2_Data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis of LendingClub Loan Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In credit business, company has to make decision about loans approval based on the applicant's profile. Company shall able to identify if the loan applicants is likely to repay the loan or not. The consequences are:\n",
    "\n",
    "- If the applicants is able to repay the loan, missing this kind of applicants may result in business loss\n",
    "- If the applicants is not able to repay the loan, accepting the loan from this applicants is also result in business loss.\n",
    "\n",
    "Lending Club is peer-to-peer lending company, which people in needs can apply for loan, and some can become the investor by providing loan funding and later enjoy the interest as the profit.\n",
    "\n",
    "While data growth is significant in this era, we can utilize it to become more useful. In this occasion, writer is trying to utilize *Lending Club Data* to predict creditworthiness of future loan applicants by developing a Machine Learning Model to predict if specific loan (defined by the loan structure and applicants background) will defaulted or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Dataset Characteristic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first recall our dataset features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>verification_status_joint</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501.00</td>\n",
       "      <td>1296599.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>4975.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more 10 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>24000.00</td>\n",
       "      <td>Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I need to upgrade...</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>Computer</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>27.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13648.00</td>\n",
       "      <td>83.70</td>\n",
       "      <td>9.00</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5861.07</td>\n",
       "      <td>5831.78</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>861.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>171.62</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430.00</td>\n",
       "      <td>1314167.00</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>2500.00</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>less 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I plan to use thi...</td>\n",
       "      <td>car</td>\n",
       "      <td>bike</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1999-04-01</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1687.00</td>\n",
       "      <td>9.40</td>\n",
       "      <td>4.00</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>1008.71</td>\n",
       "      <td>456.46</td>\n",
       "      <td>435.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>119.66</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175.00</td>\n",
       "      <td>1313524.00</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more 10 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>12252.00</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "      <td>small_business</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>8.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2001-11-01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2956.00</td>\n",
       "      <td>98.50</td>\n",
       "      <td>10.00</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>3003.65</td>\n",
       "      <td>2400.00</td>\n",
       "      <td>603.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-06-01</td>\n",
       "      <td>649.91</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863.00</td>\n",
       "      <td>1277178.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>AIR RESOURCES BOARD</td>\n",
       "      <td>more 10 years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>49200.00</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>Borrower added on 12/21/11 &gt; to pay for proper...</td>\n",
       "      <td>other</td>\n",
       "      <td>personel</td>\n",
       "      <td>917xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5598.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>f</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>12226.30</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>2209.33</td>\n",
       "      <td>16.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>357.48</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358.00</td>\n",
       "      <td>1311748.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>University Medical Group</td>\n",
       "      <td>1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td>Borrower added on 12/21/11 &gt; I plan on combini...</td>\n",
       "      <td>other</td>\n",
       "      <td>Personal</td>\n",
       "      <td>972xx</td>\n",
       "      <td>OR</td>\n",
       "      <td>17.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>27783.00</td>\n",
       "      <td>53.90</td>\n",
       "      <td>38.00</td>\n",
       "      <td>f</td>\n",
       "      <td>766.90</td>\n",
       "      <td>766.90</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>3242.17</td>\n",
       "      <td>2233.10</td>\n",
       "      <td>1009.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>67.79</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0 1077501.00 1296599.00    5000.00      5000.00          4975.00   36 months   \n",
       "1 1077430.00 1314167.00    2500.00      2500.00          2500.00   60 months   \n",
       "2 1077175.00 1313524.00    2400.00      2400.00          2400.00   36 months   \n",
       "3 1076863.00 1277178.00   10000.00     10000.00         10000.00   36 months   \n",
       "4 1075358.00 1311748.00    3000.00      3000.00          3000.00   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade                 emp_title  \\\n",
       "0     10.65       162.87     B        B2                       NaN   \n",
       "1     15.27        59.83     C        C4                     Ryder   \n",
       "2     15.96        84.33     C        C5                       NaN   \n",
       "3     13.49       339.31     C        C1       AIR RESOURCES BOARD   \n",
       "4     12.69        67.79     B        B5  University Medical Group   \n",
       "\n",
       "      emp_length home_ownership  annual_inc verification_status    issue_d  \\\n",
       "0  more 10 years           RENT    24000.00            Verified 2011-12-01   \n",
       "1    less 1 year           RENT    30000.00     Source Verified 2011-12-01   \n",
       "2  more 10 years           RENT    12252.00        Not Verified 2011-12-01   \n",
       "3  more 10 years           RENT    49200.00     Source Verified 2011-12-01   \n",
       "4         1 year           RENT    80000.00     Source Verified 2011-12-01   \n",
       "\n",
       "   loan_status pymnt_plan                                               desc  \\\n",
       "0   Fully Paid          n  Borrower added on 12/22/11 > I need to upgrade...   \n",
       "1  Charged Off          n  Borrower added on 12/22/11 > I plan to use thi...   \n",
       "2   Fully Paid          n                                                NaN   \n",
       "3   Fully Paid          n  Borrower added on 12/21/11 > to pay for proper...   \n",
       "4      Current          n  Borrower added on 12/21/11 > I plan on combini...   \n",
       "\n",
       "          purpose                 title zip_code addr_state   dti  \\\n",
       "0     credit_card              Computer    860xx         AZ 27.65   \n",
       "1             car                  bike    309xx         GA  1.00   \n",
       "2  small_business  real estate business    606xx         IL  8.72   \n",
       "3           other              personel    917xx         CA 20.00   \n",
       "4           other              Personal    972xx         OR 17.94   \n",
       "\n",
       "   delinq_2yrs earliest_cr_line  inq_last_6mths  mths_since_last_delinq  \\\n",
       "0         0.00       1985-01-01            1.00                     NaN   \n",
       "1         0.00       1999-04-01            5.00                     NaN   \n",
       "2         0.00       2001-11-01            2.00                     NaN   \n",
       "3         0.00       1996-02-01            1.00                   35.00   \n",
       "4         0.00       1996-01-01            0.00                   38.00   \n",
       "\n",
       "   mths_since_last_record  open_acc  pub_rec  revol_bal  revol_util  \\\n",
       "0                     NaN      3.00     0.00   13648.00       83.70   \n",
       "1                     NaN      3.00     0.00    1687.00        9.40   \n",
       "2                     NaN      2.00     0.00    2956.00       98.50   \n",
       "3                     NaN     10.00     0.00    5598.00       21.00   \n",
       "4                     NaN     15.00     0.00   27783.00       53.90   \n",
       "\n",
       "   total_acc initial_list_status  out_prncp  out_prncp_inv  total_pymnt  \\\n",
       "0       9.00                   f       0.00           0.00      5861.07   \n",
       "1       4.00                   f       0.00           0.00      1008.71   \n",
       "2      10.00                   f       0.00           0.00      3003.65   \n",
       "3      37.00                   f       0.00           0.00     12226.30   \n",
       "4      38.00                   f     766.90         766.90      3242.17   \n",
       "\n",
       "   total_pymnt_inv  total_rec_prncp  total_rec_int  total_rec_late_fee  \\\n",
       "0          5831.78          5000.00         861.07                0.00   \n",
       "1          1008.71           456.46         435.17                0.00   \n",
       "2          3003.65          2400.00         603.65                0.00   \n",
       "3         12226.30         10000.00        2209.33               16.97   \n",
       "4          3242.17          2233.10        1009.07                0.00   \n",
       "\n",
       "   recoveries  collection_recovery_fee last_pymnt_d  last_pymnt_amnt  \\\n",
       "0        0.00                     0.00   2015-01-01           171.62   \n",
       "1      117.08                     1.11   2013-04-01           119.66   \n",
       "2        0.00                     0.00   2014-06-01           649.91   \n",
       "3        0.00                     0.00   2015-01-01           357.48   \n",
       "4        0.00                     0.00   2016-01-01            67.79   \n",
       "\n",
       "  next_pymnt_d last_credit_pull_d  collections_12_mths_ex_med  \\\n",
       "0          NaT         2016-01-01                        0.00   \n",
       "1          NaT         2013-09-01                        0.00   \n",
       "2          NaT         2016-01-01                        0.00   \n",
       "3          NaT         2015-01-01                        0.00   \n",
       "4   2016-02-01         2016-01-01                        0.00   \n",
       "\n",
       "   mths_since_last_major_derog  policy_code application_type  \\\n",
       "0                          NaN         1.00       INDIVIDUAL   \n",
       "1                          NaN         1.00       INDIVIDUAL   \n",
       "2                          NaN         1.00       INDIVIDUAL   \n",
       "3                          NaN         1.00       INDIVIDUAL   \n",
       "4                          NaN         1.00       INDIVIDUAL   \n",
       "\n",
       "   annual_inc_joint  dti_joint verification_status_joint  acc_now_delinq  \\\n",
       "0               NaN        NaN                       NaN            0.00   \n",
       "1               NaN        NaN                       NaN            0.00   \n",
       "2               NaN        NaN                       NaN            0.00   \n",
       "3               NaN        NaN                       NaN            0.00   \n",
       "4               NaN        NaN                       NaN            0.00   \n",
       "\n",
       "   tot_coll_amt  tot_cur_bal  open_acc_6m  open_il_12m  open_il_24m  \\\n",
       "0           NaN          NaN          NaN          NaN          NaN   \n",
       "1           NaN          NaN          NaN          NaN          NaN   \n",
       "2           NaN          NaN          NaN          NaN          NaN   \n",
       "3           NaN          NaN          NaN          NaN          NaN   \n",
       "4           NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  open_rv_24m  \\\n",
       "0                 NaN           NaN      NaN          NaN          NaN   \n",
       "1                 NaN           NaN      NaN          NaN          NaN   \n",
       "2                 NaN           NaN      NaN          NaN          NaN   \n",
       "3                 NaN           NaN      NaN          NaN          NaN   \n",
       "4                 NaN           NaN      NaN          NaN          NaN   \n",
       "\n",
       "   max_bal_bc  all_util  total_rev_hi_lim  inq_fi  total_cu_tl  inq_last_12m  \n",
       "0         NaN       NaN               NaN     NaN          NaN           NaN  \n",
       "1         NaN       NaN               NaN     NaN          NaN           NaN  \n",
       "2         NaN       NaN               NaN     NaN          NaN           NaN  \n",
       "3         NaN       NaN               NaN     NaN          NaN           NaN  \n",
       "4         NaN       NaN               NaN     NaN          NaN           NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from modules.data_exploration import DataExploration\n",
    "from modules.data_preprocess import LoanDataPreprocess\n",
    "from joblib import dump, load\n",
    "\n",
    "pd.set_option('display.float', '{:.2f}'.format)\n",
    "pd.set_option('display.max_columns', 75)\n",
    "pd.set_option('display.max_rows', 75)\n",
    "\n",
    "data_preprocess = LoanDataPreprocess().fit()\n",
    "loan_df = pd.read_csv('dataset\\lc_2010-2015.csv', dtype={'desc': 'str', 'verification_status_joint': 'str'})\n",
    "loan_df = data_preprocess.transform(loan_df)\n",
    "loan_df.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **2.1 Data fields containing 'Zero' or 'Missing' records**\n",
    "\n",
    "I will give a more detailed review for data fields that has *non-null* less than total records. They must have *zeroes* or *missing records (NaN)* and needed to be identified, since each value may have different meaning (for example, *0* in datafield *open_acc* means no current credit account opened while *missing/NaN* records clearly explain that no information recorded).\n",
    "After this review, we can consider how to deal with this condition for preparation of machine learning features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "applicant_features = [\n",
    "    'member_id', 'emp_title', 'emp_length', 'home_ownership',\n",
    "    'annual_inc', 'verification_status', 'dti', 'delinq_2yrs',\n",
    "    'earliest_cr_line', 'inq_last_6mths', 'mths_since_last_delinq',\n",
    "    'mths_since_last_record', 'open_acc', 'revol_bal', 'revol_util',\n",
    "    'total_acc', 'collections_12_mths_ex_med', 'mths_since_last_major_derog',\n",
    "    'policy_code', 'annual_inc_joint', 'dti_joint', 'verification_status_joint',\n",
    "    'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', \n",
    "    'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il',\n",
    "    'il_util', 'open_rv_12m', 'open_rv_24m', 'all_util', 'total_rev_hi_lim', \n",
    "    'inq_fi', 'total_cu_tl', 'inq_last_12m', 'pub_rec', 'max_bal_bc'\n",
    "    ]\n",
    "loan_features = [\n",
    "    'id', 'loan_amnt', 'term', 'int_rate', 'installment', 'grade',\n",
    "    'sub_grade', 'pymnt_plan', 'desc', 'purpose', 'title',\n",
    "    'zip_code', 'addr_state', 'policy_code', 'application_type'\n",
    "    ]\n",
    "post_origin_features = [\n",
    "    'funded_amnt', 'funded_amnt_inv', 'issue_d', 'loan_status', \n",
    "    'initial_list_status', 'out_prncp', 'out_prncp_inv', 'total_rec_prncp',\n",
    "    'total_rec_int', 'total_rec_late_fee', 'recoveries', 'collection_recovery_fee',\n",
    "    'last_pymnt_d', 'last_pymnt_amnt', 'next_pymnt_d', 'last_credit_pull_d',\n",
    "    'total_pymnt', 'total_pymnt_inv'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is best to categorize the missing data based on the type of missing. Most commonly known types of missing data are as follows:\n",
    "\n",
    "- **Structurally missing data**, is the data that is missing for a logical reason, since that reason caused them to not exist. For example from our data, missing data of `annual_inc_joint` is a structural missing if the loan's `application_type` is `INDIVIDUAL` loan rather than `JOINT` loan.\n",
    "- **Missing Completely at Random (MCAR)**, is the data that is missing with no reasonable connection or pattern to another data. For example, `ann_inc` is very specific record of each observation, hence missing this record is categorized as MCAR. Since we can do nothing to overcome this missing value, the most possible action is to drop the observations if the feature is crucial to avoid any possible biases.\n",
    "- **Missing at Random (MAR)**, is the data that is missing but the connection or pattern can be predicted (e.g. using extrapolation) or extracted from combination of other features (e.g. substraction, summation, etc.). For example, if an observation has a missing `int_rate` record, we can possibly calculate it by using `loan_amnt` and `total_pymnt`.\n",
    "- **Missing not at Random (MNAR)**, is the data where the reason or mechanism for why the data is missing is known. For example, missing loan `desc` is known because the loan applicant didn't input it to the system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Applicant Profile**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Applicant Profile* and *Loan Profile* is our main features for developing the machine learning model. Action to overcome the missing features will be explained accordingly. Let's start with *Applicant Profile*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nans Count</th>\n",
       "      <th>Nans Percentage (%)</th>\n",
       "      <th>Data Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dti_joint</th>\n",
       "      <td>886870</td>\n",
       "      <td>99.94</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <td>886868</td>\n",
       "      <td>99.94</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>verification_status_joint</th>\n",
       "      <td>886868</td>\n",
       "      <td>99.94</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>il_util</th>\n",
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       "      <td>float64</td>\n",
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       "      <th>mths_since_rcnt_il</th>\n",
       "      <td>866569</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_12m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>total_cu_tl</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>inq_fi</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>all_util</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>open_rv_24m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "      <th>open_rv_12m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>total_bal_il</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "      <th>open_il_24m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>open_il_12m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc_6m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_bal_bc</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.59</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <td>750326</td>\n",
       "      <td>84.56</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <td>665676</td>\n",
       "      <td>75.02</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>454312</td>\n",
       "      <td>51.20</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>70276</td>\n",
       "      <td>7.92</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <td>70276</td>\n",
       "      <td>7.92</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <td>70276</td>\n",
       "      <td>7.92</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_title</th>\n",
       "      <td>51462</td>\n",
       "      <td>5.80</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>44825</td>\n",
       "      <td>5.05</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>502</td>\n",
       "      <td>0.06</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <td>145</td>\n",
       "      <td>0.02</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>0</td>\n",
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       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                             Nans Count  Nans Percentage (%)      Data Types\n",
       "dti_joint                        886870                99.94         float64\n",
       "annual_inc_joint                 886868                99.94         float64\n",
       "verification_status_joint        886868                99.94        category\n",
       "il_util                          868762                97.90         float64\n",
       "mths_since_rcnt_il               866569                97.65         float64\n",
       "inq_last_12m                     866007                97.59         float64\n",
       "total_cu_tl                      866007                97.59         float64\n",
       "inq_fi                           866007                97.59         float64\n",
       "all_util                         866007                97.59         float64\n",
       "open_rv_24m                      866007                97.59         float64\n",
       "open_rv_12m                      866007                97.59         float64\n",
       "total_bal_il                     866007                97.59         float64\n",
       "open_il_24m                      866007                97.59         float64\n",
       "open_il_12m                      866007                97.59         float64\n",
       "open_acc_6m                      866007                97.59         float64\n",
       "max_bal_bc                       866007                97.59         float64\n",
       "mths_since_last_record           750326                84.56         float64\n",
       "mths_since_last_major_derog      665676                75.02         float64\n",
       "mths_since_last_delinq           454312                51.20         float64\n",
       "total_rev_hi_lim                  70276                 7.92         float64\n",
       "tot_coll_amt                      70276                 7.92         float64\n",
       "tot_cur_bal                       70276                 7.92         float64\n",
       "emp_title                         51462                 5.80          object\n",
       "emp_length                        44825                 5.05        category\n",
       "revol_util                          502                 0.06         float64\n",
       "collections_12_mths_ex_med          145                 0.02         float64\n",
       "open_acc                             29                 0.00         float64\n",
       "acc_now_delinq                       29                 0.00         float64\n",
       "inq_last_6mths                       29                 0.00         float64\n",
       "earliest_cr_line                     29                 0.00  datetime64[ns]\n",
       "delinq_2yrs                          29                 0.00         float64\n",
       "total_acc                            29                 0.00         float64\n",
       "pub_rec                              29                 0.00         float64\n",
       "annual_inc                            4                 0.00         float64\n",
       "revol_bal                             0                 0.00         float64\n",
       "dti                                   0                 0.00         float64\n",
       "verification_status                   0                 0.00        category\n",
       "home_ownership                        0                 0.00        category\n",
       "policy_code                           0                 0.00         float64\n",
       "member_id                             0                 0.00         float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_exploration = DataExploration(loan_df)\n",
    "data_exploration.show_nans_or_zeroes('nans', applicant_features).sort_values('Nans Count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Structurally Missing: `total_rev_hi_lim`, `tot_coll_amnt`, `tot_curr_bal`, `revol_util`, `collections_12_mths_ex_med`, `inq_last_6mths`, `delinq_2yrs`, `earliest_cr_line`, `total_acc`, `open_acc`, `acc_now_delinq`, and most of the features that has a high percentage of missing data ($>$ 50%). Note on missing percentage of `_joint` features, we can easily confirm that most of the loan is `INDIVIDUAL` loan. We will drop the observations with `JOINT` type loan due to limited data compared to `INDIVIDUAL` loan hence limiting the scope of the model to covers only `INDIVIDUAL` loan. For features related to `mnths_since_`, `NaN` will be replaced with some large values (happens 'long ago'), otherwise `1` (happens recently).\n",
    "\n",
    "2. MNAR: `emp_title`. High cardinal data won't be used for machine learning features.\n",
    "\n",
    "3. MCAR: `annual_inc`, `emp_length`, `earliest_cr_line`. Observations with misisng `annual_inc` will be dropped. For `emp_length`, we will one-hot encode this and also provide *out-of-vocabulary* dummy variables for this missing data. `earlist_cr_line` will be utilized for feature extraction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More on *Structurally Missing* data that possibly includes *MCAR*, *MAR*, or *MNAR* (this will be a bit detailed, [click to skip](#skip_to_cardinality)):\n",
    "\n",
    "- `mths_since_last_record` is linked with `pub_rec`. Missing records is proportional with *zeroes* records in `pub_rec`. For this features. Replacing missing records in `mths_since_last_record` with *zeroes* while `pub_rec` is zero is valid. But if any of the missing value when `pub_rec` is not zero (leads to MCAR), the exact method to replace this missing value is non-existence. Let's see how many of this condition. We will proceed to replace as `1` if MCAR (assume worst condition, which pub record happened recently). Since the higher the `mths_since_last_record` associated with the lower power of delinquecies, we replace the structurally missing data with value large enough but sufficient.\n",
    "\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>mths_since_last_record</th>\n",
    "      <th>pub_rec</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>> 0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    </tbody> \n",
    " </table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887371</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887372</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887373</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>750297 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_record  pub_rec  loan_status\n",
       "0                          NaN     0.00   Fully Paid\n",
       "1                          NaN     0.00  Charged Off\n",
       "2                          NaN     0.00   Fully Paid\n",
       "3                          NaN     0.00   Fully Paid\n",
       "4                          NaN     0.00      Current\n",
       "...                        ...      ...          ...\n",
       "887371                     NaN     0.00  Charged Off\n",
       "887372                     NaN     0.00      Current\n",
       "887373                     NaN     0.00      Current\n",
       "887375                     NaN     0.00      Current\n",
       "887377                     NaN     0.00      Current\n",
       "\n",
       "[750297 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_condition_1 = (loan_df.mths_since_last_record.isna()) & (loan_df.pub_rec == 0)\n",
    "rec_condition_2 = (loan_df.mths_since_last_record.isna()) & (loan_df.pub_rec > 0)\n",
    "rec_condition_3 = (loan_df.mths_since_last_record.isna()) & (loan_df.pub_rec.isna())\n",
    "\n",
    "loan_df.loc[rec_condition_1, ['mths_since_last_record', 'pub_rec', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>mths_since_last_record</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [mths_since_last_record, pub_rec, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[rec_condition_2, ['mths_since_last_record', 'pub_rec', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>mths_since_last_record</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42449</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42450</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42459</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mths_since_last_record  pub_rec  \\\n",
       "42449                     NaN      NaN   \n",
       "42450                     NaN      NaN   \n",
       "42459                     NaN      NaN   \n",
       "42472                     NaN      NaN   \n",
       "42480                     NaN      NaN   \n",
       "\n",
       "                                             loan_status  \n",
       "42449  Does not meet the credit policy. Status:Fully ...  \n",
       "42450  Does not meet the credit policy. Status:Fully ...  \n",
       "42459  Does not meet the credit policy. Status:Fully ...  \n",
       "42472  Does not meet the credit policy. Status:Fully ...  \n",
       "42480  Does not meet the credit policy. Status:Fully ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[rec_condition_3, ['mths_since_last_record', 'pub_rec', 'loan_status']].head() #length = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    As we can see, observations with missing `mths_since_last_record` and `pub_rec` togetherly are tagged as `Does not meet the credit policy`. It is possibly caused by an incomplete data submission, which doesn't meet the LendingClub policy. That is why I tagged them as MNAR (this is subjective, assumming that condition). All of the cases and assumption that we use above ideally shall be communicated to the party that responsible in data recording or maybe the stakeholders. More features explained in this section are also shown similar results.\n",
    "\n",
    "- `mths_since_last_delinq` is linked with `delinq_2yrs` and `acc_now_delinq` this way: 1) records with `mths_since_last_delinq` $\\leq$ 24 will prove `delinq_2yrs` at least 1, if > 24 then `delinq_2yrs` must be 0; 2) if `acc_now_delinq` is > 0, then `mths_since_last_delinq` should be recent = 0. We can ignore the MCAR for `condition 1` since it is possibly rare.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>mths_since_last_delinq</th>\n",
    "      <th>delinq_2yrs</th>\n",
    "      <th>acc_now_delinq</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td> 0</td>\n",
    "      <td>Structurally Missing, MCAR (if last delinq > 2 years)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>> 0</td>\n",
    "      <td>MAR, should be 0</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>0</td>\n",
    "      <td>MCAR. Impute median from similar available records</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>0</th>\n",
    "      <td>>0</td>\n",
    "      <td>0</td>\n",
    "      <td>Error. Impute similar to Case #3</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>0 < x < 24</th>\n",
    "      <td>0</td>\n",
    "      <td>0</td>\n",
    "      <td>Error. Worst case impute, delinq_2_yrs = 1.</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>6</th>\n",
    "      <td>0</th>\n",
    "      <td>0</td>\n",
    "      <td>0</td>\n",
    "      <td>Error. mnths_since_last_delinq = 99</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>8</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>Charged Off</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
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       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887369</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887373</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452180 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_delinq  delinq_2yrs  acc_now_delinq  loan_status\n",
       "0                          NaN         0.00            0.00   Fully Paid\n",
       "1                          NaN         0.00            0.00  Charged Off\n",
       "2                          NaN         0.00            0.00   Fully Paid\n",
       "5                          NaN         0.00            0.00   Fully Paid\n",
       "6                          NaN         0.00            0.00      Current\n",
       "...                        ...          ...             ...          ...\n",
       "887369                     NaN         0.00            0.00   Fully Paid\n",
       "887373                     NaN         0.00            0.00      Current\n",
       "887374                     NaN         0.00            0.00      Current\n",
       "887376                     NaN         0.00            0.00      Current\n",
       "887378                     NaN         0.00            0.00      Current\n",
       "\n",
       "[452180 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Structurally missing will be replaced as 300\n",
    "# MAR should be replaced by 1\n",
    "# MCAR should be imputed (preferably mean)\n",
    "# Error should be dropped\n",
    "\n",
    "last_delinq_condition_1 = \\\n",
    "    (loan_df.mths_since_last_delinq.isna())\\\n",
    "    & (loan_df.delinq_2yrs == 0)\\\n",
    "    & (loan_df.acc_now_delinq == 0)\n",
    "last_delinq_condition_2 = \\\n",
    "    (loan_df.mths_since_last_delinq.isna())\\\n",
    "    & (loan_df.delinq_2yrs > 0)\\\n",
    "    & (loan_df.acc_now_delinq > 0)\n",
    "last_delinq_condition_3 = \\\n",
    "    (loan_df.mths_since_last_delinq.isna())\\\n",
    "    & (loan_df.delinq_2yrs > 0)\\\n",
    "    & (loan_df.acc_now_delinq == 0)\n",
    "last_delinq_condition_4 = \\\n",
    "    (loan_df.mths_since_last_delinq == 0)\\\n",
    "    & (loan_df.delinq_2yrs > 0)\\\n",
    "    & (loan_df.acc_now_delinq == 0)\n",
    "last_delinq_condition_5 = \\\n",
    "    (loan_df.mths_since_last_delinq.between(0, 24, inclusive='neither'))\\\n",
    "    & (loan_df.delinq_2yrs == 0)\\\n",
    "    & (loan_df.acc_now_delinq == 0)\n",
    "last_delinq_condition_6 = \\\n",
    "    (loan_df.mths_since_last_delinq == 0)\\\n",
    "    & (loan_df.delinq_2yrs == 0)\\\n",
    "    & (loan_df.acc_now_delinq == 0)\n",
    "last_delinq_condition_7 = \\\n",
    "    (loan_df.mths_since_last_delinq.isna())\\\n",
    "    & (loan_df.delinq_2yrs.isna())\\\n",
    "    & (loan_df.acc_now_delinq.isna())\n",
    "\n",
    "# Replace with large number\n",
    "loan_df.loc[last_delinq_condition_1, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>467661</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468634</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480852</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514024</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636977</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674429</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742926</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752588</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775355</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_delinq  delinq_2yrs  acc_now_delinq\n",
       "467661                     NaN         1.00            1.00\n",
       "468634                     NaN         1.00            1.00\n",
       "480852                     NaN         2.00            1.00\n",
       "514024                     NaN         1.00            1.00\n",
       "636977                     NaN         2.00            2.00\n",
       "674429                     NaN         1.00            1.00\n",
       "742926                     NaN         2.00            1.00\n",
       "752588                     NaN         7.00            1.00\n",
       "775355                     NaN         2.00            1.00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAR, shall be 0\n",
    "loan_df.loc[last_delinq_condition_2, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43607</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44894</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44932</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45305</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880608</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884799</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884875</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885177</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885476</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2094 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_delinq  delinq_2yrs  acc_now_delinq loan_status\n",
       "43607                      NaN         1.00            0.00  Fully Paid\n",
       "44894                      NaN         1.00            0.00  Fully Paid\n",
       "44932                      NaN         3.00            0.00  Fully Paid\n",
       "45181                      NaN         1.00            0.00     Current\n",
       "45305                      NaN         1.00            0.00  Fully Paid\n",
       "...                        ...          ...             ...         ...\n",
       "880608                     NaN         1.00            0.00  Fully Paid\n",
       "884799                     NaN         3.00            0.00  Fully Paid\n",
       "884875                     NaN         1.00            0.00     Current\n",
       "885177                     NaN         1.00            0.00     Current\n",
       "885476                     NaN        17.00            0.00     Current\n",
       "\n",
       "[2094 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MCAR, imputed (preferably median of similar condition)\n",
    "loan_df.loc[last_delinq_condition_3, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [mths_since_last_delinq, delinq_2yrs, acc_now_delinq, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logically Error\n",
    "loan_df.loc[last_delinq_condition_4, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>265785</th>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269893</th>\n",
       "      <td>19.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284405</th>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287086</th>\n",
       "      <td>16.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288985</th>\n",
       "      <td>21.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780603</th>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800858</th>\n",
       "      <td>22.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803082</th>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854358</th>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886917</th>\n",
       "      <td>21.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_delinq  delinq_2yrs  acc_now_delinq loan_status\n",
       "265785                   13.00         0.00            0.00     Current\n",
       "269893                   19.00         0.00            0.00     Current\n",
       "284405                   17.00         0.00            0.00     Current\n",
       "287086                   16.00         0.00            0.00  Fully Paid\n",
       "288985                   21.00         0.00            0.00  Fully Paid\n",
       "...                        ...          ...             ...         ...\n",
       "780603                   13.00         0.00            0.00     Current\n",
       "800858                   22.00         0.00            0.00     Current\n",
       "803082                   14.00         0.00            0.00     Current\n",
       "854358                   18.00         0.00            0.00     Current\n",
       "886917                   21.00         0.00            0.00     Current\n",
       "\n",
       "[78 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error. Assumes delinq_2_yrs = 1\n",
    "loan_df.loc[last_delinq_condition_5, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35075</th>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35431</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38786</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38797</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38945</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42507</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42508</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42511</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42513</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692130</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>821 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        mths_since_last_delinq  delinq_2yrs  acc_now_delinq  \\\n",
       "35075                     0.00         0.00            0.00   \n",
       "35431                     0.00         0.00            0.00   \n",
       "38786                     0.00         0.00            0.00   \n",
       "38797                     0.00         0.00            0.00   \n",
       "38945                     0.00         0.00            0.00   \n",
       "...                        ...          ...             ...   \n",
       "42507                     0.00         0.00            0.00   \n",
       "42508                     0.00         0.00            0.00   \n",
       "42511                     0.00         0.00            0.00   \n",
       "42513                     0.00         0.00            0.00   \n",
       "692130                    0.00         0.00            0.00   \n",
       "\n",
       "                                              loan_status  \n",
       "35075                                          Fully Paid  \n",
       "35431                                         Charged Off  \n",
       "38786                                          Fully Paid  \n",
       "38797                                          Fully Paid  \n",
       "38945                                          Fully Paid  \n",
       "...                                                   ...  \n",
       "42507   Does not meet the credit policy. Status:Fully ...  \n",
       "42508   Does not meet the credit policy. Status:Fully ...  \n",
       "42511   Does not meet the credit policy. Status:Fully ...  \n",
       "42513   Does not meet the credit policy. Status:Fully ...  \n",
       "692130                                            Current  \n",
       "\n",
       "[821 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Error. months_since_last_delinq = 300\n",
    "loan_df.loc[last_delinq_condition_6, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>mths_since_last_delinq</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42450</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42509</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42514</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42534</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mths_since_last_delinq  delinq_2yrs  acc_now_delinq  \\\n",
       "42450                     NaN          NaN             NaN   \n",
       "42509                     NaN          NaN             NaN   \n",
       "42514                     NaN          NaN             NaN   \n",
       "42472                     NaN          NaN             NaN   \n",
       "42534                     NaN          NaN             NaN   \n",
       "\n",
       "                                             loan_status  \n",
       "42450  Does not meet the credit policy. Status:Fully ...  \n",
       "42509  Does not meet the credit policy. Status:Fully ...  \n",
       "42514  Does not meet the credit policy. Status:Fully ...  \n",
       "42472  Does not meet the credit policy. Status:Fully ...  \n",
       "42534  Does not meet the credit policy. Status:Fully ...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preferably drop the observations that doesn't meet policy\n",
    "loan_df.loc[last_delinq_condition_7, ['mths_since_last_delinq', 'delinq_2yrs', 'acc_now_delinq', 'loan_status']].sample(5) #length = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `inq_last_6mths`, `inq_last_12mths` are linked with `inq_fi`. Missing last inquiries records is ambiguous if `inq_fi` > 0.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>inq_last_6mths</th>\n",
    "      <th>inq_last_12mths</th>\n",
    "      <th>inq_fi</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR, assume = inq_fi</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>inq_last_6mths</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [inq_last_6mths, inq_last_12m, inq_fi, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inq_condition_1 = \\\n",
    "    (loan_df.inq_last_6mths.isna() | loan_df.inq_last_12m.isna())\\\n",
    "    & (loan_df.inq_fi == 0)\n",
    "inq_condition_2 = \\\n",
    "    (loan_df.inq_last_6mths.isna() | loan_df.inq_last_12m.isna())\\\n",
    "    & (loan_df.inq_fi > 0)\n",
    "inq_condition_3 = \\\n",
    "    (loan_df.inq_last_6mths.isna() & loan_df.inq_last_12m.isna())\\\n",
    "    & (loan_df.inq_fi.isna())\n",
    "\n",
    "# Replace as 0\n",
    "loan_df.loc[inq_condition_1, ['inq_last_6mths', 'inq_last_12m', 'inq_fi', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [inq_last_6mths, inq_last_12m, inq_fi, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the observations\n",
    "loan_df.loc[inq_condition_2, ['inq_last_6mths', 'inq_last_12m', 'inq_fi', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>inq_last_6mths</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42514</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42520</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42450</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       inq_last_6mths  inq_last_12m  inq_fi  \\\n",
       "42480             NaN           NaN     NaN   \n",
       "42514             NaN           NaN     NaN   \n",
       "42520             NaN           NaN     NaN   \n",
       "42450             NaN           NaN     NaN   \n",
       "42472             NaN           NaN     NaN   \n",
       "\n",
       "                                             loan_status  \n",
       "42480  Does not meet the credit policy. Status:Fully ...  \n",
       "42514  Does not meet the credit policy. Status:Fully ...  \n",
       "42520  Does not meet the credit policy. Status:Fully ...  \n",
       "42450  Does not meet the credit policy. Status:Fully ...  \n",
       "42472  Does not meet the credit policy. Status:Fully ...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preferably drop the observations\n",
    "loan_df.loc[inq_condition_3, ['inq_last_6mths', 'inq_last_12m', 'inq_fi', 'loan_status']].sample(5) # length = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `open_acc` is linked with `total_open_acc` are linked with `inq_fi`. Missing `open_acc` while `total_open_acc` > 0 (and vice versa) is ambiguous.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>open_acc</th>\n",
    "      <th>total_open_acc</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>open_acc</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open_acc, total_acc, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_condition_1_2 = \\\n",
    "    (loan_df.open_acc.isna() | loan_df.total_acc.isna())\\\n",
    "    & ((loan_df.open_acc == 0) | (loan_df.total_acc == 0))\n",
    "acc_condition_3_4 = \\\n",
    "    (loan_df.open_acc.isna() | loan_df.total_acc.isna())\\\n",
    "    & ((loan_df.open_acc > 0) | (loan_df.total_acc > 0))\n",
    "acc_condition_5 = \\\n",
    "    loan_df.open_acc.isna() & loan_df.total_acc.isna()\n",
    "\n",
    "# Replace total open acc as 0\n",
    "loan_df.loc[acc_condition_1_2, ['open_acc', 'total_acc', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>open_acc</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open_acc, total_acc, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the observations\n",
    "loan_df.loc[acc_condition_3_4, ['open_acc', 'total_acc', 'loan_status']]"
   ]
  },
  {
   "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>open_acc</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42449</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42450</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42459</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       open_acc  total_acc                                        loan_status\n",
       "42449       NaN        NaN  Does not meet the credit policy. Status:Fully ...\n",
       "42450       NaN        NaN  Does not meet the credit policy. Status:Fully ...\n",
       "42459       NaN        NaN  Does not meet the credit policy. Status:Fully ...\n",
       "42472       NaN        NaN  Does not meet the credit policy. Status:Fully ...\n",
       "42480       NaN        NaN  Does not meet the credit policy. Status:Fully ..."
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preferably drop the observations that doesn't meet policy\n",
    "loan_df.loc[acc_condition_5, ['open_acc', 'total_acc', 'loan_status']].head(5) # length = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `total_cur_bal` is linked with `total_open_acc`. Missing `total_cur_bal` while `total_open_acc` > 0 (and vice versa) is ambiguous.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>total_open_acc</th>\n",
    "      <th>total_cur_bal</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "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>tot_cur_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tot_cur_bal, total_acc, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bal_condition_1_2 = \\\n",
    "    (loan_df.tot_cur_bal.isna() | loan_df.total_acc.isna())\\\n",
    "    & ((loan_df.tot_cur_bal == 0) | (loan_df.total_acc == 0))\n",
    "bal_condition_3_4 = \\\n",
    "    (loan_df.tot_cur_bal.isna() | loan_df.total_acc.isna())\\\n",
    "    & ((loan_df.tot_cur_bal > 0) | (loan_df.total_acc > 0))\n",
    "bal_condition_5 = \\\n",
    "    loan_df.tot_cur_bal.isna() & loan_df.total_acc.isna()\n",
    "\n",
    "# Replace as 0\n",
    "loan_df.loc[bal_condition_1_2, ['tot_cur_bal', 'total_acc', 'loan_status']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>tot_cur_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>37.00</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tot_cur_bal  total_acc  loan_status\n",
       "0          NaN       9.00   Fully Paid\n",
       "1          NaN       4.00  Charged Off\n",
       "2          NaN      10.00   Fully Paid\n",
       "3          NaN      37.00   Fully Paid\n",
       "4          NaN      38.00      Current"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the observations\n",
    "loan_df.loc[bal_condition_3_4, ['tot_cur_bal', 'total_acc', 'loan_status']].head() # drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>tot_cur_bal</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42449</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42450</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42459</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42472</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42480</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tot_cur_bal  total_acc  \\\n",
       "42449          NaN        NaN   \n",
       "42450          NaN        NaN   \n",
       "42459          NaN        NaN   \n",
       "42472          NaN        NaN   \n",
       "42480          NaN        NaN   \n",
       "\n",
       "                                             loan_status  \n",
       "42449  Does not meet the credit policy. Status:Fully ...  \n",
       "42450  Does not meet the credit policy. Status:Fully ...  \n",
       "42459  Does not meet the credit policy. Status:Fully ...  \n",
       "42472  Does not meet the credit policy. Status:Fully ...  \n",
       "42480  Does not meet the credit policy. Status:Fully ...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preferably drop the observations\n",
    "loan_df.loc[bal_condition_5, ['tot_cur_bal', 'total_acc', 'loan_status']].head() # length = 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `total_coll_amnt` is linked with `collections_12_mnths_ex_med`. Missing `total_cur_bal` while `total_open_acc` > 0 (and vice versa) is ambiguous.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>total_coll_amnt</th>\n",
    "      <th>collections_12_mnths_ex_med</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "hide-input",
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39698</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39711</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39719</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39728</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39729</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42531</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42532</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42533</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42534</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tot_coll_amt  collections_12_mths_ex_med  \\\n",
       "39698           NaN                         NaN   \n",
       "39711           NaN                         NaN   \n",
       "39719           NaN                         NaN   \n",
       "39728           NaN                         NaN   \n",
       "39729           NaN                         NaN   \n",
       "...             ...                         ...   \n",
       "42530           NaN                         NaN   \n",
       "42531           NaN                         NaN   \n",
       "42532           NaN                         NaN   \n",
       "42533           NaN                         NaN   \n",
       "42534           NaN                         NaN   \n",
       "\n",
       "                                             loan_status  \n",
       "39698                                         Fully Paid  \n",
       "39711                                         Fully Paid  \n",
       "39719                                         Fully Paid  \n",
       "39728                                         Fully Paid  \n",
       "39729                                         Fully Paid  \n",
       "...                                                  ...  \n",
       "42530  Does not meet the credit policy. Status:Fully ...  \n",
       "42531  Does not meet the credit policy. Status:Fully ...  \n",
       "42532  Does not meet the credit policy. Status:Fully ...  \n",
       "42533  Does not meet the credit policy. Status:Fully ...  \n",
       "42534  Does not meet the credit policy. Status:Fully ...  \n",
       "\n",
       "[145 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coll_condition_1 = \\\n",
    "    loan_df.tot_coll_amt.isna() & loan_df.collections_12_mths_ex_med.isna()\n",
    "coll_condition_2_3 = \\\n",
    "    (loan_df.tot_coll_amt.isna() | (loan_df.tot_coll_amt == 0))\\\n",
    "    & (loan_df.collections_12_mths_ex_med.isna() | (loan_df.collections_12_mths_ex_med == 0))\n",
    "coll_condition_4 = \\\n",
    "    loan_df.tot_coll_amt.isna() & (loan_df.collections_12_mths_ex_med > 0)\n",
    "coll_condition_5 = \\\n",
    "    (loan_df.tot_coll_amt == 0) & (loan_df.collections_12_mths_ex_med.isna())\n",
    "\n",
    "# Drop observations\n",
    "loan_df.loc[coll_condition_1, ['tot_coll_amt', 'collections_12_mths_ex_med', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>loan_status</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>887373</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768510 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        tot_coll_amt  collections_12_mths_ex_med  loan_status\n",
       "0                NaN                        0.00   Fully Paid\n",
       "1                NaN                        0.00  Charged Off\n",
       "2                NaN                        0.00   Fully Paid\n",
       "3                NaN                        0.00   Fully Paid\n",
       "4                NaN                        0.00      Current\n",
       "...              ...                         ...          ...\n",
       "887373          0.00                        0.00      Current\n",
       "887374          0.00                        0.00      Current\n",
       "887375          0.00                        0.00      Current\n",
       "887376          0.00                        0.00      Current\n",
       "887378          0.00                        0.00      Current\n",
       "\n",
       "[768510 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace as 0\n",
    "loan_df.loc[coll_condition_2_3, ['tot_coll_amt', 'collections_12_mths_ex_med', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>tot_coll_amt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tot_coll_amt, collections_12_mths_ex_med, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the observations\n",
    "loan_df.loc[coll_condition_4, ['tot_coll_amt', 'collections_12_mths_ex_med', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>tot_coll_amt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tot_coll_amt, collections_12_mths_ex_med, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the observations\n",
    "loan_df.loc[coll_condition_5, ['tot_coll_amt', 'collections_12_mths_ex_med', 'loan_status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `open_il_12m`, `open_il_24m`, `mnths_since_rcnt_il` are linked with `total_bal_il`.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>open_il_12m</th>\n",
    "      <th>open_il_24m</th>\n",
    "      <th>mnths_since_rcnt_il</th>\n",
    "      <th>total_bal_il</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>0</td>\n",
    "      <td>Structurally Missing</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>>0</th>\n",
    "      <td>>0</td>\n",
    "      <td>NaN</td>\n",
    "      <td>>0</td>\n",
    "      <td>MAR, should be 1</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR (majority), assume Structurally Missing</td>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "open_il_condition_1 = \\\n",
    "    (loan_df.open_il_12m.isna() | loan_df.open_il_24m.isna())\\\n",
    "    & (loan_df.mths_since_rcnt_il.isna())\\\n",
    "    & (loan_df.total_bal_il == 0)\n",
    "open_il_condition_2 = \\\n",
    "    (loan_df.open_il_12m.isna() | loan_df.open_il_24m.isna())\\\n",
    "    & (loan_df.mths_since_rcnt_il.isna())\\\n",
    "    & (loan_df.total_bal_il > 0)\n",
    "open_il_condition_3 = \\\n",
    "    ((loan_df.open_il_12m > 0) | (loan_df.open_il_24m > 0))\\\n",
    "    & (loan_df.mths_since_rcnt_il.isna())\\\n",
    "    & (loan_df.total_bal_il > 0)\n",
    "open_il_condition_4 = \\\n",
    "    (loan_df.open_il_12m.isna() & loan_df.open_il_24m.isna())\\\n",
    "    & (loan_df.mths_since_rcnt_il.isna())\\\n",
    "    & (loan_df.total_bal_il.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open_il_12m, open_il_24m, mths_since_rcnt_il, total_bal_il, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[open_il_condition_1, ['open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open_il_12m, open_il_24m, mths_since_rcnt_il, total_bal_il, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[open_il_condition_2, ['open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [open_il_12m, open_il_24m, mths_since_rcnt_il, total_bal_il, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[open_il_condition_3, ['open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>866007 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        open_il_12m  open_il_24m  mths_since_rcnt_il  total_bal_il  \\\n",
       "0               NaN          NaN                 NaN           NaN   \n",
       "1               NaN          NaN                 NaN           NaN   \n",
       "2               NaN          NaN                 NaN           NaN   \n",
       "3               NaN          NaN                 NaN           NaN   \n",
       "4               NaN          NaN                 NaN           NaN   \n",
       "...             ...          ...                 ...           ...   \n",
       "887374          NaN          NaN                 NaN           NaN   \n",
       "887375          NaN          NaN                 NaN           NaN   \n",
       "887376          NaN          NaN                 NaN           NaN   \n",
       "887377          NaN          NaN                 NaN           NaN   \n",
       "887378          NaN          NaN                 NaN           NaN   \n",
       "\n",
       "        loan_status  \n",
       "0        Fully Paid  \n",
       "1       Charged Off  \n",
       "2        Fully Paid  \n",
       "3        Fully Paid  \n",
       "4           Current  \n",
       "...             ...  \n",
       "887374      Current  \n",
       "887375      Current  \n",
       "887376      Current  \n",
       "887377      Current  \n",
       "887378      Current  \n",
       "\n",
       "[866007 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[open_il_condition_4, ['open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'loan_status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Majority of installment features has missing value for all of the related features. We will assume this as Structurally Missing. \n",
    "\n",
    "- `total_bal_il` is linked with `il_util`. Note that utilization ratio is the ratio of current debt that has been paid, missing `total_bal_il` while `il_util` > 0 (and vice versa) is ambiguous.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>total_bal_il</th>\n",
    "      <th>il_util</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>MAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MAR</td>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR (majority), assumes Structurally Missing</td>\n",
    "    </tr>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466294</th>\n",
       "      <td>55536.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
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       "    <tr>\n",
       "      <th>466340</th>\n",
       "      <td>352033.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466382</th>\n",
       "      <td>14477.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466455</th>\n",
       "      <td>42475.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
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       "    <tr>\n",
       "      <th>466465</th>\n",
       "      <td>32185.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488400</th>\n",
       "      <td>77148.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488417</th>\n",
       "      <td>30531.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488470</th>\n",
       "      <td>21331.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488527</th>\n",
       "      <td>17718.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489014</th>\n",
       "      <td>41039.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bal_il  il_util loan_status\n",
       "466294      55536.00      NaN      Issued\n",
       "466340     352033.00      NaN      Issued\n",
       "466382      14477.00      NaN      Issued\n",
       "466455      42475.00      NaN      Issued\n",
       "466465      32185.00      NaN      Issued\n",
       "...              ...      ...         ...\n",
       "488400      77148.00      NaN     Current\n",
       "488417      30531.00      NaN     Current\n",
       "488470      21331.00      NaN     Current\n",
       "488527      17718.00      NaN     Current\n",
       "489014      41039.00      NaN     Current\n",
       "\n",
       "[633 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bal_il_condition_1 = \\\n",
    "    (loan_df.total_bal_il > 0)\\\n",
    "    & (loan_df.il_util.isna())\n",
    "bal_il_condition_2 = \\\n",
    "    (loan_df.total_bal_il.isna())\\\n",
    "    & (loan_df.il_util > 0)\n",
    "bal_il_condition_3 = \\\n",
    "    (loan_df.total_bal_il.isna())\\\n",
    "    & (loan_df.il_util == 0)\n",
    "bal_il_condition_4 = \\\n",
    "    (loan_df.total_bal_il == 0)\\\n",
    "    & (loan_df.il_util.isna())\n",
    "bal_il_condition_5 = \\\n",
    "    (loan_df.total_bal_il.isna())\\\n",
    "    & (loan_df.il_util.isna())\n",
    "\n",
    "loan_df.loc[bal_il_condition_1, ['total_bal_il', 'il_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [total_bal_il, il_util, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[bal_il_condition_2, ['total_bal_il', 'il_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [total_bal_il, il_util, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[bal_il_condition_3, ['total_bal_il', 'il_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>466301</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466330</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466338</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466352</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466366</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Issued</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488962</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489026</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489059</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489092</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489160</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2122 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bal_il  il_util loan_status\n",
       "466301          0.00      NaN      Issued\n",
       "466330          0.00      NaN      Issued\n",
       "466338          0.00      NaN      Issued\n",
       "466352          0.00      NaN      Issued\n",
       "466366          0.00      NaN      Issued\n",
       "...              ...      ...         ...\n",
       "488962          0.00      NaN     Current\n",
       "489026          0.00      NaN     Current\n",
       "489059          0.00      NaN     Current\n",
       "489092          0.00      NaN     Current\n",
       "489160          0.00      NaN     Current\n",
       "\n",
       "[2122 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[bal_il_condition_4, ['total_bal_il', 'il_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charged Off</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887374</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887375</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887376</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887377</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887378</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>866007 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_bal_il  il_util  loan_status\n",
       "0                NaN      NaN   Fully Paid\n",
       "1                NaN      NaN  Charged Off\n",
       "2                NaN      NaN   Fully Paid\n",
       "3                NaN      NaN   Fully Paid\n",
       "4                NaN      NaN      Current\n",
       "...              ...      ...          ...\n",
       "887374           NaN      NaN      Current\n",
       "887375           NaN      NaN      Current\n",
       "887376           NaN      NaN      Current\n",
       "887377           NaN      NaN      Current\n",
       "887378           NaN      NaN      Current\n",
       "\n",
       "[866007 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[bal_il_condition_5, ['total_bal_il', 'il_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `revol_bal` is linked with `revol_util`. Missing `revol_bal` while `revol_util` > 0 (and vice versa) is ambiguous.\n",
    "<table border=\"1\" class=\"dataframe\">\n",
    "  <thead>\n",
    "    <tr style=\"text-align: left;\">\n",
    "      <th>Condition</th>\n",
    "      <th>revol_bal</th>\n",
    "      <th>revol_util</th>\n",
    "      <th>Remarks</th>\n",
    "    </tr>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td>1</th>\n",
    "      <td>>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "      <td>2</th>\n",
    "      <td>NaN</th>\n",
    "      <td>>0</td>\n",
    "      <td>MCAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>3</th>\n",
    "      <td>NaN</th>\n",
    "      <td>0</td>\n",
    "      <td>MAR</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td>4</th>\n",
    "      <td>0</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MAR</td>\n",
    "    <tr>\n",
    "      <td>5</th>\n",
    "      <td>NaN</th>\n",
    "      <td>NaN</td>\n",
    "      <td>MNAR (majority), assumes Structurally Missing</td>\n",
    "    </tr>\n",
    "    </tr>\n",
    "    </tbody>  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37609</th>\n",
       "      <td>49238.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41175</th>\n",
       "      <td>290291.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Does not meet the credit policy. Status:Fully ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49606</th>\n",
       "      <td>1545.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52145</th>\n",
       "      <td>2028.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78172</th>\n",
       "      <td>15304.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843957</th>\n",
       "      <td>24904.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848280</th>\n",
       "      <td>40156.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869440</th>\n",
       "      <td>62918.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872237</th>\n",
       "      <td>3671.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883171</th>\n",
       "      <td>91109.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        revol_bal  revol_util  \\\n",
       "37609    49238.00         NaN   \n",
       "41175   290291.00         NaN   \n",
       "49606     1545.00         NaN   \n",
       "52145     2028.00         NaN   \n",
       "78172    15304.00         NaN   \n",
       "...           ...         ...   \n",
       "843957   24904.00         NaN   \n",
       "848280   40156.00         NaN   \n",
       "869440   62918.00         NaN   \n",
       "872237    3671.00         NaN   \n",
       "883171   91109.00         NaN   \n",
       "\n",
       "                                              loan_status  \n",
       "37609                                          Fully Paid  \n",
       "41175   Does not meet the credit policy. Status:Fully ...  \n",
       "49606                                             Current  \n",
       "52145                                             Current  \n",
       "78172                                          Fully Paid  \n",
       "...                                                   ...  \n",
       "843957                                            Current  \n",
       "848280                                            Current  \n",
       "869440                                         Fully Paid  \n",
       "872237                                            Current  \n",
       "883171                                            Current  \n",
       "\n",
       "[99 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revol_condition_1 = \\\n",
    "    (loan_df.revol_bal > 0)\\\n",
    "    & (loan_df.revol_util.isna())\n",
    "revol_condition_2 = \\\n",
    "    (loan_df.revol_bal.isna())\\\n",
    "    & (loan_df.revol_util > 0)\n",
    "revol_condition_3 = \\\n",
    "    (loan_df.revol_bal.isna())\\\n",
    "    & (loan_df.revol_util == 0)\n",
    "revol_condition_4 = \\\n",
    "    (loan_df.revol_bal == 0)\\\n",
    "    & (loan_df.revol_util.isna())\n",
    "revol_condition_5 = \\\n",
    "    (loan_df.revol_bal.isna())\\\n",
    "    & (loan_df.revol_util.isna())\n",
    "\n",
    "loan_df.loc[revol_condition_1, ['revol_bal', 'revol_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "Empty DataFrame\n",
       "Columns: [revol_bal, revol_util, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[revol_condition_2, ['revol_bal', 'revol_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [revol_bal, revol_util, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[revol_condition_3, ['revol_bal', 'revol_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4730</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4960</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11315</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fully Paid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12076</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charged Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869207</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883031</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883102</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884401</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Late (31-120 days)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887229</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Current</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        revol_bal  revol_util         loan_status\n",
       "3577         0.00         NaN          Fully Paid\n",
       "4730         0.00         NaN         Charged Off\n",
       "4960         0.00         NaN          Fully Paid\n",
       "11315        0.00         NaN          Fully Paid\n",
       "12076        0.00         NaN         Charged Off\n",
       "...           ...         ...                 ...\n",
       "869207       0.00         NaN             Current\n",
       "883031       0.00         NaN             Current\n",
       "883102       0.00         NaN             Current\n",
       "884401       0.00         NaN  Late (31-120 days)\n",
       "887229       0.00         NaN             Current\n",
       "\n",
       "[403 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[revol_condition_4, ['revol_bal', 'revol_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>loan_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [revol_bal, revol_util, loan_status]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.loc[revol_condition_5, ['revol_bal', 'revol_util', 'loan_status']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For other features that has no counterpart, will be assumed as Structurally Missing.\n",
    "\n",
    "So, the missing value remedies has the flow: 1) first we check each of the condition above, replace the missing value accordingly; 2) For other missing value that exist (outside of the previous conditions), assume as Structurally Missing, replace either with 1 or 300 (for months_since_)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Profile**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For *Loan Profile*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nans Count</th>\n",
       "      <th>Nans Percentage (%)</th>\n",
       "      <th>Data Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>desc</th>\n",
       "      <td>761351</td>\n",
       "      <td>85.80</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
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       "    <tr>\n",
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       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Nans Count  Nans Percentage (%) Data Types\n",
       "desc                  761351                85.80     object\n",
       "title                    152                 0.02     object\n",
       "id                         0                 0.00    float64\n",
       "loan_amnt                  0                 0.00    float64\n",
       "term                       0                 0.00   category\n",
       "int_rate                   0                 0.00    float64\n",
       "installment                0                 0.00    float64\n",
       "grade                      0                 0.00   category\n",
       "sub_grade                  0                 0.00   category\n",
       "pymnt_plan                 0                 0.00   category\n",
       "purpose                    0                 0.00   category\n",
       "zip_code                   0                 0.00     object\n",
       "addr_state                 0                 0.00     object\n",
       "policy_code                0                 0.00    float64\n",
       "application_type           0                 0.00   category"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_exploration.show_nans_or_zeroes('nans', loan_features).sort_values('Nans Count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type of missing data is MNAR: `desc`, `title`. These features won't be used for machine learning features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Creating the Processor Class**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `LoanDataMissingHandler()` has been developed to handle missing value for LoanData."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>99.94</td>\n",
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       "    </tr>\n",
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       "      <th>desc</th>\n",
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       "      <td>90.24</td>\n",
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       "    <tr>\n",
       "      <th>emp_title</th>\n",
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       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>42723</td>\n",
       "      <td>5.23</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
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       "    <tr>\n",
       "      <th>member_id</th>\n",
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       "    <tr>\n",
       "      <th>open_rv_24m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
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       "      <th>all_util</th>\n",
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       "    <tr>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_fi</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>total_cu_tl</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_12m</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>pub_rec</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>il_util</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_rv_12m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_il_24m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bal_il</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>datetime64[ns]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_acc</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>policy_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_acc_6m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_il_12m</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Nans Count  Nans Percentage (%)      Data Types\n",
       "dti_joint                        815877                99.94         float64\n",
       "verification_status_joint        815875                99.94        category\n",
       "annual_inc_joint                 815875                99.94         float64\n",
       "desc                             736690                90.24          object\n",
       "emp_title                         47084                 5.77          object\n",
       "emp_length                        42723                 5.23        category\n",
       "title                               136                 0.02          object\n",
       "member_id                             0                 0.00         float64\n",
       "max_bal_bc                            0                 0.00         float64\n",
       "open_rv_24m                           0                 0.00         float64\n",
       "all_util                              0                 0.00         float64\n",
       "total_rev_hi_lim                      0                 0.00         float64\n",
       "inq_fi                                0                 0.00         float64\n",
       "total_cu_tl                           0                 0.00         float64\n",
       "inq_last_12m                          0                 0.00         float64\n",
       "pub_rec                               0                 0.00         float64\n",
       "loan_amnt                             0                 0.00         float64\n",
       "id                                    0                 0.00         float64\n",
       "il_util                               0                 0.00         float64\n",
       "term                                  0                 0.00        category\n",
       "int_rate                              0                 0.00         float64\n",
       "installment                           0                 0.00         float64\n",
       "grade                                 0                 0.00        category\n",
       "sub_grade                             0                 0.00        category\n",
       "pymnt_plan                            0                 0.00        category\n",
       "purpose                               0                 0.00        category\n",
       "zip_code                              0                 0.00          object\n",
       "addr_state                            0                 0.00          object\n",
       "policy_code                           0                 0.00         float64\n",
       "open_rv_12m                           0                 0.00         float64\n",
       "open_il_24m                           0                 0.00         float64\n",
       "total_bal_il                          0                 0.00         float64\n",
       "mths_since_rcnt_il                    0                 0.00         float64\n",
       "home_ownership                        0                 0.00        category\n",
       "annual_inc                            0                 0.00         float64\n",
       "verification_status                   0                 0.00        category\n",
       "dti                                   0                 0.00         float64\n",
       "delinq_2yrs                           0                 0.00         float64\n",
       "earliest_cr_line                      0                 0.00  datetime64[ns]\n",
       "inq_last_6mths                        0                 0.00         float64\n",
       "mths_since_last_delinq                0                 0.00         float64\n",
       "mths_since_last_record                0                 0.00         float64\n",
       "open_acc                              0                 0.00         float64\n",
       "revol_bal                             0                 0.00         float64\n",
       "revol_util                            0                 0.00         float64\n",
       "total_acc                             0                 0.00         float64\n",
       "collections_12_mths_ex_med            0                 0.00         float64\n",
       "mths_since_last_major_derog           0                 0.00         float64\n",
       "policy_code                           0                 0.00         float64\n",
       "acc_now_delinq                        0                 0.00         float64\n",
       "tot_coll_amt                          0                 0.00         float64\n",
       "tot_cur_bal                           0                 0.00         float64\n",
       "open_acc_6m                           0                 0.00         float64\n",
       "open_il_12m                           0                 0.00         float64\n",
       "application_type                      0                 0.00        category"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from modules.data_preprocess import LoanDataMissingHandler\n",
    "\n",
    "missing_handler = LoanDataMissingHandler()\n",
    "loan_df = missing_handler.fit_transform(loan_df)\n",
    "data_exploration = DataExploration(loan_df)\n",
    "data_exploration.show_nans_or_zeroes('nans', applicant_features + loan_features).sort_values('Nans Count', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"skip_to_cardinality\"> </a>\n",
    "### **2.2 Cardinality of data fields containing *Objects* datatype**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: emp_title, Unique Values: 250467\n",
      "Column: desc, Unique Values: 78434\n",
      "Column: title, Unique Values: 37805\n",
      "Column: zip_code, Unique Values: 932\n",
      "Column: addr_state, Unique Values: 51\n"
     ]
    }
   ],
   "source": [
    "obj_columns = loan_df.select_dtypes(include='object').columns\n",
    "\n",
    "for column in obj_columns:\n",
    "    print(f'Column: {column}, Unique Values: {len(loan_df[column].unique())}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data fields with `object` datatype, which have high cardinality, won't be used as machine learning features. Nevertheless, statistics related to those datafields will be shown for exploratory analysis to gather valuable insight. Later we will use *word clouds* as a representation of these observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Takeaway**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on explanation above, we will consider **applicant's profile** and **loan profile** for our prediction model. However, we will explore more whether we can extract some features from **post-originated loan data**. Features related to *date and time* will also be considered in feature extraction.\n",
    "\n",
    "Below list consists the features that will be dropped due to its cardinality. And note that we only consider `INDIVIDUAL` type loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "loan_df = loan_df[loan_df.application_type == 'INDIVIDUAL']\n",
    "drop_features = [\n",
    "    'emp_title', 'desc', 'title', 'zip_code', 'addr_state', 'application_type',\n",
    "    'dti_joint', 'annual_inc_joint', 'verification_status_joint'\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Exploratory Data Analytics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing categorical data gives us general understanding of the Lending Club dataset, especially about the loan status that implies general outcome of this Lending Club credit business. Summary statistics for our categorical data is shown as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "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>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>verification_status</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>desc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>title</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>application_type</th>\n",
       "      <th>verification_status_joint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>768850</td>\n",
       "      <td>773211</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>79687</td>\n",
       "      <td>815875</td>\n",
       "      <td>815747</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>815875</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>250363</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>78433</td>\n",
       "      <td>14</td>\n",
       "      <td>37804</td>\n",
       "      <td>932</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>36 months</td>\n",
       "      <td>B</td>\n",
       "      <td>B4</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>Current</td>\n",
       "      <td>n</td>\n",
       "      <td></td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>945xx</td>\n",
       "      <td>CA</td>\n",
       "      <td>w</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>566300</td>\n",
       "      <td>232453</td>\n",
       "      <td>51106</td>\n",
       "      <td>13782</td>\n",
       "      <td>274255</td>\n",
       "      <td>411574</td>\n",
       "      <td>311262</td>\n",
       "      <td>597307</td>\n",
       "      <td>815869</td>\n",
       "      <td>20</td>\n",
       "      <td>488500</td>\n",
       "      <td>411797</td>\n",
       "      <td>8848</td>\n",
       "      <td>117061</td>\n",
       "      <td>429583</td>\n",
       "      <td>815875</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              term   grade sub_grade emp_title emp_length home_ownership  \\\n",
       "count       815875  815875    815875    768850     773211         815875   \n",
       "unique           2       7        35    250363         11              6   \n",
       "top      36 months       B        B4   Teacher  10+ years       MORTGAGE   \n",
       "freq        566300  232453     51106     13782     274255         411574   \n",
       "\n",
       "       verification_status loan_status pymnt_plan   desc             purpose  \\\n",
       "count               815875      815875     815875  79687              815875   \n",
       "unique                   3           8          2  78433                  14   \n",
       "top        Source Verified     Current          n         debt_consolidation   \n",
       "freq                311262      597307     815869     20              488500   \n",
       "\n",
       "                     title zip_code addr_state initial_list_status  \\\n",
       "count               815747   815875     815875              815875   \n",
       "unique               37804      932         51                   2   \n",
       "top     Debt consolidation    945xx         CA                   w   \n",
       "freq                411797     8848     117061              429583   \n",
       "\n",
       "       application_type verification_status_joint  \n",
       "count            815875                         0  \n",
       "unique                1                         0  \n",
       "top          INDIVIDUAL                       NaN  \n",
       "freq             815875                       NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df.select_dtypes(include=['object', 'category']).describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Status**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature `loan_status` is the possible features as a target of supervised learning to predict whether particular loan is good or bad. This feature records is available after the loan is originated to the applicant.\n",
    "\n",
    "- Loan that is not yet started: `Issued`.\n",
    "- On-going loan has status: `Current`, `Late`, and `In Grace Period`.\n",
    "- Finished loan has status either `Fully Paid`, `Charged Off`, and `Default`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# df for plotting count\n",
    "plot_count = loan_df\\\n",
    "    .groupby('loan_status')\\\n",
    "    .aggregate(counts=pd.NamedAgg(column='loan_status', aggfunc='count'))\\\n",
    "    .reset_index()\\\n",
    "    .sort_values('counts', ascending=False)\n",
    "# plot\n",
    "red = sns.color_palette('colorblind')[3]\n",
    "blue = sns.color_palette('colorblind')[0]\n",
    "green = sns.color_palette('colorblind')[2]\n",
    "colors = [\n",
    "    blue, green, red, red, blue, \n",
    "    red, red, green, red, red \n",
    "    ]\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.barplot(\n",
    "    data=plot_count, x='counts', y='loan_status', order=plot_count.loan_status,\n",
    "    palette=colors, ax=ax\n",
    "    )\n",
    "for y_, x_ in enumerate(plot_count.counts.values):\n",
    "    ax.text(\n",
    "        x=(x_ + 2000), y=y_, s=f'{x_:,.0f}', size=9, verticalalignment='center'\n",
    "    )\n",
    "ax.set_title('Categories of Loan Status')\n",
    "ax.set_xlim(0, 650000)\n",
    "ax.get_xaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the loan listed in our dataset is on-going loan (`loan_status` = `Current`). However, we can identify some loan that faces some difficulties in paying-off debt on the due date: `Late` and `In Grace Period`. \n",
    "\n",
    "To make an easier representation of `loan_status`, we will create a new feature to label the loan as `Good Loan` and `Bad Loan`.\n",
    "\n",
    "- `Bad Loan` consists of: `Charged Off`, `Default`, `In Grace Period`, and `Late`.\n",
    "- `Good Loan` consists of: `Fully Paid`.\n",
    "- `Current` loan possibly may be a `Good Loan`, since up to the data recorded the loan seems to not having paying difficulties.\n",
    " \n",
    "Ideally we can't justify the `Curent Loan` to be either `Good Loan` or `Bad Loan` since it is highly subjective and maybe introducing biases to our dataset. \n",
    "\n",
    "However, there may be a pattern of `Current Loan` to be defaulted. For example, we can analyze tendencies of defaulted loan by its `Portion of Loan Amount that has been paid`. We can extract this from `total_rec_prncp` as a portion of `loan_amnt` (recall that `total_rec_prncp` is *principal received to date*). \n",
    "\n",
    "Let's see the distribution of portion of loan paid of `Late`, `Default` and `Charged Off` status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Principal of Loan Paid, Bad Loan')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bad_loan = [\n",
    "    'Charged Off', 'Late (31-120 days)', 'In Grace Period', \n",
    "    'Late (16-30 days)', 'Default', \n",
    "    'Does not meet the credit policy. Status:Charged Off'\n",
    "    ]\n",
    "plot_bad_loan = loan_df[loan_df.loan_status.isin(bad_loan)].assign(\n",
    "    portion_paid=lambda x: x.total_rec_prncp / x.loan_amnt * 100\n",
    "    )\n",
    "plot_bad_loan = plot_bad_loan[['portion_paid']]\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.histplot(data=plot_bad_loan, x='portion_paid', ax=ax, kde=True, color=red)\n",
    "ax.set_xlabel('Portion of Principal Loan Paid, %')\n",
    "ax.set_title('Principal of Loan Paid, Bad Loan')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above plot, the distribution of `Bad Loan` is proportionally lower for higher `Portion of Loan Paid`. This indicate that, by data, *the chance is smaller for loan to be defaulted after large portion of principal has been paid*.\n",
    "\n",
    "Since the `Good Loan` has more occasion than `Bad Loan`, we can, if necessary, consider `Current Loan` as a `Good Loan` only if the loan is close to be fully paid, specifically when the `total_rec_prncp` $\\geq$ `%` portion of `loan_amnt`. But adding more observations to `Good Loan` lead to more imbalanced data towards the `Good Loan`.\n",
    "\n",
    "On the other hand, we are more interested to oversample our `Bad Loan` to tighten the gap. However, considering `Current Loan` as a `Bad Loan`, let's say for portion of paid loan under `20%`, is very subjective since the `Bad Loan` isn't the majority here.\n",
    "\n",
    "Let's create a new feature called `loan_category` to classify `Good Loan` and `Bad Loan`. Then, we will look at the proportion of each label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mapping_loan_cat = {\n",
    "    'Charged Off': 'Bad Loan', \n",
    "    'Late (31-120 days)': 'Bad Loan', \n",
    "    'In Grace Period': 'Bad Loan', \n",
    "    'Late (16-30 days)': 'Bad Loan', \n",
    "    'Default': 'Bad Loan', \n",
    "    'Does not meet the credit policy. Status:Charged Off': 'Bad Loan',\n",
    "    'Fully Paid': 'Good Loan', \n",
    "    'Does not meet the credit policy. Status:Fully Paid': 'Good Loan'\n",
    "    }\n",
    "\n",
    "# loan_cat_vect = np.vectorize(loan_cat)\n",
    "loan_df = loan_df.assign(\n",
    "    loan_category = loan_df.loan_status.map(mapping_loan_cat)\n",
    "    )\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "loan_df\\\n",
    "    .loc[loan_df.loan_category.isin(['Good Loan', 'Bad Loan']), 'loan_category']\\\n",
    "    .value_counts()\\\n",
    "    .plot.pie(autopct=\"%.2f%%\", ax=ax)\n",
    "ax.set_ylabel('')\n",
    "ax.set_title('Percentage of Loan Category')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imbalanced sample between `Good Loan` and `Bad Loan` probably would lead to model with low recall in predicting potentially `Bad Loan` (false negative occurs more frequent). For credit business, it is more preferable to avoid defaulted loan rather than taking a risk for maximizing loan numbers then hoping succesfull loan will outnumber (in profit) defaulted loan. Action should be taken.\n",
    "\n",
    "From now on, we will use `loan_df` subset of `Good Loan` and `Bad Loan`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_df_subset = loan_df[loan_df.loan_category.isin(['Good Loan', 'Bad Loan'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Category Along Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nJXD0/5X/eq0Ph6ZdyR7/Dv83PIW4Zt2g81k7HzfgDlx8EqN+/YklrkWplwy5OB75NZW4hm2h4+DyxyYiIoFSQrYrLQ+FS74nnJvN1NevY8C0I9mck4vzx/VUSof8HY59EFr0CTqS6Dr8VqizP8uGPse5P8HPk2bCgH9Cv+vKd71+1+O2rOOZqSG+nJFG8ppUwsc8AAlV/jqmXhvoeRmhqR9wX9qAMl32pzlprEnPIDzgzoqXJDdqD+d+AHVbBR2JiEiFpoRsV7qdi1v2G2+9+jTnrDmfrVQlPSuHuNqVOCFr3AWA0MEnBxtH1Tpw5L+g63mRv3a91nDYTYRmfs51S/oAxtVfpzBtzjw48XHoeu7uXa/+AXDwIAomv8dneYfiHNw9dCVxdVvi+lz913FH3YMLF/DlqImk0aBMl3YOnvg1hbjGHaD9KbsXVzTVaAgXDoGOpxE+/eWgoxERqdCUkO3C+EkTuPLdCTy6+RSc/3alZOUTX6txwJEFqImXkFnb44IpP6EKHPoPuGmGl8AM/h8ceHRkyzjhUVy4kG+Hj2Cx320YdnDBpytZunQx7vRX4ICjyn69ftfhQgV8OnEpeSQBMG7JeqYuXAFH/NObCqNJF+h6LoXjX+exDbtXnx9mpbJ23TqvlawiSKgC53+Mq9GIdRM+Ia7NEdDz0qCjEhGpsJSQ7cIPq2vwS6jHdttSs/NxtZsHFFHAqtSC+m0IZa8lrnFHqNsydmVbHHQ7H26YAic8RmjNTKa/dQOZ6atxZ7/tdfdFwoHHQPtTyP/tOR7M3D7pzCsMc/r7y8hcn0F40H+9mHalal3ofiHh2V/x4ob+2+26Z2gyVKlN+Ig74Oj/w+Vs4I0xS8ii5m6FHAo7nhy1irimXaHdCWU7qUkXuGMJ9L1mt8oqk9Negv37svGrf3L0N3HMXboCd/wjUFn/bkREdkEJWTmkZeVgNfeDuEp4k+p+HQFYMu4rAMLtBsWm3KQacOmPcMZrhLZmsvy9aznrrVmcsXgQg99fQqGLJ3z+R95xeyI+EQY9QXj9Uv49dhPZ7Hy9zXmF3D8ylbhG7aDDabu+Zs9LIKkGE/74jQzqbbdr4dpNDJ22BOt7DbQ7gbyxL/Hi5t1oeSvim+mprMvMJHzk3WU7ofeVUKMRDPovnPxM5D7PA+6ArueS9/NjnDGjB9nU5NovlxGyRMKnPh+ZMkRE9jFKyMohLTsXi4uDmpWw29Lvrhw+exVrMjIJtzsx+mXGJ3ndX/v3Ifvrf3Llyz9w1PxTmOEOAmBVZg7XfjILa9Te60osTlyCd9dkSftqN4PmPeCYB6BhW9J+epJP8g8vMaSf5qSRlpFBeMAdpccelwB9ria0fCyPpnQp9pCHR6wiFArhstfw1B8byaFq6dcsQWHY8fSoVcQ1P8Rr5StNYjXodAahmUMYNWY09L4C/v6l15q3JzqdCUffR2jGZ1zyayIrnDfWclVmDk8Nm0dc2+OiM+ZPRGQvVwmbePZcWlae96R2U8heHWwwsda4My5nA5M21qb6gg1ccehhkFQT8jdHp7y4eG9i1gMGsvmrWzh+Uo9iB7v/sjiLV4ZN4x8nDYbUW+H3Z73Wn3YnQLsT4cCjvDhdGPK3ePHmb/W6YGs03K7rMTTvB66f2+HPMYPFCTt4clQKT597iFfGouHFH9jxdKjTgpQfnmSeO77YQ9Kyc7nmnd85v/Ab3ss9drfenh19MW01dx7TktpH3kX80l9KPrD9KVC1DnOn/c5lC0/gxvRfuW3w4XDlz9jH50Lmst0vvOPpMPgVQskTeeCrKUwID9xu92vjUzmrazIHDXoCWzYKNqd7iWHns6HPVYDBuydDXvbuly0ispdTQlYOadk5AIRrNyeOKQFHE2NNOuPS5jA/3JLQ/AyuPOJAOGAgLPghOuWd8hx0PI3cn+7ntMmdS73z8Mkx6RzSfD6HHn0/1vF0aHYIAOGs1biZn5O+IZv4KtVISkoiKakqCUlVIW8zWzdvYGP2ZlKz81mW5RidGs8M16PEcrb5dkYq9xzbinoD/kVccQlZXAIcdjPh9Ut5ZF7pd0z+sjyXX9jz1saCkOO50cn8e3Af7/eybHTxB3a/kPCGZJ5f0giAF6fkMH/db7x6US/ir/oVe/tEyFhQtkKr1YOTnoQu5xBePZ3PPnqTD/MH73SYc3DNF8sYeXN/7LSXiMtYCD0ugmr1CKcvwBocCGe9hX1ynia4FZFKRwlZOaRleYtCh2q3rFx9vhYH+3WkYOrHrKcZk1dksjUnh6SDTyYhGgnZcQ9Bj4vJ/+1pzhrTmOVu11ONXPHFSn6qFU+rpBA5vzzJ2AVr+HD1fkwMdyCf/Yo5o57/2H2FYcezv63ikTN6QpsjYflv2x9wynPQtBtZQ27g51AZB9pHwKdT1nDrwJbUPvp+4opLyOq0gAOOpGD0M4wOdftz888r8jnh5Un89I9+JJzyHHHvlCFBbHscnPYirnpD8n99nH/9uolvCk8v8fBl67bw0s/zuHnQCbiDjiE87wfmTRzBY0tb0633kdx1xvFet/HPD5Sj5iIiey8lZOWwYWsB+QWF3rijyqR+G0iqwdq0VKAZhWHHb4syOPGgY7wJSXdcAqg8ajX1ur46nQEt+1E46W0uHAZzXesynZ5TEGLg64toRBYZdAA67nlMpfh86hruPKY1NQbcSXzRhOyIf0KPi8gf/TTnTe9cavdnpOUVhvnvLyt47KyeXnftomHbH9DtfLA4vp+2ghAdttu1NLOQh4cv4+EzD/WWjJr9RfGFJFSBQU9Cz0sIr53HvA//xeUrjye9DMnt82NWk7BxGXVX/cormb1YjTef3fiJ6+jadBYnHX4LpM+N/tqctZp4v6daTWD0f2Dt3OiWJyJSikrVwBNJ67K3EFerSdBhxFaTrgBMWZPz56bh8zOxWo3/7B4sl8Tq0PdauHwY3L7Au8uxSm1yRjzMVd+uYbJrv5sXNDKoC0R/1vq8wjAvjkkmvs3hsL+/ckGXc+CY/6NwxmdcNqKARS72y2wNmZpK+rp1hI++f+fZ+7tfSGj577y2rvibDD6avIYVKatxxz/qjbErzukvQ89LKPj9Rf7v5Xc5ZeW5ZUrGwB9/N7MK92YOYjWNttt303cpLFm2DHfai95NFkU1bOutENHv+jKVU6Jq9bzW15tm4Hpeimt1OO6aMXDcw3t+l+6eiIv3YkusHtyKC3Hx3tQkLXpB9frBxCBSSamFrJzWZOXTpLIlZI0748KF/L426c9NoxelEw6Hce1OJn71tPJd97QXoMs5hNLmkv/Lfxk5J4V309ow3XWPactSeX04cTW3HNWaqkfcSfy4Z+H0lwkt/537v5rGuHDZlj+KtMKw49GRyTx/QQ/vzsc5X3o7Wh4K9Q9g/ejXWeyOKPbcsIObv1nON9f3xw34Fzbyvu0POOoe6HIOeT8/ykm/NGKpOzKicZ/70VJ+u6EmNc//BHv3ZGjV31uua/++ALhwCJv/PWStKvlCpzzntbKuWwgZi7zxcOsWQtNu0P8mXJVahGd+zshRP/Pi5oE8OagZHQ+7Cdf5TGzoHbBwaMTqVCb1D4ALP4cGB/21rTAPCnJgxkcw4t7ItEDvqG5LOOxm7z9bdZpDzSZeUgaQmwW/PgJT3oJwqPjzm/eAjatgS0bkYxOpZJSQldPq7Hx6tqxkyyc16Yxbt4RZBX911W7cWsCclel0bncCjHp496/Z+gjocg75o5/mguHGNNcBR6cIBh19OQUhXhubzG0nHget+hLesJL3Pv6Aj/PLMEdZFH03K5U7j1pL04H3EDfvG+9LtfuFuLzN/G9WCV+wvpkpWfw4ZREn97sWZnwAGQu9Hd0ugCP/ReG0j7jolyosdZGf6DVzSz7nvTeP76/rQ/yNUwEIZywkd/jDfL+0kHOuuR/X91riRtxb/AXqtYYeFxNOmUK4sID4tsdjPS76c3do/lAm/vIVd63uT7IbDMBJX2zl1Cnf898zOlDtgk9g3WII5flJkAMH5G+C9Uu9fesXw/ol3us9vQFh/z5wwac458ga/h82Wy0SExNITEigSv0W1Dj0H17L3Q+3RC4pq9XEmy+ux8U4F8atnEB4yW9szV7PhuzNrNkcpl2f42lw0pNwyEXw462Q4t/AVK2e1+3d81Jo1B6XOhN746iSkzYRKRMlZOWUmpULtVoEHUZsNe4MK8fvNLh+6IINdB3UxRtTl72m7NeLS4CTniS8YSWPj1rDVBfh5Y9i6J3xKVx/ZBuqhHIZ+8HDPLQp+Lm2nIN/j0jm9Yt7e4nUnK+g02DCc7/hy9xeuzz//uGrOK7L/iQOeoq490+F1ofDaS8QWvYb//pqDpNcyfO07al5aVu47oPJPNZ5NUtn/cFzK1oyIey1mDaYk8wxPS72xn0VN91Kv+twLsSQT9/lrsxTqMsouldfT78mYernreHtNS1Z4M7Y6bTvV8Tx0/MLefKwBZzUJp4ECwP2Z/ehVauLtTsR63HxXyct+AE+vbD8Fe1wGpz1BuGsNUz84P+4NO3sP5fW8oR48viJnHP0pd5NNd/ftGdJWfUGcPit0PtKXFwCoakf8PXoiTyReSTrqAPU+evYWenc2OU7bj6lDwlX/gLTPvBazzqdAYnVCK2awpY/3qF2/8u8buQ/Xix/XCKihKy81mbnYolVvHEWWzODDif6qtWDOi3ISltOaIcWrF/mp3PXoA6E255A3NR3/tqRUMWb1DVvU/HX7Hst7NeBjI+v4928k6IYfPRtyivkvFfHMTj0E/9ZdxaxGL9WFiPmpbN01RraHHkXcRYPVWoxa/pEssswxUbmlnyeGL6I+wcP8Aa/97+RcOZy3v74E74sPDX6sS/ZzIgldYDtV4N46fc1HNutDeHuFxI36bXtT6pWDw65iPCsL3km8zAANlKL0VtrMXoZwAGlllkYhlvHGreO3dbq5beQAbCe+qygc7X19NovzPG9OtG+9ylei9yGFbtfwX7XwwmPEkqZypAPX+OerPOL7aK/Y8Q6nJvAucdc7CWH391Uvla5+gfApT/gajYhNPMzhv86iocyBrCWkltyX5wdz/sLp/LacYn07X8+FOYSmv4J4yZP4ZmUg5npDmRI3aX0Puoerxu5PO+DiABKyMot1Z/6glrNKkdC1rgzAItSN+60a3H6ZtZmbqRhtwu8JKxpV2+sTqP23hfHN9fD7M+3P6lWExh4F6FFI7h29sGE94KxYrsyc20+M9nFDPkBeGB4Mh9e2Q9O/A/hzOU8t7TsXe3vTlzDpb0b0/KY/8NtTmfsBw/z6Obzoxjtrs1YtZEFK9fQrt91MPmN7ZOTXpdDUg3GjRtFOiVPv1E+Ria1GZNTmzEr4ZONML43hLqcR/yYJ8pwukHDg70B8wcdB50GE5r3PY99Noq38nZusSvqzpHrCbsJnH/sRV5L2bc37F5SVq8NXPI9Lr4K81+7mKuTjyPF767dlax8OP/HAg4e/TXdQ3P4Kbcz2f6dsQA3fbOCsbc1Jf6U54j7oGzXFJGd7f3fggFZuy0h25emvtivo/eFVpwmXkI2ZnXx3SVD560nvmVf7w7Jg44jnLWGrWNfYXPybDjrTTj8tu1POO5hXHwio3/8lOmubSRrITv4fcl65ixbBUk1yJ8+hLHh4u+uLE7YwQ1fL2XrmgUs+/g2rko/m4rQ+vfi72nE1W/jTeuxTUIV6HsNocW/8MjqXU/su6dSs3KZsXQ11vXc0g/cvw9c9A38ayX8YyKc/jLugIEUjH2eaz6azlt5ZUvi7/p5PR+NnADdL/RuWiireq3h0u9xidWY+97NnLHybFJco12etqOFW2rwWW7fndZ3TcvO5YlhC4g78CgtiyWyB9RCVk7bWsgKa++/77yJJz4OBxwJqTNgxzsmm3QhvDmdKZuKn3H+6ZFLKFj4C1syljMhqwHzXCs2cSBJ8el8es5Uehz7gHdH19DbvTv9up5LweinuGPtcdGvl3D3jyv59KwtfD11FWE679a5s1Ky6fFCBmHOJp/EKEW4e4bNTSNzwwbq9LuB+G13RHY5F2o2Zum4h1jkYtMF/vH09XQ/uys06wFrirnL2OK8BKxqXUKzv2LNqhWMTc7h54zaTAu1JYuDdj6nFPf+sp6E+Mmcd/QlkLkUxu1isfa6rbyWscQazH/3Rs5K3nGMWmS8NTGV8w9ZxYEn/gdb8jNsXR/xMkT2dWohK6eMzXmEw2HCtfeRgf0N23nJGBA64s6d9zfuAmlzmF/CnFqb8wp5bHFznt94OBNdBzbhLeSdHwpz5qdpfDtqHPS6DM7/5M+B/E+MSiWT2lGrkvxl9uosDnlhAfdnlm/FgFyqVJhkDCAUdrz2x2ri2xzmLXhvBv1vIJw6mwcXtYxZHMPmpFJYUECo29+KP6DDadCwHek/PkbbLxsyYFIf7k07klGhQ8iiZrnK/NeIdCbMmoc75kFvTdKS1G3ptYxVqcWC927kjCglY+DdZ3Ddl8twSbUJn/B4VMoQ2dcpISunUNixYdNWbF+Zi6zX5bhQPvMmDCe+/SBoXGTgflwCNDqY/LSFZJfzS+Tm4Rt54atRuAOPgf06kvHT47yTNzAysUuZ5JO4T4zV2+aTyavJz8sl1O9Gb0xWo/ZkjnuXP8KxmzYlO7eQUfPXENdpsPd3sqMjbie8bgkPzKoX0Tn1LhmykpSUZNyZr3vjNYsy87o1r/wFV6UOC9+7gcEro5eMbbM4fTNv/zafuG7nwoEVbyylSEUXyL/OZnarmc01szlm9omZVTWzNmY20cyWmNlnZhbdfz0iIC07l7ja+0BCllgduv+N8NzvuGl4Fnm5OYSOuOOv/Q3bQUIVVqel7VExz0zays3vjCJl2LNcM7v9PpUcSOxl5xbyxdRVxHU+A46+j3BWCo/OqEqsx7h9Nj0dq9kIDjxq+x0HHQtNu5I19nWGhyI7pi2vMMyZ7y9h69atuAs+85YcA2jeE674GQa/QmjDSqa9fTODV5wV9WRsmydHrWb9+nWEBhTTyi4ipYr5N6KZNQduAno55zoD8cD5wBPAs865g4ANwBWxjm13rcoqIK7WPjA5bJdzoGodZk/6lSU5Nfhg/AriOp7+16zh/oD+iWvy97io75bkc/jodsxwuzd2RqQ4r41L8VqmmnYjd/xbfF/YJ+Yx/LYog81btlDYdYf5yI64nXBWCo9MS4zKihMZm/M45925hKvU9pKywa/AVb/i6rRg45e3ccWrIzlr1bnkUiXiZZckrzDMRzMyidu/t5ZeEtlNQTVRJADVzCwBqA6kAkcD21Yyfg8YHExoZZeWnYcL+i7L4rpJdlfvKwmnzeWJZa0B+N/vKRQWFhA6/J/e/sZdcIV5/J5edc/LEomgleu38vv8ZMjZyCuTN1AYwC02BSHHd7PSiG9/4l9rf7bqD636s/X3V/mmoG/Uyp6XtoUbP57mraLR+WwKxj7PM8/+h96Tj2R0qNuuLxAFI+elY3HxuLbHB1K+yN4q5gmZc2418BSQjJeIZQFTgY3OuUL/sBQg8muyRFhaVg5x1eoEtyDxIX+HO5Z4XYrl1aIXNO3KpskfMz7cEYD1W/IZMmkFcV3P8QYGN+mMy1jI3NA+cgOD7FP+8flCHnr5Td7eUvzanLHwxfQ0LLHaX4Psj7gdtzmDpyblESI+qmUPXZjNta8O49MX/sWhPzbixa3HUxDgvd9z1mSxIXsToXal3HAgIjsJosuyHnA60AZoBtSAMkwb/tf5V5vZFDObkpER7IK2aVl53pOgui0PPBqq1SN83keQVL7B9vS+Epe3iVem5VJ07M2LY1IIO0f48NugSRdc6myS3X6RiVskgrJzC3l7XQe2ElwL7rTkjaxdv4FQ1wugaXc46Fjyxr/OJ3n9Y1L+8GS4O/04f/mjYDkHw+evI/7Agd5KHSJSJkF0WR4LLHfOZTjnCoCvgMOAun4XJkALYHVxJzvnXnfO9XLO9WrUaPcnN4yktOwc70lQ3ZbNeuAyFmENDsSdVo515KrXh05nEprxGZ/kbN+tkpady7dTV2CH/B1qNCJzbXJUxsGI7Cs+m55OXJsj4MTHcLlZ/G9CeswG01c0I+avw6rW8tY/FZEyCeIbNhnoZ2bVzcyAY4B5wCjgbP+YS4BvA4htt6T5k8OGahc/N1dUVa8P9duQNf1rXh0+A+t8JvS9ZveucchFkFCF3yZN3Wn2bYBnf1v1ZxI2L7WE9ShFBICvpq/B4uKg1WEUTHyLNwPsQg3auCXryM/Pp/BgdVuKlFUQY8gm4g3enwbM9mN4HfgXcJuZLQEaAG/FOrbdlZbtJWTh2gEMd2vm3UY/O2UD/x2TxrR5i3DHPwr7l3EAsRn0upzQij94ek3xM7evysxh2IzluHCIsWuCXy5HpCJbsX4rC5LTIH8L749bwhaqBR1SYPIKw4xfkk58Ow3sFymrQPqgnHMPOOfaO+c6O+cucs7lOeeWOef6OOcOcs6d45zLCyK23ZFbEGbT1hxcrQC6LJv3xLkwI1bF4xxcOmQ5WRszcee8BzXK0JXb9gSo15q1k75inmtd4mH/+m4pr739Ot9vbh+52EX2UTd8sYDvP3iWF7OPDDqUwP04LxOru//2k0yLSIk0KGgPpQc1OWxzb/zYpFzvzsfs3EL+9uECXLV6hM9+B+JKubMrsTqc+Djh9ct4anbpA6E35RXy+JKWrEVzConsypL0Ldy4uPzLIu1LRi1IByCkbkuRMlFCtodSsvKJD2L5pOY9cSlTWeL+6i6dl7qZ+7+e6Q0sHnh3yececz/Ub8OKbx/h6yjOkSQilVfG5jzmJ6/F2pVv/VSRykYJ2R5KDWJy2LotoUYjMlYv3WmOow+nrWPE5Lkw4A44qJj15Fr2g77XUDjxTS5bfJjunBSRqPlh/gbiWvSEmjtMmROfCAcPggRNNi2yjb6N91BaVi5Wo1FkZswvq+Y9AZi8akuxu2/8Npm1aatxZ74BRW84SKwGp79MeOMqnh0+h5VuH1iHU0QqrF/me92W4XZFpppsfThcOw4u+BR3/keaq0zEp4RsD6Vl5Xq3usey27JZD1xhLiPXFL9GXV5hmAs+XEQovpo/nsxPFo+6FxocRMq3D/G/rcW0nomIRNCCtE1kbMgi3O5kqNEQBv8PLv2RcEJVNvz+FnbQsbiz3y59zKtIJaGEbA9tm4ssppPDNu+BS53NtFCbEg9Ztm4rd385i7iWfb0xYy16Qb/rKZz8Lpct7EtYv3oRiYGf5q/3Zu2/YQquy9kU/PYMjz3/Aj1/2I8Xvx+PdTgVTn/Fm4pHpBLTt/Ie+nMusloxmossLh6aHUJo9QxSXOnTW3w+az0/TJgNh90M532E25TKK8OmstRV+GVCRWQf8ePstVhiVVzaXCa8fDX9f9qPN3OPJkwcT4/L5N3hE6Hb+XDS00GHWrqkGt4Y3FgOT5FKRZ+sPZS6bbb+Oi23z24TqkLH02D2F+DCkSuw4cGQVIOUlGS8pUBLd/sPKfRoWY9mzVqQ+v5VPL/l1MjFIiKyCxOXZ3LhEx8RzlrN+PA5O+1/cNQ6alWZwlkDr4CCrTDivgCiLEWTLtDzMuh6DlSpDRkLYfg9sOTnoCOTfYwSsj2UlVNAfkHBzguMH/8w9LkatqyDpb9GrkB/QP/YVWWbNzevMMypb83jnhaf8sqi3jvdlSkiEm3jNtQF6pa4//Zha6lRZTon9r/RW21k5P9B8oTyF2hx0PlMOORirys0b5P3yN8MeZuhYAvkb4H8rf7zrUX+4+y8FdJrNYUeF3lTDBXkEJ77LUtWrKLN4eeQ9PcvYfFIGHGvl6CJRIASsghIz9pKk9pFErJWh3nJGFDYoh8JEU3IeuByNjJqXe0yn7J+SwG3L+wQuRhERCLsuu/WcNeaNK487hDiLx8OC36Anx+EdYt3Ptjiiu95iEuArufCEbdDg4MIr1+KbU6Huq1wVWphVWpCUk0sofgbonYUTl/A1qEP8Na0LN7f3If1dCRp8hye7D+b044eANf9gU1+E37+t9e6J7IHlJBFQGp2Hs1qNfZeJFaD018inLmcLeEkqrXoGdnCmvfErZ7OzNCBkb2uiEiAnIP/TA7z8owpPDfAcdQRA+H6Cdisz6Agx5vCp04LqLM/VKsL2WsgcymsX+b9DOVD32uhXmvCqbNJ//Qm7p3diF9D3anKGmqSSw3LoQa51IrLo16So25SmDpVoFYSJBEC/74CA0IF+fyWXpXx4V7bzdeYHzJuHmv8d9oYXj++Gh17XwVNumEfn+O1womUkxKyCEjJKqBXa38819H/B/UPIPnty5nY+X7O7dwjcgUlVIXGncgd+wqZKCETkX1PdoFx+S/G/hN+5ZVjq9K599mQvwWXtRrbmEL+yilsyc0lqU4Tqtdvhh18ElbTu8EptHoaqUOf5p65TRkTPp5tGVYOVcmhKhmurldICMjxH+W0ekscJ3+dx0VLfuOh84/AXfQt9uGZkLtxD2ovlZkSsghYm5ULtVp4d+D0u47CiW9x6eL+9K2TzXl9WkP9AyBz2Z4X1LQrxCWwbHUaKCETkX3Yqi0JnPptIft99zktWcsa15C11CNE0Vn/M2jIUjpWy6RTrS1MXxfHhPAJ/NnUFQMfzM5ha+E4nrrwUNwl32MfDIat62NWvuw7NO1FBKRm53pjEs56i/DGVTw/fBYrXFOmJ28AINyiT2QKaua1tv2cHIrM9UREKrh0V48prj1raFjMTUnGOuowJqcN/0vvzIRwR2KZjG3z5fwt3PjeH7iG7XCXDoWajWMeg+z9lJBFwJ+Tw9ZpwepvH+LlrccCsCR9Mzm5eYRaRGgB7+Y9CWetZlzWfrs+VkREYuaHxVu5+p3xuDotcTdMhvM+hN5XQsO2QYcmewl1WUbA6o3e3TXhye9waZFZ8MMO5qRsoGfzcowjq9MCcjZ4t2Zv07wnrJ7GHNc6AlGLiEgk/bxsK+e9No4XDsujyQFdvVUIwLsBYd638MtDuhtTSqSELALmrM7mqY+Hsnbhcpa67VvDJiZvpteRnbwB+YW5Zbtgh1O9/12BNxZhY7L3aHAgm6Z9SQ6dIlwDERGJhMlr8jn0cyORGZxQfwRntYunV9vm1OxzDRwwEPvs77B+SdBhSgWkLssIeWmW4/O8nbsmZ6zagMUnQtNuZbtQfBIc/wjh9AWsGvkSWXNHUrhlA65RB9yWdUxfnBzhyEVEJNIKSOCHzGZcNqExXT4o5Na3RxCu0Rh39W/Q6Yygw5MKSC1kUTZj1UYAQvv3I37VxF2f0PcaqNealPeuZsD80/AGqOZQhbm0sAwy3BHRDFdERKLgmyUhpj0/ka//3poG57z714oEoYKgQ5MKQglZlK3bnE9aZhYNm/fe9cHVG8CAOwgtGskNCzpT9G6hPJK0KLiIyF4sOTtMv9eW8+agdRx5+HW4Qy7EQoXeTvP/vU+fB0MugS0ZwQUqgVBCFgNTkrM5uU0ZBvYPvAuXVIPxwz5lljsz+oGJiEhMFYQcl/yQzcVLh3JLh2yquHzA4QCzOKp3PwsuH469fxpkpQQdrsSQErIYmLoqm1O6d/IWq92UWvxBjQ6GXpcTmvwed6w5MrYBiohITL0/3/H+/Fo7bR8wdTzvXNqbuG1J2fqlAUQnQdCg/hiYnrzRe9KiV8kHHfcwLn8LH/46mVQaxCQuERGpWMaszOWcNyZRmFADd9kwaFzkrvqqdaHTmTD4Ffj7V3DKc3D4bdD5LG9apKSaQYVdMVkc/O0zaHt80JGUiVrIYmDemmwKCwtxLQ4lcf73Ox9w4NHQ7gTyhv+b/2YdF/sARUSkwpi2Jo9TX53Kt1d2JenSodjkN6DVYd6NAHHxuJwNkLkCmnbDajT868St6+GDMyB1ZmCxVyj794V2JxLekknc4hFBR7NLSshiID8UZuGaTNo3P2TnnRbnTXOxYSUPjdvCVqrGPkAREalQFmTkcdyrM/npyo7UGHAH4TUzyR/7AlMWreLDFbX5I9SRqszloCob6Vq/kA4NEjjx5DNJuuR7b5HzlClBVyF47U/2fu7XIdg4ykgJWYxMTN5Mxz7dIS4ewkXWojzidmjcifWf3cin+XtHs6qIiERf8oZ8DnlmOqdXe585W+ow37UF2v25P5sapOfV449UIBVapMxj+FUdqX7Rt9hHZ0Py+MBirxD8hMwaHew1frhwwAGVTmPIYmTGqiwsqTrsV2Q8wMEnwdH3UThzCNfMOODPJZdEREQA8kPG55u7Mt+1YlcLp6dszOOY1+ayKXsD7u9fQpsdbhCrWgdaH+51f1rsF2GPqf06QP0DyFs1w/vurdc66Ih2SS1kMTIteQMA4Ra9iUub5X1Yznyd8OppPPrVJKa5owKOUERE9nZp2fkc/dp8fr7qYOpcOASb8KqXjDTtBvXb/HVg5nKY9h5M/3DnOc8Sq3vfUQVbIX1+TOOPGL91bNjo3zj9ou64xp2wzGUBB1U6JWQxkrIhh6zNW6jeoi9xc7+CCz7F5W9h6Ecv8k7e2UGHJyIi+4h1mws46rUFjLi8kEaH30I4czmkzmLTtC9ZtGYDa6u04pg+3al27IO4o+7FFvwI6XO9HpwmnaH+AV4XH8DSUTD2aVgxNthK7a72pxBeNYU3F9fg1HCYUONuxd9UV4EoIYuhaclZDNy/F5zzHq5WU5a+cxW3ZJ4VdFgiIrKP2bC1kN4vzefYqt+SlpfIYteCPIoMbp+ewXH7fcwd/WrQtvsArNNgL3FLm82WWT+wIm0dOfU60uuwY4m79AdImewlZouGgXPBVawsajeHZoeQM/I/zM7vQmpmNo0btQ86ql1SQhZDk5OzOapje2hwIJu/upVzlw6iUL8CERGJCuPn3INL3DsyvQ4jv4PaP47miMSFrMirxRLXnDzaAm0BqPX7BB7unc+pA/oSf8GnsHIcvH96xV6D8+BBAIyYlwZ0Zd7arTTbr+T3oaLQKPIYmrLSG0cW+uMVzp3clkxqBxyRiIhUdtmhKvyY25W5rg15JG23b1NhPLeMr0aHJ2fw+fffezcEHHZLMIEWVa3eX92qO2p/CuF1i/k4rSUA89K2QIMDIaFiTyulhCyGJi3P5J//+5K//7jFv2NGRESk4ssPx3HHuDjGzFyEG3AnBNkF2GYA3DYPzn5n531V60LrwymcP4ypzpsiZGFaNhYX7y1RWIEpIYuxL1ZWZXyoY9BhiIiI7LZbvltJXl4u4dNfLrmFKpraHAl/G4IryIVOg6HPVdvvb3scxCcyaf6yP6eSWpi2CYDwfhX7u1cJmYiIiJRJ5pZ87v1uIXEtekG/62Jb+AED4W+fEc5czucv3MH0+Ytxxz8KzYqsgtP+ZMKb1vLuynp/blqxfisFBYWEGneNbby7SQmZiIiIlNmXM9OZMn8p7uj7vCkyYuGAo+CCTwlnLmfI209z58YzuOzz5WzZvInw2e9CldqQUAUOOha34CfGhrr8eWoo7FiRkUXcfhX7TkslZCIiIrJb/vH1cgpCjvCpL0Z/1v8Dj4YLPiG8fhmfvf0Md2WdARgbtxZw2cfzsTotcKe95HVnVqnFwgVzdro5YXbaVuIq+JqWSshERERkt6zNzuORHxcQ1+ZwOPPN6C3gXaMhnPch4fVL+fSdZ7k7azBFl5CanJzNS8NnYp1Oh5OfxuVt5p1FSTtdZmHaZqx2U+/uzApKCZmIiIjstvcnp/HtbxNwBw+C6yfA3z7zpsXYJj4RmveEftfDGa95A/Cr1Nq9QnpdDkk1GDfkGe7ZIRnb5pmxacxYsATqtiS8eCQjC3YeK7ZtYH/UEscI0KykIiIiUi43/7SeZ34bzuP9oe+hRxJ32VBYPRUKcrxkLLEaAG7LOqzb+bhjHsBmfgqT34CMhaVfPD4Jel9JaNFI/p3al5IWV3cOLh2yjB8uzGXL5BFs5JSdjlngJ2Sh/boQv/KPPapztCghExERkXJbubUKF/wMtUf/zkO98zilbxfiwo7Cye+xIjmZYSsKGJ3VjLYtGnFz/0Y0PeQirM9VsHwMDL0DMhYUf+HOZ0LNxiwb/w1L3EmlxrBxawGHv7GSBE4sdn9adi5bcnJJatyZ+D2tcJQoIRMREZE9ll2YwC3jE7ht/BKakMkaWgD7/7l/2ir47LNCDvz+R/7dN0z/w4+Bs98h7rUjIFy48wX7XU84fQGPLGxe5hhKW45wUVo23Rq1250qxZTGkImIiEjEhIljDQ0pqYtx6daq/H1UdW74aglxjTtC32t3PqhVf2jajewJ7/FbODLzh81J24pV4DFkSshEREQk5obOzWDKgmW4o+6B2ju0gvW7Hrd1Pc9MLaSkxG53LUzbhFWrA3VaROR6kaaETERERAJxy7crCBFPeNB//9pYrzW0P5mCKe8zJO/QiJW14M87LSvmEkpKyERERCQQKRtyePnXRcR1OAXaHu9t7HMVLhziiwmLyaVKxMpalO4lZIUVdAklJWQiIiISmJfGppCWvpbwSU9BjUbQ42LCc7/lhcx+ES0nO6eQdRs34SroODIlZCIiIhKYgpDj5q+XElevFe6yoVClNlP/+Jk0GkS8rHlpm0mooGtaKiETERGRQE1cvoER0xZiDdsRSp7Ioyuj04o1L20rNGwHcRVv1i8lZCIiIhK4u39MJiNlKSmj3mamOygqZSxcuwlLSIIGB0bl+ntCCZmIiIgEbv2WfHq/NJ8T5x8ftTK2rWnpel0BCVWjVk55BJKQmVldM/vCzBaY2XwzO9TM6pvZSDNb7P+suEuyi4iISBQYOUQvUVqQls2EWfOwvtfgbpgEnc+KWlm7K6gWsueBYc659kA3YD5wF/CLc64t8Iv/WkRERCQiwg7O/3g5d7/xNbk5uXD223DFCG8h9IDtMiEzs3Zm9oaZjTCzX7c9ylugmdUBBgBvATjn8p1zG4HTgff8w94DBpe3DBEREZGSfLI0iU4vLOb9L78lVLcNXPUrBZ2CbS0ry20GnwOvAm8AoQiU2QbIAN4xs27AVOBmoLFzLtU/Jg1oXNzJZnY1cDVAy5YtIxCOiIiIVDZhZ9w/OYFnZ07ipaPiyE7eyKDILQyw28rSZVnonPufc26Sc27qtscelJkA9AD+55w7BNjCDt2TzjkHuOJOds697pzr5Zzr1ahRoz0IQ0RERCq7DfnGhcMdb2wZEGgcZUnIvjez682sqT/wvr6Z1d+DMlOAFOfcRP/1F3gJ2lozawrg/0zfgzJERERE9hpl6bK8xP95R5FtDjigPAU659LMbJWZHeycWwgcA8zzH5cAj/s/vy3P9UVERET2NrtMyJxzbaJQ7o3AR2aWBCwDLsNrrRtiZlcAK4Fzo1CuiIiISIVTprUDzKwz0BH+mhzEOfd+eQt1zs0AehWz65jyXlNERERkb7XLhMzMHgAG4iVkQ4FBwO9AuRMyEREREflLWQb1n43XcpXmnLsMbyLXOlGNSkRERKQSKUtCluOcCwOFZlYb7+7H/aMbloiIiEjlUZYxZFPMrC7exLBTgc3A+GgGJSIiIlKZlOUuy+v9p6+a2TCgtnNuVnTDEhEREak8ynqX5Wl4608C/AYoIRMRERGJkLIsLv443lqT2yZvvcnMHot2YCIiIiKVRVlayE4CuvsD+zGz94DpwD3RDExERESksijLXZYAdYs815QXIiIiIhFUlhay/wDTzWwUYHhjye6KalQiIiIilUhZ7rL8xMxGA739Tf8CWkUzKBEREZHKpEx3WTrnUoHvtr02s0lAy2gFJSIiIlKZlHUM2Y4solGIiIiIVGLlTchcRKMQERERqcRK7LI0s+8pPvEyoEHUIhIRERGpZEobQ/ZUOfeJiIiIyG4oMSFzzv0Wy0BEREREKqvyjiETERERkQhRQiYiIiISMCVkIiIiIgErz12WADjnTotKRCIiIiKVTFnusjwTaAJ86L++AFgbzaBEREREKpNd3mVpZk8753oV2fW9mU2JemQiIiIilURZxpDVMLMDtr0wszZAjeiFJCIiIlK5lGVx8VuB0Wa2DG+W/lbA1VGNSkRERKQS2WVC5pwbZmZtgfb+pgXOubzohiUiIiJSeewyITOzROAaYIC/abSZveacK4hqZCIiIiKVRFm6LP8HJAKv+K8v8rddGa2gRERERCqTsiRkvZ1z3Yq8/tXMZkYrIBEREZHKpix3WYbM7MBtL/w7LkPRC0lERESkcilLC9kdwKgd7rK8LKpRiYiIiFQiZbnL8hf/LsuD/U0LdZeliIiISOToLksRERGRgOkuSxEREZGA6S5LERERkYDpLksRERGRgOkuSxEREZGA6S5LERERkYCV2GVpZrX9RAw/ATsY6A6cZ2aNYxOeiIiIyL6vtDFkTwGHFXn9GNALb/qLf0czKBEREZHKpLQuy954849ts9k5dxOAmf0e1ahEREREKpHSWsgSnHOuyOuLijyvG51wRERERCqf0hKysJk12fbCOTcHwMyaA+FoByYiIiJSWZSWkD0JfG9mA8yslv84EvjG3yciIiIiEVDiGDLn3Idmtg54BOgEOGAucL9z7qcYxSciIiKyzyt1HjLn3DBgWIxiEREREamUyrJ0koiIiIhEkRIyERERkYApIRMREREJWJkTMjPrZ2bDzGy0mQ2OYkwiIiIilUppa1k22WHTbcAZwEnAw3tasJnFm9l0M/vBf93GzCaa2RIz+8zMkva0DBEREZG9QWktZK+a2f1mVtV/vRE4Gy8py45A2TcD84u8fgJ41jl3ELABuCICZYiIiIhUeCUmZM65wcB04Aczuxi4BagCNAAG70mhZtYCOBl4039twNHAF/4h7+1pGSIiIiJ7i1LHkDnnvgdOAOoAXwOLnHMvOOcy9rDc54A7+WsJpgbARudcof86BWhe3IlmdrWZTTGzKRkZexqGiIiISPBKG0N2mpmNwpsYdg5wHnC6mX1qZgeWt0AzOwVId85NLc/5zrnXnXO9nHO9GjVqVN4wRERERCqM0mbqfwToA1QDhjvn+gC3m1lb4FHg/HKWeRhwmpmdBFQFagPPA3XNLMFvJWsBrC7n9UVERET2KqV1WWYBZwJnAenbNjrnFjvnypuM4Zy72znXwjnXGi+p+9U5dyEwCu+mAYBLgG/LW4aIiIjI3qS0hOwMvLFdCcDfYhDLv4DbzGyJX+5bMShTREREJHAldlk659YBL0azcOfcaGC0/3wZXhepiIiISKWipZNEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgSshEREREAqaETERERCRgMU/IzGx/MxtlZvPMbK6Z3exvr29mI81ssf+zXqxjExEREQlCEC1khcDtzrmOQD/gH2bWEbgL+MU51xb4xX8tIiIiss+LeULmnEt1zk3zn28C5gPNgdOB9/zD3gMGxzo2ERERkSAEOobMzFoDhwATgcbOuVR/VxrQOKi4RERERGIpsITMzGoCXwK3OOeyi+5zzjnAlXDe1WY2xcymZGRkxCBSERERkegKJCEzs0S8ZOwj59xX/ua1ZtbU398USC/uXOfc6865Xs65Xo0aNYpNwCIiIiJRFMRdlga8Bcx3zj1TZNd3wCX+80uAb2Mdm4iIiEgQEgIo8zDgImC2mc3wt90DPA4MMbMrgJXAuQHEJiIiIhJzMU/InHO/A1bC7mNiGYuIiIhIRaCZ+kVEREQCpoRMREREJGBKyEREREQCpoRMREREJGBKyEREREQCpoRMREREJGBKyEREREQCpoRMREREJGBBzNQvIiJSKdWuEseNfevRqm4iVuIc6VIeDsfKjQW8OHED2XnhoMPZbUrIREREYuTGvvXocWAzEqrXwlvaWSLFOUeDBtncCDw6Zn3Q4ew2dVmKiIjESKu6iUrGosTMSKhem1Z1E4MOpVyUkImIiMSIYUrGosjM9tquYCVkIiIiIgHTGDIREZGAXPzWJDbmFETsenWrJfL+FX1KPWZ9RjpP/vseZk2fQu06dUlMTOLSa2/imEGn7HH5V5xzCrfd9zCduh1Spu3yFyVkIiIiAYlkMlaW6znnuOXKv3Pq2efz+EtvArAmJZnRI4dFNA7ZfUrIREREKolJ48aQmJjIuRdd/ue2Zi1a8rfLrgYgLzeXR+65nXmzphOfkMA/73+UPv2PKHF7bk4O99/+DxbNn0vrA9uSm5tb5liyNmzggX/eQEryCqpWq879TzxLuw6dmT19Kv998G7y8nKpWrUqDz39Mq0PbMu3Qz5m9MifyM3JIWXlco4+8WRuvfehiL9HQVFCJiIiUkksWbSADp27lbj/0/fexMz48uc/WL5kEddeeCbf/TalxO1DPnibqtWq882oiSyaP4fzBw0scyyvPPMf2nfuynNvfcTEcWO475brGDJ8LG0Oass7Xw4lISGBCWNH88ITD/PM6+8DsHDebD776TeSkqpw+sDeXHDZ1TRp1mIP35WKQQmZiIhIJfXYvf9k+uQJJCYm8fGPvzJ98gQu8FvL2hzUjqbN92fl8iUlbp828Q8uuPwaANp16EzbDp3KXPb0yRN45jUv0ep72AA2bshk86ZsNm/K5r5bryd5+VLMjMLCwj/P6XvYkdSqXQeAA9oezJqUVftMQqa7LEVERCqJg9q1Z/6cmX++vufRp3j902/ZkLkuwKi29/KTj9G7/xF89ct4XnjnU/Lz/uoGTUxK+vN5XHw8oVAoiBCjQgmZiIhIJdHnsAHk5eUx5P23/tyWm7P1z+c9+hzK0K8/B2DFsiWkrUmh9QFtS97etz8/ffMFAIsXzGPx/LlljqVHn0P50b/m5PG/U7d+A2rWqs2mTdk0btIUgO8+/3jPKrwXUZeliIhIQOpWS4z4tBelMTOee/NDnvz3Pbzz6gvUq9+QatWrc/PdDwJw3sVX8Mg9t3PWsf2JT0jgoWdeIalKlRK3n3vR5dx/+z8YfFRf2hzUjg5dupdY9g2XnkdCghdft569+b//PMsD/7yBs487jKrVqvPIs68AcNl1N3Hfrdfz+gtPMeDo4yPyvuwNzDkXdAzl1qtXLzdlypSolnHv17P5aGJyVMsQEZHK4Y3TmtK45QFBh7FPW5u8jKu+S93t83q0rMdX1/ePQkR/MbOpzrlexe1Tl6WIiIhIwJSQiYiIiARMCZmIiIhIwJSQiYiIiARMCZmIiIhIwJSQiYiIiARM85CJiIgEpMNHPUnMidws+QXVGjL/wqmlHnNIqwa0bd8R5xxx8fHc/fB/6d6rb5nL+N8zj1O9eg0uufbGMm2XslFCJiIiEpBIJmNlvV6VqtUYMnwsAONG/8ILjz/E21/8GNE4ZPepy1JERKSS2rJ5E7Xr1AVg65bNXHX+6Zw36EjOOrY/o4YP/fO4N154ilMH9OKSM09kxdLFZb6+c45nHvk/zjzmUM46tj/Dvvuq1LJWr0pm8FF9+fedN3PGMYdyzd/OJDcnJ3IVrsDUQiYiIlKJ5OXmcO4JR5CXl8e69DTe+PQ7AJKqVOXZNz6gZq3abMhcz0WnHcfA4wcxf/ZMhn33FUOGjyFUWMj5gwbSsZQlkor65afvWThvDp+P+J2Nmev52ylH07Nvf+o1aFhsWQDJy5fy+Etv8sB/n+eO6y7j55++45Qzz4vW21FhKCETERGpRIp2Wc6cOol7b72Or37+A+ccLzzxMNMm/kFcXBzpaamsz0hn2qTxHH3iKVSrVh2AI48bVOaypk+awImnnUV8fDwNGu1Hz36HMXfmNA476rhiywJovn8r2nfqAkCHLt1Ys2pVhN+BikkJmYiISCXVrWcfNmauJ3P9On7/dSQb1q/nk6GjSUxMZNChXcnLy4tKuUO//rzEshKTkv48Lj4unrxQblRiqGg0hkxERKSSWr5kEeFQiLr16rN5Uzb1GzYkMTGRSX+MZU2K1zLVs29/Rg3/kdycHLZs3sSYn4eV+fqH9D2U4d9/TSgUInP9OqZN/IPO3XuWWFZlphYyERGRgBRUaxjxaS92ZdsYMvAG3T/87CvEx8dz0hnncNNlF3DWsf3p2PUQ2hzUDvC6DU849QzOOeEI6jdsSKduh5R47TdefIoP33r1z9cjJs1h1tRJnHP84ZgZt9zzbxru17jEsiozc84FHUO59erVy02ZMiWqZdz79Ww+mpgc1TJERKRyeOO0pjRueUDQYezT1iYv46rvUnf7vB4t6/HV9f2jENFfzGyqc65XcfvUZSkiIiISMCVkIiIiIgFTQiYiIhIjDsfePFSoonPO4dg7318lZCIiIjGycmMBhVuzlZRFgXOOwq3ZrNxYEHQo5aK7LEVERGLkxYkbuBFoVXcdhgUdzj7F4Vi5sYAXJ24IOpRyUUImIiISI9l5YR4dsz7oMKQCUpeliIiISMCUkImIiIgETAmZiIiISMCUkImIiIgETAmZiIiISMCUkImIiIgETAmZiIiISMCUkImIiIgErEIlZGZ2opktNLMlZnZX0PGIiIiIxEKFScjMLB54GRgEdAQuMLOOwUYlIiIiEn0VJiED+gBLnHPLnHP5wKfA6QHHJCIiIhJ1FWkty+bAqiKvU4C+Ox5kZlcDVwO0bNky6kHVrJJAfJwRb1oEVkREZF8Udo7a1YJNiSpSQlYmzrnXgdcBevXq5aJd3p0ntuf6gQdFuxgREREJUPUq8YGWX5ESstXA/kVet/C3BSo+zqhTPTHoMERERGQfVpHGkE0G2ppZGzNLAs4Hvgs4JhEREZGoqzAtZM65QjO7ARgOxANvO+fmBhyWiIiISNRVmIQMwDk3FBgadBwiIiIisVSRuixFREREKiUlZCIiIiIBU0ImIiIiEjAlZCIiIiIBU0ImIiIiEjAlZCIiIiIBU0ImIiIiEjAlZCIiIiIBU0ImIiIiEjBzzgUdQ7mZWQawMug4oqghsC7oIGKkMtUVVN99WWWqK6i++7LKVFeITX1bOecaFbdjr07I9nVmNsU51yvoOGKhMtUVVN99WWWqK6i++7LKVFcIvr7qshQREREJmBIyERERkYApIavYXg86gBiqTHUF1XdfVpnqCqrvvqwy1RUCrq/GkImIiIgETC1kIiIiIgFTQhZDZra/mY0ys3lmNtfMbva31zezkWa22P9Zz99uZvaCmS0xs1lm1qPItf7rX2O+f4wFVa+SRLi+T5jZHP9xXlB1Kkk56trezMabWZ6Z/XOHa51oZgv99+GuIOqzKxGu79tmlm5mc4Koy65Eqq4lXaeiiWB9q5rZJDOb6V/n30HVqTSR/Cz7++PNbLqZ/RDruuxKhP9uV5jZbDObYWZTgqjPrkS4vnXN7AszW2De9+6hEQ/YOadHjB5AU6CH/7wWsAjoCPwXuMvffhfwhP/8JOAnwIB+wER/e39gHBDvP8YDA4OuXxTrezIwEkgAagCTgdpB128P67of0Bt4FPhnkevEA0uBA4AkYCbQMej6Rau+/r4BQA9gTtD1ivLvttjrBF2/KNbXgJr+80RgItAv6PpF87Ps778N+Bj4Iei6RbOuwAqgYdB1imF93wOu9J8nAXUjHa9ayGLIOZfqnJvmP98EzAeaA6fj/bLxfw72n58OvO88E4C6ZtYUcEBVvA9FFbx/7NbGqh5lFcH6dgTGOOcKnXNbgFnAibGrya7tbl2dc+nOuclAwQ6X6gMscc4tc87lA5/616hQIlhfnHNjgMwYhF0ukaprKdepUCJYX+ec2+y/TPQfFW7QciQ/y2bWAu8/kG9GP/LdF8m67g0iVV8zq4P3H8e3/OPynXMbIx2vErKAmFlr4BC8/zU2ds6l+rvSgMb+8+bAqiKnpQDNnXPjgVFAqv8Y7pybH4u4y2tP6ovXSnSimVU3s4bAUcD+sYi7PMpY15KU9B5UWHtY371KpOq6w3UqrD2tr999NwNIB0Y65/bp+gLPAXcC4WjEF0kRqKsDRpjZVDO7OjpRRs4e1rcNkAG843dHv2lmNSIdoxKyAJhZTeBL4BbnXHbRfc5rDy31f5FmdhDQAWiB92V9tJkdEaVw99ie1tc5NwIYCvwBfILXRRuKTrR7Zk/rurepTPWNVF1Lu05FEon6OudCzrnueP9W9TGzztGINRIi8O/yKUC6c25q9KKMjAh9lg93zvUABgH/MLMBkY80MiJQ3wS8YRX/c84dAmzB6+qMKCVkMWZmiXgfjI+cc1/5m9f6XXP4P9P97avZviWohb/tDGCCc26z3yXwExD5AYYREKH64px71DnX3Tl3HN7YlEWxiH937GZdS1Lie1DRRKi+e4VI1bWE61Q4kf7d+t07o6hgQw22iVB9DwNOM7MVeEMNjjazD6MUcrlF6nfrnNv2b3M68DXecIsKJ0L1TQFSirTwfoGXoEWUErIYMjPD64Oe75x7psiu74BL/OeXAN8W2X6xefoBWX4zazJwpJkl+B+2I/H6xiuUSNXX7/Zo4F+zK9AVGBGTSpRROepakslAWzNrY2ZJwPn+NSqUCNa3wotUXUu5ToUSwfo2MrO6/vNqwHHAgogHvIciVV/n3N3OuRbOudZ4f7e/Ouf+HoWQyy2Cv9saZlZr23PgeKDC3SUdwd9tGrDKzA72Nx0DzItwuLrLMpYP4HC8ptFZwAz/cRLQAPgFWAz8DNT3jzfgZby77mYDvfzt8cBreEnYPOCZoOsW5fpW9es5D5gAdA+6bhGoaxO8/3VlAxv957X9fSfhtQAuBe4Num4xqO8neGMhC/ztVwRdv2jUtaTrBF2/KNa3KzDdv84c4P6g6xbtz3KRaw6kYt5lGanf7QF4Y3tnAnOpHP9OdQem+Nf6BqgX6Xg1U7+IiIhIwNRlKSIiIhIwJWQiIiIiAVNCJiIiIhIwJWQiIiIiAVNCJiIiIhIwJWQiIiIiAVNCJiL7DDP7I+gYAMzsXTM7O+g4RGTvoYRMRPYZzrn+QccgIlIeSshEZJ9hZpv9n03NbIyZzTCzOWZ2hL8E17v+69lmdqt/7Ggz6+U/b+ivRYh//JNmNtnMZpnZNaWUa2b2kpktNLOfgf2iX1sR2ZckBB2AiEgU/A0Y7px71Mzigep4S580d851Bti2zmIprsBbT7W3mVUBxpnZCOfc8mKOPQM4GOgINMZb5uvtiNRERCoFJWQisi+aDLxtZonAN865GWa2DDjAzF4EfmTXC9QfD3QtMhasDtAWKC4hGwB84pwLAWvM7NeI1EJEKg11WYrIPsc5NwYvSVoNvGtmFzvnNgDdgNHAtcCb/uGF/PVvYdUilzHgRudcd//Rxjm3qyRORKRclJCJyD7HzFoBa51zb+AlXj3MrCEQ55z7ErgP6OEfvgLo6T8vemfkcOA6v5UNM2tnZjVKKHIMcJ4/7qwpcFREKyQi+zx1WYrIvmggcIeZFQCbgYuB5sA7ZrbtP6J3+z+fAoaY2dV4XZnbvAm0BqaZmQEZwOASyvsaOBpv7FgyMD5SFRGRysGcc0HHICIiIlKpqctSREREJGDqshQRKSMz6wJ8sMPmPOdc3yDiEZF9h7osRURERAKmLksRERGRgCkhExEREQmYEjIRERGRgCkhExEREQmYEjIRERGRgP0/TICGL4sk14MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggr_loan_cat = loan_df\\\n",
    "    .set_index('issue_d')\\\n",
    "    .assign(loan_cat_map=lambda x: x.loan_category.map({'Good Loan': 100, 'Bad Loan': 0}))\\\n",
    "    .groupby([pd.Grouper(freq='M')])\\\n",
    "    .agg({'loan_cat_map': 'mean'})\\\n",
    "    .reset_index()\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "color_blue = sns.color_palette()[0]\n",
    "color_orange= sns.color_palette()[1]\n",
    "ax.plot(aggr_loan_cat.issue_d, aggr_loan_cat.loan_cat_map, color='white')\n",
    "ax.fill_between(\n",
    "    data=aggr_loan_cat, color=color_blue,\n",
    "    x='issue_d', y1='loan_cat_map', y2=0, label='Good Loan')\n",
    "ax.fill_between(\n",
    "    data=aggr_loan_cat, color=color_orange,\n",
    "    x='issue_d', y1=100, y2='loan_cat_map', label='Bad Loan')\n",
    "ax.set_xlabel('issue_d')\n",
    "ax.set_ylabel('% Good Loan')\n",
    "ax.set_title('Portion of Good Loan and Bad Loan Over Time')\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.6, 0.07, 0.5, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decreasing portion of good loan may suggest lower loan ecosystem quality over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "source": [
    "#### **Loan Grade by Loan Category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "def plot_categorical_count(category, flip=False):\n",
    "    left = loan_df_subset\\\n",
    "        .value_counts([category, 'loan_category'])\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns={0: 'counts'})\n",
    "    right = loan_df_subset\\\n",
    "        .value_counts(category)\\\n",
    "        .reset_index()\\\n",
    "        .rename(columns={0: 'total_count'})\n",
    "    emp_length_count = pd.merge(\n",
    "        left, right, on=category, how='left'\n",
    "        )\n",
    "    emp_length_count = emp_length_count.assign(\n",
    "        portion=lambda x: x.counts / x.total_count * 100\n",
    "        )\n",
    "    # plot\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
    "    if flip:\n",
    "        plot_count = emp_length_count\\\n",
    "            .pivot(index=category, columns='loan_category', values='counts')\\\n",
    "            .plot.barh\n",
    "        plot_portion = emp_length_count\\\n",
    "            .pivot(index=category, columns='loan_category', values='portion')\\\n",
    "            .plot.barh\n",
    "        ax[0].set_xlabel('count')\n",
    "        ax[1].set_xlabel('%')\n",
    "    else:\n",
    "        plot_count = emp_length_count\\\n",
    "            .pivot(index=category, columns='loan_category', values='counts')\\\n",
    "            .plot.bar\n",
    "        plot_portion = emp_length_count\\\n",
    "            .pivot(index=category, columns='loan_category', values='portion')\\\n",
    "            .plot.bar\n",
    "        ax[0].set_ylabel('count')\n",
    "        ax[1].set_ylabel('%')\n",
    "    plot_count(y=['Good Loan', 'Bad Loan'], ax=ax[0])\n",
    "    plot_portion(y=['Good Loan', 'Bad Loan'], ax=ax[1], stacked=True)\n",
    "    ax[1].get_legend().set_visible(False)\n",
    "    fig.suptitle(f'{category} by loan_category')\n",
    "    fig.subplots_adjust(wspace=0.15, hspace=0.1)\n",
    "\n",
    "plot_categorical_count('grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using above code, we generate two chart: left-hand plot is to show comparable `counts` of loan between each `grade` classes, while right-hand plot is to show portion of Good-Bad loan between `grade` classes, scaled to `100%` (normalized). \n",
    "\n",
    "From the above plot, we can see that, for lower grade, the chance that the loan will be a `Good Loan` is become more similar than its likeliness to be a `Bad Loan`. For `F` and `G` Grade, the almost 50-50 percent portion is just like a flipping coin to decide if the loan will be good or bad!\n",
    "\n",
    "So what is the loan `Grade` actually for the LendingClub Loan Data? Turns out that it is the *Ranking* according to *LendingClub Scoring Model,* which analyzes the performance of borrower members taking into account FICO Score, credit attributes, and other application data (See LendingClub website for more detailed information). \n",
    "\n",
    "Looking at the occurence of `Bad Loan` on the plot above, is it worth to consider investing on the lower grade loan? Higher risk, higher profit! Lower-graded loan has a higher interest rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df for plotting count\n",
    "avg_int = loan_df_subset\\\n",
    "    .groupby('grade')\\\n",
    "    .aggregate(avg_int_rate=pd.NamedAgg(column='int_rate', aggfunc='mean'))\\\n",
    "    .reset_index()\n",
    "# plot\n",
    "colors = sns.color_palette('rainbow', len(avg_int))\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.barplot(\n",
    "    data=avg_int, x='avg_int_rate', y='grade',\n",
    "    palette=colors, ax=ax\n",
    "    )\n",
    "for y_, x_ in enumerate(avg_int.avg_int_rate.values):\n",
    "    ax.text(\n",
    "        x=(x_ - 2.6), y=y_, s=f'{x_:,.2f}%', size=14, verticalalignment='center'\n",
    "    )\n",
    "ax.set_title('Average Interest Rate, Loan Grade')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So how come *higher risk, higher profit* relate to our investment to lending? The most simple way to explain it by looking at its loan `Value` of an investment unit:\n",
    "\n",
    "$$Value=Probability\\times\\;Return-1$$\n",
    "\n",
    "For example, the investment of *Grade-G* loan (`1.25x` return) may be profittable if we can estimate the probability of that particular loan to be a `Good Loan` for above `80%`.\n",
    "\n",
    "Furthermore, how much we can expect loan payment if somehow the loan would be defaulted? Below graph shows the `Bad Loan` average portion of loan paid according to its loan `grade`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loan_df_subset = loan_df_subset\\\n",
    "    .assign(\n",
    "        portion_paid=lambda x: x.total_rec_prncp / x.loan_amnt * 100\n",
    "        )\n",
    "avg_portion = loan_df_subset\\\n",
    "    [loan_df_subset.loan_category == 'Bad Loan']\\\n",
    "    .groupby('grade')\\\n",
    "    .aggregate(avg_portion_paid=pd.NamedAgg(column='portion_paid', aggfunc='mean'))\\\n",
    "    .reset_index()\n",
    "\n",
    "# plot\n",
    "colors = sns.color_palette('rainbow', len(avg_portion))\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.barplot(\n",
    "    data=avg_portion, x='avg_portion_paid', y='grade',\n",
    "    palette=colors, ax=ax\n",
    "    )\n",
    "for y_, x_ in enumerate(avg_portion.avg_portion_paid.values):\n",
    "    ax.text(\n",
    "        x=(x_ - 4), y=y_, s=f'{x_:,.2f}%', size=14, verticalalignment='center'\n",
    "        )\n",
    "ax.set_title('Average of Portion Paid according to Loan Grade, \\nBad Loan')\n",
    "ax.get_xaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the loan `grade` explains the loan risk very well. Based on evidence above, we expect lower fund recovery for defaulted lower grade loan. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Employment Length by Loan Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categorical_count('emp_length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the loaner has a senior employment status with 10 years experience or more. There is no distinguishable pattern whether lower employment length or higher employment length provide (significant) evidence of more likely to be defaulted on loan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Home Ownership by Loan Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categorical_count('home_ownership')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there is no distinguishable pattern whether particular home ownership is (significantly) more likely to be defaulted on loan. However, we can see that `RENT` ownership (by proportion of `Bad Loan` within classes) has a slighter evidence of more likely to be defaulted than `MORTGAGE`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Status by Purpose**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categorical_count('purpose')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on above plot, there is evidence that loan for `small_business` has a slightly higher defaulted portion among other classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Status by Term**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_categorical_count('term')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering the frequency of submitted loan, 36-months term dominates the longer 60-months term. Moreover, there is slightly higher evidence of defaulted loan for 60-months term."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Numerical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": [
     "output_scroll"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_rec_int</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>dti_joint</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>open_acc_6m</th>\n",
       "      <th>open_il_12m</th>\n",
       "      <th>open_il_24m</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "      <th>portion_paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "      <td>210406.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17644379.16</td>\n",
       "      <td>19422532.73</td>\n",
       "      <td>14336.58</td>\n",
       "      <td>14336.53</td>\n",
       "      <td>14329.64</td>\n",
       "      <td>14.41</td>\n",
       "      <td>442.15</td>\n",
       "      <td>73727.65</td>\n",
       "      <td>17.55</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.85</td>\n",
       "      <td>67.79</td>\n",
       "      <td>95.43</td>\n",
       "      <td>11.39</td>\n",
       "      <td>0.18</td>\n",
       "      <td>15673.67</td>\n",
       "      <td>55.46</td>\n",
       "      <td>25.87</td>\n",
       "      <td>1180.41</td>\n",
       "      <td>1179.98</td>\n",
       "      <td>13214.67</td>\n",
       "      <td>13208.02</td>\n",
       "      <td>11137.08</td>\n",
       "      <td>1921.02</td>\n",
       "      <td>0.84</td>\n",
       "      <td>155.73</td>\n",
       "      <td>16.57</td>\n",
       "      <td>6886.24</td>\n",
       "      <td>0.01</td>\n",
       "      <td>84.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205.38</td>\n",
       "      <td>136847.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98.92</td>\n",
       "      <td>38.95</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.98</td>\n",
       "      <td>0.06</td>\n",
       "      <td>29431.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16276714.56</td>\n",
       "      <td>17185356.83</td>\n",
       "      <td>8260.50</td>\n",
       "      <td>8260.42</td>\n",
       "      <td>8256.93</td>\n",
       "      <td>4.45</td>\n",
       "      <td>247.72</td>\n",
       "      <td>61681.61</td>\n",
       "      <td>7.98</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.07</td>\n",
       "      <td>35.66</td>\n",
       "      <td>13.75</td>\n",
       "      <td>5.01</td>\n",
       "      <td>0.50</td>\n",
       "      <td>19915.88</td>\n",
       "      <td>23.83</td>\n",
       "      <td>11.82</td>\n",
       "      <td>4309.23</td>\n",
       "      <td>4307.63</td>\n",
       "      <td>9563.25</td>\n",
       "      <td>9558.63</td>\n",
       "      <td>8601.51</td>\n",
       "      <td>2016.14</td>\n",
       "      <td>6.02</td>\n",
       "      <td>724.20</td>\n",
       "      <td>102.95</td>\n",
       "      <td>7603.61</td>\n",
       "      <td>0.11</td>\n",
       "      <td>26.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.07</td>\n",
       "      <td>20022.01</td>\n",
       "      <td>151386.21</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1672.06</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.14</td>\n",
       "      <td>246.75</td>\n",
       "      <td>2.08</td>\n",
       "      <td>29448.80</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>35.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>57167.00</td>\n",
       "      <td>137225.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>950.00</td>\n",
       "      <td>5.32</td>\n",
       "      <td>27.85</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5414798.50</td>\n",
       "      <td>6418125.50</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>11.14</td>\n",
       "      <td>264.94</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>11.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6221.00</td>\n",
       "      <td>38.20</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5873.48</td>\n",
       "      <td>5870.61</td>\n",
       "      <td>4186.32</td>\n",
       "      <td>617.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>536.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28371.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13200.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10844643.00</td>\n",
       "      <td>12637728.50</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>12000.00</td>\n",
       "      <td>14.09</td>\n",
       "      <td>388.00</td>\n",
       "      <td>64000.00</td>\n",
       "      <td>17.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11272.00</td>\n",
       "      <td>56.60</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11081.14</td>\n",
       "      <td>11076.12</td>\n",
       "      <td>9600.00</td>\n",
       "      <td>1309.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4483.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>78664.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27651024.25</td>\n",
       "      <td>30143527.75</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>19950.00</td>\n",
       "      <td>17.56</td>\n",
       "      <td>577.01</td>\n",
       "      <td>89000.00</td>\n",
       "      <td>23.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19537.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18314.52</td>\n",
       "      <td>18305.10</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>2470.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10772.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>205955.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>36500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>68604659.00</td>\n",
       "      <td>73507418.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>28.99</td>\n",
       "      <td>1424.57</td>\n",
       "      <td>8900060.00</td>\n",
       "      <td>39.99</td>\n",
       "      <td>29.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>152.00</td>\n",
       "      <td>121.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1746716.00</td>\n",
       "      <td>892.30</td>\n",
       "      <td>150.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>56087.91</td>\n",
       "      <td>56047.85</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>21087.91</td>\n",
       "      <td>358.68</td>\n",
       "      <td>33520.27</td>\n",
       "      <td>5694.09</td>\n",
       "      <td>36475.59</td>\n",
       "      <td>6.00</td>\n",
       "      <td>159.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.00</td>\n",
       "      <td>9152545.00</td>\n",
       "      <td>8000078.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>193.00</td>\n",
       "      <td>249212.00</td>\n",
       "      <td>129.20</td>\n",
       "      <td>8.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>30567.00</td>\n",
       "      <td>102.80</td>\n",
       "      <td>2013133.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id   member_id  loan_amnt  funded_amnt  funded_amnt_inv  \\\n",
       "count   210406.00   210406.00  210406.00    210406.00        210406.00   \n",
       "mean  17644379.16 19422532.73   14336.58     14336.53         14329.64   \n",
       "std   16276714.56 17185356.83    8260.50      8260.42          8256.93   \n",
       "min      57167.00   137225.00    1000.00      1000.00           950.00   \n",
       "25%    5414798.50  6418125.50    8000.00      8000.00          8000.00   \n",
       "50%   10844643.00 12637728.50   12000.00     12000.00         12000.00   \n",
       "75%   27651024.25 30143527.75   20000.00     20000.00         19950.00   \n",
       "max   68604659.00 73507418.00   35000.00     35000.00         35000.00   \n",
       "\n",
       "       int_rate  installment  annual_inc       dti  delinq_2yrs  \\\n",
       "count 210406.00    210406.00   210406.00 210406.00    210406.00   \n",
       "mean      14.41       442.15    73727.65     17.55         0.31   \n",
       "std        4.45       247.72    61681.61      7.98         0.83   \n",
       "min        5.32        27.85     3000.00      0.00         0.00   \n",
       "25%       11.14       264.94    45000.00     11.56         0.00   \n",
       "50%       14.09       388.00    64000.00     17.13         0.00   \n",
       "75%       17.56       577.01    89000.00     23.24         0.00   \n",
       "max       28.99      1424.57  8900060.00     39.99        29.00   \n",
       "\n",
       "       inq_last_6mths  mths_since_last_delinq  mths_since_last_record  \\\n",
       "count       210406.00               210406.00               210406.00   \n",
       "mean             0.85                   67.79                   95.43   \n",
       "std              1.07                   35.66                   13.75   \n",
       "min              0.00                    0.00                    0.00   \n",
       "25%              0.00                   32.00                   99.00   \n",
       "50%              1.00                   99.00                   99.00   \n",
       "75%              1.00                   99.00                   99.00   \n",
       "max              7.00                  152.00                  121.00   \n",
       "\n",
       "       open_acc   pub_rec  revol_bal  revol_util  total_acc  out_prncp  \\\n",
       "count 210406.00 210406.00  210406.00   210406.00  210406.00  210406.00   \n",
       "mean      11.39      0.18   15673.67       55.46      25.87    1180.41   \n",
       "std        5.01      0.50   19915.88       23.83      11.82    4309.23   \n",
       "min        0.00      0.00       0.00        0.00       2.00       0.00   \n",
       "25%        8.00      0.00    6221.00       38.20      17.00       0.00   \n",
       "50%       11.00      0.00   11272.00       56.60      24.00       0.00   \n",
       "75%       14.00      0.00   19537.00       74.00      33.00       0.00   \n",
       "max       76.00     20.00 1746716.00      892.30     150.00   35000.00   \n",
       "\n",
       "       out_prncp_inv  total_pymnt  total_pymnt_inv  total_rec_prncp  \\\n",
       "count      210406.00    210406.00        210406.00        210406.00   \n",
       "mean         1179.98     13214.67         13208.02         11137.08   \n",
       "std          4307.63      9563.25          9558.63          8601.51   \n",
       "min             0.00         0.00             0.00             0.00   \n",
       "25%             0.00      5873.48          5870.61          4186.32   \n",
       "50%             0.00     11081.14         11076.12          9600.00   \n",
       "75%             0.00     18314.52         18305.10         16000.00   \n",
       "max         35000.00     56087.91         56047.85         35000.00   \n",
       "\n",
       "       total_rec_int  total_rec_late_fee  recoveries  collection_recovery_fee  \\\n",
       "count      210406.00           210406.00   210406.00                210406.00   \n",
       "mean         1921.02                0.84      155.73                    16.57   \n",
       "std          2016.14                6.02      724.20                   102.95   \n",
       "min             0.00                0.00        0.00                     0.00   \n",
       "25%           617.55                0.00        0.00                     0.00   \n",
       "50%          1309.17                0.00        0.00                     0.00   \n",
       "75%          2470.88                0.00        0.00                     0.00   \n",
       "max         21087.91              358.68    33520.27                  5694.09   \n",
       "\n",
       "       last_pymnt_amnt  collections_12_mths_ex_med  \\\n",
       "count        210406.00                   210406.00   \n",
       "mean           6886.24                        0.01   \n",
       "std            7603.61                        0.11   \n",
       "min               0.00                        0.00   \n",
       "25%             536.01                        0.00   \n",
       "50%            4483.09                        0.00   \n",
       "75%           10772.06                        0.00   \n",
       "max           36475.59                        6.00   \n",
       "\n",
       "       mths_since_last_major_derog  policy_code  annual_inc_joint  dti_joint  \\\n",
       "count                    210406.00    210406.00              0.00       0.00   \n",
       "mean                         84.75         1.00               NaN        NaN   \n",
       "std                          26.64         0.00               NaN        NaN   \n",
       "min                           0.00         1.00               NaN        NaN   \n",
       "25%                          80.00         1.00               NaN        NaN   \n",
       "50%                          99.00         1.00               NaN        NaN   \n",
       "75%                          99.00         1.00               NaN        NaN   \n",
       "max                         159.00         1.00               NaN        NaN   \n",
       "\n",
       "       acc_now_delinq  tot_coll_amt  tot_cur_bal  open_acc_6m  open_il_12m  \\\n",
       "count       210406.00     210406.00    210406.00    210406.00    210406.00   \n",
       "mean             0.00        205.38    136847.80         0.00         0.00   \n",
       "std              0.07      20022.01    151386.21         0.06         0.05   \n",
       "min              0.00          0.00         0.00         0.00         0.00   \n",
       "25%              0.00          0.00     28371.25         0.00         0.00   \n",
       "50%              0.00          0.00     78664.00         0.00         0.00   \n",
       "75%              0.00          0.00    205955.75         0.00         0.00   \n",
       "max              5.00    9152545.00   8000078.00         6.00         6.00   \n",
       "\n",
       "       open_il_24m  mths_since_rcnt_il  total_bal_il   il_util  open_rv_12m  \\\n",
       "count    210406.00           210406.00     210406.00 210406.00    210406.00   \n",
       "mean          0.00               98.92         38.95      0.07         0.00   \n",
       "std           0.09                2.67       1672.06      2.36         0.07   \n",
       "min           0.00                0.00          0.00      0.00         0.00   \n",
       "25%           0.00               99.00          0.00      0.00         0.00   \n",
       "50%           0.00               99.00          0.00      0.00         0.00   \n",
       "75%           0.00               99.00          0.00      0.00         0.00   \n",
       "max          14.00              193.00     249212.00    129.20         8.00   \n",
       "\n",
       "       open_rv_24m  max_bal_bc  all_util  total_rev_hi_lim    inq_fi  \\\n",
       "count    210406.00   210406.00 210406.00         210406.00 210406.00   \n",
       "mean          0.00        5.98      0.06          29431.11      0.00   \n",
       "std           0.14      246.75      2.08          29448.80      0.07   \n",
       "min           0.00        0.00      0.00              0.00      0.00   \n",
       "25%           0.00        0.00      0.00          13200.00      0.00   \n",
       "50%           0.00        0.00      0.00          22000.00      0.00   \n",
       "75%           0.00        0.00      0.00          36500.00      0.00   \n",
       "max          21.00    30567.00    102.80        2013133.00      9.00   \n",
       "\n",
       "       total_cu_tl  inq_last_12m  portion_paid  \n",
       "count    210406.00     210406.00     210406.00  \n",
       "mean          0.00          0.00         79.46  \n",
       "std           0.14          0.15         35.23  \n",
       "min           0.00         -4.00          0.00  \n",
       "25%           0.00          0.00         57.32  \n",
       "50%           0.00          0.00        100.00  \n",
       "75%           0.00          0.00        100.00  \n",
       "max          21.00         19.00        100.00  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_df_subset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Loan Ecosystem Growth**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggr_funding = loan_df\\\n",
    "    .set_index('issue_d')\\\n",
    "    .groupby([pd.Grouper(freq='M')])\\\n",
    "    .agg({'funded_amnt_inv': 'mean'})\\\n",
    "    .reset_index()\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.lineplot(\n",
    "    data=aggr_funding, x='issue_d', y='funded_amnt_inv', ax=ax)\n",
    "ax.set_title('Monthly Average Funded Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that participation of loan was increasing along time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Stability of Interest Rate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aggr_int_rate = loan_df\\\n",
    "    .set_index('issue_d')\\\n",
    "    .groupby([pd.Grouper(freq='M'), 'loan_category'])\\\n",
    "    .agg({'int_rate': 'mean'})\\\n",
    "    .reset_index()\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "color_order = [sns.color_palette()[1], sns.color_palette()[0]]\n",
    "sns.lineplot(\n",
    "    data=aggr_int_rate, x='issue_d', y='int_rate', \n",
    "    hue='loan_category', ax=ax, palette=color_order)\n",
    "ax.set_title('Monthly Average Interest Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upward trend in successful loan may suggest healthy ecosystem of investment and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Correlation Coefficient**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use correlation coefficient that works with categorical problem: `Point Biserial` and `Phi-k` correlation coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import phik\n",
    "from phik import resources, report\n",
    "from scipy import stats\n",
    "\n",
    "def categorical_corr_table(dataframe, sample_size=None):\n",
    "    if sample_size:\n",
    "        X = dataframe.sample(sample_size)\n",
    "    else:\n",
    "        X = dataframe.copy()\n",
    "    # phi-k correlation matrix\n",
    "    X_phik_corr = X.phik_matrix()['loan_category']\n",
    "    # point biserial correlation\n",
    "    pbis_corr, pbis_p = [], []\n",
    "    X.loan_category = X.loan_category.map({'Good Loan': 0, 'Bad Loan': 1})\n",
    "    for var_ in X.columns:\n",
    "        try:\n",
    "            pbis_corr.append(\n",
    "                stats.pointbiserialr(X[var_], \n",
    "                X['loan_category'])[0]\n",
    "                )\n",
    "            pbis_p.append(\n",
    "                stats.pointbiserialr(X[var_], \n",
    "                X['loan_category'])[1]\n",
    "                )\n",
    "        except (ValueError, AttributeError): # Error caused by NaN records\n",
    "            pbis_corr.append(\n",
    "                stats.pointbiserialr(X.loc[~X[var_].isna(), var_], \n",
    "                X.loc[~X[var_].isna(), 'loan_category'])[0]\n",
    "                )\n",
    "            pbis_p.append(\n",
    "                stats.pointbiserialr(X.loc[~X[var_].isna(), var_], \n",
    "                X.loc[~X[var_].isna(), 'loan_category'])[1]\n",
    "                )\n",
    "    X_pbis_corr = pd.Series(pbis_corr, index=X.columns)\n",
    "    X_pbis_p = pd.Series(pbis_p, index=X.columns)\n",
    "    #combining subset\n",
    "    X_corr = pd.concat([X_phik_corr,\n",
    "                                    X_pbis_corr,\n",
    "                                    np.abs(X_pbis_corr),\n",
    "                                    X_pbis_p],\n",
    "                                    axis=1)\n",
    "    X_corr.columns = [\n",
    "        'phi-k corr.', 'point biserial corr.', 'point biserial corr. (mag.)', 'point biserial p-value'\n",
    "        ]\n",
    "    return X_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval columns not set, guessing: ['annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'revol_bal', 'revol_util', 'total_acc', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m', 'pub_rec', 'max_bal_bc']\n",
      "interval columns not set, guessing: ['loan_amnt', 'int_rate', 'installment']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "applicant_ftrs_nums = (\n",
    "    ['loan_category']\n",
    "    + loan_df_subset[applicant_features].select_dtypes(exclude=['object', 'category', 'datetime64']).columns.tolist()\n",
    "    ) \n",
    "loan_ftrs_nums = (\n",
    "    ['loan_category']\n",
    "    + loan_df_subset[loan_features].select_dtypes(exclude=['object', 'category', 'datetime64']).columns.tolist()\n",
    "    )\n",
    "dfs_to_plot = [\n",
    "    loan_df_subset[applicant_ftrs_nums].drop(columns=['annual_inc_joint', 'dti_joint', 'member_id', 'policy_code']), \n",
    "    loan_df_subset[loan_ftrs_nums].drop(columns=['id', 'policy_code'])] \n",
    "# plot point biserial correlation\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 8))\n",
    "for idx, df_ in enumerate(dfs_to_plot):\n",
    "    corr_df = categorical_corr_table(df_, sample_size=100000)\n",
    "    plot_corr_df = corr_df\\\n",
    "        .drop('loan_category')\\\n",
    "        .reset_index()\\\n",
    "        .melt(id_vars=['index'], \n",
    "              value_vars=corr_df.columns[[0, 2]])\n",
    "    plot_order = corr_df.drop('loan_category').sort_values('point biserial corr. (mag.)', ascending=False).index\n",
    "    sns.barplot(y='index', x='value', hue='variable', data = plot_corr_df, order=plot_order, ax=ax[idx])\n",
    "    ax[idx].grid(visible=True, axis='x')\n",
    "    ax[idx].set_axisbelow(True)\n",
    "    ax[idx].set_xlabel('Correlation Coefficient, loan_category')\n",
    "fig.suptitle('Correlation Coefficient of Numerical Features')\n",
    "fig.subplots_adjust(wspace=0.35, hspace=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High difference in correlation coefficient magnitude occurs for features `revol_util`, `tot_cur_bal`, `ann_inc`, and `total_rev_hi_lim`.\n",
    "\n",
    "Let's check the distribution of each parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interval columns not set, guessing: ['annual_inc', 'dti', 'delinq_2yrs', 'inq_last_6mths', 'mths_since_last_delinq', 'mths_since_last_record', 'open_acc', 'revol_bal', 'revol_util', 'total_acc', 'collections_12_mths_ex_med', 'mths_since_last_major_derog', 'acc_now_delinq', 'tot_coll_amt', 'tot_cur_bal', 'open_acc_6m', 'open_il_12m', 'open_il_24m', 'mths_since_rcnt_il', 'total_bal_il', 'il_util', 'open_rv_12m', 'open_rv_24m', 'all_util', 'total_rev_hi_lim', 'inq_fi', 'total_cu_tl', 'inq_last_12m', 'pub_rec', 'max_bal_bc', 'loan_amnt', 'int_rate', 'installment']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x2160 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ftrs_nums = (\n",
    "    ['loan_category']\n",
    "    + loan_df_subset[applicant_features].select_dtypes(exclude=['object', 'category', 'datetime64']).columns.tolist()\n",
    "    + loan_df_subset[loan_features].select_dtypes(exclude=['object', 'category', 'datetime64']).columns.tolist()\n",
    "    )\n",
    "plot_df = loan_df_subset[ftrs_nums]\\\n",
    "    .drop(columns=['dti_joint', 'annual_inc_joint', 'member_id', 'id', 'policy_code'])\n",
    "corr_df = categorical_corr_table(plot_df, sample_size=100000)\n",
    "x = corr_df.drop('loan_category').sort_values('point biserial corr. (mag.)', ascending=False).index[:5]\n",
    "fig, ax= plt.subplots(len(x), 2, figsize=(14, 6 * len(x)))\n",
    "for x, axes in zip(x, ax):\n",
    "    sns.histplot(data=plot_df, x=x, bins=30, hue='loan_category', ax=axes[0], alpha=0.5)\n",
    "    sns.boxplot(data=plot_df, x=x, y='loan_category', ax=axes[1])\n",
    "    axes[0].grid(visible=True, axis='y')\n",
    "    axes[0].set_axisbelow(True)\n",
    "    axes[0].set_title(f'Histogram, {x}')\n",
    "    axes[1].set_title(f'Central Tendency, {x}')\n",
    "fig.subplots_adjust(wspace=0.35, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above plots, points observed:\n",
    "\n",
    "- Variables from above to below is sorted by the highest to lowest correlation coefficient.\n",
    "- Interest rate (`int_rate`) is the most highly associated with `Bad Loan` among others. We know previously that higher `int_rate` is associated with lower loan `grade`, which reflects the quality of the loan.\n",
    "- Each variables has a highly different value scales, needs to be standardized.\n",
    "- From above to below, the location of box plot (reflects the center of distribution) between `Good Loan` and `Bad Loan` class is becoming less different. It may inform that the more different the distribution between classes, the more that variable may be useful and importance to the model as classification features. \n",
    "- We should focus on several features that has skewed (right-skewed, specifically) distributions and have a high number of outliers.\n",
    "- it is clear that only `dti` has a more normal-distribution than others, identified by the histogram and well-balanced boxplot (centered median and balanced whisker for class `Bad Loan`, however class `Good Loan` is less Gaussian). Other than that, especially `revol_util`, `tot_cur_bal`, `ann_inc`, `total_rev_hi_lim` and other variables that has correlation coefficient close to zero, has a lot of outliers.\n",
    "- For features that majority records zeroes (for example `pub_rec`, `acc_now_delinq`, etc.), the outliers is (probably) not exactly an anomaly. Hence if we simply discard it we may lose important information that may reduce our data representativeness. In the next process we will conduct more detailed outlier identification for each of the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Closing note for our numerical features: less gaussian distribution, a lot of outliers, and a less linearly related variables make our feature analysis by utilizing correlation coefficient is not properly suitable for our dataset. Indeed we still can  observe generally which variables may be potential for our model features and which one may be discarded to avoid overfitting.\n",
    "\n",
    "Later, more robust methods for feature selection will be conducted if necessary. Feature transformation to make the distribution more Gaussian will also be conducted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Multicollinearity**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collinearity between predictor variables can mask the importance of a variable that is collinear with such. In multiple regression setting with high degree of predictor variables, it is also possible for collinearity to exist between three or more variables even if no pair of variables has a particularly high correlation, which is called *multicollinearity*.\n",
    "\n",
    "The `VIF` (variance inflation factor) is known to be a better way to assess mulicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input",
     "output_scroll"
    ]
   },
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "check_vif = plot_df.drop(columns=['loan_category'])\n",
    "pd.DataFrame({\n",
    "    'vif': \n",
    "        [variance_inflation_factor(check_vif, i) for i in range(check_vif.shape[1])]\n",
    "    }, index=check_vif.columns).sort_values(by='vif', ascending=False).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a pair of features that show high collinearity: `loan_amnt` and `installment`. According to the explanation, it is clear because installment is a monthly payment based on loan amount (+ *interest*) divided by number of payment (i.e. loan `term`). So we can just use `installment` to cover both `loan_amnt` and `term`.\n",
    "\n",
    "Multicollinearity only impacts multiple regression model and should be considered carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4. Summary\n",
    "\n",
    "We already got some understanding for a credit business and the motivation behind developing machine learning classifier, specifically for *Lending Club Peer-to-peer Loan*, although the provided dataset may set constraint for our model here and there.\n",
    "\n",
    "Then, we discovered that our dataset has several variables that uninsightful for machine learning modeling. Also, the characteristic of our data is not ideal enough to implement statistical method directly hence need some cleaning and transformations. "
   ]
  },
  {
   "cell_type": "code",
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