Credit_risk_model_nn_new.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREDIT RISK MODEL USING NEURAL NETWORKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/praveen/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#Importing the necessary packages\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Loading the data\n",
    "data = pd.read_csv('/home/praveen/Documents/Data/Loan/loan.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A little Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887379"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'member_id',\n",
       " 'loan_amnt',\n",
       " 'funded_amnt',\n",
       " 'funded_amnt_inv',\n",
       " 'term',\n",
       " 'int_rate',\n",
       " 'installment',\n",
       " 'grade',\n",
       " 'sub_grade',\n",
       " 'emp_title',\n",
       " 'emp_length',\n",
       " 'home_ownership',\n",
       " 'annual_inc',\n",
       " 'verification_status',\n",
       " 'issue_d',\n",
       " 'loan_status',\n",
       " 'pymnt_plan',\n",
       " 'url',\n",
       " 'desc',\n",
       " 'purpose',\n",
       " 'title',\n",
       " 'zip_code',\n",
       " 'addr_state',\n",
       " 'dti',\n",
       " 'delinq_2yrs',\n",
       " 'earliest_cr_line',\n",
       " 'inq_last_6mths',\n",
       " 'mths_since_last_delinq',\n",
       " 'mths_since_last_record',\n",
       " 'open_acc',\n",
       " 'pub_rec',\n",
       " 'revol_bal',\n",
       " 'revol_util',\n",
       " 'total_acc',\n",
       " 'initial_list_status',\n",
       " 'out_prncp',\n",
       " 'out_prncp_inv',\n",
       " 'total_pymnt',\n",
       " 'total_pymnt_inv',\n",
       " 'total_rec_prncp',\n",
       " 'total_rec_int',\n",
       " 'total_rec_late_fee',\n",
       " 'recoveries',\n",
       " 'collection_recovery_fee',\n",
       " 'last_pymnt_d',\n",
       " 'last_pymnt_amnt',\n",
       " 'next_pymnt_d',\n",
       " 'last_credit_pull_d',\n",
       " 'collections_12_mths_ex_med',\n",
       " 'mths_since_last_major_derog',\n",
       " 'policy_code',\n",
       " 'application_type',\n",
       " 'annual_inc_joint',\n",
       " 'dti_joint',\n",
       " 'verification_status_joint',\n",
       " 'acc_now_delinq',\n",
       " 'tot_coll_amt',\n",
       " 'tot_cur_bal',\n",
       " 'open_acc_6m',\n",
       " 'open_il_6m',\n",
       " 'open_il_12m',\n",
       " 'open_il_24m',\n",
       " 'mths_since_rcnt_il',\n",
       " 'total_bal_il',\n",
       " 'il_util',\n",
       " 'open_rv_12m',\n",
       " 'open_rv_24m',\n",
       " 'max_bal_bc',\n",
       " 'all_util',\n",
       " 'total_rev_hi_lim',\n",
       " 'inq_fi',\n",
       " 'total_cu_tl',\n",
       " 'inq_last_12m']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fully Paid', 'Charged Off', 'Current', 'Default',\n",
       "       'Late (31-120 days)', 'In Grace Period', 'Late (16-30 days)',\n",
       "       'Does not meet the credit policy. Status:Fully Paid',\n",
       "       'Does not meet the credit policy. Status:Charged Off', 'Issued'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['loan_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['last_delinq_none'] = data['mths_since_last_delinq'].apply(lambda x : 0 if x > 0 else +1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['last_major_derog_none'] = data['mths_since_last_major_derog'].apply(lambda x : 0 if x > 0 else +1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['short_emp'] = data['emp_length'].apply(lambda x : +1 if x == '< 1 year' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Target feature that we are going to predict. 1 means safe, 0 is risky\n",
    "data['safe_loans'] = data['loan_status'].apply(lambda x : 0 \n",
    "                                               if x in [\"Charged Off \",\n",
    "                                                        \"Default\",\n",
    "                                                        \"Does not meet the credit policy. Status:Charged Off\",\n",
    "                                                        \"In Grace Period\", \n",
    "                                                        \"Default Receiver\", \n",
    "                                                        \"Late (16-30 days)\",\n",
    "                                                        \"Late (31-120 days)\"] \n",
    "                                               else +1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div>\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>...</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>last_delinq_none</th>\n",
       "      <th>last_major_derog_none</th>\n",
       "      <th>short_emp</th>\n",
       "      <th>safe_loans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1075269</td>\n",
       "      <td>1311441</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1069639</td>\n",
       "      <td>1304742</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>170.08</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1072053</td>\n",
       "      <td>1288686</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>18.64</td>\n",
       "      <td>109.43</td>\n",
       "      <td>E</td>\n",
       "      <td>E1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1071795</td>\n",
       "      <td>1306957</td>\n",
       "      <td>5600</td>\n",
       "      <td>5600</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>21.28</td>\n",
       "      <td>152.39</td>\n",
       "      <td>F</td>\n",
       "      <td>F2</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1071570</td>\n",
       "      <td>1306721</td>\n",
       "      <td>5375</td>\n",
       "      <td>5375</td>\n",
       "      <td>5350.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>121.45</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1070078</td>\n",
       "      <td>1305201</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500</td>\n",
       "      <td>6500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.65</td>\n",
       "      <td>153.45</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1069908</td>\n",
       "      <td>1305008</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>402.54</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1064687</td>\n",
       "      <td>1298717</td>\n",
       "      <td>9000</td>\n",
       "      <td>9000</td>\n",
       "      <td>9000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>305.38</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1069866</td>\n",
       "      <td>1304956</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.91</td>\n",
       "      <td>96.68</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1069057</td>\n",
       "      <td>1303503</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>325.74</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1069759</td>\n",
       "      <td>1304871</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>16.29</td>\n",
       "      <td>35.31</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1065775</td>\n",
       "      <td>1299699</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>347.98</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1069971</td>\n",
       "      <td>1304884</td>\n",
       "      <td>3600</td>\n",
       "      <td>3600</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.03</td>\n",
       "      <td>109.57</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1062474</td>\n",
       "      <td>1294539</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>198.46</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1069742</td>\n",
       "      <td>1304855</td>\n",
       "      <td>9200</td>\n",
       "      <td>9200</td>\n",
       "      <td>9200.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.03</td>\n",
       "      <td>280.01</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1069740</td>\n",
       "      <td>1284848</td>\n",
       "      <td>20250</td>\n",
       "      <td>20250</td>\n",
       "      <td>19142.161077</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>484.63</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1039153</td>\n",
       "      <td>1269083</td>\n",
       "      <td>21000</td>\n",
       "      <td>21000</td>\n",
       "      <td>21000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.42</td>\n",
       "      <td>701.73</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1069710</td>\n",
       "      <td>1304821</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>330.76</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1069700</td>\n",
       "      <td>1304810</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>330.76</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1069559</td>\n",
       "      <td>1304634</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>198.46</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1069697</td>\n",
       "      <td>1273773</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.91</td>\n",
       "      <td>483.38</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1069800</td>\n",
       "      <td>1304679</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>8725.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.27</td>\n",
       "      <td>514.64</td>\n",
       "      <td>C</td>\n",
       "      <td>C2</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1069657</td>\n",
       "      <td>1304764</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>16.77</td>\n",
       "      <td>123.65</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1069799</td>\n",
       "      <td>1304678</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>132.31</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1047704</td>\n",
       "      <td>1278806</td>\n",
       "      <td>8500</td>\n",
       "      <td>8500</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.71</td>\n",
       "      <td>281.15</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887349</th>\n",
       "      <td>36691397</td>\n",
       "      <td>39434133</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.99</td>\n",
       "      <td>617.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887350</th>\n",
       "      <td>36500819</td>\n",
       "      <td>39232937</td>\n",
       "      <td>10300</td>\n",
       "      <td>10300</td>\n",
       "      <td>10300.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.66</td>\n",
       "      <td>237.86</td>\n",
       "      <td>C</td>\n",
       "      <td>C3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887351</th>\n",
       "      <td>36743377</td>\n",
       "      <td>39486112</td>\n",
       "      <td>4200</td>\n",
       "      <td>4200</td>\n",
       "      <td>4200.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.99</td>\n",
       "      <td>147.64</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887352</th>\n",
       "      <td>36231083</td>\n",
       "      <td>38942474</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>498.15</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887353</th>\n",
       "      <td>36371802</td>\n",
       "      <td>39103231</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>11.44</td>\n",
       "      <td>329.44</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887354</th>\n",
       "      <td>36250801</td>\n",
       "      <td>38962184</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>17.86</td>\n",
       "      <td>216.50</td>\n",
       "      <td>D</td>\n",
       "      <td>D5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887355</th>\n",
       "      <td>36331554</td>\n",
       "      <td>39042967</td>\n",
       "      <td>26950</td>\n",
       "      <td>26950</td>\n",
       "      <td>26950.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.19</td>\n",
       "      <td>846.88</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887356</th>\n",
       "      <td>36460697</td>\n",
       "      <td>39182359</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.59</td>\n",
       "      <td>554.32</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887357</th>\n",
       "      <td>35773415</td>\n",
       "      <td>38468842</td>\n",
       "      <td>18700</td>\n",
       "      <td>18700</td>\n",
       "      <td>18700.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>17.86</td>\n",
       "      <td>473.44</td>\n",
       "      <td>D</td>\n",
       "      <td>D5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887358</th>\n",
       "      <td>36440728</td>\n",
       "      <td>39152101</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.99</td>\n",
       "      <td>607.82</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887359</th>\n",
       "      <td>35591131</td>\n",
       "      <td>38288343</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.59</td>\n",
       "      <td>602.52</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887360</th>\n",
       "      <td>36361815</td>\n",
       "      <td>39093260</td>\n",
       "      <td>26500</td>\n",
       "      <td>26500</td>\n",
       "      <td>26500.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>8.67</td>\n",
       "      <td>545.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887361</th>\n",
       "      <td>36201871</td>\n",
       "      <td>38913321</td>\n",
       "      <td>21000</td>\n",
       "      <td>21000</td>\n",
       "      <td>21000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.03</td>\n",
       "      <td>639.15</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887362</th>\n",
       "      <td>36381756</td>\n",
       "      <td>39013193</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.19</td>\n",
       "      <td>251.40</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887363</th>\n",
       "      <td>36231724</td>\n",
       "      <td>38943173</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.49</td>\n",
       "      <td>384.34</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887364</th>\n",
       "      <td>36231718</td>\n",
       "      <td>38943165</td>\n",
       "      <td>10775</td>\n",
       "      <td>10775</td>\n",
       "      <td>10775.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.03</td>\n",
       "      <td>327.95</td>\n",
       "      <td>A</td>\n",
       "      <td>A1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887365</th>\n",
       "      <td>36381704</td>\n",
       "      <td>39013139</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>14.31</td>\n",
       "      <td>240.30</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887366</th>\n",
       "      <td>36241316</td>\n",
       "      <td>38952731</td>\n",
       "      <td>6225</td>\n",
       "      <td>6225</td>\n",
       "      <td>6225.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>16.49</td>\n",
       "      <td>220.37</td>\n",
       "      <td>D</td>\n",
       "      <td>D3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887367</th>\n",
       "      <td>36301729</td>\n",
       "      <td>39023153</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.49</td>\n",
       "      <td>306.45</td>\n",
       "      <td>A</td>\n",
       "      <td>A2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887368</th>\n",
       "      <td>36250208</td>\n",
       "      <td>38961532</td>\n",
       "      <td>13150</td>\n",
       "      <td>13150</td>\n",
       "      <td>13150.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>14.99</td>\n",
       "      <td>312.77</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887369</th>\n",
       "      <td>36421485</td>\n",
       "      <td>39142898</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>8.67</td>\n",
       "      <td>126.59</td>\n",
       "      <td>B</td>\n",
       "      <td>B1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887370</th>\n",
       "      <td>35094480</td>\n",
       "      <td>37777829</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500</td>\n",
       "      <td>7500.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.49</td>\n",
       "      <td>240.22</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887371</th>\n",
       "      <td>36260758</td>\n",
       "      <td>38972123</td>\n",
       "      <td>10850</td>\n",
       "      <td>10850</td>\n",
       "      <td>10850.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>19.24</td>\n",
       "      <td>399.04</td>\n",
       "      <td>E</td>\n",
       "      <td>E2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887372</th>\n",
       "      <td>36330911</td>\n",
       "      <td>39042282</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>398.52</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887373</th>\n",
       "      <td>36221446</td>\n",
       "      <td>38932858</td>\n",
       "      <td>11575</td>\n",
       "      <td>11575</td>\n",
       "      <td>11575.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.59</td>\n",
       "      <td>404.61</td>\n",
       "      <td>D</td>\n",
       "      <td>D1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>36371250</td>\n",
       "      <td>39102635</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>332.10</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>36441262</td>\n",
       "      <td>39152692</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>797.03</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>36271333</td>\n",
       "      <td>38982739</td>\n",
       "      <td>13000</td>\n",
       "      <td>13000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.99</td>\n",
       "      <td>316.07</td>\n",
       "      <td>D</td>\n",
       "      <td>D2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>36490806</td>\n",
       "      <td>39222577</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000.000000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>19.99</td>\n",
       "      <td>317.86</td>\n",
       "      <td>E</td>\n",
       "      <td>E3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>36271262</td>\n",
       "      <td>38982659</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>11.99</td>\n",
       "      <td>664.20</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>887379 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "0        1077501    1296599       5000         5000      4975.000000   \n",
       "1        1077430    1314167       2500         2500      2500.000000   \n",
       "2        1077175    1313524       2400         2400      2400.000000   \n",
       "3        1076863    1277178      10000        10000     10000.000000   \n",
       "4        1075358    1311748       3000         3000      3000.000000   \n",
       "5        1075269    1311441       5000         5000      5000.000000   \n",
       "6        1069639    1304742       7000         7000      7000.000000   \n",
       "7        1072053    1288686       3000         3000      3000.000000   \n",
       "8        1071795    1306957       5600         5600      5600.000000   \n",
       "9        1071570    1306721       5375         5375      5350.000000   \n",
       "10       1070078    1305201       6500         6500      6500.000000   \n",
       "11       1069908    1305008      12000        12000     12000.000000   \n",
       "12       1064687    1298717       9000         9000      9000.000000   \n",
       "13       1069866    1304956       3000         3000      3000.000000   \n",
       "14       1069057    1303503      10000        10000     10000.000000   \n",
       "15       1069759    1304871       1000         1000      1000.000000   \n",
       "16       1065775    1299699      10000        10000     10000.000000   \n",
       "17       1069971    1304884       3600         3600      3600.000000   \n",
       "18       1062474    1294539       6000         6000      6000.000000   \n",
       "19       1069742    1304855       9200         9200      9200.000000   \n",
       "20       1069740    1284848      20250        20250     19142.161077   \n",
       "21       1039153    1269083      21000        21000     21000.000000   \n",
       "22       1069710    1304821      10000        10000     10000.000000   \n",
       "23       1069700    1304810      10000        10000     10000.000000   \n",
       "24       1069559    1304634       6000         6000      6000.000000   \n",
       "25       1069697    1273773      15000        15000     15000.000000   \n",
       "26       1069800    1304679      15000        15000      8725.000000   \n",
       "27       1069657    1304764       5000         5000      5000.000000   \n",
       "28       1069799    1304678       4000         4000      4000.000000   \n",
       "29       1047704    1278806       8500         8500      8500.000000   \n",
       "...          ...        ...        ...          ...              ...   \n",
       "887349  36691397   39434133      20000        20000     20000.000000   \n",
       "887350  36500819   39232937      10300        10300     10300.000000   \n",
       "887351  36743377   39486112       4200         4200      4200.000000   \n",
       "887352  36231083   38942474      15000        15000     15000.000000   \n",
       "887353  36371802   39103231      15000        15000     15000.000000   \n",
       "887354  36250801   38962184       6000         6000      6000.000000   \n",
       "887355  36331554   39042967      26950        26950     26950.000000   \n",
       "887356  36460697   39182359      23000        23000     23000.000000   \n",
       "887357  35773415   38468842      18700        18700     18700.000000   \n",
       "887358  36440728   39152101      25000        25000     25000.000000   \n",
       "887359  35591131   38288343      25000        25000     25000.000000   \n",
       "887360  36361815   39093260      26500        26500     26500.000000   \n",
       "887361  36201871   38913321      21000        21000     21000.000000   \n",
       "887362  36381756   39013193       8000         8000      8000.000000   \n",
       "887363  36231724   38943173      12000        12000     12000.000000   \n",
       "887364  36231718   38943165      10775        10775     10775.000000   \n",
       "887365  36381704   39013139       7000         7000      7000.000000   \n",
       "887366  36241316   38952731       6225         6225      6225.000000   \n",
       "887367  36301729   39023153      10000        10000     10000.000000   \n",
       "887368  36250208   38961532      13150        13150     13150.000000   \n",
       "887369  36421485   39142898       4000         4000      4000.000000   \n",
       "887370  35094480   37777829       7500         7500      7500.000000   \n",
       "887371  36260758   38972123      10850        10850     10850.000000   \n",
       "887372  36330911   39042282      12000        12000     12000.000000   \n",
       "887373  36221446   38932858      11575        11575     11575.000000   \n",
       "887374  36371250   39102635      10000        10000     10000.000000   \n",
       "887375  36441262   39152692      24000        24000     24000.000000   \n",
       "887376  36271333   38982739      13000        13000     13000.000000   \n",
       "887377  36490806   39222577      12000        12000     12000.000000   \n",
       "887378  36271262   38982659      20000        20000     20000.000000   \n",
       "\n",
       "              term  int_rate  installment grade sub_grade    ...      \\\n",
       "0        36 months     10.65       162.87     B        B2    ...       \n",
       "1        60 months     15.27        59.83     C        C4    ...       \n",
       "2        36 months     15.96        84.33     C        C5    ...       \n",
       "3        36 months     13.49       339.31     C        C1    ...       \n",
       "4        60 months     12.69        67.79     B        B5    ...       \n",
       "5        36 months      7.90       156.46     A        A4    ...       \n",
       "6        60 months     15.96       170.08     C        C5    ...       \n",
       "7        36 months     18.64       109.43     E        E1    ...       \n",
       "8        60 months     21.28       152.39     F        F2    ...       \n",
       "9        60 months     12.69       121.45     B        B5    ...       \n",
       "10       60 months     14.65       153.45     C        C3    ...       \n",
       "11       36 months     12.69       402.54     B        B5    ...       \n",
       "12       36 months     13.49       305.38     C        C1    ...       \n",
       "13       36 months      9.91        96.68     B        B1    ...       \n",
       "14       36 months     10.65       325.74     B        B2    ...       \n",
       "15       36 months     16.29        35.31     D        D1    ...       \n",
       "16       36 months     15.27       347.98     C        C4    ...       \n",
       "17       36 months      6.03       109.57     A        A1    ...       \n",
       "18       36 months     11.71       198.46     B        B3    ...       \n",
       "19       36 months      6.03       280.01     A        A1    ...       \n",
       "20       60 months     15.27       484.63     C        C4    ...       \n",
       "21       36 months     12.42       701.73     B        B4    ...       \n",
       "22       36 months     11.71       330.76     B        B3    ...       \n",
       "23       36 months     11.71       330.76     B        B3    ...       \n",
       "24       36 months     11.71       198.46     B        B3    ...       \n",
       "25       36 months      9.91       483.38     B        B1    ...       \n",
       "26       36 months     14.27       514.64     C        C2    ...       \n",
       "27       60 months     16.77       123.65     D        D2    ...       \n",
       "28       36 months     11.71       132.31     B        B3    ...       \n",
       "29       36 months     11.71       281.15     B        B3    ...       \n",
       "...            ...       ...          ...   ...       ...    ...       \n",
       "887349   36 months      6.99       617.46     A        A3    ...       \n",
       "887350   60 months     13.66       237.86     C        C3    ...       \n",
       "887351   36 months     15.99       147.64     D        D2    ...       \n",
       "887352   36 months     11.99       498.15     B        B5    ...       \n",
       "887353   60 months     11.44       329.44     B        B4    ...       \n",
       "887354   36 months     17.86       216.50     D        D5    ...       \n",
       "887355   36 months      8.19       846.88     A        A5    ...       \n",
       "887356   60 months     15.59       554.32     D        D1    ...       \n",
       "887357   60 months     17.86       473.44     D        D5    ...       \n",
       "887358   60 months     15.99       607.82     D        D2    ...       \n",
       "887359   60 months     15.59       602.52     D        D1    ...       \n",
       "887360   60 months      8.67       545.87     B        B1    ...       \n",
       "887361   36 months      6.03       639.15     A        A1    ...       \n",
       "887362   36 months      8.19       251.40     A        A5    ...       \n",
       "887363   36 months      9.49       384.34     B        B2    ...       \n",
       "887364   36 months      6.03       327.95     A        A1    ...       \n",
       "887365   36 months     14.31       240.30     C        C4    ...       \n",
       "887366   36 months     16.49       220.37     D        D3    ...       \n",
       "887367   36 months      6.49       306.45     A        A2    ...       \n",
       "887368   60 months     14.99       312.77     C        C5    ...       \n",
       "887369   36 months      8.67       126.59     B        B1    ...       \n",
       "887370   36 months      9.49       240.22     B        B2    ...       \n",
       "887371   36 months     19.24       399.04     E        E2    ...       \n",
       "887372   36 months     11.99       398.52     B        B5    ...       \n",
       "887373   36 months     15.59       404.61     D        D1    ...       \n",
       "887374   36 months     11.99       332.10     B        B5    ...       \n",
       "887375   36 months     11.99       797.03     B        B5    ...       \n",
       "887376   60 months     15.99       316.07     D        D2    ...       \n",
       "887377   60 months     19.99       317.86     E        E3    ...       \n",
       "887378   36 months     11.99       664.20     B        B5    ...       \n",
       "\n",
       "       max_bal_bc all_util total_rev_hi_lim  inq_fi total_cu_tl inq_last_12m  \\\n",
       "0             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "1             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "2             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "3             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "4             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "5             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "6             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "7             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "8             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "9             NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "10            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "11            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "12            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "13            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "14            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "15            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "16            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "17            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "18            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "19            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "20            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "21            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "22            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "23            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "24            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "25            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "26            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "27            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "28            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "29            NaN      NaN              NaN     NaN         NaN          NaN   \n",
       "...           ...      ...              ...     ...         ...          ...   \n",
       "887349        NaN      NaN            39700     NaN         NaN          NaN   \n",
       "887350        NaN      NaN            22000     NaN         NaN          NaN   \n",
       "887351        NaN      NaN            20400     NaN         NaN          NaN   \n",
       "887352        NaN      NaN             9700     NaN         NaN          NaN   \n",
       "887353        NaN      NaN            26100     NaN         NaN          NaN   \n",
       "887354        NaN      NaN             8500     NaN         NaN          NaN   \n",
       "887355        NaN      NaN           129100     NaN         NaN          NaN   \n",
       "887356        NaN      NaN            65600     NaN         NaN          NaN   \n",
       "887357        NaN      NaN            10200     NaN         NaN          NaN   \n",
       "887358        NaN      NaN            18700     NaN         NaN          NaN   \n",
       "887359        NaN      NaN            24400     NaN         NaN          NaN   \n",
       "887360        NaN      NaN           118600     NaN         NaN          NaN   \n",
       "887361        NaN      NaN            76300     NaN         NaN          NaN   \n",
       "887362        NaN      NaN             7300     NaN         NaN          NaN   \n",
       "887363        NaN      NaN            11900     NaN         NaN          NaN   \n",
       "887364        NaN      NaN            41700     NaN         NaN          NaN   \n",
       "887365        NaN      NaN             9900     NaN         NaN          NaN   \n",
       "887366        NaN      NaN             1800     NaN         NaN          NaN   \n",
       "887367        NaN      NaN            30300     NaN         NaN          NaN   \n",
       "887368        NaN      NaN            20700     NaN         NaN          NaN   \n",
       "887369        NaN      NaN            30100     NaN         NaN          NaN   \n",
       "887370        NaN      NaN            14500     NaN         NaN          NaN   \n",
       "887371        NaN      NaN            16800     NaN         NaN          NaN   \n",
       "887372        NaN      NaN            30300     NaN         NaN          NaN   \n",
       "887373        NaN      NaN            28100     NaN         NaN          NaN   \n",
       "887374        NaN      NaN            17100     NaN         NaN          NaN   \n",
       "887375        NaN      NaN            10200     NaN         NaN          NaN   \n",
       "887376        NaN      NaN            18000     NaN         NaN          NaN   \n",
       "887377        NaN      NaN            27000     NaN         NaN          NaN   \n",
       "887378        NaN      NaN            41700     NaN         NaN          NaN   \n",
       "\n",
       "       last_delinq_none last_major_derog_none short_emp safe_loans  \n",
       "0                     1                     1         0          1  \n",
       "1                     1                     1         1          1  \n",
       "2                     1                     1         0          1  \n",
       "3                     0                     1         0          1  \n",
       "4                     0                     1         0          1  \n",
       "5                     1                     1         0          1  \n",
       "6                     1                     1         0          1  \n",
       "7                     1                     1         0          1  \n",
       "8                     1                     1         0          1  \n",
       "9                     1                     1         1          1  \n",
       "10                    1                     1         0          1  \n",
       "11                    1                     1         0          1  \n",
       "12                    1                     1         1          1  \n",
       "13                    1                     1         0          1  \n",
       "14                    1                     1         0          1  \n",
       "15                    1                     1         1          1  \n",
       "16                    0                     1         0          1  \n",
       "17                    1                     1         0          1  \n",
       "18                    0                     1         0          1  \n",
       "19                    1                     1         0          1  \n",
       "20                    1                     1         0          1  \n",
       "21                    1                     1         0          1  \n",
       "22                    1                     1         0          1  \n",
       "23                    1                     1         0          1  \n",
       "24                    1                     1         0          1  \n",
       "25                    1                     1         0          1  \n",
       "26                    1                     1         0          1  \n",
       "27                    0                     1         0          1  \n",
       "28                    0                     1         0          1  \n",
       "29                    1                     1         1          1  \n",
       "...                 ...                   ...       ...        ...  \n",
       "887349                0                     0         0          1  \n",
       "887350                0                     1         0          1  \n",
       "887351                0                     0         0          1  \n",
       "887352                1                     1         1          1  \n",
       "887353                1                     1         0          1  \n",
       "887354                1                     1         0          1  \n",
       "887355                1                     1         0          1  \n",
       "887356                1                     1         0          1  \n",
       "887357                0                     0         0          1  \n",
       "887358                1                     1         0          1  \n",
       "887359                1                     1         0          1  \n",
       "887360                0                     1         0          1  \n",
       "887361                1                     1         0          1  \n",
       "887362                0                     0         0          1  \n",
       "887363                0                     0         0          1  \n",
       "887364                0                     0         1          1  \n",
       "887365                0                     0         0          1  \n",
       "887366                1                     1         0          1  \n",
       "887367                0                     1         0          1  \n",
       "887368                1                     1         0          1  \n",
       "887369                1                     1         0          1  \n",
       "887370                0                     0         0          1  \n",
       "887371                0                     1         0          1  \n",
       "887372                0                     0         1          1  \n",
       "887373                1                     1         0          1  \n",
       "887374                1                     1         0          1  \n",
       "887375                0                     0         0          1  \n",
       "887376                1                     1         0          1  \n",
       "887377                0                     0         0          1  \n",
       "887378                1                     1         0          1  \n",
       "\n",
       "[887379 rows x 78 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22181\n",
      "865198\n"
     ]
    }
   ],
   "source": [
    "print sum(data['safe_loans']==0)\n",
    "print sum(data['safe_loans']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Choosing only the required columns.\n",
    "#We intuitively chose few of the features, few by trail and error and few from the forums.\n",
    "col_subset = ['grade',                   # grade of the loan\n",
    "            'sub_grade',                 # sub-grade of the loan\n",
    "            'short_emp',                 # one year or less of employment\n",
    "            'home_ownership',            # home_ownership status: own, mortgage or rent\n",
    "            'dti',                       # debt to income ratio\n",
    "            'purpose',                   # the purpose of the loan\n",
    "            'term',                      # the term of the loan\n",
    "            'last_delinq_none',          # has borrower had a delinquincy\n",
    "            'last_major_derog_none',     # has borrower had 90 day or worse rating\n",
    "            'revol_util',                # percent of available credit being used\n",
    "            'total_rec_late_fee',        # total late fees received to day\n",
    "            'safe_loans'      \n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Few of the above columns have null values. There are ways to fill in the null/unknown values \n",
    "#but since we have enough data we're dropping the rows with null values. \n",
    "data_clean  = data[col_subset].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22161\n",
      "864716\n"
     ]
    }
   ],
   "source": [
    "print sum(data_clean['safe_loans']==0)\n",
    "print sum(data_clean['safe_loans']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#As displayed in the previous print statements we have a very large number of safe loans compared to the risky loans. \n",
    "#Initially, we tried with equal proportions of safe and risky loans but the result was not as expected. \n",
    "#So we chose approximately 80-20 of safe and risky loans to train and test the model.\n",
    "good_loans = data_clean[data_clean['safe_loans']==1]\n",
    "bad_loans = data_clean[data_clean['safe_loans']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "good_loans_subset = good_loans.sample(n=80000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "good_loans_remain = good_loans.loc[good_loans.index.drop(good_loans_subset.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_clean_subset = pd.concat([good_loans_subset,bad_loans])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Splitting the dataset for training and testing\n",
    "training_set, test_set = train_test_split(data_clean_subset, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loans_remain_data = pd.concat([good_loans_remain,test_set])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17659\n",
      "64069\n"
     ]
    }
   ],
   "source": [
    "print sum(training_set['safe_loans']==0)\n",
    "print sum(training_set['safe_loans']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Creates tensors, which is the input for the model\n",
    "def input_fn(df):\n",
    "    # Creates a dictionary mapping from each continuous feature column name (k) to\n",
    "    # the values of that column stored in a constant Tensor.\n",
    "    continuous_cols = {k: tf.constant(df[k].values)\n",
    "                       for k in CONTINUOUS_COLUMNS}\n",
    "    # Creates a dictionary mapping from each categorical feature column name (k)\n",
    "    # to the values of that column stored in a tf.SparseTensor.\n",
    "    categorical_cols = {k: tf.SparseTensor(\n",
    "        indices=[[i, 0] for i in range(df[k].size)],\n",
    "        values=df[k].values,\n",
    "        shape=[df[k].size, 1])\n",
    "                        for k in CATEGORICAL_COLUMNS}\n",
    "    # Merges the two dictionaries into one.\n",
    "    feature_cols = dict(continuous_cols.items() + categorical_cols.items())\n",
    "    # Converts the label column into a constant Tensor.\n",
    "    label = tf.constant(df[LABEL_COLUMN].values)\n",
    "    # Returns the feature columns and the label.\n",
    "    return feature_cols, label\n",
    "\n",
    "def train_input_fn():\n",
    "    return input_fn(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Choosing the features to create the model\n",
    "features = col_subset[:-1]\n",
    "\n",
    "LABEL_COLUMN = 'safe_loans'              # prediction target (y) (+1 means safe, 0 is risky)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grade',\n",
       " 'sub_grade',\n",
       " 'short_emp',\n",
       " 'home_ownership',\n",
       " 'dti',\n",
       " 'purpose',\n",
       " 'term',\n",
       " 'last_delinq_none',\n",
       " 'last_major_derog_none',\n",
       " 'revol_util',\n",
       " 'total_rec_late_fee']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Segregating the categorical and continuous columns to create the tensors with the respective data type \n",
    "tensor_features = {}\n",
    "CATEGORICAL_COLUMNS = []\n",
    "CONTINUOUS_COLUMNS = []\n",
    "\n",
    "for i in features:\n",
    "    if (data[i].dtype == 'int64') or (data[i].dtype == 'float64'):\n",
    "        CONTINUOUS_COLUMNS.append(i) \n",
    "        tensor_features[i] = tf.contrib.layers.real_valued_column(i)\n",
    "    else:\n",
    "        CATEGORICAL_COLUMNS.append(i)\n",
    "        tensor_features[i] = tf.contrib.layers.sparse_column_with_keys(\n",
    "                              column_name=i, keys=list(data[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['grade', 'sub_grade', 'home_ownership', 'purpose', 'term']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CATEGORICAL_COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['short_emp',\n",
       " 'dti',\n",
       " 'last_delinq_none',\n",
       " 'last_major_derog_none',\n",
       " 'revol_util',\n",
       " 'total_rec_late_fee']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CONTINUOUS_COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wide_columns = [tensor_features['short_emp'],\n",
    "                tensor_features['dti'], \n",
    "                tensor_features['last_delinq_none'], \n",
    "                tensor_features['last_major_derog_none'], \n",
    "                tensor_features['revol_util'], \n",
    "                tensor_features['total_rec_late_fee']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Dimensions = k * log base 2 of vocab size, chosen k = 9\n",
    "deep_columns = [tf.contrib.layers.embedding_column(tensor_features['grade'], dimension=27),\n",
    "                tf.contrib.layers.embedding_column(tensor_features['sub_grade'], dimension=45),\n",
    "                tf.contrib.layers.embedding_column(tensor_features['purpose'], dimension=36),\n",
    "                tf.contrib.layers.embedding_column(tensor_features['home_ownership'], dimension=23),\n",
    "                tf.contrib.layers.embedding_column(tensor_features['term'], dimension=9)\n",
    "               ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tempfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Creating the model\n",
    "model_dir = tempfile.mkdtemp()\n",
    "model = model = tf.contrib.learn.DNNLinearCombinedClassifier(\n",
    "    model_dir=model_dir,\n",
    "    linear_feature_columns=wide_columns,\n",
    "    dnn_feature_columns=deep_columns,\n",
    "    dnn_hidden_units=[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-11-13 20:57:16.351729\n",
      "2016-11-13 21:11:55.028410\n"
     ]
    }
   ],
   "source": [
    "#Fitting the model on the training data\n",
    "print datetime.datetime.now()\n",
    "model.fit(input_fn=train_input_fn, steps=2000)\n",
    "print datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.796508\n",
      "accuracy/baseline_target_mean: 0.78393\n",
      "accuracy/threshold_0.500000_mean: 0.796508\n",
      "auc: 0.701852\n",
      "global_step: 2000\n",
      "labels/actual_target_mean: 0.78393\n",
      "labels/prediction_mean: 0.792137\n",
      "loss: 0.474371\n",
      "precision/positive_threshold_0.500000_mean: 0.797253\n",
      "recall/positive_threshold_0.500000_mean: 0.99293\n"
     ]
    }
   ],
   "source": [
    "#Evaluating the model on the test data\n",
    "results = model.evaluate(input_fn=train_input_fn, steps=1)\n",
    "for key in sorted(results):\n",
    "    print \"%s: %s\" % (key, results[key])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting using the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_random_dataset = loans_remain_data.sample(n=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "555\n",
      "99445\n"
     ]
    }
   ],
   "source": [
    "print sum(test_random_dataset['safe_loans']==0)\n",
    "print sum(test_random_dataset['safe_loans']==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def eval_input_fn():\n",
    "    return input_fn(test_random_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.98762\n",
      "accuracy/baseline_target_mean: 0.99445\n",
      "accuracy/threshold_0.500000_mean: 0.98762\n",
      "auc: 0.718179\n",
      "global_step: 2000\n",
      "labels/actual_target_mean: 0.99445\n",
      "labels/prediction_mean: 0.812793\n",
      "loss: 0.225133\n",
      "precision/positive_threshold_0.500000_mean: 0.99499\n",
      "recall/positive_threshold_0.500000_mean: 0.992549\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(input_fn=eval_input_fn, steps=1)\n",
    "for key in sorted(results):\n",
    "    print \"%s: %s\" % (key, results[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We need the precited lables to calculate the number of correct and wrong predictions\n",
    "pred_labels = model.predict(input_fn=eval_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "799\n",
      "99201\n"
     ]
    }
   ],
   "source": [
    "print sum(pred_labels==0)\n",
    "print sum(pred_labels==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true_labels = np.array(test_random_dataset['safe_loans']).astype(int)\n",
    "pred_labels = np.array(pred_labels).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Predicted as safe and the actual label is also safe\n",
    "true_positive = sum((pred_labels == 1) & (true_labels == 1)) \n",
    "\n",
    "#Predicted as risky and the actual label is also risky\n",
    "true_negative = sum((pred_labels == 0) & (true_labels == 0))\n",
    "\n",
    "#Predicted as safe but the actual label is risky\n",
    "false_positive = sum((pred_labels == 1) & (true_labels == 0))\n",
    "\n",
    "#Predicted as risky but the actual label is safe\n",
    "false_negative = sum((pred_labels == 0) & (true_labels == 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positive:  98704\n",
      "False Positive:  497\n",
      "True Negative:  58\n",
      "False Negative:  741\n"
     ]
    }
   ],
   "source": [
    "print 'True Positive: ',true_positive\n",
    "print 'False Positive: ',false_positive\n",
    "print 'True Negative: ',true_negative\n",
    "print 'False Negative: ',false_negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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