk/aUDjK3Lvgk8G3gW8H1gn56y/YFLBhDTfDDW9fYN4Hrm4fWWqpp4L01bkq/STLK2FPg58CHgHcNdq9VIsgZ444jN/1ZV+yfZkWbc5EHArcB7qurNcxxiJ43VbsCf09xsPazddjlwclV9eM6C67AkD6Rpk1+z5beQH6uql3nNjW68dgOOxWtuVEnuCZwNPJrmhv46moTOG6tqk9ebNDhtb5T30Axr2xbYSPNH+O00ScUjqsqhWyMkOZ8te10c0zOnyp/S/H577CBi67ok96AZajlEMyT60qq6dbBRzU9JdgK2rSpX1xvDQrreTFxIkiRpUVtIN/eD1v4xubSqrh90LF2T5ACaJcT/Z+TE1UlOraqXDyaybrPdpi7JUuA1NMNS303zpcla4EHA54CjqmrzwAKcBhMXkiRJ0ghJtqUZ0vWkQccyn9huo0vyAppJS79CM0/UpcCzhnv0JLm5qnYcYIidZLtNTzvf0QqaoVyPBN5Hs3z9dsDrgX+pqmMGF+HUmbiQJEmSRkiyPbCpqpYMOpb5xHYbXZKLgRdU1QXt/GSn0Xwb/pSqurldPtylsEew3aYnyZU0iYttaCYaflhVXdqWPQI4q6oeNMAQp8zEhSRJkhalJJ8ap3gb4Bn+Ab41223qktxUVTuN2PY+4A+ApwBX2HNga7bb9PS222i9UuZjT5Wlgw5AkiRJGpCnAx+gmWV/pG2BZ8xtOPOG7TZ11yfZs6ouH97QTgh+KvBl/LtsLLbb9NyU5B7tXD1v6S1o56H59WDCmj57XEiSJGlRSvJfwHFV9ZlRypbRDHnYZu4j6zbbbeqSfAC4uqrWjFL2XuCvbbOt2W7Tk+TdwHuHh4eMKFsFHFpVT53zwGbAxIUkSZIWpSSvAH5eVWePUrYEWF1Vb5r7yLrNdpu6JNvRrLayaYzyParqZ3McVufZbv3XLtNeY7VpV5m4kCRJkiRJnWW3GkmSJEmS1FkmLiTNSJLvJzlo0HFIkiRJWpgcKiJpTiRZA6yoqoPnY/2SJEmSBsMeF5I6L4lLXUmSJEmLlIkLSTOS5PIkBydZleSiJMcmuS7JtUmObPc5GHgdcFCSDUk2TFDncF1vSnINsC7JUJJPt3XflOSrSR41Xv1p/E2SS5KsT3J+kofNaoNIkqRZ4xBVaXEycSGpnx4ObAbuD6wE3pHkf7XLpb0VOKeqhqpqaBJ1/S5wB7AH8Jc0v6/OBH4H+C3g28Ank2Sc+v8aeBHwDODewKeAf2mX1pIkSfNMVT28qs6ZaL8ka5JstVxrv8x2/ZK2ZOJCUj9dX1V/X1W3V9X5wE+BFdOs6ybgLVV1W1Vtqqqbq2pdVW2sqs3AG4EHA/cbp45XAG+oqh9W1R1V9W7gHsC+04xJkiQtcA5RlbrHxIWkfrpmxOuNwA7TrOvnVXXX8Isk90hyajs05Wbg8rbo3uPUsSfwsXaYyPok64FdgAdMMyZJkjRADlGVFicTF5Lmyl0T7zLu/q8Cfh/Yr6p2pElKAGSc+q8E/ryqdu55LK+qj08xFkmS1D0OUZUWCRMXkubKtcADkyyZ5vE70tyc3JhkiOaGYaL63wu8OclDAJLsmOSZSabbC0SSJHWHQ1SlRcLEhaS58k/AzcCv2iEbU3UicCdNguJ7wDcmUf97gLXAp9rhJT8ADp3GuSVJUvc4RFVaJJx4RtKMVNWePS/Xjihb0fP8BuCPJlnn2lHqugZ40ohd/3G8+quqgFPbhyRJWjz6OUT1qiQ7Azcy8RDVI6vqc1M8t6QJ2ONCkiRJ0kLjEFVpATFxIWkgknx/eCbuEY/3DTo2SZI07zlEVVpA0vSkliRJkiRJ6h57XEiSJEmSpM4ycSFJkiRpQXOIqjS/OVREkiRJkiR1lj0uJEmSJElSZ5m4kCRJkiRJnWXiQpIkSZIkdZaJC0mSJEmS1FkmLiRJkiRJUmeZuJAkSZIkSZ1l4kKSJEmSJHXW/wOGpauRO4AGfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('int_rate', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like applicants who could not afford to pay back or were charged off had higher interest rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### grade and subgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    129972\n",
       "B    123078\n",
       "A     75560\n",
       "D     74329\n",
       "E     38835\n",
       "F     12509\n",
       "G      3445\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1    27783\n",
       "C2    27383\n",
       "B5    26522\n",
       "C3    26375\n",
       "B4    26218\n",
       "C4    25615\n",
       "B3    25569\n",
       "B2    22889\n",
       "C5    22816\n",
       "A5    22738\n",
       "B1    21880\n",
       "D1    19249\n",
       "A4    17546\n",
       "D2    16052\n",
       "D3    14561\n",
       "D4    13405\n",
       "A3    11973\n",
       "A2    11711\n",
       "A1    11592\n",
       "D5    11062\n",
       "E1    10364\n",
       "E2     9177\n",
       "E3     7538\n",
       "E4     6260\n",
       "E5     5496\n",
       "F1     3990\n",
       "F2     2804\n",
       "F3     2449\n",
       "F4     1883\n",
       "F5     1383\n",
       "G1     1121\n",
       "G2      864\n",
       "G3      603\n",
       "G4      461\n",
       "G5      396\n",
       "Name: sub_grade, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub_grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('grade', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('sub_grade', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that grade and sub grade have same shape and relation with loan status. IN this case I would keep sub_grade, because it carries more information than the grade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### emp_title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128310"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.emp_title.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like emp_title has lots of unique value, which may not be strongly associated with predicted loan amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['emp_title', 'id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11-20 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</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>url</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>111.97</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Code/Compliance Inspector</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>other</td>\n",
       "      <td>Other</td>\n",
       "      <td>467xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1037.38</td>\n",
       "      <td>E</td>\n",
       "      <td>E4</td>\n",
       "      <td>Human Resource Specialist LER</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>Home improvement</td>\n",
       "      <td>224xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>528.73</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>Air Traffic Controller</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>840xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>329.72</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>General manager</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>087xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>173.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Lead Supervisor</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>Mar-2017</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>945xx</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    installment grade sub_grade                      emp_title emp_length  \\\n",
       "16       111.97     A        A4      Code/Compliance Inspector  10+ years   \n",
       "38      1037.38     E        E4  Human Resource Specialist LER  10+ years   \n",
       "40       528.73     A        A4         Air Traffic Controller  10+ years   \n",
       "51       329.72     B        B5                General manager  10+ years   \n",
       "56       173.31     C        C4                Lead Supervisor  10+ years   \n",
       "\n",
       "   home_ownership  annual_inc verification_status   issue_d loan_status  \\\n",
       "16       MORTGAGE    120000.0        Not Verified  Mar-2017  Fully Paid   \n",
       "38       MORTGAGE    114000.0     Source Verified  Mar-2017  Fully Paid   \n",
       "40       MORTGAGE    120000.0        Not Verified  Mar-2017  Fully Paid   \n",
       "51            OWN    145000.0        Not Verified  Mar-2017  Fully Paid   \n",
       "56           RENT     68000.0        Not Verified  Mar-2017  Fully Paid   \n",
       "\n",
       "                                                  url             purpose  \\\n",
       "16  https://lendingclub.com/browse/loanDetail.acti...               other   \n",
       "38  https://lendingclub.com/browse/loanDetail.acti...    home_improvement   \n",
       "40  https://lendingclub.com/browse/loanDetail.acti...  debt_consolidation   \n",
       "51  https://lendingclub.com/browse/loanDetail.acti...  debt_consolidation   \n",
       "56  https://lendingclub.com/browse/loanDetail.acti...  debt_consolidation   \n",
       "\n",
       "                 title zip_code  \n",
       "16               Other    467xx  \n",
       "38    Home improvement    224xx  \n",
       "40  Debt consolidation    840xx  \n",
       "51  Debt consolidation    087xx  \n",
       "56  Debt consolidation    945xx  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 6: 20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### emp_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10+ years    151369\n",
       "2 years       41008\n",
       "< 1 year      36781\n",
       "3 years       36372\n",
       "1 year        29681\n",
       "4 years       26812\n",
       "5 years       26720\n",
       "8 years       22753\n",
       "7 years       22333\n",
       "6 years       20774\n",
       "9 years       18240\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.emp_length.fillna(value=0,inplace=True)\n",
    "df['emp_length'].replace(to_replace='[^0-9]+', value='', inplace=True, regex=True)\n",
    "df['emp_length'] = df['emp_length'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('emp_length', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like emp lenght is not good predictor to determine the loan status. Sicne number of loanees remain same with the employment length."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### home_ownership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    228469\n",
       "RENT        181308\n",
       "OWN          47875\n",
       "ANY             75\n",
       "NONE             1\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.home_ownership.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('home_ownership', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "home_ownership is also not that much discreminatory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### verification_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source Verified    187996\n",
       "Not Verified       135931\n",
       "Verified           133801\n",
       "Name: verification_status, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.verification_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.verification_status = df.verification_status.map(lambda x: 1 if x == 'Not Verified' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('verification_status', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verification_status is somewhat discreminative in the sense that, among the loanes whose source was verified are more charged off which is a bit wired."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### issue_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Oct-2014    33699\n",
       "Jul-2014    25974\n",
       "Jul-2015    23990\n",
       "Jan-2015    22750\n",
       "Oct-2015    22431\n",
       "Nov-2014    21186\n",
       "Apr-2015    20097\n",
       "Dec-2015    19106\n",
       "May-2015    17964\n",
       "Aug-2015    17492\n",
       "Apr-2014    17140\n",
       "May-2014    17133\n",
       "Aug-2014    16780\n",
       "Nov-2015    16540\n",
       "Jun-2015    15304\n",
       "Jun-2014    15207\n",
       "Mar-2015    14997\n",
       "Mar-2014    14988\n",
       "Feb-2015    14464\n",
       "Jan-2014    14464\n",
       "Feb-2014    13945\n",
       "Sep-2015    13838\n",
       "Sep-2014     9344\n",
       "Dec-2014     8283\n",
       "Jan-2017     5912\n",
       "Mar-2017     5008\n",
       "Feb-2017     4241\n",
       "May-2017     3644\n",
       "Apr-2017     3431\n",
       "Jun-2017     2849\n",
       "Jul-2017     2366\n",
       "Aug-2017     1863\n",
       "Sep-2017     1298\n",
       "Name: issue_d, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.issue_d.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['issue_month'] = pd.Series(df.issue_d).str.replace(r'-\\d+', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('issue_month', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like people who borrowed in December, are more charged off than those who borrowed in other months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.issue_month = df.issue_month.astype(\"category\", categories=np.unique(df.issue_month)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    56130\n",
       "5     52330\n",
       "4     43126\n",
       "0     40668\n",
       "8     38741\n",
       "9     37726\n",
       "1     36135\n",
       "7     34993\n",
       "6     33360\n",
       "3     32650\n",
       "2     27389\n",
       "11    24480\n",
       "Name: issue_month, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.issue_month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['issue_year'] = pd.Series(df.issue_d).str.replace(r'\\w+-', '').astype(np.int) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    218973\n",
       "2014    208143\n",
       "2017     30612\n",
       "Name: issue_year, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.issue_year.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     358629\n",
       "Charged Off     99099\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loan_status = df.loan_status.map(lambda x: 1 if x == 'Charged Off' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.append('url')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    276597\n",
       "credit_card           102367\n",
       "home_improvement       27628\n",
       "other                  22100\n",
       "major_purchase          8256\n",
       "medical                 4786\n",
       "small_business          4206\n",
       "car                     3963\n",
       "moving                  2954\n",
       "vacation                2755\n",
       "house                   1836\n",
       "renewable_energy         268\n",
       "wedding                   11\n",
       "educational                1\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('purpose', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like, purpose can be a good discrimnatory. For exmaple people who had a purpose for renewable energy are more charged off while people borrwed loan for car or educational purpose are less charged off."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1935"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.title.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.append('title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### zip_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "910"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.zip_code.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.append('zip_code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### addr_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    67209\n",
       "TX    37406\n",
       "NY    36650\n",
       "FL    31741\n",
       "IL    17478\n",
       "NJ    16260\n",
       "PA    15857\n",
       "OH    14976\n",
       "GA    14691\n",
       "VA    13204\n",
       "NC    12875\n",
       "MI    12183\n",
       "AZ    11150\n",
       "MD    10692\n",
       "CO    10559\n",
       "MA    10188\n",
       "WA    10153\n",
       "MN     8392\n",
       "IN     7955\n",
       "TN     7405\n",
       "MO     7170\n",
       "NV     6881\n",
       "CT     6246\n",
       "WI     6011\n",
       "AL     5818\n",
       "OR     5805\n",
       "SC     5358\n",
       "LA     5336\n",
       "KY     4423\n",
       "OK     4122\n",
       "KS     3778\n",
       "UT     3603\n",
       "AR     3408\n",
       "NM     2671\n",
       "MS     2384\n",
       "HI     2333\n",
       "NH     2141\n",
       "RI     1983\n",
       "WV     1974\n",
       "MT     1319\n",
       "DE     1292\n",
       "DC     1204\n",
       "AK     1125\n",
       "WY     1038\n",
       "SD      943\n",
       "VT      941\n",
       "NE      732\n",
       "ME      291\n",
       "ND      280\n",
       "ID       93\n",
       "IA        1\n",
       "Name: addr_state, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.addr_state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature('addr_state', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "addr_state can be a good discreminatory feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### dti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_feature('dti', True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21 - 30 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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_status</th>\n",
       "      <th>url</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>fico_range_low</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>other</td>\n",
       "      <td>Other</td>\n",
       "      <td>467xx</td>\n",
       "      <td>IN</td>\n",
       "      <td>18.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-1992</td>\n",
       "      <td>720.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>Home improvement</td>\n",
       "      <td>224xx</td>\n",
       "      <td>VA</td>\n",
       "      <td>14.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-1998</td>\n",
       "      <td>660.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>840xx</td>\n",
       "      <td>UT</td>\n",
       "      <td>8.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Oct-2005</td>\n",
       "      <td>735.0</td>\n",
       "      <td>739.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>087xx</td>\n",
       "      <td>NJ</td>\n",
       "      <td>13.31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sep-2005</td>\n",
       "      <td>705.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0</td>\n",
       "      <td>https://lendingclub.com/browse/loanDetail.acti...</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>945xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Apr-2003</td>\n",
       "      <td>660.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_status                                                url  \\\n",
       "16            0  https://lendingclub.com/browse/loanDetail.acti...   \n",
       "38            0  https://lendingclub.com/browse/loanDetail.acti...   \n",
       "40            0  https://lendingclub.com/browse/loanDetail.acti...   \n",
       "51            0  https://lendingclub.com/browse/loanDetail.acti...   \n",
       "56            0  https://lendingclub.com/browse/loanDetail.acti...   \n",
       "\n",
       "               purpose               title zip_code addr_state    dti  \\\n",
       "16               other               Other    467xx         IN  18.90   \n",
       "38    home_improvement    Home improvement    224xx         VA  14.04   \n",
       "40  debt_consolidation  Debt consolidation    840xx         UT   8.41   \n",
       "51  debt_consolidation  Debt consolidation    087xx         NJ  13.31   \n",
       "56  debt_consolidation  Debt consolidation    945xx         CA  22.50   \n",
       "\n",
       "    delinq_2yrs earliest_cr_line  fico_range_low  fico_range_high  \\\n",
       "16          0.0         Aug-1992           720.0            724.0   \n",
       "38          0.0         Nov-1998           660.0            664.0   \n",
       "40          0.0         Oct-2005           735.0            739.0   \n",
       "51          2.0         Sep-2005           705.0            709.0   \n",
       "56          0.0         Apr-2003           660.0            664.0   \n",
       "\n",
       "    inq_last_6mths  mths_since_last_delinq  mths_since_last_record  open_acc  \n",
       "16             1.0                     NaN                    96.0      18.0  \n",
       "38             1.0                    30.0                     NaN      17.0  \n",
       "40             0.0                    72.0                    89.0       9.0  \n",
       "51             0.0                     8.0                     NaN      13.0  \n",
       "56             0.0                    27.0                     NaN       6.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 15: 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### earliest_cr_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['earliest_cr_year'] = df.earliest_cr_line.str.replace(r'\\w+-', '').astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['credit_history'] = np.absolute(df['issue_year']- df['earliest_cr_year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    32959\n",
       "14    32690\n",
       "12    31406\n",
       "15    29670\n",
       "11    28940\n",
       "16    26000\n",
       "10    23389\n",
       "17    21970\n",
       "18    19927\n",
       "19    19155\n",
       "20    17403\n",
       "9     17389\n",
       "21    15139\n",
       "8     15041\n",
       "22    11740\n",
       "7     11408\n",
       "23     9530\n",
       "24     9333\n",
       "25     8890\n",
       "26     7844\n",
       "6      7647\n",
       "27     6844\n",
       "28     6125\n",
       "5      5866\n",
       "4      5559\n",
       "29     5200\n",
       "30     4842\n",
       "31     4073\n",
       "32     3320\n",
       "33     2380\n",
       "3      2078\n",
       "34     1957\n",
       "35     1799\n",
       "36     1684\n",
       "37     1535\n",
       "38     1302\n",
       "39      988\n",
       "40      785\n",
       "41      682\n",
       "42      632\n",
       "43      497\n",
       "44      389\n",
       "45      363\n",
       "47      278\n",
       "46      272\n",
       "48      204\n",
       "49      158\n",
       "50      130\n",
       "51       85\n",
       "52       60\n",
       "53       41\n",
       "54       32\n",
       "56       27\n",
       "55       26\n",
       "59       10\n",
       "57        9\n",
       "58        8\n",
       "60        6\n",
       "64        4\n",
       "61        2\n",
       "65        2\n",
       "66        1\n",
       "63        1\n",
       "62        1\n",
       "70        1\n",
       "Name: credit_history, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.credit_history.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['issue_d', 'mths_since_last_delinq', 'mths_since_last_record', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 31 - 40 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>724.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5658.0</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>33.0</td>\n",
       "      <td>w</td>\n",
       "      <td>3765.098751</td>\n",
       "      <td>3765.10</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>165.10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>664.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35694.0</td>\n",
       "      <td>74.8%</td>\n",
       "      <td>31.0</td>\n",
       "      <td>w</td>\n",
       "      <td>36875.078917</td>\n",
       "      <td>36875.08</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>1875.08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>739.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15406.0</td>\n",
       "      <td>31.6%</td>\n",
       "      <td>34.0</td>\n",
       "      <td>w</td>\n",
       "      <td>17282.802792</td>\n",
       "      <td>17282.80</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>282.80</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>709.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>31.9%</td>\n",
       "      <td>30.0</td>\n",
       "      <td>w</td>\n",
       "      <td>10365.368358</td>\n",
       "      <td>10365.37</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>365.37</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>664.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10276.0</td>\n",
       "      <td>90.1%</td>\n",
       "      <td>18.0</td>\n",
       "      <td>f</td>\n",
       "      <td>5168.906377</td>\n",
       "      <td>5168.91</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>168.91</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fico_range_high  inq_last_6mths  mths_since_last_delinq  \\\n",
       "16            724.0             1.0                     NaN   \n",
       "38            664.0             1.0                    30.0   \n",
       "40            739.0             0.0                    72.0   \n",
       "51            709.0             0.0                     8.0   \n",
       "56            664.0             0.0                    27.0   \n",
       "\n",
       "    mths_since_last_record  open_acc  pub_rec  revol_bal revol_util  \\\n",
       "16                    96.0      18.0      1.0     5658.0      14.9%   \n",
       "38                     NaN      17.0      0.0    35694.0      74.8%   \n",
       "40                    89.0       9.0      1.0    15406.0      31.6%   \n",
       "51                     NaN      13.0      0.0     7936.0      31.9%   \n",
       "56                     NaN       6.0      0.0    10276.0      90.1%   \n",
       "\n",
       "    total_acc initial_list_status   total_pymnt  total_pymnt_inv  \\\n",
       "16       33.0                   w   3765.098751          3765.10   \n",
       "38       31.0                   w  36875.078917         36875.08   \n",
       "40       34.0                   w  17282.802792         17282.80   \n",
       "51       30.0                   w  10365.368358         10365.37   \n",
       "56       18.0                   f   5168.906377          5168.91   \n",
       "\n",
       "    total_rec_prncp  total_rec_int  total_rec_late_fee  \n",
       "16           3600.0         165.10                 0.0  \n",
       "38          35000.0        1875.08                 0.0  \n",
       "40          17000.0         282.80                 0.0  \n",
       "51          10000.0         365.37                 0.0  \n",
       "56           5000.0         168.91                 0.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 25: 40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.revol_util = df.revol_util.str.replace('%', '').astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "w    259204\n",
       "f    198524\n",
       "Name: initial_list_status, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.initial_list_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.initial_list_status = df.initial_list_status.map(lambda x: 1 if x== 'w' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 41 - 50 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>last_fico_range_high</th>\n",
       "      <th>last_fico_range_low</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>application_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3765.098751</td>\n",
       "      <td>3765.10</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>165.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>2984.31</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>724.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>36875.078917</td>\n",
       "      <td>36875.08</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>1875.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-2017</td>\n",
       "      <td>35565.10</td>\n",
       "      <td>Jun-2017</td>\n",
       "      <td>709.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17282.802792</td>\n",
       "      <td>17282.80</td>\n",
       "      <td>17000.0</td>\n",
       "      <td>282.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-2017</td>\n",
       "      <td>16768.22</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>764.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>10365.368358</td>\n",
       "      <td>10365.37</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>365.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Aug-2017</td>\n",
       "      <td>9388.98</td>\n",
       "      <td>Nov-2017</td>\n",
       "      <td>734.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Individual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5168.906377</td>\n",
       "      <td>5168.91</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>168.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jun-2017</td>\n",
       "      <td>5003.93</td>\n",
       "      <td>Dec-2017</td>\n",
       "      <td>659.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Individual</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_pymnt  total_pymnt_inv  total_rec_prncp  total_rec_int  \\\n",
       "16   3765.098751          3765.10           3600.0         165.10   \n",
       "38  36875.078917         36875.08          35000.0        1875.08   \n",
       "40  17282.802792         17282.80          17000.0         282.80   \n",
       "51  10365.368358         10365.37          10000.0         365.37   \n",
       "56   5168.906377          5168.91           5000.0         168.91   \n",
       "\n",
       "    total_rec_late_fee  recoveries  collection_recovery_fee last_pymnt_d  \\\n",
       "16                 0.0         0.0                      0.0     Dec-2017   \n",
       "38                 0.0         0.0                      0.0     Jun-2017   \n",
       "40                 0.0         0.0                      0.0     Jun-2017   \n",
       "51                 0.0         0.0                      0.0     Aug-2017   \n",
       "56                 0.0         0.0                      0.0     Jun-2017   \n",
       "\n",
       "    last_pymnt_amnt last_credit_pull_d  last_fico_range_high  \\\n",
       "16          2984.31           Dec-2017                 724.0   \n",
       "38         35565.10           Jun-2017                 709.0   \n",
       "40         16768.22           Dec-2017                 764.0   \n",
       "51          9388.98           Nov-2017                 734.0   \n",
       "56          5003.93           Dec-2017                 659.0   \n",
       "\n",
       "    last_fico_range_low  collections_12_mths_ex_med  \\\n",
       "16                720.0                         0.0   \n",
       "38                705.0                         0.0   \n",
       "40                760.0                         0.0   \n",
       "51                730.0                         0.0   \n",
       "56                655.0                         0.0   \n",
       "\n",
       "    mths_since_last_major_derog application_type  \n",
       "16                          NaN       Individual  \n",
       "38                         30.0       Individual  \n",
       "40                         75.0       Individual  \n",
       "51                         26.0       Individual  \n",
       "56                          NaN       Individual  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 35: 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Individual    456197\n",
       "Joint App       1531\n",
       "Name: application_type, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.application_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.application_type = df.application_type.map(lambda x: 0 if x == 'Individual' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 51 - 60 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>last_fico_range_high</th>\n",
       "      <th>last_fico_range_low</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>724.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36506.0</td>\n",
       "      <td>37900.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2147.0</td>\n",
       "      <td>7980.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>709.0</td>\n",
       "      <td>705.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>394086.0</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24630.0</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>764.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>290743.0</td>\n",
       "      <td>48800.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32305.0</td>\n",
       "      <td>32394.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>734.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>324984.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24999.0</td>\n",
       "      <td>11199.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>659.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>36168.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6028.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>90.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    last_fico_range_high  last_fico_range_low  collections_12_mths_ex_med  \\\n",
       "16                 724.0                720.0                         0.0   \n",
       "38                 709.0                705.0                         0.0   \n",
       "40                 764.0                760.0                         0.0   \n",
       "51                 734.0                730.0                         0.0   \n",
       "56                 659.0                655.0                         0.0   \n",
       "\n",
       "    mths_since_last_major_derog  application_type  acc_now_delinq  \\\n",
       "16                          NaN                 0             0.0   \n",
       "38                         30.0                 0             0.0   \n",
       "40                         75.0                 0             0.0   \n",
       "51                         26.0                 0             0.0   \n",
       "56                          NaN                 0             0.0   \n",
       "\n",
       "    tot_coll_amt  tot_cur_bal  total_rev_hi_lim  acc_open_past_24mths  \\\n",
       "16           0.0      36506.0           37900.0                  13.0   \n",
       "38           0.0     394086.0           47700.0                   2.0   \n",
       "40           0.0     290743.0           48800.0                   3.0   \n",
       "51           0.0     324984.0           24900.0                   8.0   \n",
       "56         531.0      36168.0           11400.0                   2.0   \n",
       "\n",
       "    avg_cur_bal  bc_open_to_buy  bc_util  chargeoff_within_12_mths  \\\n",
       "16       2147.0          7980.0     11.3                       0.0   \n",
       "38      24630.0          1481.0     95.5                       0.0   \n",
       "40      32305.0         32394.0     32.2                       0.0   \n",
       "51      24999.0         11199.0     40.1                       0.0   \n",
       "56       6028.0          1124.0     90.1                       0.0   \n",
       "\n",
       "    delinq_amnt  \n",
       "16          0.0  \n",
       "38          0.0  \n",
       "40          0.0  \n",
       "51          0.0  \n",
       "56          0.0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 45: 60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend([ 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 61 - 70 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>avg_cur_bal</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>chargeoff_within_12_mths</th>\n",
       "      <th>delinq_amnt</th>\n",
       "      <th>mo_sin_old_il_acct</th>\n",
       "      <th>mo_sin_old_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_rev_tl_op</th>\n",
       "      <th>mo_sin_rcnt_tl</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_bc_dlq</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>mths_since_recent_revol_delinq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2147.0</td>\n",
       "      <td>7980.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>24630.0</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>32305.0</td>\n",
       "      <td>32394.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>24999.0</td>\n",
       "      <td>11199.0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6028.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>90.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    avg_cur_bal  bc_open_to_buy  bc_util  chargeoff_within_12_mths  \\\n",
       "16       2147.0          7980.0     11.3                       0.0   \n",
       "38      24630.0          1481.0     95.5                       0.0   \n",
       "40      32305.0         32394.0     32.2                       0.0   \n",
       "51      24999.0         11199.0     40.1                       0.0   \n",
       "56       6028.0          1124.0     90.1                       0.0   \n",
       "\n",
       "    delinq_amnt  mo_sin_old_il_acct  mo_sin_old_rev_tl_op  \\\n",
       "16          0.0               166.0                 295.0   \n",
       "38          0.0               157.0                 220.0   \n",
       "40          0.0               137.0                 119.0   \n",
       "51          0.0               133.0                 138.0   \n",
       "56          0.0               167.0                 111.0   \n",
       "\n",
       "    mo_sin_rcnt_rev_tl_op  mo_sin_rcnt_tl  mort_acc  mths_since_recent_bc  \\\n",
       "16                    3.0             2.0       0.0                   9.0   \n",
       "38                    0.0             0.0       4.0                  26.0   \n",
       "40                   24.0             9.0       2.0                  24.0   \n",
       "51                    4.0             4.0       1.0                   4.0   \n",
       "56                   46.0            18.0       0.0                  46.0   \n",
       "\n",
       "    mths_since_recent_bc_dlq  mths_since_recent_inq  \\\n",
       "16                       NaN                    2.0   \n",
       "38                       NaN                    0.0   \n",
       "40                       NaN                    9.0   \n",
       "51                      64.0                    2.0   \n",
       "56                       NaN                   18.0   \n",
       "\n",
       "    mths_since_recent_revol_delinq  num_accts_ever_120_pd  \n",
       "16                             NaN                    0.0  \n",
       "38                             NaN                    4.0  \n",
       "40                             NaN                    0.0  \n",
       "51                            29.0                    0.0  \n",
       "56                             NaN                    0.0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 55: 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 71 - 80 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>mths_since_recent_bc</th>\n",
       "      <th>mths_since_recent_bc_dlq</th>\n",
       "      <th>mths_since_recent_inq</th>\n",
       "      <th>mths_since_recent_revol_delinq</th>\n",
       "      <th>num_accts_ever_120_pd</th>\n",
       "      <th>num_actv_bc_tl</th>\n",
       "      <th>num_actv_rev_tl</th>\n",
       "      <th>num_bc_sats</th>\n",
       "      <th>num_bc_tl</th>\n",
       "      <th>num_il_tl</th>\n",
       "      <th>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mths_since_recent_bc  mths_since_recent_bc_dlq  mths_since_recent_inq  \\\n",
       "16                   9.0                       NaN                    2.0   \n",
       "38                  26.0                       NaN                    0.0   \n",
       "40                  24.0                       NaN                    9.0   \n",
       "51                   4.0                      64.0                    2.0   \n",
       "56                  46.0                       NaN                   18.0   \n",
       "\n",
       "    mths_since_recent_revol_delinq  num_accts_ever_120_pd  num_actv_bc_tl  \\\n",
       "16                             NaN                    0.0             1.0   \n",
       "38                             NaN                    4.0             9.0   \n",
       "40                             NaN                    0.0             4.0   \n",
       "51                            29.0                    0.0             6.0   \n",
       "56                             NaN                    0.0             4.0   \n",
       "\n",
       "    num_actv_rev_tl  num_bc_sats  num_bc_tl  num_il_tl  num_op_rev_tl  \\\n",
       "16              4.0          2.0        3.0       17.0           12.0   \n",
       "38             11.0         10.0       11.0        8.0           14.0   \n",
       "40              4.0          5.0        6.0       24.0            6.0   \n",
       "51              8.0          6.0        8.0       10.0            9.0   \n",
       "56              4.0          4.0        6.0        8.0            4.0   \n",
       "\n",
       "    num_rev_accts  num_rev_tl_bal_gt_0  num_sats  num_tl_120dpd_2m  \n",
       "16           16.0                  4.0      18.0               0.0  \n",
       "38           16.0                 11.0      17.0               0.0  \n",
       "40            8.0                  4.0       9.0               0.0  \n",
       "51           19.0                  8.0      13.0               0.0  \n",
       "56            9.0                  4.0       6.0               0.0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 65: 80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 81 - 90 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>num_op_rev_tl</th>\n",
       "      <th>num_rev_accts</th>\n",
       "      <th>num_rev_tl_bal_gt_0</th>\n",
       "      <th>num_sats</th>\n",
       "      <th>num_tl_120dpd_2m</th>\n",
       "      <th>num_tl_30dpd</th>\n",
       "      <th>num_tl_90g_dpd_24m</th>\n",
       "      <th>num_tl_op_past_12m</th>\n",
       "      <th>pct_tl_nvr_dlq</th>\n",
       "      <th>percent_bc_gt_75</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>tax_liens</th>\n",
       "      <th>tot_hi_cred_lim</th>\n",
       "      <th>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93599.0</td>\n",
       "      <td>36506.0</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>424707.0</td>\n",
       "      <td>81780.0</td>\n",
       "      <td>32700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>374071.0</td>\n",
       "      <td>48682.0</td>\n",
       "      <td>47800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>411827.0</td>\n",
       "      <td>68390.0</td>\n",
       "      <td>18700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52113.0</td>\n",
       "      <td>36168.0</td>\n",
       "      <td>11400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num_op_rev_tl  num_rev_accts  num_rev_tl_bal_gt_0  num_sats  \\\n",
       "16           12.0           16.0                  4.0      18.0   \n",
       "38           14.0           16.0                 11.0      17.0   \n",
       "40            6.0            8.0                  4.0       9.0   \n",
       "51            9.0           19.0                  8.0      13.0   \n",
       "56            4.0            9.0                  4.0       6.0   \n",
       "\n",
       "    num_tl_120dpd_2m  num_tl_30dpd  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "16               0.0           0.0                 0.0                 6.0   \n",
       "38               0.0           0.0                 0.0                 1.0   \n",
       "40               0.0           0.0                 0.0                 1.0   \n",
       "51               0.0           0.0                 0.0                 3.0   \n",
       "56               0.0           0.0                 0.0                 0.0   \n",
       "\n",
       "    pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  tax_liens  \\\n",
       "16           100.0               0.0                   1.0        0.0   \n",
       "38            87.1             100.0                   0.0        0.0   \n",
       "40            94.1              20.0                   1.0        0.0   \n",
       "51            66.7              16.7                   0.0        0.0   \n",
       "56            94.4              75.0                   0.0        0.0   \n",
       "\n",
       "    tot_hi_cred_lim  total_bal_ex_mort  total_bc_limit  \n",
       "16          93599.0            36506.0          9000.0  \n",
       "38         424707.0            81780.0         32700.0  \n",
       "40         374071.0            48682.0         47800.0  \n",
       "51         411827.0            68390.0         18700.0  \n",
       "56          52113.0            36168.0         11400.0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 75: 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 91 to rest of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>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>disbursement_method</th>\n",
       "      <th>debt_settlement_flag</th>\n",
       "      <th>issue_month</th>\n",
       "      <th>issue_year</th>\n",
       "      <th>earliest_cr_year</th>\n",
       "      <th>credit_history</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93599.0</td>\n",
       "      <td>36506.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>55699.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>1992</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>424707.0</td>\n",
       "      <td>81780.0</td>\n",
       "      <td>32700.0</td>\n",
       "      <td>52309.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>1998</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>374071.0</td>\n",
       "      <td>48682.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>38667.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2005</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>411827.0</td>\n",
       "      <td>68390.0</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>82927.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2005</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52113.0</td>\n",
       "      <td>36168.0</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>40713.0</td>\n",
       "      <td>Cash</td>\n",
       "      <td>N</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>2003</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pub_rec_bankruptcies  tax_liens  tot_hi_cred_lim  total_bal_ex_mort  \\\n",
       "16                   1.0        0.0          93599.0            36506.0   \n",
       "38                   0.0        0.0         424707.0            81780.0   \n",
       "40                   1.0        0.0         374071.0            48682.0   \n",
       "51                   0.0        0.0         411827.0            68390.0   \n",
       "56                   0.0        0.0          52113.0            36168.0   \n",
       "\n",
       "    total_bc_limit  total_il_high_credit_limit disbursement_method  \\\n",
       "16          9000.0                     55699.0                Cash   \n",
       "38         32700.0                     52309.0                Cash   \n",
       "40         47800.0                     38667.0                Cash   \n",
       "51         18700.0                     82927.0                Cash   \n",
       "56         11400.0                     40713.0                Cash   \n",
       "\n",
       "   debt_settlement_flag  issue_month  issue_year  earliest_cr_year  \\\n",
       "16                    N            7        2017              1992   \n",
       "38                    N            7        2017              1998   \n",
       "40                    N            7        2017              2005   \n",
       "51                    N            7        2017              2005   \n",
       "56                    N            7        2017              2003   \n",
       "\n",
       "    credit_history  \n",
       "16              25  \n",
       "38              19  \n",
       "40              12  \n",
       "51              12  \n",
       "56              14  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:5, 85:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cash         457716\n",
       "DirectPay        12\n",
       "Name: disbursement_method, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.disbursement_method.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.disbursement_method = df.disbursement_method.map(lambda x: 0 if x == 'Cash' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N    449510\n",
       "Y      8218\n",
       "Name: debt_settlement_flag, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.debt_settlement_flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.debt_settlement_flag = df.debt_settlement_flag.map(lambda x: 0 if x == 'N' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_be_removed.extend(['debt_settlement_flag', 'total_il_high_credit_limit'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removed _ features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['emp_title', 'id', 'url', 'title', 'zip_code', 'issue_d', 'mths_since_last_delinq', 'mths_since_last_record', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'total_pymnt', 'total_pymnt_inv', 'total_rec_prncp', 'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee', 'last_pymnt_d', 'last_pymnt_amnt', 'last_credit_pull_d', 'last_fico_range_high', 'last_fico_range_low', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'total_rev_hi_lim', 'avg_cur_bal', 'bc_open_to_buy', 'bc_util', 'chargeoff_within_12_mths', 'delinq_amnt', 'mo_sin_old_il_acct', 'mo_sin_old_rev_tl_op', 'mo_sin_rcnt_rev_tl_op', 'mo_sin_rcnt_tl', 'mths_since_recent_bc', 'mths_since_recent_bc_dlq', 'mths_since_recent_inq', 'mths_since_recent_revol_delinq', 'num_accts_ever_120_pd', 'num_actv_bc_tl', 'num_actv_rev_tl', 'num_bc_sats', 'num_bc_tl', 'num_il_tl', 'num_op_rev_tl', 'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats', 'num_tl_120dpd_2m', 'num_tl_30dpd', 'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq', 'percent_bc_gt_75', 'tot_hi_cred_lim', 'total_bal_ex_mort', 'total_bc_limit', 'debt_settlement_flag', 'total_il_high_credit_limit']\n"
     ]
    }
   ],
   "source": [
    "print(features_to_be_removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(features_to_be_removed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected = df.drop(list(set(features_to_be_removed)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457728, 36)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dtypes = pd.merge(df_selected.isnull().sum(axis = 0).sort_values().to_frame('missing_value').reset_index(),\n",
    "         df_selected.dtypes.to_frame('feature_type').reset_index(),\n",
    "         on = 'index',\n",
    "         how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>index</th>\n",
       "      <th>missing_value</th>\n",
       "      <th>feature_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>issue_month</td>\n",
       "      <td>0</td>\n",
       "      <td>int8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>initial_list_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>application_type</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>disbursement_method</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>issue_year</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>earliest_cr_year</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>emp_length</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>term</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>verification_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>loan_status</td>\n",
       "      <td>0</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>int_rate</td>\n",
       "      <td>0</td>\n",
       "      <td>float32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fico_range_high</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>open_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pub_rec</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>total_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fico_range_low</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mort_acc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pub_rec_bankruptcies</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tax_liens</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>acc_open_past_24mths</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>funded_amnt_inv</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>installment</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>annual_inc</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>delinq_2yrs</td>\n",
       "      <td>0</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>earliest_cr_line</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>sub_grade</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>home_ownership</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>purpose</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>addr_state</td>\n",
       "      <td>0</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>dti</td>\n",
       "      <td>21</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>revol_util</td>\n",
       "      <td>223</td>\n",
       "      <td>float32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   index  missing_value feature_type\n",
       "13           issue_month              0         int8\n",
       "6    initial_list_status              0        int64\n",
       "8       application_type              0        int64\n",
       "12   disbursement_method              0        int64\n",
       "14            issue_year              0        int64\n",
       "16      earliest_cr_year              0        int64\n",
       "18            emp_length              0        int64\n",
       "21                  term              0        int64\n",
       "26        credit_history              0        int64\n",
       "29   verification_status              0        int64\n",
       "30           loan_status              0        int64\n",
       "22              int_rate              0      float32\n",
       "0              loan_amnt              0      float64\n",
       "1        fico_range_high              0      float64\n",
       "2               open_acc              0      float64\n",
       "3                pub_rec              0      float64\n",
       "4              revol_bal              0      float64\n",
       "5              total_acc              0      float64\n",
       "7         fico_range_low              0      float64\n",
       "9               mort_acc              0      float64\n",
       "10  pub_rec_bankruptcies              0      float64\n",
       "11             tax_liens              0      float64\n",
       "15  acc_open_past_24mths              0      float64\n",
       "19           funded_amnt              0      float64\n",
       "20       funded_amnt_inv              0      float64\n",
       "23           installment              0      float64\n",
       "28            annual_inc              0      float64\n",
       "33           delinq_2yrs              0      float64\n",
       "17      earliest_cr_line              0       object\n",
       "24                 grade              0       object\n",
       "25             sub_grade              0       object\n",
       "27        home_ownership              0       object\n",
       "31               purpose              0       object\n",
       "32            addr_state              0       object\n",
       "34                   dti             21      float64\n",
       "35            revol_util            223      float32"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtypes.sort_values(['missing_value', 'feature_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(457484, 36)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.drop('earliest_cr_line', axis = True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    276479\n",
       "credit_card           102340\n",
       "home_improvement       27591\n",
       "other                  22073\n",
       "major_purchase          8246\n",
       "medical                 4776\n",
       "small_business          4204\n",
       "car                     3959\n",
       "moving                  2950\n",
       "vacation                2751\n",
       "house                   1835\n",
       "renewable_energy         268\n",
       "wedding                   11\n",
       "educational                1\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.purpose = df_selected.purpose.astype(\"category\", categories=np.unique(df_selected.purpose)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     276479\n",
       "1     102340\n",
       "4      27591\n",
       "9      22073\n",
       "6       8246\n",
       "7       4776\n",
       "11      4204\n",
       "0       3959\n",
       "8       2950\n",
       "12      2751\n",
       "5       1835\n",
       "10       268\n",
       "13        11\n",
       "3          1\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.home_ownership = df_selected.home_ownership.astype(\"category\", categories = np.unique(df_selected.home_ownership)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    228337\n",
       "4    181232\n",
       "3     47839\n",
       "0        75\n",
       "2         1\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.home_ownership.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.grade = df_selected.grade.astype(\"category\", categories = np.unique(df_selected.grade)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    129897\n",
       "1    123032\n",
       "0     75537\n",
       "3     74262\n",
       "4     38812\n",
       "5     12501\n",
       "6      3443\n",
       "Name: grade, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.sub_grade = df_selected.sub_grade.astype(\"category\", categories = np.unique(df_selected.sub_grade)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    27774\n",
       "11    27365\n",
       "9     26514\n",
       "12    26361\n",
       "8     26205\n",
       "13    25596\n",
       "7     25556\n",
       "6     22881\n",
       "14    22801\n",
       "4     22731\n",
       "5     21876\n",
       "15    19234\n",
       "3     17541\n",
       "16    16039\n",
       "17    14551\n",
       "18    13390\n",
       "2     11969\n",
       "1     11707\n",
       "0     11589\n",
       "19    11048\n",
       "20    10361\n",
       "21     9170\n",
       "22     7532\n",
       "23     6255\n",
       "24     5494\n",
       "25     3989\n",
       "26     2802\n",
       "27     2447\n",
       "28     1882\n",
       "29     1381\n",
       "30     1121\n",
       "31      863\n",
       "32      603\n",
       "33      461\n",
       "34      395\n",
       "Name: sub_grade, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.sub_grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.addr_state = df_selected.addr_state.astype(\"category\", categories = np.unique(df_selected.addr_state)).cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    27774\n",
       "11    27365\n",
       "9     26514\n",
       "12    26361\n",
       "8     26205\n",
       "13    25596\n",
       "7     25556\n",
       "6     22881\n",
       "14    22801\n",
       "4     22731\n",
       "5     21876\n",
       "15    19234\n",
       "3     17541\n",
       "16    16039\n",
       "17    14551\n",
       "18    13390\n",
       "2     11969\n",
       "1     11707\n",
       "0     11589\n",
       "19    11048\n",
       "20    10361\n",
       "21     9170\n",
       "22     7532\n",
       "23     6255\n",
       "24     5494\n",
       "25     3989\n",
       "26     2802\n",
       "27     2447\n",
       "28     1882\n",
       "29     1381\n",
       "30     1121\n",
       "31      863\n",
       "32      603\n",
       "33      461\n",
       "34      395\n",
       "Name: sub_grade, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.sub_grade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'funded_amnt', 'funded_amnt_inv', 'term', 'int_rate',\n",
       "       'installment', 'grade', 'sub_grade', 'emp_length', 'home_ownership',\n",
       "       'annual_inc', 'verification_status', 'loan_status', 'purpose',\n",
       "       'addr_state', 'dti', 'delinq_2yrs', 'fico_range_low', 'fico_range_high',\n",
       "       'open_acc', 'pub_rec', 'revol_bal', 'revol_util', 'total_acc',\n",
       "       'initial_list_status', 'application_type', 'acc_open_past_24mths',\n",
       "       'mort_acc', 'pub_rec_bankruptcies', 'tax_liens', 'disbursement_method',\n",
       "       'issue_month', 'issue_year', 'earliest_cr_year', 'credit_history'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save selected features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_selected.to_csv('./data/df_selected.csv', index = False)"
   ]
  }
 ],
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