Light_house_Test1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supress Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv('2017Q4.csv.gz', compression='gzip', header=0, sep=' ', quotechar='\"', error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('2017Q4.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df5 = pd.read_csv('2016Q1.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df5' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-582e4cbd163b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf5\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df5' is not defined"
     ]
    }
   ],
   "source": [
    "#df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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>funded_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</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_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>application_type</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</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_revol_delinq</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>revol_bal_joint</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>119702702</td>\n",
       "      <td>6000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>187.94</td>\n",
       "      <td>Panel operator</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>601xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>20.08</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-92</td>\n",
       "      <td>684</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>3002</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>12</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>32.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9057.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18032</td>\n",
       "      <td>11400</td>\n",
       "      <td>28868.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>Oct-09</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>122369339</td>\n",
       "      <td>10000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>225.60</td>\n",
       "      <td>MACHINIST</td>\n",
       "      <td>5 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>In Grace Period</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>029xx</td>\n",
       "      <td>RI</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-02</td>\n",
       "      <td>714</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>13</td>\n",
       "      <td>8184</td>\n",
       "      <td>18.20%</td>\n",
       "      <td>38</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>44900</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>24942.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>8184</td>\n",
       "      <td>31600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>124136825</td>\n",
       "      <td>1500</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.72%</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Sales rep</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30975.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>770xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-01</td>\n",
       "      <td>814</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>537</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>8</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>1</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>537</td>\n",
       "      <td>1800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>124065154</td>\n",
       "      <td>8400</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>270.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RENT</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>177xx</td>\n",
       "      <td>PA</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-98</td>\n",
       "      <td>754</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>3215</td>\n",
       "      <td>26.60%</td>\n",
       "      <td>12</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8885.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10176</td>\n",
       "      <td>12100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>119971787</td>\n",
       "      <td>24000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>804.27</td>\n",
       "      <td>Owner</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>447xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-92</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>690</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>10</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>22.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9610.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>10300</td>\n",
       "      <td>8372.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>Aug-05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>122339127</td>\n",
       "      <td>26450</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.07%</td>\n",
       "      <td>817.55</td>\n",
       "      <td>sales rep</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>OWN</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>208xx</td>\n",
       "      <td>MD</td>\n",
       "      <td>10.48</td>\n",
       "      <td>0</td>\n",
       "      <td>Feb-00</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>7392</td>\n",
       "      <td>16.20%</td>\n",
       "      <td>28</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45700</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>21461.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>4</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25494</td>\n",
       "      <td>28500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>124960857</td>\n",
       "      <td>28000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>20.00%</td>\n",
       "      <td>1040.59</td>\n",
       "      <td>Support Staff</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>731xx</td>\n",
       "      <td>OK</td>\n",
       "      <td>24.69</td>\n",
       "      <td>0</td>\n",
       "      <td>Mar-96</td>\n",
       "      <td>709</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11</td>\n",
       "      <td>21170</td>\n",
       "      <td>76.40%</td>\n",
       "      <td>18</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>109000.0</td>\n",
       "      <td>34.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>27700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1424.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0</td>\n",
       "      <td>21170</td>\n",
       "      <td>21800</td>\n",
       "      <td>65628.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>Mar-96</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>124812621</td>\n",
       "      <td>20000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>13.59%</td>\n",
       "      <td>461.13</td>\n",
       "      <td>REGIONAL SALES</td>\n",
       "      <td>2 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>295xx</td>\n",
       "      <td>SC</td>\n",
       "      <td>46.29</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-04</td>\n",
       "      <td>709</td>\n",
       "      <td>2</td>\n",
       "      <td>43.0</td>\n",
       "      <td>14</td>\n",
       "      <td>14421</td>\n",
       "      <td>61.90%</td>\n",
       "      <td>17</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>111500.0</td>\n",
       "      <td>22.73</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>23300</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6616.0</td>\n",
       "      <td>67.4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94.1</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1</td>\n",
       "      <td>107558</td>\n",
       "      <td>20300</td>\n",
       "      <td>29122.0</td>\n",
       "      <td>684.0</td>\n",
       "      <td>Jan-12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>122340066</td>\n",
       "      <td>10000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.91%</td>\n",
       "      <td>326.97</td>\n",
       "      <td>lead technician</td>\n",
       "      <td>8 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>458xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>12.17</td>\n",
       "      <td>0</td>\n",
       "      <td>Nov-03</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6809</td>\n",
       "      <td>46.60%</td>\n",
       "      <td>5</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>14600</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>70.3</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6809</td>\n",
       "      <td>9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>124975705</td>\n",
       "      <td>40000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.35%</td>\n",
       "      <td>1241.50</td>\n",
       "      <td>Account executive</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>other</td>\n",
       "      <td>787xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>Apr-96</td>\n",
       "      <td>844</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>353</td>\n",
       "      <td>0.40%</td>\n",
       "      <td>20</td>\n",
       "      <td>Individual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89400</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>89047.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>353</td>\n",
       "      <td>89400</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  funded_amnt        term int_rate  installment  \\\n",
       "0  119702702         6000   36 months    7.97%       187.94   \n",
       "1  122369339        10000   60 months   12.62%       225.60   \n",
       "2  124136825         1500   36 months    6.72%        46.13   \n",
       "3  124065154         8400   36 months    9.93%       270.77   \n",
       "4  119971787        24000   36 months   12.62%       804.27   \n",
       "5  122339127        26450   36 months    7.07%       817.55   \n",
       "6  124960857        28000   36 months   20.00%      1040.59   \n",
       "7  124812621        20000   60 months   13.59%       461.13   \n",
       "8  122340066        10000   36 months   10.91%       326.97   \n",
       "9  124975705        40000   36 months    7.35%      1241.50   \n",
       "\n",
       "           emp_title emp_length home_ownership  annual_inc      loan_status  \\\n",
       "0     Panel operator   < 1 year       MORTGAGE     30000.0          Current   \n",
       "1          MACHINIST    5 years       MORTGAGE     47300.0  In Grace Period   \n",
       "2         Sales rep     9 years       MORTGAGE     30975.0          Current   \n",
       "3                NaN        NaN           RENT     18000.0       Fully Paid   \n",
       "4              Owner   < 1 year           RENT     20000.0          Current   \n",
       "5          sales rep  10+ years            OWN    100000.0          Current   \n",
       "6      Support Staff  10+ years       MORTGAGE     33000.0          Current   \n",
       "7    REGIONAL SALES     2 years           RENT     45000.0          Current   \n",
       "8    lead technician    8 years           RENT     35000.0       Fully Paid   \n",
       "9  Account executive    9 years       MORTGAGE    300000.0       Fully Paid   \n",
       "\n",
       "              purpose zip_code addr_state    dti  delinq_2yrs  \\\n",
       "0               house    601xx         IL  20.08            0   \n",
       "1      major_purchase    029xx         RI   4.87            0   \n",
       "2               house    770xx         TX   1.63            0   \n",
       "3  debt_consolidation    177xx         PA  16.33            0   \n",
       "4  debt_consolidation    447xx         OH   3.00            0   \n",
       "5  debt_consolidation    208xx         MD  10.48            0   \n",
       "6  debt_consolidation    731xx         OK  24.69            0   \n",
       "7  debt_consolidation    295xx         SC  46.29            0   \n",
       "8  debt_consolidation    458xx         OH  12.17            0   \n",
       "9               other    787xx         TX   0.20            1   \n",
       "\n",
       "  earliest_cr_line  fico_range_high  inq_last_6mths  mths_since_last_delinq  \\\n",
       "0           Jun-92              684               1                     NaN   \n",
       "1           Jan-02              714               1                    41.0   \n",
       "2           Jan-01              814               0                     NaN   \n",
       "3           Jun-98              754               0                     NaN   \n",
       "4           Jan-92              794               0                     NaN   \n",
       "5           Feb-00              794               0                     NaN   \n",
       "6           Mar-96              709               0                    30.0   \n",
       "7           Nov-04              709               2                    43.0   \n",
       "8           Nov-03              664               0                    77.0   \n",
       "9           Apr-96              844               1                     8.0   \n",
       "\n",
       "   open_acc  revol_bal revol_util  total_acc application_type  \\\n",
       "0         9       3002     13.80%         12        Joint App   \n",
       "1        13       8184     18.20%         38       Individual   \n",
       "2         4        537      7.10%          8       Individual   \n",
       "3         5       3215     26.60%         12       Individual   \n",
       "4         5        690      5.70%         10        Joint App   \n",
       "5        14       7392     16.20%         28       Individual   \n",
       "6        11      21170     76.40%         18        Joint App   \n",
       "7        14      14421     61.90%         17        Joint App   \n",
       "8         2       6809     46.60%          5       Individual   \n",
       "9         8        353      0.40%         20       Individual   \n",
       "\n",
       "   annual_inc_joint  dti_joint  open_act_il  open_il_24m  il_util  \\\n",
       "0           83000.0      32.88            1            1     70.0   \n",
       "1               NaN        NaN            0            2      NaN   \n",
       "2               NaN        NaN            0            0      NaN   \n",
       "3               NaN        NaN            1            2     63.0   \n",
       "4           70000.0      22.73            0            0      NaN   \n",
       "5               NaN        NaN            1            3     78.0   \n",
       "6          109000.0      34.85            0            0      NaN   \n",
       "7          111500.0      22.73            7            2     72.0   \n",
       "8               NaN        NaN            0            0      NaN   \n",
       "9               NaN        NaN            0            0      NaN   \n",
       "\n",
       "   open_rv_24m  all_util  total_rev_hi_lim  inq_last_12m  \\\n",
       "0            3      42.0             21700             1   \n",
       "1            4      18.0             44900             5   \n",
       "2            0       7.0              7600             0   \n",
       "3            2      44.0             12100             0   \n",
       "4            4       6.0             12100             0   \n",
       "5            2      37.0             45700             0   \n",
       "6            0      76.0             27700             1   \n",
       "7            3      71.0             23300             4   \n",
       "8            1      47.0             14600             1   \n",
       "9            1       0.0             89400             1   \n",
       "\n",
       "   acc_open_past_24mths  bc_open_to_buy  bc_util  mort_acc  \\\n",
       "0                     4          9057.0     20.6         0   \n",
       "1                     7         24942.0     21.1         4   \n",
       "2                     0          1457.0     19.1         1   \n",
       "3                     4          8885.0     26.6         0   \n",
       "4                     4          9610.0      6.7         0   \n",
       "5                     5         21461.0     24.7         4   \n",
       "6                     0          1424.0     93.5         2   \n",
       "7                     5          6616.0     67.4         1   \n",
       "8                     1          2673.0     70.3         0   \n",
       "9                     3         89047.0      0.4         9   \n",
       "\n",
       "   mths_since_recent_bc  mths_since_recent_bc_dlq  \\\n",
       "0                  15.0                       NaN   \n",
       "1                  32.0                       NaN   \n",
       "2                 110.0                       NaN   \n",
       "3                  13.0                       NaN   \n",
       "4                   0.0                       NaN   \n",
       "5                  49.0                       NaN   \n",
       "6                  27.0                       NaN   \n",
       "7                  16.0                       NaN   \n",
       "8                  38.0                       NaN   \n",
       "9                  17.0                       8.0   \n",
       "\n",
       "   mths_since_recent_revol_delinq  num_accts_ever_120_pd  num_rev_accts  \\\n",
       "0                             NaN                      0             11   \n",
       "1                             NaN                      0             29   \n",
       "2                             NaN                      0              6   \n",
       "3                             NaN                      0              9   \n",
       "4                             NaN                      0             10   \n",
       "5                             NaN                      0             19   \n",
       "6                            30.0                      0             10   \n",
       "7                             NaN                      1              7   \n",
       "8                             NaN                      0              2   \n",
       "9                             8.0                      0             10   \n",
       "\n",
       "   num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0                    5         9                   0                   1   \n",
       "1                    6        13                   0                   3   \n",
       "2                    2         4                   0                   0   \n",
       "3                    2         5                   0                   0   \n",
       "4                    2         5                   0                   2   \n",
       "5                    6        14                   0                   2   \n",
       "6                    7        11                   0                   0   \n",
       "7                    7        14                   0                   1   \n",
       "8                    2         2                   0                   1   \n",
       "9                    2         8                   0                   1   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  total_bal_ex_mort  \\\n",
       "0           100.0               0.0                     1              18032   \n",
       "1            97.4              14.3                     0               8184   \n",
       "2           100.0               0.0                     0                537   \n",
       "3           100.0              25.0                     0              10176   \n",
       "4           100.0               0.0                     0                690   \n",
       "5           100.0               0.0                     0              25494   \n",
       "6            94.4              83.3                     0              21170   \n",
       "7            94.1              33.3                     1             107558   \n",
       "8            80.0               0.0                     1               6809   \n",
       "9            94.7               0.0                     0                353   \n",
       "\n",
       "   total_bc_limit  revol_bal_joint  sec_app_fico_range_high  \\\n",
       "0           11400          28868.0                    674.0   \n",
       "1           31600              NaN                      NaN   \n",
       "2            1800              NaN                      NaN   \n",
       "3           12100              NaN                      NaN   \n",
       "4           10300           8372.0                    669.0   \n",
       "5           28500              NaN                      NaN   \n",
       "6           21800          65628.0                    649.0   \n",
       "7           20300          29122.0                    684.0   \n",
       "8            9000              NaN                      NaN   \n",
       "9           89400              NaN                      NaN   \n",
       "\n",
       "  sec_app_earliest_cr_line  sec_app_inq_last_6mths  \n",
       "0                   Oct-09                     1.0  \n",
       "1                      NaN                     NaN  \n",
       "2                      NaN                     NaN  \n",
       "3                      NaN                     NaN  \n",
       "4                   Aug-05                     2.0  \n",
       "5                      NaN                     NaN  \n",
       "6                   Mar-96                     2.0  \n",
       "7                   Jan-12                     1.0  \n",
       "8                      NaN                     NaN  \n",
       "9                      NaN                     NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59324 entries, 0 to 59323\n",
      "Data columns (total 55 columns):\n",
      "id                                59324 non-null int64\n",
      "funded_amnt                       59324 non-null int64\n",
      "term                              59324 non-null object\n",
      "int_rate                          59324 non-null object\n",
      "installment                       59324 non-null float64\n",
      "emp_title                         54381 non-null object\n",
      "emp_length                        54456 non-null object\n",
      "home_ownership                    59324 non-null object\n",
      "annual_inc                        59324 non-null float64\n",
      "loan_status                       59324 non-null object\n",
      "purpose                           59324 non-null object\n",
      "zip_code                          59324 non-null object\n",
      "addr_state                        59324 non-null object\n",
      "dti                               59216 non-null float64\n",
      "delinq_2yrs                       59324 non-null int64\n",
      "earliest_cr_line                  59324 non-null object\n",
      "fico_range_high                   59324 non-null int64\n",
      "inq_last_6mths                    59324 non-null int64\n",
      "mths_since_last_delinq            26952 non-null float64\n",
      "open_acc                          59324 non-null int64\n",
      "revol_bal                         59324 non-null int64\n",
      "revol_util                        59232 non-null object\n",
      "total_acc                         59324 non-null int64\n",
      "application_type                  59324 non-null object\n",
      "annual_inc_joint                  8633 non-null float64\n",
      "dti_joint                         8633 non-null float64\n",
      "open_act_il                       59324 non-null int64\n",
      "open_il_24m                       59324 non-null int64\n",
      "il_util                           50007 non-null float64\n",
      "open_rv_24m                       59324 non-null int64\n",
      "all_util                          59306 non-null float64\n",
      "total_rev_hi_lim                  59324 non-null int64\n",
      "inq_last_12m                      59324 non-null int64\n",
      "acc_open_past_24mths              59324 non-null int64\n",
      "bc_open_to_buy                    58351 non-null float64\n",
      "bc_util                           58327 non-null float64\n",
      "mort_acc                          59324 non-null int64\n",
      "mths_since_recent_bc              58413 non-null float64\n",
      "mths_since_recent_bc_dlq          12381 non-null float64\n",
      "mths_since_recent_revol_delinq    17768 non-null float64\n",
      "num_accts_ever_120_pd             59324 non-null int64\n",
      "num_rev_accts                     59324 non-null int64\n",
      "num_rev_tl_bal_gt_0               59324 non-null int64\n",
      "num_sats                          59324 non-null int64\n",
      "num_tl_90g_dpd_24m                59324 non-null int64\n",
      "num_tl_op_past_12m                59324 non-null int64\n",
      "pct_tl_nvr_dlq                    59324 non-null float64\n",
      "percent_bc_gt_75                  58347 non-null float64\n",
      "pub_rec_bankruptcies              59324 non-null int64\n",
      "total_bal_ex_mort                 59324 non-null int64\n",
      "total_bc_limit                    59324 non-null int64\n",
      "revol_bal_joint                   8632 non-null float64\n",
      "sec_app_fico_range_high           8633 non-null float64\n",
      "sec_app_earliest_cr_line          8633 non-null object\n",
      "sec_app_inq_last_6mths            8633 non-null float64\n",
      "dtypes: float64(18), int64(24), object(13)\n",
      "memory usage: 24.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                                 0.00\n",
      "funded_amnt                        0.00\n",
      "term                               0.00\n",
      "int_rate                           0.00\n",
      "installment                        0.00\n",
      "emp_title                          8.33\n",
      "emp_length                         8.21\n",
      "home_ownership                     0.00\n",
      "annual_inc                         0.00\n",
      "loan_status                        0.00\n",
      "purpose                            0.00\n",
      "zip_code                           0.00\n",
      "addr_state                         0.00\n",
      "dti                                0.18\n",
      "delinq_2yrs                        0.00\n",
      "earliest_cr_line                   0.00\n",
      "fico_range_high                    0.00\n",
      "inq_last_6mths                     0.00\n",
      "mths_since_last_delinq            54.57\n",
      "open_acc                           0.00\n",
      "revol_bal                          0.00\n",
      "revol_util                         0.16\n",
      "total_acc                          0.00\n",
      "application_type                   0.00\n",
      "annual_inc_joint                  85.45\n",
      "dti_joint                         85.45\n",
      "open_act_il                        0.00\n",
      "open_il_24m                        0.00\n",
      "il_util                           15.71\n",
      "open_rv_24m                        0.00\n",
      "all_util                           0.03\n",
      "total_rev_hi_lim                   0.00\n",
      "inq_last_12m                       0.00\n",
      "acc_open_past_24mths               0.00\n",
      "bc_open_to_buy                     1.64\n",
      "bc_util                            1.68\n",
      "mort_acc                           0.00\n",
      "mths_since_recent_bc               1.54\n",
      "mths_since_recent_bc_dlq          79.13\n",
      "mths_since_recent_revol_delinq    70.05\n",
      "num_accts_ever_120_pd              0.00\n",
      "num_rev_accts                      0.00\n",
      "num_rev_tl_bal_gt_0                0.00\n",
      "num_sats                           0.00\n",
      "num_tl_90g_dpd_24m                 0.00\n",
      "num_tl_op_past_12m                 0.00\n",
      "pct_tl_nvr_dlq                     0.00\n",
      "percent_bc_gt_75                   1.65\n",
      "pub_rec_bankruptcies               0.00\n",
      "total_bal_ex_mort                  0.00\n",
      "total_bc_limit                     0.00\n",
      "revol_bal_joint                   85.45\n",
      "sec_app_fico_range_high           85.45\n",
      "sec_app_earliest_cr_line          85.45\n",
      "sec_app_inq_last_6mths            85.45\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "percent_missing = round(100*(df1.isnull().sum()/len(df1.index)), 2)\n",
    "print(percent_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#annual_inc_joint\tThe combined self-reported annual income provided by the co-borrowers during registration\n",
    "#dti_joint :A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income\n",
    "#mths_since_last_delinq: The number of months since the borrower's last delinquency.\n",
    "#il_util :Ratio of total current balance to high credit/credit limit on all install acct\n",
    "#mths_since_recent_bc_dlq :Months since most recent bankcard delinquency\n",
    "#mths_since_recent_revol_delinq:Months since most recent revolving delinquency.\n",
    "#revol_bal_joint :Sum of revolving credit balance of the co-borrowers, net of duplicate balances\n",
    "#sec_app_fico_range_high\n",
    "#sec_app_earliest_cr_line :Earliest credit line at time of application for the secondary applicant\n",
    "#sec_app_inq_last_6mths: Credit inquiries in the last 6 months at time of application for the secondary applicant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the columns which has null value more than 50 percentage\n",
    "columns_with_missing_values = list(percent_missing[percent_missing >= 50].index)\n",
    "\n",
    "len(columns_with_missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59324, 46)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df1.drop(columns_with_missing_values,axis=1)\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 59324 entries, 0 to 59323\n",
      "Data columns (total 46 columns):\n",
      "id                       59324 non-null int64\n",
      "funded_amnt              59324 non-null int64\n",
      "term                     59324 non-null object\n",
      "int_rate                 59324 non-null object\n",
      "installment              59324 non-null float64\n",
      "emp_title                54381 non-null object\n",
      "emp_length               54456 non-null object\n",
      "home_ownership           59324 non-null object\n",
      "annual_inc               59324 non-null float64\n",
      "loan_status              59324 non-null object\n",
      "purpose                  59324 non-null object\n",
      "zip_code                 59324 non-null object\n",
      "addr_state               59324 non-null object\n",
      "dti                      59216 non-null float64\n",
      "delinq_2yrs              59324 non-null int64\n",
      "earliest_cr_line         59324 non-null object\n",
      "fico_range_high          59324 non-null int64\n",
      "inq_last_6mths           59324 non-null int64\n",
      "open_acc                 59324 non-null int64\n",
      "revol_bal                59324 non-null int64\n",
      "revol_util               59232 non-null object\n",
      "total_acc                59324 non-null int64\n",
      "application_type         59324 non-null object\n",
      "open_act_il              59324 non-null int64\n",
      "open_il_24m              59324 non-null int64\n",
      "il_util                  50007 non-null float64\n",
      "open_rv_24m              59324 non-null int64\n",
      "all_util                 59306 non-null float64\n",
      "total_rev_hi_lim         59324 non-null int64\n",
      "inq_last_12m             59324 non-null int64\n",
      "acc_open_past_24mths     59324 non-null int64\n",
      "bc_open_to_buy           58351 non-null float64\n",
      "bc_util                  58327 non-null float64\n",
      "mort_acc                 59324 non-null int64\n",
      "mths_since_recent_bc     58413 non-null float64\n",
      "num_accts_ever_120_pd    59324 non-null int64\n",
      "num_rev_accts            59324 non-null int64\n",
      "num_rev_tl_bal_gt_0      59324 non-null int64\n",
      "num_sats                 59324 non-null int64\n",
      "num_tl_90g_dpd_24m       59324 non-null int64\n",
      "num_tl_op_past_12m       59324 non-null int64\n",
      "pct_tl_nvr_dlq           59324 non-null float64\n",
      "percent_bc_gt_75         58347 non-null float64\n",
      "pub_rec_bankruptcies     59324 non-null int64\n",
      "total_bal_ex_mort        59324 non-null int64\n",
      "total_bc_limit           59324 non-null int64\n",
      "dtypes: float64(10), int64(24), object(12)\n",
      "memory usage: 20.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emp_title                8.33\n",
       "emp_length               8.21\n",
       "dti                      0.18\n",
       "revol_util               0.16\n",
       "il_util                 15.71\n",
       "all_util                 0.03\n",
       "bc_open_to_buy           1.64\n",
       "bc_util                  1.68\n",
       "mths_since_recent_bc     1.54\n",
       "percent_bc_gt_75         1.65\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = round(100*(df2.isnull().sum()/len(df2.index)), 2)\n",
    "percent_missing[percent_missing != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique emp_title : 23492\n",
      "unique emp_length : 12\n",
      "unique revol_util : 1053\n",
      "unique il_util : 191\n",
      "unique all_util : 147\n",
      "unique bc_open_to_buy : 27159\n",
      "unique bc_util : 1091\n",
      "unique mths_since_recent_bc : 332\n",
      "unique percent_bc_gt_75 : 136\n"
     ]
    }
   ],
   "source": [
    "print(\"unique emp_title : %d\"  % len(df2.emp_title.unique()))\n",
    "print(\"unique emp_length : %d\"  % len(df2.emp_length.unique()))\n",
    "print(\"unique revol_util : %d\"  % len(df2.revol_util.unique()))\n",
    "print(\"unique il_util : %d\"  % len(df2.il_util.unique()))\n",
    "print(\"unique all_util : %d\"  % len(df2.all_util.unique()))\n",
    "print(\"unique bc_open_to_buy : %d\"  % len(df2.bc_open_to_buy.unique()))\n",
    "print(\"unique bc_util : %d\"  % len(df2.bc_util.unique()))\n",
    "print(\"unique mths_since_recent_bc : %d\"  % len(df2.mths_since_recent_bc.unique()))\n",
    "print(\"unique percent_bc_gt_75 : %d\"  % len(df2.percent_bc_gt_75.unique()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the null values which columns has less than 1 percentage\n",
    "df2=df2[~df2.dti.isnull()]\n",
    "df2=df2[~df2.revol_util.isnull()]\n",
    "df2=df2[~df2.all_util.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>funded_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</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_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>application_type</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>119702702</td>\n",
       "      <td>6000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>187.94</td>\n",
       "      <td>Panel operator</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>601xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>20.08</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-92</td>\n",
       "      <td>684</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3002</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>12</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9057.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18032</td>\n",
       "      <td>11400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>122369339</td>\n",
       "      <td>10000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>225.60</td>\n",
       "      <td>MACHINIST</td>\n",
       "      <td>5 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>In Grace Period</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>029xx</td>\n",
       "      <td>RI</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-02</td>\n",
       "      <td>714</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8184</td>\n",
       "      <td>18.20%</td>\n",
       "      <td>38</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>44900</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>24942.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>8184</td>\n",
       "      <td>31600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>124136825</td>\n",
       "      <td>1500</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.72%</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Sales rep</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30975.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>770xx</td>\n",
       "      <td>TX</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-01</td>\n",
       "      <td>814</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>537</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>8</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>1</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>537</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>124065154</td>\n",
       "      <td>8400</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>270.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RENT</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>177xx</td>\n",
       "      <td>PA</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-98</td>\n",
       "      <td>754</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3215</td>\n",
       "      <td>26.60%</td>\n",
       "      <td>12</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8885.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10176</td>\n",
       "      <td>12100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>119971787</td>\n",
       "      <td>24000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>804.27</td>\n",
       "      <td>Owner</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>447xx</td>\n",
       "      <td>OH</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-92</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>690</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>10</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9610.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>10300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  funded_amnt        term int_rate  installment       emp_title  \\\n",
       "0  119702702         6000   36 months    7.97%       187.94  Panel operator   \n",
       "1  122369339        10000   60 months   12.62%       225.60       MACHINIST   \n",
       "2  124136825         1500   36 months    6.72%        46.13      Sales rep    \n",
       "3  124065154         8400   36 months    9.93%       270.77             NaN   \n",
       "4  119971787        24000   36 months   12.62%       804.27           Owner   \n",
       "\n",
       "  emp_length home_ownership  annual_inc      loan_status             purpose  \\\n",
       "0   < 1 year       MORTGAGE     30000.0          Current               house   \n",
       "1    5 years       MORTGAGE     47300.0  In Grace Period      major_purchase   \n",
       "2    9 years       MORTGAGE     30975.0          Current               house   \n",
       "3        NaN           RENT     18000.0       Fully Paid  debt_consolidation   \n",
       "4   < 1 year           RENT     20000.0          Current  debt_consolidation   \n",
       "\n",
       "  zip_code addr_state    dti  delinq_2yrs earliest_cr_line  fico_range_high  \\\n",
       "0    601xx         IL  20.08            0           Jun-92              684   \n",
       "1    029xx         RI   4.87            0           Jan-02              714   \n",
       "2    770xx         TX   1.63            0           Jan-01              814   \n",
       "3    177xx         PA  16.33            0           Jun-98              754   \n",
       "4    447xx         OH   3.00            0           Jan-92              794   \n",
       "\n",
       "   inq_last_6mths  open_acc  revol_bal revol_util  total_acc application_type  \\\n",
       "0               1         9       3002     13.80%         12        Joint App   \n",
       "1               1        13       8184     18.20%         38       Individual   \n",
       "2               0         4        537      7.10%          8       Individual   \n",
       "3               0         5       3215     26.60%         12       Individual   \n",
       "4               0         5        690      5.70%         10        Joint App   \n",
       "\n",
       "   open_act_il  open_il_24m  il_util  open_rv_24m  all_util  total_rev_hi_lim  \\\n",
       "0            1            1     70.0            3      42.0             21700   \n",
       "1            0            2      NaN            4      18.0             44900   \n",
       "2            0            0      NaN            0       7.0              7600   \n",
       "3            1            2     63.0            2      44.0             12100   \n",
       "4            0            0      NaN            4       6.0             12100   \n",
       "\n",
       "   inq_last_12m  acc_open_past_24mths  bc_open_to_buy  bc_util  mort_acc  \\\n",
       "0             1                     4          9057.0     20.6         0   \n",
       "1             5                     7         24942.0     21.1         4   \n",
       "2             0                     0          1457.0     19.1         1   \n",
       "3             0                     4          8885.0     26.6         0   \n",
       "4             0                     4          9610.0      6.7         0   \n",
       "\n",
       "   mths_since_recent_bc  num_accts_ever_120_pd  num_rev_accts  \\\n",
       "0                  15.0                      0             11   \n",
       "1                  32.0                      0             29   \n",
       "2                 110.0                      0              6   \n",
       "3                  13.0                      0              9   \n",
       "4                   0.0                      0             10   \n",
       "\n",
       "   num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0                    5         9                   0                   1   \n",
       "1                    6        13                   0                   3   \n",
       "2                    2         4                   0                   0   \n",
       "3                    2         5                   0                   0   \n",
       "4                    2         5                   0                   2   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  total_bal_ex_mort  \\\n",
       "0           100.0               0.0                     1              18032   \n",
       "1            97.4              14.3                     0               8184   \n",
       "2           100.0               0.0                     0                537   \n",
       "3           100.0              25.0                     0              10176   \n",
       "4           100.0               0.0                     0                690   \n",
       "\n",
       "   total_bc_limit  \n",
       "0           11400  \n",
       "1           31600  \n",
       "2            1800  \n",
       "3           12100  \n",
       "4           10300  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.0     936\n",
      "73.0     934\n",
      "72.0     915\n",
      "81.0     912\n",
      "83.0     910\n",
      "        ... \n",
      "153.0      1\n",
      "160.0      1\n",
      "181.0      1\n",
      "323.0      1\n",
      "164.0      1\n",
      "Name: il_util, Length: 190, dtype: int64\n",
      "Median: 70.0\n",
      "Mean: 67.77253950429133\n"
     ]
    }
   ],
   "source": [
    "print(df2[\"il_util\"].value_counts())\n",
    "print(\"Median: {}\".format(df2[\"il_util\"].median()))\n",
    "print(\"Mean: {}\".format(df2[\"il_util\"].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is not much difference with median and mode ,we replace the nan values with median\n",
    "df2['il_util'] = df2['il_util'].replace(np.nan, df2['il_util'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0        427\n",
      "3000.0      69\n",
      "2000.0      67\n",
      "500.0       66\n",
      "5000.0      64\n",
      "          ... \n",
      "27789.0      1\n",
      "5194.0       1\n",
      "12114.0      1\n",
      "16013.0      1\n",
      "39609.0      1\n",
      "Name: bc_open_to_buy, Length: 27135, dtype: int64\n",
      "Median: 7612.5\n",
      "Mean: 14304.047115384616\n"
     ]
    }
   ],
   "source": [
    "print(df2[\"bc_open_to_buy\"].value_counts())\n",
    "print(\"Median: {}\".format(df2[\"bc_open_to_buy\"].median()))\n",
    "print(\"Mean: {}\".format(df2[\"bc_open_to_buy\"].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59124, 46)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove all null values\n",
    "df3=df2[~df2.emp_title.isnull()]\n",
    "df3=df2[~df2.emp_length.isnull()]\n",
    "df3=df2[~df2.bc_open_to_buy.isnull()]\n",
    "df3=df2[~df2.bc_util.isnull()]\n",
    "df3=df2[~df2.mths_since_recent_bc.isnull()]\n",
    "df3=df2[~df2.percent_bc_gt_75.isnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58236, 46)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 58236 entries, 0 to 59322\n",
      "Data columns (total 46 columns):\n",
      "id                       58236 non-null int64\n",
      "funded_amnt              58236 non-null int64\n",
      "term                     58236 non-null object\n",
      "int_rate                 58236 non-null object\n",
      "installment              58236 non-null float64\n",
      "emp_title                53520 non-null object\n",
      "emp_length               53595 non-null object\n",
      "home_ownership           58236 non-null object\n",
      "annual_inc               58236 non-null float64\n",
      "loan_status              58236 non-null object\n",
      "purpose                  58236 non-null object\n",
      "zip_code                 58236 non-null object\n",
      "addr_state               58236 non-null object\n",
      "dti                      58236 non-null float64\n",
      "delinq_2yrs              58236 non-null int64\n",
      "earliest_cr_line         58236 non-null object\n",
      "fico_range_high          58236 non-null int64\n",
      "inq_last_6mths           58236 non-null int64\n",
      "open_acc                 58236 non-null int64\n",
      "revol_bal                58236 non-null int64\n",
      "revol_util               58236 non-null object\n",
      "total_acc                58236 non-null int64\n",
      "application_type         58236 non-null object\n",
      "open_act_il              58236 non-null int64\n",
      "open_il_24m              58236 non-null int64\n",
      "il_util                  58236 non-null float64\n",
      "open_rv_24m              58236 non-null int64\n",
      "all_util                 58236 non-null float64\n",
      "total_rev_hi_lim         58236 non-null int64\n",
      "inq_last_12m             58236 non-null int64\n",
      "acc_open_past_24mths     58236 non-null int64\n",
      "bc_open_to_buy           58236 non-null float64\n",
      "bc_util                  58220 non-null float64\n",
      "mort_acc                 58236 non-null int64\n",
      "mths_since_recent_bc     58236 non-null float64\n",
      "num_accts_ever_120_pd    58236 non-null int64\n",
      "num_rev_accts            58236 non-null int64\n",
      "num_rev_tl_bal_gt_0      58236 non-null int64\n",
      "num_sats                 58236 non-null int64\n",
      "num_tl_90g_dpd_24m       58236 non-null int64\n",
      "num_tl_op_past_12m       58236 non-null int64\n",
      "pct_tl_nvr_dlq           58236 non-null float64\n",
      "percent_bc_gt_75         58236 non-null float64\n",
      "pub_rec_bankruptcies     58236 non-null int64\n",
      "total_bal_ex_mort        58236 non-null int64\n",
      "total_bc_limit           58236 non-null int64\n",
      "dtypes: float64(10), int64(24), object(12)\n",
      "memory usage: 20.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the columns which are not required for analysis i.e. id, zip_code, addr_state\n",
    "df3 = df3.drop('id',axis=1)\n",
    "df3 = df3.drop('zip_code',axis=1)\n",
    "df3 = df3.drop('addr_state',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imbalance check\n",
    "#df3[\"loan_status\"].value_counts()/len(df3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,7))\n",
    "ax = df3.groupby('loan_status').funded_amnt.count().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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>funded_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>purpose</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>application_type</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>187.94</td>\n",
       "      <td>Panel operator</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>20.08</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-92</td>\n",
       "      <td>684</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3002</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>12</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9057.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18032</td>\n",
       "      <td>11400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>225.60</td>\n",
       "      <td>MACHINIST</td>\n",
       "      <td>5 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>In Grace Period</td>\n",
       "      <td>major_purchase</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-02</td>\n",
       "      <td>714</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8184</td>\n",
       "      <td>18.20%</td>\n",
       "      <td>38</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>44900</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>24942.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>8184</td>\n",
       "      <td>31600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.72%</td>\n",
       "      <td>46.13</td>\n",
       "      <td>Sales rep</td>\n",
       "      <td>9 years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30975.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>house</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-01</td>\n",
       "      <td>814</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>537</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>8</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>1</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>537</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8400</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>270.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RENT</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0</td>\n",
       "      <td>Jun-98</td>\n",
       "      <td>754</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3215</td>\n",
       "      <td>26.60%</td>\n",
       "      <td>12</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8885.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10176</td>\n",
       "      <td>12100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>804.27</td>\n",
       "      <td>Owner</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Current</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Jan-92</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>690</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>10</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9610.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>10300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   funded_amnt        term int_rate  installment       emp_title emp_length  \\\n",
       "0         6000   36 months    7.97%       187.94  Panel operator   < 1 year   \n",
       "1        10000   60 months   12.62%       225.60       MACHINIST    5 years   \n",
       "2         1500   36 months    6.72%        46.13      Sales rep     9 years   \n",
       "3         8400   36 months    9.93%       270.77             NaN        NaN   \n",
       "4        24000   36 months   12.62%       804.27           Owner   < 1 year   \n",
       "\n",
       "  home_ownership  annual_inc      loan_status             purpose    dti  \\\n",
       "0       MORTGAGE     30000.0          Current               house  20.08   \n",
       "1       MORTGAGE     47300.0  In Grace Period      major_purchase   4.87   \n",
       "2       MORTGAGE     30975.0          Current               house   1.63   \n",
       "3           RENT     18000.0       Fully Paid  debt_consolidation  16.33   \n",
       "4           RENT     20000.0          Current  debt_consolidation   3.00   \n",
       "\n",
       "   delinq_2yrs earliest_cr_line  fico_range_high  inq_last_6mths  open_acc  \\\n",
       "0            0           Jun-92              684               1         9   \n",
       "1            0           Jan-02              714               1        13   \n",
       "2            0           Jan-01              814               0         4   \n",
       "3            0           Jun-98              754               0         5   \n",
       "4            0           Jan-92              794               0         5   \n",
       "\n",
       "   revol_bal revol_util  total_acc application_type  open_act_il  open_il_24m  \\\n",
       "0       3002     13.80%         12        Joint App            1            1   \n",
       "1       8184     18.20%         38       Individual            0            2   \n",
       "2        537      7.10%          8       Individual            0            0   \n",
       "3       3215     26.60%         12       Individual            1            2   \n",
       "4        690      5.70%         10        Joint App            0            0   \n",
       "\n",
       "   il_util  open_rv_24m  all_util  total_rev_hi_lim  inq_last_12m  \\\n",
       "0     70.0            3      42.0             21700             1   \n",
       "1     70.0            4      18.0             44900             5   \n",
       "2     70.0            0       7.0              7600             0   \n",
       "3     63.0            2      44.0             12100             0   \n",
       "4     70.0            4       6.0             12100             0   \n",
       "\n",
       "   acc_open_past_24mths  bc_open_to_buy  bc_util  mort_acc  \\\n",
       "0                     4          9057.0     20.6         0   \n",
       "1                     7         24942.0     21.1         4   \n",
       "2                     0          1457.0     19.1         1   \n",
       "3                     4          8885.0     26.6         0   \n",
       "4                     4          9610.0      6.7         0   \n",
       "\n",
       "   mths_since_recent_bc  num_accts_ever_120_pd  num_rev_accts  \\\n",
       "0                  15.0                      0             11   \n",
       "1                  32.0                      0             29   \n",
       "2                 110.0                      0              6   \n",
       "3                  13.0                      0              9   \n",
       "4                   0.0                      0             10   \n",
       "\n",
       "   num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0                    5         9                   0                   1   \n",
       "1                    6        13                   0                   3   \n",
       "2                    2         4                   0                   0   \n",
       "3                    2         5                   0                   0   \n",
       "4                    2         5                   0                   2   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  total_bal_ex_mort  \\\n",
       "0           100.0               0.0                     1              18032   \n",
       "1            97.4              14.3                     0               8184   \n",
       "2           100.0               0.0                     0                537   \n",
       "3           100.0              25.0                     0              10176   \n",
       "4           100.0               0.0                     0                690   \n",
       "\n",
       "   total_bc_limit  \n",
       "0           11400  \n",
       "1           31600  \n",
       "2            1800  \n",
       "3           12100  \n",
       "4           10300  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the columns emp_title,emp_length,earliest_cr_line,purpose\n",
    "df3 = df3.drop('emp_title',axis=1)\n",
    "df3 = df3.drop('emp_length',axis=1)\n",
    "df3 = df3.drop('earliest_cr_line',axis=1)\n",
    "df3 = df3.drop('purpose',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 36 months    40513\n",
       " 60 months    17723\n",
       "Name: term, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dummies for the following columns below\n",
    "#term\n",
    "df3['term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    27877\n",
       "RENT        22837\n",
       "OWN          7521\n",
       "ANY             1\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#home_ownership\n",
    "df3['home_ownership'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current               46258\n",
       "Fully Paid             8403\n",
       "Charged Off            1492\n",
       "Late (31-120 days)     1155\n",
       "In Grace Period         612\n",
       "Late (16-30 days)       316\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loan_status\n",
    "df3['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#purpose\t\n",
    "#df3['purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Individual    49970\n",
       "Joint App      8266\n",
       "Name: application_type, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#application_type\n",
    "df3['application_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3['loan_status'].replace(['Fully Paid', 'Charged Off', 'Late (31-120 days)', 'In Grace Period','Late (16-30 days)'], ['FullyPaid', 'ChargedOff', 'Late_31_120', 'InGracePeriod', 'Late_16_30'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "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>loan_status_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>In Grace Period</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59318</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59319</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59320</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59321</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59322</td>\n",
       "      <td>Current</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58236 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           loan_status  loan_status_encoded\n",
       "0              Current                    5\n",
       "1      In Grace Period                    3\n",
       "2              Current                    5\n",
       "3           Fully Paid                    1\n",
       "4              Current                    5\n",
       "...                ...                  ...\n",
       "59318          Current                    5\n",
       "59319          Current                    5\n",
       "59320          Current                    5\n",
       "59321          Current                    5\n",
       "59322          Current                    5\n",
       "\n",
       "[58236 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['loan_status_encoded'] = df3['loan_status'].map( {'Charged Off':0, 'Fully Paid':1, 'Late (31-120 days)':2, 'In Grace Period':3, 'Late (16-30 days)':4, 'Current':5})\n",
    "df3[['loan_status', 'loan_status_encoded']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.drop(['loan_status'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3['loan_status'].replace(['Fully Paid', 'Charged Off', 'InGracePeriod', 'Late_31_120', 'Late_16_30', 'Current'],[1, 0, 2, 3, 4,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3['loan_status'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# term : \n",
    "\n",
    "d_term = pd.get_dummies(df3['term'], prefix='term')\n",
    "# Dropping quick Add Form column\n",
    "#d_term1 = d_term.drop(['term_Quick Add Form'], 1)\n",
    "#Adding the results to the master dataframe\n",
    "df3 = pd.concat([df3, d_term], axis = 1)\n",
    "\n",
    "\n",
    "#home_ownership\n",
    "d_home_ownership = pd.get_dummies(df3['home_ownership'], prefix='home_ownership')\n",
    "# Dropping quick Add Form column\n",
    "d_home_ownership1 = d_home_ownership.drop(['home_ownership_ANY'], 1)\n",
    "#Adding the results to the master dataframe\n",
    "df3 = pd.concat([df3, d_home_ownership1], axis = 1)\n",
    "\n",
    "#loan_status\n",
    "\n",
    "#d_loan_status = pd.get_dummies(df3['loan_status'], prefix='loan_status')\n",
    "# Dropping quick Add Form column\n",
    "#d_term1 = d_term.drop(['term_Quick Add Form'], 1)\n",
    "#Adding the results to the master dataframe\n",
    "#df3 = pd.concat([df3, d_loan_status], axis = 1)\n",
    "                 \n",
    "                 \n",
    "#application_type\n",
    "\n",
    "d_application_type = pd.get_dummies(df3['application_type'], prefix='application_type')\n",
    "# Dropping quick Add Form column\n",
    "#d_purpose1 = d_purpose.drop(['purpose_educational'], 1)\n",
    "#Adding the results to the master dataframe\n",
    "df3 = pd.concat([df3, d_application_type], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>funded_amnt</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>application_type</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>loan_status_encoded</th>\n",
       "      <th>term_ 36 months</th>\n",
       "      <th>term_ 60 months</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.97%</td>\n",
       "      <td>187.94</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>20.08</td>\n",
       "      <td>0</td>\n",
       "      <td>684</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3002</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>12</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9057.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>18032</td>\n",
       "      <td>11400</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>225.60</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0</td>\n",
       "      <td>714</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8184</td>\n",
       "      <td>18.20%</td>\n",
       "      <td>38</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>44900</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>24942.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>8184</td>\n",
       "      <td>31600</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>36 months</td>\n",
       "      <td>6.72%</td>\n",
       "      <td>46.13</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>30975.0</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>814</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>537</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>8</td>\n",
       "      <td>Individual</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>1</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>537</td>\n",
       "      <td>1800</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8400</td>\n",
       "      <td>36 months</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>270.77</td>\n",
       "      <td>RENT</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0</td>\n",
       "      <td>754</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3215</td>\n",
       "      <td>26.60%</td>\n",
       "      <td>12</td>\n",
       "      <td>Individual</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8885.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10176</td>\n",
       "      <td>12100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24000</td>\n",
       "      <td>36 months</td>\n",
       "      <td>12.62%</td>\n",
       "      <td>804.27</td>\n",
       "      <td>RENT</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>794</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>690</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>10</td>\n",
       "      <td>Joint App</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12100</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>9610.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>10300</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   funded_amnt        term int_rate  installment home_ownership  annual_inc  \\\n",
       "0         6000   36 months    7.97%       187.94       MORTGAGE     30000.0   \n",
       "1        10000   60 months   12.62%       225.60       MORTGAGE     47300.0   \n",
       "2         1500   36 months    6.72%        46.13       MORTGAGE     30975.0   \n",
       "3         8400   36 months    9.93%       270.77           RENT     18000.0   \n",
       "4        24000   36 months   12.62%       804.27           RENT     20000.0   \n",
       "\n",
       "     dti  delinq_2yrs  fico_range_high  inq_last_6mths  open_acc  revol_bal  \\\n",
       "0  20.08            0              684               1         9       3002   \n",
       "1   4.87            0              714               1        13       8184   \n",
       "2   1.63            0              814               0         4        537   \n",
       "3  16.33            0              754               0         5       3215   \n",
       "4   3.00            0              794               0         5        690   \n",
       "\n",
       "  revol_util  total_acc application_type  open_act_il  open_il_24m  il_util  \\\n",
       "0     13.80%         12        Joint App            1            1     70.0   \n",
       "1     18.20%         38       Individual            0            2     70.0   \n",
       "2      7.10%          8       Individual            0            0     70.0   \n",
       "3     26.60%         12       Individual            1            2     63.0   \n",
       "4      5.70%         10        Joint App            0            0     70.0   \n",
       "\n",
       "   open_rv_24m  all_util  total_rev_hi_lim  inq_last_12m  \\\n",
       "0            3      42.0             21700             1   \n",
       "1            4      18.0             44900             5   \n",
       "2            0       7.0              7600             0   \n",
       "3            2      44.0             12100             0   \n",
       "4            4       6.0             12100             0   \n",
       "\n",
       "   acc_open_past_24mths  bc_open_to_buy  bc_util  mort_acc  \\\n",
       "0                     4          9057.0     20.6         0   \n",
       "1                     7         24942.0     21.1         4   \n",
       "2                     0          1457.0     19.1         1   \n",
       "3                     4          8885.0     26.6         0   \n",
       "4                     4          9610.0      6.7         0   \n",
       "\n",
       "   mths_since_recent_bc  num_accts_ever_120_pd  num_rev_accts  \\\n",
       "0                  15.0                      0             11   \n",
       "1                  32.0                      0             29   \n",
       "2                 110.0                      0              6   \n",
       "3                  13.0                      0              9   \n",
       "4                   0.0                      0             10   \n",
       "\n",
       "   num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  num_tl_op_past_12m  \\\n",
       "0                    5         9                   0                   1   \n",
       "1                    6        13                   0                   3   \n",
       "2                    2         4                   0                   0   \n",
       "3                    2         5                   0                   0   \n",
       "4                    2         5                   0                   2   \n",
       "\n",
       "   pct_tl_nvr_dlq  percent_bc_gt_75  pub_rec_bankruptcies  total_bal_ex_mort  \\\n",
       "0           100.0               0.0                     1              18032   \n",
       "1            97.4              14.3                     0               8184   \n",
       "2           100.0               0.0                     0                537   \n",
       "3           100.0              25.0                     0              10176   \n",
       "4           100.0               0.0                     0                690   \n",
       "\n",
       "   total_bc_limit  loan_status_encoded  term_ 36 months  term_ 60 months  \\\n",
       "0           11400                    5                1                0   \n",
       "1           31600                    3                0                1   \n",
       "2            1800                    5                1                0   \n",
       "3           12100                    1                1                0   \n",
       "4           10300                    5                1                0   \n",
       "\n",
       "   home_ownership_MORTGAGE  home_ownership_OWN  home_ownership_RENT  \\\n",
       "0                        1                   0                    0   \n",
       "1                        1                   0                    0   \n",
       "2                        1                   0                    0   \n",
       "3                        0                   0                    1   \n",
       "4                        0                   0                    1   \n",
       "\n",
       "   application_type_Individual  application_type_Joint App  \n",
       "0                            0                           1  \n",
       "1                            1                           0  \n",
       "2                            1                           0  \n",
       "3                            1                           0  \n",
       "4                            0                           1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove following columns from the df3\n",
    "# term\n",
    "#home_ownership\n",
    "#loan_status\n",
    "#purpose\n",
    "#application_type\n",
    "\n",
    "df3 = df3.drop(['term', 'home_ownership', 'application_type'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3[\"int_rate\"] = df3[\"int_rate\"].str.rstrip('%').astype('float') / 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3[\"revol_util\"] = df3[\"revol_util\"].str.rstrip('%').astype('float') / 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['funded_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',\n",
       "       'delinq_2yrs', 'fico_range_high', 'inq_last_6mths', 'open_acc',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'open_act_il', 'open_il_24m',\n",
       "       'il_util', 'open_rv_24m', 'all_util', 'total_rev_hi_lim',\n",
       "       'inq_last_12m', 'acc_open_past_24mths', 'bc_open_to_buy', 'bc_util',\n",
       "       'mort_acc', 'mths_since_recent_bc', 'num_accts_ever_120_pd',\n",
       "       'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',\n",
       "       'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq',\n",
       "       'percent_bc_gt_75', 'pub_rec_bankruptcies', 'total_bal_ex_mort',\n",
       "       'total_bc_limit', 'loan_status_encoded', 'term_ 36 months',\n",
       "       'term_ 60 months', 'home_ownership_MORTGAGE', 'home_ownership_OWN',\n",
       "       'home_ownership_RENT', 'application_type_Individual',\n",
       "       'application_type_Joint App'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1847</td>\n",
       "      <td>application_type_Joint App</td>\n",
       "      <td>application_type_Individual</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1169</td>\n",
       "      <td>num_sats</td>\n",
       "      <td>open_acc</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1627</td>\n",
       "      <td>term_ 60 months</td>\n",
       "      <td>term_ 36 months</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>installment</td>\n",
       "      <td>funded_amnt</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>913</td>\n",
       "      <td>bc_util</td>\n",
       "      <td>revol_util</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1482</td>\n",
       "      <td>total_bc_limit</td>\n",
       "      <td>bc_open_to_buy</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>832</td>\n",
       "      <td>acc_open_past_24mths</td>\n",
       "      <td>open_rv_24m</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1354</td>\n",
       "      <td>percent_bc_gt_75</td>\n",
       "      <td>bc_util</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1479</td>\n",
       "      <td>total_bc_limit</td>\n",
       "      <td>total_rev_hi_lim</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>740</td>\n",
       "      <td>total_rev_hi_lim</td>\n",
       "      <td>revol_bal</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1086</td>\n",
       "      <td>num_rev_accts</td>\n",
       "      <td>total_acc</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1758</td>\n",
       "      <td>home_ownership_RENT</td>\n",
       "      <td>home_ownership_MORTGAGE</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1266</td>\n",
       "      <td>num_tl_op_past_12m</td>\n",
       "      <td>acc_open_past_24mths</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1343</td>\n",
       "      <td>percent_bc_gt_75</td>\n",
       "      <td>revol_util</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>481</td>\n",
       "      <td>total_acc</td>\n",
       "      <td>open_acc</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1172</td>\n",
       "      <td>num_sats</td>\n",
       "      <td>total_acc</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1186</td>\n",
       "      <td>num_sats</td>\n",
       "      <td>num_rev_accts</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1083</td>\n",
       "      <td>num_rev_accts</td>\n",
       "      <td>open_acc</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1209</td>\n",
       "      <td>num_tl_90g_dpd_24m</td>\n",
       "      <td>delinq_2yrs</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1126</td>\n",
       "      <td>num_rev_tl_bal_gt_0</td>\n",
       "      <td>open_acc</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Var1                         Var2  Correlation\n",
       "1847  application_type_Joint App  application_type_Individual         1.00\n",
       "1169                    num_sats                     open_acc         1.00\n",
       "1627             term_ 60 months              term_ 36 months         1.00\n",
       "86                   installment                  funded_amnt         0.95\n",
       "913                      bc_util                   revol_util         0.89\n",
       "1482              total_bc_limit               bc_open_to_buy         0.85\n",
       "832         acc_open_past_24mths                  open_rv_24m         0.84\n",
       "1354            percent_bc_gt_75                      bc_util         0.84\n",
       "1479              total_bc_limit             total_rev_hi_lim         0.80\n",
       "740             total_rev_hi_lim                    revol_bal         0.78\n",
       "1086               num_rev_accts                    total_acc         0.77\n",
       "1758         home_ownership_RENT      home_ownership_MORTGAGE         0.77\n",
       "1266          num_tl_op_past_12m         acc_open_past_24mths         0.77\n",
       "1343            percent_bc_gt_75                   revol_util         0.75\n",
       "481                    total_acc                     open_acc         0.74\n",
       "1172                    num_sats                    total_acc         0.74\n",
       "1186                    num_sats                num_rev_accts         0.71\n",
       "1083               num_rev_accts                     open_acc         0.71\n",
       "1209          num_tl_90g_dpd_24m                  delinq_2yrs         0.69\n",
       "1126         num_rev_tl_bal_gt_0                     open_acc         0.68"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr1 = df3.corr()\n",
    "corr1 = corr1.where(np.triu(np.ones(corr1.shape), k=1).astype(np.bool))\n",
    "corr_df1 = corr1.unstack().reset_index()\n",
    "corr_df1.columns = ['Var1', 'Var2', 'Correlation']\n",
    "corr_df1.dropna(subset = ['Correlation'], inplace=True)\n",
    "corr_df1['Correlation'] = round(corr_df1['Correlation'].abs(), 2)\n",
    "corr_df1.sort_values(by = 'Correlation', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bc_util    0.03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = round(100*(df3.isnull().sum()/len(df3.index)), 2)\n",
    "percent_missing[percent_missing != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.dropna(how='any',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = round(100*(df3.isnull().sum()/len(df3.index)), 2)\n",
    "percent_missing[percent_missing != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting feature variables into X\n",
    "X = df3.drop(['loan_status_encoded'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting target variable to y\n",
    "y = df3['loan_status_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train and test set 80:20\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46576, 42)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>funded_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>term_ 36 months</th>\n",
       "      <th>term_ 60 months</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>34288</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.0993</td>\n",
       "      <td>644.69</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>12.86</td>\n",
       "      <td>2</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11490</td>\n",
       "      <td>0.530</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>39836</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57024</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.0993</td>\n",
       "      <td>161.18</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>36.66</td>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>11827</td>\n",
       "      <td>0.351</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33700</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>18673.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50132</td>\n",
       "      <td>30500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5345</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.0944</td>\n",
       "      <td>419.46</td>\n",
       "      <td>88415.0</td>\n",
       "      <td>20.14</td>\n",
       "      <td>0</td>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>23906</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>70200</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5863.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "      <td>39804</td>\n",
       "      <td>46900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51778</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.1408</td>\n",
       "      <td>102.65</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1</td>\n",
       "      <td>694</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4527</td>\n",
       "      <td>0.312</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>88.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4527</td>\n",
       "      <td>11800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24102</td>\n",
       "      <td>6400</td>\n",
       "      <td>0.1091</td>\n",
       "      <td>209.26</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>35.91</td>\n",
       "      <td>1</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>29568</td>\n",
       "      <td>0.632</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>46800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92.6</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0</td>\n",
       "      <td>32290</td>\n",
       "      <td>25200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       funded_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "34288        20000    0.0993       644.69    125000.0  12.86            2   \n",
       "57024         5000    0.0993       161.18     38000.0  36.66            0   \n",
       "5345         20000    0.0944       419.46     88415.0  20.14            0   \n",
       "51778         3000    0.1408       102.65     19000.0  12.82            1   \n",
       "24102         6400    0.1091       209.26     42000.0  35.91            1   \n",
       "\n",
       "       fico_range_high  inq_last_6mths  open_acc  revol_bal  revol_util  \\\n",
       "34288              664               0         6      11490       0.530   \n",
       "57024              699               0        17      11827       0.351   \n",
       "5345               684               0        20      23906       0.340   \n",
       "51778              694               0         6       4527       0.312   \n",
       "24102              699               0        23      29568       0.632   \n",
       "\n",
       "       total_acc  open_act_il  open_il_24m  il_util  open_rv_24m  all_util  \\\n",
       "34288         17            1            1     83.0            0      53.0   \n",
       "57024         28            6            1     38.0            5      36.0   \n",
       "5345          27            1            0     35.0            7      34.0   \n",
       "51778          9            0            0     70.0            3      31.0   \n",
       "24102         27            1            1     50.0            0      62.0   \n",
       "\n",
       "       total_rev_hi_lim  inq_last_12m  acc_open_past_24mths  bc_open_to_buy  \\\n",
       "34288             21700             3                     1          5425.0   \n",
       "57024             33700             5                     6         18673.0   \n",
       "5345              70200             1                     8          5863.0   \n",
       "51778             14500             0                     3          7770.0   \n",
       "24102             46800             0                     1          3896.0   \n",
       "\n",
       "       bc_util  mort_acc  mths_since_recent_bc  num_accts_ever_120_pd  \\\n",
       "34288     53.0         3                  32.0                      1   \n",
       "57024     38.8         0                   7.0                      0   \n",
       "5345      37.0         1                   7.0                      0   \n",
       "51778     34.2         0                  23.0                      0   \n",
       "24102     84.5         0                  32.0                      2   \n",
       "\n",
       "       num_rev_accts  num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  \\\n",
       "34288              9                    4         6                   0   \n",
       "57024             14                    7        17                   0   \n",
       "5345              21                   12        20                   0   \n",
       "51778              6                    5         6                   0   \n",
       "24102             24                   18        23                   1   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "34288                   1            88.0              25.0   \n",
       "57024                   4           100.0               0.0   \n",
       "5345                    2           100.0              12.5   \n",
       "51778                   2            88.9               0.0   \n",
       "24102                   0            92.6              83.3   \n",
       "\n",
       "       pub_rec_bankruptcies  total_bal_ex_mort  total_bc_limit  \\\n",
       "34288                     0              39836           21700   \n",
       "57024                     0              50132           30500   \n",
       "5345                      0              39804           46900   \n",
       "51778                     0               4527           11800   \n",
       "24102                     0              32290           25200   \n",
       "\n",
       "       term_ 36 months  term_ 60 months  home_ownership_MORTGAGE  \\\n",
       "34288                1                0                        1   \n",
       "57024                1                0                        0   \n",
       "5345                 0                1                        1   \n",
       "51778                1                0                        0   \n",
       "24102                1                0                        1   \n",
       "\n",
       "       home_ownership_OWN  home_ownership_RENT  application_type_Individual  \\\n",
       "34288                   0                    0                            1   \n",
       "57024                   1                    0                            1   \n",
       "5345                    0                    0                            1   \n",
       "51778                   0                    1                            1   \n",
       "24102                   0                    0                            1   \n",
       "\n",
       "       application_type_Joint App  \n",
       "34288                           0  \n",
       "57024                           0  \n",
       "5345                            0  \n",
       "51778                           0  \n",
       "24102                           0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling the Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the scaling library - StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>term_ 36 months</th>\n",
       "      <th>term_ 60 months</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>34288</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.0993</td>\n",
       "      <td>644.69</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>12.86</td>\n",
       "      <td>2</td>\n",
       "      <td>664</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>11490</td>\n",
       "      <td>0.530</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>21700</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5425.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>39836</td>\n",
       "      <td>21700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57024</td>\n",
       "      <td>5000</td>\n",
       "      <td>0.0993</td>\n",
       "      <td>161.18</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>36.66</td>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>11827</td>\n",
       "      <td>0.351</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>33700</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>18673.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>50132</td>\n",
       "      <td>30500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5345</td>\n",
       "      <td>20000</td>\n",
       "      <td>0.0944</td>\n",
       "      <td>419.46</td>\n",
       "      <td>88415.0</td>\n",
       "      <td>20.14</td>\n",
       "      <td>0</td>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>23906</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>70200</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5863.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "      <td>39804</td>\n",
       "      <td>46900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51778</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.1408</td>\n",
       "      <td>102.65</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1</td>\n",
       "      <td>694</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4527</td>\n",
       "      <td>0.312</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>88.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4527</td>\n",
       "      <td>11800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24102</td>\n",
       "      <td>6400</td>\n",
       "      <td>0.1091</td>\n",
       "      <td>209.26</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>35.91</td>\n",
       "      <td>1</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>29568</td>\n",
       "      <td>0.632</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>46800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92.6</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0</td>\n",
       "      <td>32290</td>\n",
       "      <td>25200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       funded_amnt  int_rate  installment  annual_inc    dti  delinq_2yrs  \\\n",
       "34288        20000    0.0993       644.69    125000.0  12.86            2   \n",
       "57024         5000    0.0993       161.18     38000.0  36.66            0   \n",
       "5345         20000    0.0944       419.46     88415.0  20.14            0   \n",
       "51778         3000    0.1408       102.65     19000.0  12.82            1   \n",
       "24102         6400    0.1091       209.26     42000.0  35.91            1   \n",
       "\n",
       "       fico_range_high  inq_last_6mths  open_acc  revol_bal  revol_util  \\\n",
       "34288              664               0         6      11490       0.530   \n",
       "57024              699               0        17      11827       0.351   \n",
       "5345               684               0        20      23906       0.340   \n",
       "51778              694               0         6       4527       0.312   \n",
       "24102              699               0        23      29568       0.632   \n",
       "\n",
       "       total_acc  open_act_il  open_il_24m  il_util  open_rv_24m  all_util  \\\n",
       "34288         17            1            1     83.0            0      53.0   \n",
       "57024         28            6            1     38.0            5      36.0   \n",
       "5345          27            1            0     35.0            7      34.0   \n",
       "51778          9            0            0     70.0            3      31.0   \n",
       "24102         27            1            1     50.0            0      62.0   \n",
       "\n",
       "       total_rev_hi_lim  inq_last_12m  acc_open_past_24mths  bc_open_to_buy  \\\n",
       "34288             21700             3                     1          5425.0   \n",
       "57024             33700             5                     6         18673.0   \n",
       "5345              70200             1                     8          5863.0   \n",
       "51778             14500             0                     3          7770.0   \n",
       "24102             46800             0                     1          3896.0   \n",
       "\n",
       "       bc_util  mort_acc  mths_since_recent_bc  num_accts_ever_120_pd  \\\n",
       "34288     53.0         3                  32.0                      1   \n",
       "57024     38.8         0                   7.0                      0   \n",
       "5345      37.0         1                   7.0                      0   \n",
       "51778     34.2         0                  23.0                      0   \n",
       "24102     84.5         0                  32.0                      2   \n",
       "\n",
       "       num_rev_accts  num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  \\\n",
       "34288              9                    4         6                   0   \n",
       "57024             14                    7        17                   0   \n",
       "5345              21                   12        20                   0   \n",
       "51778              6                    5         6                   0   \n",
       "24102             24                   18        23                   1   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "34288                   1            88.0              25.0   \n",
       "57024                   4           100.0               0.0   \n",
       "5345                    2           100.0              12.5   \n",
       "51778                   2            88.9               0.0   \n",
       "24102                   0            92.6              83.3   \n",
       "\n",
       "       pub_rec_bankruptcies  total_bal_ex_mort  total_bc_limit  \\\n",
       "34288                     0              39836           21700   \n",
       "57024                     0              50132           30500   \n",
       "5345                      0              39804           46900   \n",
       "51778                     0               4527           11800   \n",
       "24102                     0              32290           25200   \n",
       "\n",
       "       term_ 36 months  term_ 60 months  home_ownership_MORTGAGE  \\\n",
       "34288                1                0                        1   \n",
       "57024                1                0                        0   \n",
       "5345                 0                1                        1   \n",
       "51778                1                0                        0   \n",
       "24102                1                0                        1   \n",
       "\n",
       "       home_ownership_OWN  home_ownership_RENT  application_type_Individual  \\\n",
       "34288                   0                    0                            1   \n",
       "57024                   1                    0                            1   \n",
       "5345                    0                    0                            1   \n",
       "51778                   0                    1                            1   \n",
       "24102                   0                    0                            1   \n",
       "\n",
       "       application_type_Joint App  \n",
       "34288                           0  \n",
       "57024                           0  \n",
       "5345                            0  \n",
       "51778                           0  \n",
       "24102                           0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[['funded_amnt','int_rate','installment','annual_inc','dti','fico_range_high','open_acc']]= scaler.fit_transform(X_train[['funded_amnt','int_rate','installment','annual_inc','dti','fico_range_high','open_acc']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[['revol_bal','revol_util','total_acc','open_act_il','open_il_24m','il_util','open_rv_24m']]= scaler.fit_transform(X_train[['revol_bal','revol_util','total_acc','open_act_il','open_il_24m','il_util','open_rv_24m']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[['all_util','total_rev_hi_lim','inq_last_12m','acc_open_past_24mths','bc_open_to_buy','bc_util','mort_acc','total_bc_limit']]= scaler.fit_transform(X_train[['all_util','total_rev_hi_lim','inq_last_12m','acc_open_past_24mths','bc_open_to_buy','bc_util','mort_acc','total_bc_limit']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train[['mths_since_recent_bc','num_accts_ever_120_pd','num_rev_accts','num_rev_tl_bal_gt_0','num_sats','num_tl_90g_dpd_24m','num_tl_op_past_12m','pct_tl_nvr_dlq','percent_bc_gt_75','pub_rec_bankruptcies','total_bal_ex_mort']]= scaler.fit_transform(X_train[['mths_since_recent_bc','num_accts_ever_120_pd','num_rev_accts','num_rev_tl_bal_gt_0','num_sats','num_tl_90g_dpd_24m','num_tl_op_past_12m','pct_tl_nvr_dlq','percent_bc_gt_75','pub_rec_bankruptcies','total_bal_ex_mort']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>fico_range_high</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>open_act_il</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>acc_open_past_24mths</th>\n",
       "      <th>bc_open_to_buy</th>\n",
       "      <th>bc_util</th>\n",
       "      <th>mort_acc</th>\n",
       "      <th>mths_since_recent_bc</th>\n",
       "      <th>num_accts_ever_120_pd</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_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>total_bal_ex_mort</th>\n",
       "      <th>total_bc_limit</th>\n",
       "      <th>term_ 36 months</th>\n",
       "      <th>term_ 60 months</th>\n",
       "      <th>home_ownership_MORTGAGE</th>\n",
       "      <th>home_ownership_OWN</th>\n",
       "      <th>home_ownership_RENT</th>\n",
       "      <th>application_type_Individual</th>\n",
       "      <th>application_type_Joint App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>34288</td>\n",
       "      <td>0.467854</td>\n",
       "      <td>-0.574978</td>\n",
       "      <td>0.687221</td>\n",
       "      <td>0.542893</td>\n",
       "      <td>-0.317629</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.265859</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.925822</td>\n",
       "      <td>-0.172564</td>\n",
       "      <td>0.345362</td>\n",
       "      <td>-0.470146</td>\n",
       "      <td>-0.584228</td>\n",
       "      <td>-0.353209</td>\n",
       "      <td>0.677001</td>\n",
       "      <td>-1.052063</td>\n",
       "      <td>-0.061307</td>\n",
       "      <td>-0.406233</td>\n",
       "      <td>0.441070</td>\n",
       "      <td>-1.088700</td>\n",
       "      <td>-0.465868</td>\n",
       "      <td>0.090164</td>\n",
       "      <td>0.945261</td>\n",
       "      <td>0.201394</td>\n",
       "      <td>0.404647</td>\n",
       "      <td>-0.504577</td>\n",
       "      <td>-0.371729</td>\n",
       "      <td>-0.921834</td>\n",
       "      <td>-0.139761</td>\n",
       "      <td>-0.550023</td>\n",
       "      <td>-0.678421</td>\n",
       "      <td>-0.246186</td>\n",
       "      <td>-0.383565</td>\n",
       "      <td>-0.210872</td>\n",
       "      <td>-0.166033</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57024</td>\n",
       "      <td>-1.037954</td>\n",
       "      <td>-0.574978</td>\n",
       "      <td>-1.004996</td>\n",
       "      <td>-0.490662</td>\n",
       "      <td>0.810388</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.299700</td>\n",
       "      <td>0</td>\n",
       "      <td>0.981316</td>\n",
       "      <td>-0.159724</td>\n",
       "      <td>-0.368798</td>\n",
       "      <td>0.455060</td>\n",
       "      <td>1.127673</td>\n",
       "      <td>-0.353209</td>\n",
       "      <td>-1.361982</td>\n",
       "      <td>0.948038</td>\n",
       "      <td>-0.859883</td>\n",
       "      <td>-0.091313</td>\n",
       "      <td>1.299832</td>\n",
       "      <td>0.484741</td>\n",
       "      <td>0.234088</td>\n",
       "      <td>-0.396299</td>\n",
       "      <td>-0.798086</td>\n",
       "      <td>-0.542698</td>\n",
       "      <td>-0.344641</td>\n",
       "      <td>0.137080</td>\n",
       "      <td>0.555904</td>\n",
       "      <td>0.990227</td>\n",
       "      <td>-0.139761</td>\n",
       "      <td>1.077681</td>\n",
       "      <td>0.611327</td>\n",
       "      <td>-0.951560</td>\n",
       "      <td>-0.383565</td>\n",
       "      <td>-0.016848</td>\n",
       "      <td>0.184250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5345</td>\n",
       "      <td>0.467854</td>\n",
       "      <td>-0.671013</td>\n",
       "      <td>-0.101053</td>\n",
       "      <td>0.108266</td>\n",
       "      <td>0.027412</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713768</td>\n",
       "      <td>0</td>\n",
       "      <td>1.501444</td>\n",
       "      <td>0.300493</td>\n",
       "      <td>-0.412685</td>\n",
       "      <td>0.370950</td>\n",
       "      <td>-0.584228</td>\n",
       "      <td>-0.989959</td>\n",
       "      <td>-1.497914</td>\n",
       "      <td>1.748079</td>\n",
       "      <td>-0.953833</td>\n",
       "      <td>0.866569</td>\n",
       "      <td>-0.417691</td>\n",
       "      <td>1.114118</td>\n",
       "      <td>-0.442726</td>\n",
       "      <td>-0.457963</td>\n",
       "      <td>-0.216970</td>\n",
       "      <td>-0.542698</td>\n",
       "      <td>-0.344641</td>\n",
       "      <td>1.035399</td>\n",
       "      <td>2.101958</td>\n",
       "      <td>1.511699</td>\n",
       "      <td>-0.139761</td>\n",
       "      <td>-0.007455</td>\n",
       "      <td>0.611327</td>\n",
       "      <td>-0.598873</td>\n",
       "      <td>-0.383565</td>\n",
       "      <td>-0.211475</td>\n",
       "      <td>0.837050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51778</td>\n",
       "      <td>-1.238729</td>\n",
       "      <td>0.238384</td>\n",
       "      <td>-1.209843</td>\n",
       "      <td>-0.716380</td>\n",
       "      <td>-0.319524</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.437723</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.925822</td>\n",
       "      <td>-0.437858</td>\n",
       "      <td>-0.524397</td>\n",
       "      <td>-1.143023</td>\n",
       "      <td>-0.926608</td>\n",
       "      <td>-0.989959</td>\n",
       "      <td>0.087962</td>\n",
       "      <td>0.147998</td>\n",
       "      <td>-1.094758</td>\n",
       "      <td>-0.595185</td>\n",
       "      <td>-0.847072</td>\n",
       "      <td>-0.459324</td>\n",
       "      <td>-0.341970</td>\n",
       "      <td>-0.553885</td>\n",
       "      <td>-0.798086</td>\n",
       "      <td>-0.066479</td>\n",
       "      <td>-0.344641</td>\n",
       "      <td>-0.889572</td>\n",
       "      <td>-0.062518</td>\n",
       "      <td>-0.921834</td>\n",
       "      <td>-0.139761</td>\n",
       "      <td>-0.007455</td>\n",
       "      <td>-0.581689</td>\n",
       "      <td>-0.951560</td>\n",
       "      <td>-0.383565</td>\n",
       "      <td>-0.876257</td>\n",
       "      <td>-0.560102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24102</td>\n",
       "      <td>-0.897412</td>\n",
       "      <td>-0.382907</td>\n",
       "      <td>-0.836723</td>\n",
       "      <td>-0.443142</td>\n",
       "      <td>0.774841</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.299700</td>\n",
       "      <td>0</td>\n",
       "      <td>2.021573</td>\n",
       "      <td>0.516218</td>\n",
       "      <td>0.752313</td>\n",
       "      <td>0.370950</td>\n",
       "      <td>-0.584228</td>\n",
       "      <td>-0.353209</td>\n",
       "      <td>-0.818253</td>\n",
       "      <td>-1.052063</td>\n",
       "      <td>0.361469</td>\n",
       "      <td>0.252475</td>\n",
       "      <td>-0.847072</td>\n",
       "      <td>-1.088700</td>\n",
       "      <td>-0.546652</td>\n",
       "      <td>1.169289</td>\n",
       "      <td>-0.798086</td>\n",
       "      <td>0.201394</td>\n",
       "      <td>1.153934</td>\n",
       "      <td>1.420393</td>\n",
       "      <td>3.957222</td>\n",
       "      <td>2.033170</td>\n",
       "      <td>1.865011</td>\n",
       "      <td>-1.092592</td>\n",
       "      <td>-0.184017</td>\n",
       "      <td>1.398747</td>\n",
       "      <td>-0.383565</td>\n",
       "      <td>-0.353074</td>\n",
       "      <td>-0.026716</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       funded_amnt  int_rate  installment  annual_inc       dti  delinq_2yrs  \\\n",
       "34288     0.467854 -0.574978     0.687221    0.542893 -0.317629            2   \n",
       "57024    -1.037954 -0.574978    -1.004996   -0.490662  0.810388            0   \n",
       "5345      0.467854 -0.671013    -0.101053    0.108266  0.027412            0   \n",
       "51778    -1.238729  0.238384    -1.209843   -0.716380 -0.319524            1   \n",
       "24102    -0.897412 -0.382907    -0.836723   -0.443142  0.774841            1   \n",
       "\n",
       "       fico_range_high  inq_last_6mths  open_acc  revol_bal  revol_util  \\\n",
       "34288        -1.265859               0 -0.925822  -0.172564    0.345362   \n",
       "57024        -0.299700               0  0.981316  -0.159724   -0.368798   \n",
       "5345         -0.713768               0  1.501444   0.300493   -0.412685   \n",
       "51778        -0.437723               0 -0.925822  -0.437858   -0.524397   \n",
       "24102        -0.299700               0  2.021573   0.516218    0.752313   \n",
       "\n",
       "       total_acc  open_act_il  open_il_24m   il_util  open_rv_24m  all_util  \\\n",
       "34288  -0.470146    -0.584228    -0.353209  0.677001    -1.052063 -0.061307   \n",
       "57024   0.455060     1.127673    -0.353209 -1.361982     0.948038 -0.859883   \n",
       "5345    0.370950    -0.584228    -0.989959 -1.497914     1.748079 -0.953833   \n",
       "51778  -1.143023    -0.926608    -0.989959  0.087962     0.147998 -1.094758   \n",
       "24102   0.370950    -0.584228    -0.353209 -0.818253    -1.052063  0.361469   \n",
       "\n",
       "       total_rev_hi_lim  inq_last_12m  acc_open_past_24mths  bc_open_to_buy  \\\n",
       "34288         -0.406233      0.441070             -1.088700       -0.465868   \n",
       "57024         -0.091313      1.299832              0.484741        0.234088   \n",
       "5345           0.866569     -0.417691              1.114118       -0.442726   \n",
       "51778         -0.595185     -0.847072             -0.459324       -0.341970   \n",
       "24102          0.252475     -0.847072             -1.088700       -0.546652   \n",
       "\n",
       "        bc_util  mort_acc  mths_since_recent_bc  num_accts_ever_120_pd  \\\n",
       "34288  0.090164  0.945261              0.201394               0.404647   \n",
       "57024 -0.396299 -0.798086             -0.542698              -0.344641   \n",
       "5345  -0.457963 -0.216970             -0.542698              -0.344641   \n",
       "51778 -0.553885 -0.798086             -0.066479              -0.344641   \n",
       "24102  1.169289 -0.798086              0.201394               1.153934   \n",
       "\n",
       "       num_rev_accts  num_rev_tl_bal_gt_0  num_sats  num_tl_90g_dpd_24m  \\\n",
       "34288      -0.504577            -0.371729 -0.921834           -0.139761   \n",
       "57024       0.137080             0.555904  0.990227           -0.139761   \n",
       "5345        1.035399             2.101958  1.511699           -0.139761   \n",
       "51778      -0.889572            -0.062518 -0.921834           -0.139761   \n",
       "24102       1.420393             3.957222  2.033170            1.865011   \n",
       "\n",
       "       num_tl_op_past_12m  pct_tl_nvr_dlq  percent_bc_gt_75  \\\n",
       "34288           -0.550023       -0.678421         -0.246186   \n",
       "57024            1.077681        0.611327         -0.951560   \n",
       "5345            -0.007455        0.611327         -0.598873   \n",
       "51778           -0.007455       -0.581689         -0.951560   \n",
       "24102           -1.092592       -0.184017          1.398747   \n",
       "\n",
       "       pub_rec_bankruptcies  total_bal_ex_mort  total_bc_limit  \\\n",
       "34288             -0.383565          -0.210872       -0.166033   \n",
       "57024             -0.383565          -0.016848        0.184250   \n",
       "5345              -0.383565          -0.211475        0.837050   \n",
       "51778             -0.383565          -0.876257       -0.560102   \n",
       "24102             -0.383565          -0.353074       -0.026716   \n",
       "\n",
       "       term_ 36 months  term_ 60 months  home_ownership_MORTGAGE  \\\n",
       "34288                1                0                        1   \n",
       "57024                1                0                        0   \n",
       "5345                 0                1                        1   \n",
       "51778                1                0                        0   \n",
       "24102                1                0                        1   \n",
       "\n",
       "       home_ownership_OWN  home_ownership_RENT  application_type_Individual  \\\n",
       "34288                   0                    0                            1   \n",
       "57024                   1                    0                            1   \n",
       "5345                    0                    0                            1   \n",
       "51778                   0                    1                            1   \n",
       "24102                   0                    0                            1   \n",
       "\n",
       "       application_type_Joint App  \n",
       "34288                           0  \n",
       "57024                           0  \n",
       "5345                            0  \n",
       "51778                           0  \n",
       "24102                           0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Scaling the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['funded_amnt','int_rate','installment','annual_inc','dti','fico_range_high','open_acc']]= scaler.fit_transform(X_test[['funded_amnt','int_rate','installment','annual_inc','dti','fico_range_high','open_acc']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['revol_bal','revol_util','total_acc','open_act_il','open_il_24m','il_util','open_rv_24m']]= scaler.fit_transform(X_test[['revol_bal','revol_util','total_acc','open_act_il','open_il_24m','il_util','open_rv_24m']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['all_util','total_rev_hi_lim','inq_last_12m','acc_open_past_24mths','bc_open_to_buy','bc_util','mort_acc']]= scaler.fit_transform(X_test[['all_util','total_rev_hi_lim','inq_last_12m','acc_open_past_24mths','bc_open_to_buy','bc_util','mort_acc']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['mths_since_recent_bc','num_accts_ever_120_pd','num_rev_accts','num_rev_tl_bal_gt_0','num_sats','num_tl_90g_dpd_24m','num_tl_op_past_12m','pct_tl_nvr_dlq','percent_bc_gt_75','pub_rec_bankruptcies','total_bal_ex_mort','total_bc_limit']]= scaler.fit_transform(X_test[['mths_since_recent_bc','num_accts_ever_120_pd','num_rev_accts','num_rev_tl_bal_gt_0','num_sats','num_tl_90g_dpd_24m','num_tl_op_past_12m','pct_tl_nvr_dlq','percent_bc_gt_75','pub_rec_bankruptcies','total_bal_ex_mort','total_bc_limit']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mitigate skewness with power transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['funded_amnt', 'int_rate', 'installment', 'annual_inc', 'dti',\n",
       "       'delinq_2yrs', 'fico_range_high', 'inq_last_6mths', 'open_acc',\n",
       "       'revol_bal', 'revol_util', 'total_acc', 'open_act_il', 'open_il_24m',\n",
       "       'il_util', 'open_rv_24m', 'all_util', 'total_rev_hi_lim',\n",
       "       'inq_last_12m', 'acc_open_past_24mths', 'bc_open_to_buy', 'bc_util',\n",
       "       'mort_acc', 'mths_since_recent_bc', 'num_accts_ever_120_pd',\n",
       "       'num_rev_accts', 'num_rev_tl_bal_gt_0', 'num_sats',\n",
       "       'num_tl_90g_dpd_24m', 'num_tl_op_past_12m', 'pct_tl_nvr_dlq',\n",
       "       'percent_bc_gt_75', 'pub_rec_bankruptcies', 'total_bal_ex_mort',\n",
       "       'total_bc_limit', 'term_ 36 months', 'term_ 60 months',\n",
       "       'home_ownership_MORTGAGE', 'home_ownership_OWN', 'home_ownership_RENT',\n",
       "       'application_type_Individual', 'application_type_Joint App'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = X_train.columns\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 30, not 31",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-72-5d78be41689e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcols\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mskew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1142\u001b[1;33m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1143\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m     \u001b[0maxes_to_delete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1400\u001b[0m                     \u001b[1;31m# more similar to add_axes.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1401\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1402\u001b[1;33m             \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1403\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1404\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSubplotSpec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_from_subplot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[1;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\gridspec.py\u001b[0m in \u001b[0;36m_from_subplot_args\u001b[1;34m(figure, args)\u001b[0m\n\u001b[0;32m    688\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mrows\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m                     raise ValueError(\n\u001b[1;32m--> 690\u001b[1;33m                         f\"num must be 1 <= num <= {rows*cols}, not {num}\")\n\u001b[0m\u001b[0;32m    691\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m   \u001b[1;31m# -1 due to MATLAB indexing.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: num must be 1 <= num <= 30, not 31"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x2016 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "plt.figure(figsize=(17,28))\n",
    "for col in cols :    \n",
    "    k=k+1\n",
    "    plt.subplot(6, 5,k)    \n",
    "    sns.distplot(X_train[col])\n",
    "    plt.title(col+' '+str(X_train[col].skew()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing PowerTransformer\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "# Instantiate the powertransformer\n",
    "pt = PowerTransformer(method='yeo-johnson', standardize=True, copy=False)\n",
    "# Fit and transform the PT on training data\n",
    "X_train[cols] = pt.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the test set\n",
    "X_test[cols] = pt.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "num must be 1 <= num <= 30, not 31",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-76-9525078b1b14>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcols\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mskew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1142\u001b[1;33m     \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1143\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m     \u001b[0maxes_to_delete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1400\u001b[0m                     \u001b[1;31m# more similar to add_axes.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1401\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1402\u001b[1;33m             \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1403\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1404\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_subplotspec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSubplotSpec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_from_subplot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[1;31m# _axes_class is set in the subplot_class_factory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\gridspec.py\u001b[0m in \u001b[0;36m_from_subplot_args\u001b[1;34m(figure, args)\u001b[0m\n\u001b[0;32m    688\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mrows\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m                     raise ValueError(\n\u001b[1;32m--> 690\u001b[1;33m                         f\"num must be 1 <= num <= {rows*cols}, not {num}\")\n\u001b[0m\u001b[0;32m    691\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mgs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m   \u001b[1;31m# -1 due to MATLAB indexing.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: num must be 1 <= num <= 30, not 31"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x2016 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of the variables (skewness) of all the columns\n",
    "k=0\n",
    "plt.figure(figsize=(17,28))\n",
    "for col in cols :    \n",
    "    k=k+1\n",
    "    plt.subplot(6, 5,k)    \n",
    "    sns.distplot(X_train[col])\n",
    "    plt.title(col+' '+str(X_train[col].skew()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model building using Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2 = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9200"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_test == pred2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00       286\n",
      "           1       0.22      0.02      0.03      1672\n",
      "           2       0.00      0.00      0.00       224\n",
      "           3       0.00      0.00      0.00       118\n",
      "           4       0.00      0.00      0.00        70\n",
      "           5       0.80      0.99      0.88      9274\n",
      "\n",
      "    accuracy                           0.79     11644\n",
      "   macro avg       0.17      0.17      0.15     11644\n",
      "weighted avg       0.67      0.79      0.71     11644\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    3    0    0    0  283]\n",
      " [   1   31    0    0    0 1640]\n",
      " [   0    0    0    0    0  224]\n",
      " [   0    2    0    0    0  116]\n",
      " [   0    1    0    0    0   69]\n",
      " [   0  105    0    0    0 9169]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection Using RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=100, max_depth=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFE(estimator=RandomForestRegressor(max_depth=10), n_features_to_select=1)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "# here we want only one final feature, we do this to produce a ranking\n",
    "n_features_to_select = 1\n",
    "rfe = RFE(regressor, n_features_to_select=n_features_to_select)\n",
    "rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 bc_open_to_buy\n",
      "2 revol_util\n",
      "3 installment\n",
      "4 dti\n",
      "5 total_bal_ex_mort\n",
      "6 int_rate\n",
      "7 bc_util\n",
      "8 revol_bal\n",
      "9 acc_open_past_24mths\n",
      "10 annual_inc\n",
      "11 total_acc\n",
      "12 il_util\n",
      "13 mths_since_recent_bc\n",
      "14 total_rev_hi_lim\n",
      "15 all_util\n",
      "16 funded_amnt\n",
      "17 open_il_24m\n",
      "18 total_bc_limit\n",
      "19 inq_last_12m\n",
      "20 num_rev_accts\n",
      "21 fico_range_high\n",
      "22 num_rev_tl_bal_gt_0\n",
      "23 num_sats\n",
      "24 num_tl_op_past_12m\n",
      "25 pct_tl_nvr_dlq\n",
      "26 mort_acc\n",
      "27 open_act_il\n",
      "28 inq_last_6mths\n",
      "29 open_rv_24m\n",
      "30 open_acc\n",
      "31 percent_bc_gt_75\n",
      "32 term_ 60 months\n",
      "33 num_accts_ever_120_pd\n",
      "34 delinq_2yrs\n",
      "35 pub_rec_bankruptcies\n",
      "36 term_ 36 months\n",
      "37 home_ownership_OWN\n",
      "38 home_ownership_RENT\n",
      "39 application_type_Individual\n",
      "40 home_ownership_MORTGAGE\n",
      "41 application_type_Joint App\n",
      "42 num_tl_90g_dpd_24m\n"
     ]
    }
   ],
   "source": [
    "from operator import itemgetter\n",
    "features = X_train.columns.to_list()\n",
    "for x, y in (sorted(zip(rfe.ranking_ , features), key=itemgetter(0))):\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFE(estimator=RandomForestRegressor(max_depth=10), n_features_to_select=10)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features_to_select = 10\n",
    "rfe = RFE(regressor, n_features_to_select=n_features_to_select)\n",
    "rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use the model to predict \n",
    "predictions = rfe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'head'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-92-70c11d2378a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredictions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'head'"
     ]
    }
   ],
   "source": [
    "#predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    3    0    0    0  283]\n",
      " [   1   31    0    0    0 1640]\n",
      " [   0    0    0    0    0  224]\n",
      " [   0    2    0    0    0  116]\n",
      " [   0    1    0    0    0   69]\n",
      " [   0  105    0    0    0 9169]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_test == print(confusion_matrix(y_test,pred2))).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "target     = 'loan_status_encoded'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({\"Id\":y_test.index, target:predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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_status_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>51930</td>\n",
       "      <td>3.680166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>23693</td>\n",
       "      <td>4.590790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1237</td>\n",
       "      <td>4.231269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>19982</td>\n",
       "      <td>3.208638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7057</td>\n",
       "      <td>4.118029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>39964</td>\n",
       "      <td>4.075982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>49344</td>\n",
       "      <td>4.358274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>23911</td>\n",
       "      <td>4.218291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>39675</td>\n",
       "      <td>4.700162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>7468</td>\n",
       "      <td>3.218792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  loan_status_encoded\n",
       "0  51930             3.680166\n",
       "1  23693             4.590790\n",
       "2   1237             4.231269\n",
       "3  19982             3.208638\n",
       "4   7057             4.118029\n",
       "5  39964             4.075982\n",
       "6  49344             4.358274\n",
       "7  23911             4.218291\n",
       "8  39675             4.700162\n",
       "9   7468             3.218792"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.790106492614222\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(accuracy_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Outlier is not checked,Note:As the target value contain more than two values,prediction is confusing. for missing value one can use random forst or MICE imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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