Giovanni_Facchinetti_18202941_COMP47350_Homework1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Data quality report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read csv file\n",
    "In order to perform our activities on the dataset, we need to convert csv file into a dataframe object using Pandas.\n",
    "\n",
    "N.B.: in case the original categorical data has some space character in front of the value, we eliminate it with an extra parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('CreditRisk-18202941.csv', keep_default_na=True, sep=',\\s+', delimiter=',', skipinitialspace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First glance at the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has 1000 rows and 24 columns.\n"
     ]
    }
   ],
   "source": [
    "print(\"The dataset has %s rows and %s columns.\" % df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We examine the first 5 rows of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RiskPerformance</th>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <th>...</th>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>...</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "      <td>308</td>\n",
       "      <td>7</td>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Good</td>\n",
       "      <td>75</td>\n",
       "      <td>381</td>\n",
       "      <td>15</td>\n",
       "      <td>108</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>64</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bad</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>...</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bad</td>\n",
       "      <td>58</td>\n",
       "      <td>216</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>-8</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
       "0             Bad                    -9                     -9   \n",
       "1            Good                    71                    308   \n",
       "2            Good                    75                    381   \n",
       "3             Bad                    -9                     -9   \n",
       "4             Bad                    58                    216   \n",
       "\n",
       "   MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
       "0                         -9              -9                     -9   \n",
       "1                          7             104                     39   \n",
       "2                         15             108                     20   \n",
       "3                         -9              -9                     -9   \n",
       "4                         12              86                     23   \n",
       "\n",
       "   NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
       "0                           -9                           -9   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                           -9                           -9   \n",
       "4                            0                            0   \n",
       "\n",
       "   PercentTradesNeverDelq  MSinceMostRecentDelq  ...  PercentInstallTrades  \\\n",
       "0                      -9                    -9  ...                    -9   \n",
       "1                      98                    73  ...                    30   \n",
       "2                      95                    14  ...                    45   \n",
       "3                      -9                    -9  ...                    -9   \n",
       "4                      88                     3  ...                    29   \n",
       "\n",
       "   MSinceMostRecentInqexcl7days  NumInqLast6M  NumInqLast6Mexcl7days  \\\n",
       "0                            -9            -9                     -9   \n",
       "1                             0             0                      0   \n",
       "2                             1             2                      2   \n",
       "3                            -9            -9                     -9   \n",
       "4                             1             1                      1   \n",
       "\n",
       "   NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
       "0                          -9                        -9   \n",
       "1                          75                        99   \n",
       "2                          32                        64   \n",
       "3                          -9                        -9   \n",
       "4                          84                        -8   \n",
       "\n",
       "   NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
       "0                          -9                        -9   \n",
       "1                          11                         1   \n",
       "2                           6                         4   \n",
       "3                          -9                        -9   \n",
       "4                           7                         1   \n",
       "\n",
       "   NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \n",
       "0                                  -9                     -9  \n",
       "1                                   5                     81  \n",
       "2                                   3                     77  \n",
       "3                                  -9                     -9  \n",
       "4                                   5                     75  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now the last 5 rows:"
   ]
  },
  {
   "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>RiskPerformance</th>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <th>...</th>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Good</td>\n",
       "      <td>89</td>\n",
       "      <td>193</td>\n",
       "      <td>9</td>\n",
       "      <td>114</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>88</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Good</td>\n",
       "      <td>89</td>\n",
       "      <td>353</td>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>-8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Good</td>\n",
       "      <td>78</td>\n",
       "      <td>188</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Bad</td>\n",
       "      <td>63</td>\n",
       "      <td>291</td>\n",
       "      <td>9</td>\n",
       "      <td>88</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Bad</td>\n",
       "      <td>61</td>\n",
       "      <td>250</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>-7</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
       "995            Good                    89                    193   \n",
       "996            Good                    89                    353   \n",
       "997            Good                    78                    188   \n",
       "998             Bad                    63                    291   \n",
       "999             Bad                    61                    250   \n",
       "\n",
       "     MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
       "995                          9             114                     13   \n",
       "996                         40             140                     19   \n",
       "997                          3              72                     20   \n",
       "998                          9              88                     25   \n",
       "999                          4              66                     47   \n",
       "\n",
       "     NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
       "995                            0                            0   \n",
       "996                            0                            0   \n",
       "997                            1                            1   \n",
       "998                            0                            0   \n",
       "999                            1                            1   \n",
       "\n",
       "     PercentTradesNeverDelq  MSinceMostRecentDelq  ...  PercentInstallTrades  \\\n",
       "995                     100                    -7  ...                    43   \n",
       "996                     100                    -7  ...                    11   \n",
       "997                      96                    45  ...                    43   \n",
       "998                      84                     3  ...                    32   \n",
       "999                     100                    -7  ...                    39   \n",
       "\n",
       "     MSinceMostRecentInqexcl7days  NumInqLast6M  NumInqLast6Mexcl7days  \\\n",
       "995                             0             0                      0   \n",
       "996                             0             0                      0   \n",
       "997                             0             2                      2   \n",
       "998                             0             0                      0   \n",
       "999                             0             2                      2   \n",
       "\n",
       "     NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
       "995                           1                        88   \n",
       "996                           7                        -8   \n",
       "997                           1                        91   \n",
       "998                          30                        73   \n",
       "999                          73                        61   \n",
       "\n",
       "     NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
       "995                           2                         2   \n",
       "996                           3                         1   \n",
       "997                           1                         5   \n",
       "998                           7                         2   \n",
       "999                          14                         4   \n",
       "\n",
       "     NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \n",
       "995                                   0                     67  \n",
       "996                                   0                     36  \n",
       "997                                   0                     43  \n",
       "998                                   1                     82  \n",
       "999                                   5                     90  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types and conversions\n",
    "First of all, we list all the features in the dataset with their types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RiskPerformance                       object\n",
       "ExternalRiskEstimate                   int64\n",
       "MSinceOldestTradeOpen                  int64\n",
       "MSinceMostRecentTradeOpen              int64\n",
       "AverageMInFile                         int64\n",
       "NumSatisfactoryTrades                  int64\n",
       "NumTrades60Ever2DerogPubRec            int64\n",
       "NumTrades90Ever2DerogPubRec            int64\n",
       "PercentTradesNeverDelq                 int64\n",
       "MSinceMostRecentDelq                   int64\n",
       "MaxDelq2PublicRecLast12M               int64\n",
       "MaxDelqEver                            int64\n",
       "NumTotalTrades                         int64\n",
       "NumTradesOpeninLast12M                 int64\n",
       "PercentInstallTrades                   int64\n",
       "MSinceMostRecentInqexcl7days           int64\n",
       "NumInqLast6M                           int64\n",
       "NumInqLast6Mexcl7days                  int64\n",
       "NetFractionRevolvingBurden             int64\n",
       "NetFractionInstallBurden               int64\n",
       "NumRevolvingTradesWBalance             int64\n",
       "NumInstallTradesWBalance               int64\n",
       "NumBank2NatlTradesWHighUtilization     int64\n",
       "PercentTradesWBalance                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After having imported and examined the CSV file in a spreadsheet, we decided to make the following type conversions:\n",
    "\n",
    "- **RiskPerformance** feature (i.e. our target feature) is converted to **categorical**, as it is a binary variable showing only two values: 'good' and 'bad'.\n",
    "- **MaxDelq2PublicRecLast12M** and **MaxDelqEver** features are converted to **categorical** as they contain finite set of possible values. This is an evidence from the Data Dictionary spreadsheet.\n",
    "- **PercentTradesNeverDelq** feature is converted to **floating-point** since its values are percentages.\n",
    "- **NetFractionRevolvingBurden** feature is converted to **floating-point** since it represents a fraction between two numbers.\n",
    "- **NetFractionInstallBurden** feature is converted to **floating-point** since it represents a fraction between two numbers.\n",
    "- **PercentTradesWBalance** feature is converted to **floating-point** since its values are percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RiskPerformance                       category\n",
       "ExternalRiskEstimate                     int64\n",
       "MSinceOldestTradeOpen                    int64\n",
       "MSinceMostRecentTradeOpen                int64\n",
       "AverageMInFile                           int64\n",
       "NumSatisfactoryTrades                    int64\n",
       "NumTrades60Ever2DerogPubRec              int64\n",
       "NumTrades90Ever2DerogPubRec              int64\n",
       "PercentTradesNeverDelq                 float64\n",
       "MSinceMostRecentDelq                     int64\n",
       "MaxDelq2PublicRecLast12M              category\n",
       "MaxDelqEver                           category\n",
       "NumTotalTrades                           int64\n",
       "NumTradesOpeninLast12M                   int64\n",
       "PercentInstallTrades                     int64\n",
       "MSinceMostRecentInqexcl7days             int64\n",
       "NumInqLast6M                             int64\n",
       "NumInqLast6Mexcl7days                    int64\n",
       "NetFractionRevolvingBurden             float64\n",
       "NetFractionInstallBurden               float64\n",
       "NumRevolvingTradesWBalance               int64\n",
       "NumInstallTradesWBalance                 int64\n",
       "NumBank2NatlTradesWHighUtilization       int64\n",
       "PercentTradesWBalance                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select columns containing categorical data\n",
    "categorical_columns = df[['RiskPerformance','MaxDelq2PublicRecLast12M','MaxDelqEver']].columns\n",
    "\n",
    "# Convert data type to category for these columns\n",
    "for column in categorical_columns:\n",
    "    df[column] = df[column].astype('category')\n",
    "\n",
    "# Select columns containing ratio or percentage data\n",
    "float_columns = df[['PercentTradesNeverDelq','NetFractionRevolvingBurden','NetFractionInstallBurden','PercentTradesWBalance']].columns\n",
    "\n",
    "# Convert data type to float for these columns\n",
    "for column in float_columns:\n",
    "    df[column] = df[column].astype('float')\n",
    "\n",
    "continuous_columns = df.select_dtypes(['float64', 'int64'])\n",
    "\n",
    "# Show feature data types again to check that conversion was successful\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check for duplicates and constant columns\n",
    "We need first to check for the presence of duplicated rows and/or columns in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In our dataset there are:\n",
      "- 56 duplicate rows\n",
      "- 0 duplicate columns.\n"
     ]
    }
   ],
   "source": [
    "print('In our dataset there are:\\n- %s duplicate rows\\n- %s duplicate columns.' % \\\n",
    "      (df.duplicated()[df.duplicated() == True].shape[0],\\\n",
    "       df.columns.size - df.columns.unique().size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After having assessed the presence of duplicates in rows, we proceed to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset has now 942 rows and 24 columns.\n"
     ]
    }
   ],
   "source": [
    "# The 'keep' and 'inplace' optional parameters are used in order to drop all the duplicates\n",
    "# Otherwise the method would leave two rows with -9 values for all the features\n",
    "# except for the target feature, as the two values are 'good' and 'bad'\n",
    "df.drop_duplicates(keep = False, inplace = True)\n",
    "print(\"The dataset has now %s rows and %s columns.\" % df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the descriptive statistics for the categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>942</td>\n",
       "      <td>2</td>\n",
       "      <td>Bad</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>942</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>942</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique  top freq\n",
       "RiskPerformance            942      2  Bad  490\n",
       "MaxDelq2PublicRecLast12M   942      8    7  388\n",
       "MaxDelqEver                942      7    8  416"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['category']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to check for the presence of constant values in columns, we need to look at the descriptive statistics for the continuous features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>942.0</td>\n",
       "      <td>72.150743</td>\n",
       "      <td>10.039486</td>\n",
       "      <td>37.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>196.628450</td>\n",
       "      <td>101.707191</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>183.5</td>\n",
       "      <td>252.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>9.325902</td>\n",
       "      <td>11.255838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>942.0</td>\n",
       "      <td>78.936306</td>\n",
       "      <td>32.789643</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>21.399151</td>\n",
       "      <td>11.398674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.587049</td>\n",
       "      <td>1.202354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.384289</td>\n",
       "      <td>0.905109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>942.0</td>\n",
       "      <td>91.898089</td>\n",
       "      <td>12.294483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>942.0</td>\n",
       "      <td>8.498938</td>\n",
       "      <td>20.495340</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>23.289809</td>\n",
       "      <td>13.362273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.817410</td>\n",
       "      <td>1.731313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>35.298301</td>\n",
       "      <td>17.510804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.004246</td>\n",
       "      <td>5.912754</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.450106</td>\n",
       "      <td>1.871299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.397028</td>\n",
       "      <td>1.834697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>33.200637</td>\n",
       "      <td>29.168736</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>41.003185</td>\n",
       "      <td>41.344217</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>3.832272</td>\n",
       "      <td>3.339058</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.553079</td>\n",
       "      <td>3.510550</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.639066</td>\n",
       "      <td>2.535881</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>66.690021</td>\n",
       "      <td>22.626372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count        mean         std   min  \\\n",
       "ExternalRiskEstimate                942.0   72.150743   10.039486  37.0   \n",
       "MSinceOldestTradeOpen               942.0  196.628450  101.707191  -8.0   \n",
       "MSinceMostRecentTradeOpen           942.0    9.325902   11.255838   0.0   \n",
       "AverageMInFile                      942.0   78.936306   32.789643   5.0   \n",
       "NumSatisfactoryTrades               942.0   21.399151   11.398674   1.0   \n",
       "NumTrades60Ever2DerogPubRec         942.0    0.587049    1.202354   0.0   \n",
       "NumTrades90Ever2DerogPubRec         942.0    0.384289    0.905109   0.0   \n",
       "PercentTradesNeverDelq              942.0   91.898089   12.294483   0.0   \n",
       "MSinceMostRecentDelq                942.0    8.498938   20.495340  -8.0   \n",
       "NumTotalTrades                      942.0   23.289809   13.362273   0.0   \n",
       "NumTradesOpeninLast12M              942.0    1.817410    1.731313   0.0   \n",
       "PercentInstallTrades                942.0   35.298301   17.510804   0.0   \n",
       "MSinceMostRecentInqexcl7days        942.0    0.004246    5.912754  -8.0   \n",
       "NumInqLast6M                        942.0    1.450106    1.871299   0.0   \n",
       "NumInqLast6Mexcl7days               942.0    1.397028    1.834697   0.0   \n",
       "NetFractionRevolvingBurden          942.0   33.200637   29.168736  -8.0   \n",
       "NetFractionInstallBurden            942.0   41.003185   41.344217  -8.0   \n",
       "NumRevolvingTradesWBalance          942.0    3.832272    3.339058  -8.0   \n",
       "NumInstallTradesWBalance            942.0    1.553079    3.510550  -8.0   \n",
       "NumBank2NatlTradesWHighUtilization  942.0    0.639066    2.535881  -8.0   \n",
       "PercentTradesWBalance               942.0   66.690021   22.626372   0.0   \n",
       "\n",
       "                                      25%    50%    75%    max  \n",
       "ExternalRiskEstimate                 65.0   72.0   80.0   93.0  \n",
       "MSinceOldestTradeOpen               134.0  183.5  252.0  528.0  \n",
       "MSinceMostRecentTradeOpen             3.0    6.0   12.0  145.0  \n",
       "AverageMInFile                       58.0   75.5   98.0  256.0  \n",
       "NumSatisfactoryTrades                13.0   20.0   28.0   70.0  \n",
       "NumTrades60Ever2DerogPubRec           0.0    0.0    1.0   12.0  \n",
       "NumTrades90Ever2DerogPubRec           0.0    0.0    0.0    9.0  \n",
       "PercentTradesNeverDelq               88.0   97.0  100.0  100.0  \n",
       "MSinceMostRecentDelq                 -7.0    1.0   18.0   81.0  \n",
       "NumTotalTrades                       14.0   21.0   31.0   85.0  \n",
       "NumTradesOpeninLast12M                0.0    1.0    3.0   11.0  \n",
       "PercentInstallTrades                 22.0   33.0   46.0  100.0  \n",
       "MSinceMostRecentInqexcl7days         -7.0    0.0    1.0   24.0  \n",
       "NumInqLast6M                          0.0    1.0    2.0   16.0  \n",
       "NumInqLast6Mexcl7days                 0.0    1.0    2.0   16.0  \n",
       "NetFractionRevolvingBurden            7.0   27.0   55.0  112.0  \n",
       "NetFractionInstallBurden             -8.0   49.5   79.0  142.0  \n",
       "NumRevolvingTradesWBalance            2.0    3.0    5.0   20.0  \n",
       "NumInstallTradesWBalance              1.0    2.0    3.0   14.0  \n",
       "NumBank2NatlTradesWHighUtilization    0.0    0.5    2.0   18.0  \n",
       "PercentTradesWBalance                50.0   67.0   83.0  100.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(['int64','float64']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the **standard deviation** dispersion indicator, we can see that it is not zero for all the continuous features in the dataset. Thus, there are no constant continuous features.\n",
    "\n",
    "We can now write the updated and cleaned dataframe object to a new csv file, in order to proceed with the data quality report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RiskPerformance                        object\n",
       "ExternalRiskEstimate                    int64\n",
       "MSinceOldestTradeOpen                   int64\n",
       "MSinceMostRecentTradeOpen               int64\n",
       "AverageMInFile                          int64\n",
       "NumSatisfactoryTrades                   int64\n",
       "NumTrades60Ever2DerogPubRec             int64\n",
       "NumTrades90Ever2DerogPubRec             int64\n",
       "PercentTradesNeverDelq                float64\n",
       "MSinceMostRecentDelq                    int64\n",
       "MaxDelq2PublicRecLast12M                int64\n",
       "MaxDelqEver                             int64\n",
       "NumTotalTrades                          int64\n",
       "NumTradesOpeninLast12M                  int64\n",
       "PercentInstallTrades                    int64\n",
       "MSinceMostRecentInqexcl7days            int64\n",
       "NumInqLast6M                            int64\n",
       "NumInqLast6Mexcl7days                   int64\n",
       "NetFractionRevolvingBurden            float64\n",
       "NetFractionInstallBurden              float64\n",
       "NumRevolvingTradesWBalance              int64\n",
       "NumInstallTradesWBalance                int64\n",
       "NumBank2NatlTradesWHighUtilization      int64\n",
       "PercentTradesWBalance                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('CreditRisk-18202941_clean.csv', index = False)\n",
    "\n",
    "# Import new csv in new dataframe object\n",
    "df_clean = pd.read_csv('CreditRisk-18202941_clean.csv', keep_default_na=True, sep=',\\s+', delimiter=',', skipinitialspace=True)\n",
    "# Show feature types in new/updated dataframe\n",
    "df_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RiskPerformance                       category\n",
       "ExternalRiskEstimate                     int64\n",
       "MSinceOldestTradeOpen                    int64\n",
       "MSinceMostRecentTradeOpen                int64\n",
       "AverageMInFile                           int64\n",
       "NumSatisfactoryTrades                    int64\n",
       "NumTrades60Ever2DerogPubRec              int64\n",
       "NumTrades90Ever2DerogPubRec              int64\n",
       "PercentTradesNeverDelq                 float64\n",
       "MSinceMostRecentDelq                     int64\n",
       "MaxDelq2PublicRecLast12M              category\n",
       "MaxDelqEver                           category\n",
       "NumTotalTrades                           int64\n",
       "NumTradesOpeninLast12M                   int64\n",
       "PercentInstallTrades                     int64\n",
       "MSinceMostRecentInqexcl7days             int64\n",
       "NumInqLast6M                             int64\n",
       "NumInqLast6Mexcl7days                    int64\n",
       "NetFractionRevolvingBurden             float64\n",
       "NetFractionInstallBurden               float64\n",
       "NumRevolvingTradesWBalance               int64\n",
       "NumInstallTradesWBalance                 int64\n",
       "NumBank2NatlTradesWHighUtilization       int64\n",
       "PercentTradesWBalance                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We need to re-convert former category types, as they become lost during write/read process\n",
    "\n",
    "# Select columns containing categorical data\n",
    "cat_columns = df_clean[['RiskPerformance','MaxDelq2PublicRecLast12M','MaxDelqEver']].columns\n",
    "\n",
    "# Convert data type to category for these columns\n",
    "for column in cat_columns:\n",
    "    df_clean[column] = df_clean[column].astype('category')\n",
    "\n",
    "# Show feature data types again to check that conversion was successful\n",
    "df_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DOUBLE CHECK PASSED: datasets are coherent.\n"
     ]
    }
   ],
   "source": [
    "# Double check to see if the original dataframe is coherent with the updated one.\n",
    "if (df.shape == df_clean.shape):\n",
    "    print(\"DOUBLE CHECK PASSED: datasets are coherent.\")\n",
    "elif (df.shape[0] != df_clean.shape[0]):\n",
    "    print(\"WARNING: rows are incoherent.\")\n",
    "elif (df.shape[1] != df_clean.shape[1]):\n",
    "    print(\"WARNING: columns are incoherent.\")\n",
    "else:\n",
    "    print(\"WARNING: incoherent datasets.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select continuous columns\n",
    "continuous_columns = df_clean[['ExternalRiskEstimate','MSinceOldestTradeOpen',\n",
    "                               'MSinceMostRecentTradeOpen','AverageMInFile',\n",
    "                               'NumSatisfactoryTrades','NumTrades60Ever2DerogPubRec',\n",
    "                               'NumTrades90Ever2DerogPubRec','PercentTradesNeverDelq',\n",
    "                               'MSinceMostRecentDelq','NumTotalTrades',\n",
    "                               'NumTradesOpeninLast12M','PercentInstallTrades',\n",
    "                               'MSinceMostRecentInqexcl7days','NumInqLast6M',\n",
    "                               'NumInqLast6Mexcl7days','NetFractionRevolvingBurden',\n",
    "                               'NetFractionInstallBurden','NumRevolvingTradesWBalance',\n",
    "                               'NumInstallTradesWBalance','NumBank2NatlTradesWHighUtilization',\n",
    "                               'PercentTradesWBalance',]].columns\n",
    "\n",
    "# Select categorical columns\n",
    "categorical_columns = df_clean[['RiskPerformance','MaxDelq2PublicRecLast12M','MaxDelqEver']].columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate descriptive statistics table for categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>942</td>\n",
       "      <td>2</td>\n",
       "      <td>Bad</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>942</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>942</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique  top freq\n",
       "RiskPerformance            942      2  Bad  490\n",
       "MaxDelq2PublicRecLast12M   942      8    7  388\n",
       "MaxDelqEver                942      7    8  416"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.select_dtypes(['category']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate descriptive statistics table for continuous features:"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>942.0</td>\n",
       "      <td>72.150743</td>\n",
       "      <td>10.039486</td>\n",
       "      <td>37.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>196.628450</td>\n",
       "      <td>101.707191</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>183.5</td>\n",
       "      <td>252.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>9.325902</td>\n",
       "      <td>11.255838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>942.0</td>\n",
       "      <td>78.936306</td>\n",
       "      <td>32.789643</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>21.399151</td>\n",
       "      <td>11.398674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.587049</td>\n",
       "      <td>1.202354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.384289</td>\n",
       "      <td>0.905109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>942.0</td>\n",
       "      <td>91.898089</td>\n",
       "      <td>12.294483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentDelq</th>\n",
       "      <td>942.0</td>\n",
       "      <td>8.498938</td>\n",
       "      <td>20.495340</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>23.289809</td>\n",
       "      <td>13.362273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.817410</td>\n",
       "      <td>1.731313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>35.298301</td>\n",
       "      <td>17.510804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.004246</td>\n",
       "      <td>5.912754</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.450106</td>\n",
       "      <td>1.871299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.397028</td>\n",
       "      <td>1.834697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>33.200637</td>\n",
       "      <td>29.168736</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>41.003185</td>\n",
       "      <td>41.344217</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>3.832272</td>\n",
       "      <td>3.339058</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.553079</td>\n",
       "      <td>3.510550</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.639066</td>\n",
       "      <td>2.535881</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>66.690021</td>\n",
       "      <td>22.626372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count        mean         std   min  \\\n",
       "ExternalRiskEstimate                942.0   72.150743   10.039486  37.0   \n",
       "MSinceOldestTradeOpen               942.0  196.628450  101.707191  -8.0   \n",
       "MSinceMostRecentTradeOpen           942.0    9.325902   11.255838   0.0   \n",
       "AverageMInFile                      942.0   78.936306   32.789643   5.0   \n",
       "NumSatisfactoryTrades               942.0   21.399151   11.398674   1.0   \n",
       "NumTrades60Ever2DerogPubRec         942.0    0.587049    1.202354   0.0   \n",
       "NumTrades90Ever2DerogPubRec         942.0    0.384289    0.905109   0.0   \n",
       "PercentTradesNeverDelq              942.0   91.898089   12.294483   0.0   \n",
       "MSinceMostRecentDelq                942.0    8.498938   20.495340  -8.0   \n",
       "NumTotalTrades                      942.0   23.289809   13.362273   0.0   \n",
       "NumTradesOpeninLast12M              942.0    1.817410    1.731313   0.0   \n",
       "PercentInstallTrades                942.0   35.298301   17.510804   0.0   \n",
       "MSinceMostRecentInqexcl7days        942.0    0.004246    5.912754  -8.0   \n",
       "NumInqLast6M                        942.0    1.450106    1.871299   0.0   \n",
       "NumInqLast6Mexcl7days               942.0    1.397028    1.834697   0.0   \n",
       "NetFractionRevolvingBurden          942.0   33.200637   29.168736  -8.0   \n",
       "NetFractionInstallBurden            942.0   41.003185   41.344217  -8.0   \n",
       "NumRevolvingTradesWBalance          942.0    3.832272    3.339058  -8.0   \n",
       "NumInstallTradesWBalance            942.0    1.553079    3.510550  -8.0   \n",
       "NumBank2NatlTradesWHighUtilization  942.0    0.639066    2.535881  -8.0   \n",
       "PercentTradesWBalance               942.0   66.690021   22.626372   0.0   \n",
       "\n",
       "                                      25%    50%    75%    max  \n",
       "ExternalRiskEstimate                 65.0   72.0   80.0   93.0  \n",
       "MSinceOldestTradeOpen               134.0  183.5  252.0  528.0  \n",
       "MSinceMostRecentTradeOpen             3.0    6.0   12.0  145.0  \n",
       "AverageMInFile                       58.0   75.5   98.0  256.0  \n",
       "NumSatisfactoryTrades                13.0   20.0   28.0   70.0  \n",
       "NumTrades60Ever2DerogPubRec           0.0    0.0    1.0   12.0  \n",
       "NumTrades90Ever2DerogPubRec           0.0    0.0    0.0    9.0  \n",
       "PercentTradesNeverDelq               88.0   97.0  100.0  100.0  \n",
       "MSinceMostRecentDelq                 -7.0    1.0   18.0   81.0  \n",
       "NumTotalTrades                       14.0   21.0   31.0   85.0  \n",
       "NumTradesOpeninLast12M                0.0    1.0    3.0   11.0  \n",
       "PercentInstallTrades                 22.0   33.0   46.0  100.0  \n",
       "MSinceMostRecentInqexcl7days         -7.0    0.0    1.0   24.0  \n",
       "NumInqLast6M                          0.0    1.0    2.0   16.0  \n",
       "NumInqLast6Mexcl7days                 0.0    1.0    2.0   16.0  \n",
       "NetFractionRevolvingBurden            7.0   27.0   55.0  112.0  \n",
       "NetFractionInstallBurden             -8.0   49.5   79.0  142.0  \n",
       "NumRevolvingTradesWBalance            2.0    3.0    5.0   20.0  \n",
       "NumInstallTradesWBalance              1.0    2.0    3.0   14.0  \n",
       "NumBank2NatlTradesWHighUtilization    0.0    0.5    2.0   18.0  \n",
       "PercentTradesWBalance                50.0   67.0   83.0  100.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.select_dtypes(['int64','float64']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For continuous features we build two different kinds of plots and save to pdf files:\n",
    "- histograms\n",
    "- box-plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean[continuous_columns].hist(figsize=(30,30))\n",
    "plt.savefig('continuous_histo_1-1.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAEyCAYAAADeAVWKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAGL5JREFUeJzt3X+U1Xed3/HnG4bMmFEyJEFPSGxIrV0xJIrOWot2m5isR0sPSVs5McfauIdDAnZHa4w7LLR11z1wooGmlm5LWPE0ti5G0nShatelZHS1K6mDiQFCu0mNuuPEzazDj3QMgYF3/7jfoQMhYcJnZu5c7vNxDmfu93O/9/t93eGc5MXn+7nfG5mJJEmSzs20egeQJElqZJYpSZKkApYpSZKkApYpSZKkApYpSZKkApYpSZKkApYpSZKkApYpSZKkApYpSZKkAi2TebJLL700586dO5mnlCRJOie7d+/+q8ycfbb9JrVMzZ07l97e3sk8pSRJ0jmJiJ+MZT8v80mSJBWwTEmSJBWwTEmSJBWwTEmSJBWwTEmSJBWwTEmSJBWwTEmSJBWwTElqWFu2bGH+/PlMnz6d+fPns2XLlnpHktSEJvWmnZI0XrZs2cLq1avZvHkz7373u/nud7/L0qVLAbj11lvrnE5SM4nMnLSTdXZ2pndAlzQe5s+fz4YNG7j++utPjvX09NDV1cXevXvrmEzS+SIidmdm59n2G9Nlvoj4RETsi4i9EbElItoi4qqIeCQinoyIByLigvLYkjQ2+/fvp6+v75TLfH19fezfv7/e0SQ1mbOWqYi4HPgY0JmZ84HpwAeBzwL3ZuYbgQPA0okMKkmjzZkzh+7ubjZs2MCRI0fYsGED3d3dzJkzp97RJDWZsS5AbwFeFREtwIXAM8B7gAer5+8Hbh7/eJL00k5fpjCZyxYkacRZy1Rm/gxYB/yUWok6BOwGDmbmcLVbH3D5mV4fEbdHRG9E9A4MDIxPaklNr7+/n/nz53PDDTdwwQUXcMMNNzB//nz6+/vrHU1SkxnLZb5ZwE3AVcAcoB14/xl2PeM/CTNzU2Z2Zmbn7NmzS7JK0kkdHR309PSwbt06hoaGWLduHT09PXR0dNQ7mqQmM5bLfDcCT2fmQGYeAx4CFgId1WU/gCsA/zkoadIcPnyYiy66iAULFjBjxgwWLFjARRddxOHDh+sdTVKTGUuZ+inwzoi4MCICuAF4AugBPlDtcxuwbWIiStKLDQ8Ps27dOrq6umhra6Orq4t169YxPDx89hdL0jgay5qpR6gtNP8BsKd6zSagG7gzIp4CLgE2T2BOSTpFa2srBw4cYO/evRw/fpy9e/dy4MABWltb6x1NUpMZ0x3QM/PTwKdPG/4R8I5xTyRJY7Bs2TI+9alP8bnPfY5nn32W1772tQwMDPDRj3603tEkNRm/m09SQ1q4cCHt7e0MDg6SmQwODtLe3s7ChQvrHU1Sk7FMSWpIa9asYdu2bRw9epTM5OjRo2zbto01a9bUO5qkJmOZktSQ9u/fz9atW2lrayMiaGtrY+vWrX6djKRJZ5mS1JA6Ojq47777WLt2LUNDQ6xdu5b77rvP+0xJmnSWKUkN6fDhw3R0dJxyn6mOjg7vMyVp0lmmJDWk4eFh1q9ff8p9ptavX+99piRNOsuUpIbU2trK4ODgKfeZGhwc9D5TkibdmO4zJUlTzbJly+ju7gZg+fLlbNy4ke7ubpYvX17nZJKajWVKUkPasGEDAKtWreKTn/wkra2tLF++/OS4JE2WyMxJO1lnZ2f29vZO2vkkSZLOVUTszszOs+3nmilJkqQClilJDWvkk3wjN+3s6uqqdyRJTcgyJakhdXV1sXHjxlNu2rlx40YLlaRJ55opSQ2pra2Nzs5Oent7eeGFF2htbT25feTIkXrHk3QecM2UpPPaCy+8wK5du06Zmdq1axcvvPBCvaNJajKWKUkNa9GiRdx5551ceOGF3HnnnSxatKjekSQ1Ie8zJalhbd++nYiodwxJTc6ZKUkNaaRETZs27ZSflitJk80yJakhZSYRwT333MPQ0BD33HMPEcFkfqhGksBP80lqUBHBnDlz6O/vPzk2sm2hkjQe/DSfpPNef38/K1as4ODBg6xYseKUYiVJk8UyJUmSVMDLfJIaUkQwb9489u/ff3JsZNvLfJLGg5f5JJ33+vr6ePjhhzl69CgPP/wwfX199Y4kqQlZpiQ1pPb2dp577jm2bt3KL3/5S7Zu3cpzzz1He3t7vaNJajKWKUkN6fnnn+fGG29k48aNdHR0sHHjRm688Uaef/75ekeT1GS8A7qkhjRv3jxWrVrFjh07To719PTwzDPP1DGVpGZkmZLUkFavXs0tt9xCe3s7P/nJT7jyyisZGhri85//fL2jSWoyXuaT1PD8ChlJ9WSZktSQ1qxZwwMPPMDTTz/N8ePHefrpp3nggQdYs2ZNvaNJajKWKUkNaf/+/WzdupW2tjYigra2NrZu3XrKfackaTJYpiQ1pI6ODjZt2sTatWsZGhpi7dq1bNq0iY6OjnpHk9RkLFOSGtLhw4eZOXMmCxYsYMaMGSxYsICZM2dy+PDhekeT1GQsU5Ia0vDwMOvXr6erq4u2tja6urpYv349w8PD9Y4mqclYpiQ1pNbWVg4cOMDevXs5fvw4e/fu5cCBA7S2ttY7mqQm432mJDWkZcuW0d3dDcDy5cvZuHEj3d3dLF++vM7JJDWbmMxvV+/s7Mze3t5JO5+k89sll1zC4ODgye2LL76YX/ziF3VMJOl8EhG7M7PzbPt5mU9SQ7r22msZHBxk8eLFDAwMsHjxYgYHB7n22mvrHU1Sk7FMSWpIe/bsYfHixWzbto1LL72Ubdu2sXjxYvbs2VPvaJKajGVKUsPavHnzy25L0mSwTElqWEuXLn3ZbUmaDH6aT1JDuuaaa9i+ffuLvuT4mmuuqVMiSc3KmSlJDemyyy57ReOSNFEsU5Ia0o4dO1ixYgWZefLPihUr2LFjR72jSWoylilJDSkzOXbsGG1tbUQEbW1tHDt2jMm8d54kgWVKUgPbvHkza9euZWhoiLVr1/ppPkl1MaYyFREdEfFgRPyviNgfEX87Ii6OiB0R8WT1c9ZEh5WkERFBZvLUU09x7NgxnnrqKTLzRQvSJWmijenrZCLifuA7mfmFiLgAuBBYBQxm5t0RsRKYlZndL3ccv05G0niJCK644gr6+vpOjo1se6lP0ngYt6+TiYiZwK8BmwEy82hmHgRuAu6vdrsfuPnc40rSKzNt2jT6+/tZv349Q0NDrF+/nv7+fqZNc/WCpMl11pmpiHgrsAl4AngLsBv4OPCzzOwYtd+BzHzZS33OTEkaLy0tLRw/fvxF49OnT2d4eLgOiSSdb8bzi45bgLcB/z4zFwBDwMpXEOT2iOiNiN6BgYGxvkySXtZIkRpZIzXy80wFS5Im0ljKVB/Ql5mPVNsPUitXfxkRlwFUP58904szc1NmdmZm5+zZs8cjsyQBcPXVV3PixAkykxMnTnD11VfXO5KkJnTWMpWZPwf+IiJ+pRq6gdolv+3AbdXYbcC2CUkoSS9h3759tLS0EBG0tLSwb9++ekeS1ITG+t18XcCXq0/y/Qj4DWpF7KsRsRT4KbBkYiJKkiRNXWP62EtmPlZdqrs2M2/OzAOZ+YvMvCEz31j9HJzosJI0YmSN1KJFixgYGGDRokWnjEvSZBnrzJQkTSmZyaxZs9i+fTsj6zFnzZrFgQMH6pxMUrPxhiySGtL06dM5dOjQKfeZOnToENOnT693NElNxjIlqSG91D3yvPu5pMlmmZLUkE6cOMF1113HXXfdRXt7O3fddRfXXXcdJ06cqHc0SU3GMiWpIbW0tPDYY4+xc+dOjh49ys6dO3nsscdoaXEpqKTJZZmS1JBmzpzJwYMHefTRRzl27BiPPvooBw8eZObMmfWOJqnJWKYkNaSDBw9yxx13sGrVKtrb21m1ahV33HEHBw8erHc0SU3GMiWpIc2bN48lS5Zw5MgRMpMjR46wZMkS5s2bV+9okpqMZUpSQ1q9ejVLly6lp6eHY8eO0dPTw9KlS1m9enW9o0lqMjGZHyPu7OzM3t7eSTufpKmnke5Q7m0WpOYWEbszs/Ns+zkzJWlSZea4/7my+2sTclxJGgvLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUgHLlCRJUoExl6mImB4Rj0bE16rtqyLikYh4MiIeiIgLJi6mJEnS1PRKZqY+Duwftf1Z4N7MfCNwAFg6nsEkSZIawZjKVERcASwCvlBtB/Ae4MFql/uBmycioCRJ0lQ21pmpfw38FnCi2r4EOJiZw9V2H3D5mV4YEbdHRG9E9A4MDBSFlSRJmmrOWqYi4u8Dz2bm7tHDZ9g1z/T6zNyUmZ2Z2Tl79uxzjClJkjQ1tYxhn3cBiyPi7wFtwExqM1UdEdFSzU5dAfRPXExJkqSp6awzU5n525l5RWbOBT4IPJyZHwJ6gA9Uu90GbJuwlJIkSVNUyX2muoE7I+IpamuoNo9PJEmSpMYxlst8J2Xmt4BvVY9/BLxj/CNJkiQ1Du+ALkmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVMAyJUmSVOCsZSoiXh8RPRGxPyL2RcTHq/GLI2JHRDxZ/Zw18XElSZKmlrHMTA0Dn8zMecA7gX8aEW8GVgI7M/ONwM5qW5IkqamctUxl5jOZ+YPq8XPAfuBy4Cbg/mq3+4GbJyqkJEnSVPWK1kxFxFxgAfAI8LrMfAZqhQt47XiHkyRJmurGXKYi4tXAfwb+WWYefgWvuz0ieiOid2Bg4FwySpIkTVljKlMRMYNakfpyZj5UDf9lRFxWPX8Z8OyZXpuZmzKzMzM7Z8+ePR6ZJUmSpoyxfJovgM3A/sz8V6Oe2g7cVj2+Ddg2/vEkSZKmtpYx7PMu4MPAnoh4rBpbBdwNfDUilgI/BZZMTERJkqSp66xlKjO/C8RLPH3D+MaRJElqLGOZmZLUpN7yu3/CoeeP1TvGmMxd+fV6Rziri141gx9++r31jiFpnFmmJL2kQ88f48d3L6p3jPNGIxQ+Sa+c380nSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUwDIlSZJUoKXkxRHxPuDzwHTgC5l597ikkjQlvGbeSq65f2W9Y5w3XjMPYFG9Y0gaZ+dcpiJiOvD7wK8DfcD3I2J7Zj4xXuEk1ddz++/mx3f7P//xMnfl1+sdQdIEKLnM9w7gqcz8UWYeBb4C3DQ+sSRJkhpDSZm6HPiLUdt91dgpIuL2iOiNiN6BgYGC00mSJE09JWUqzjCWLxrI3JSZnZnZOXv27ILTSZIkTT0lZaoPeP2o7SuA/rI4kiRJjaWkTH0feGNEXBURFwAfBLaPTyxJkqTGcM6f5svM4Yj4TeCb1G6N8MXM3DduySRJkhpA0X2mMvMbwDfGKYskSVLD8Q7okiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBSxTkiRJBVrqHUDS1DZ35dfrHeG8cdGrZtQ7gqQJYJmS9JJ+fPeiekcYk7krv94wWSWdfyxTkiZVREzMcT87/sfMzPE/qKTzjmVK0qSyoEg637gAXZIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqYBlSpIkqUBM5vdkRcQA8JNJO6GkZnEp8Ff1DiHpvHNlZs4+206TWqYkaSJERG9mdtY7h6Tm5GU+SZKkApYpSZKkApYpSeeDTfUOIKl5uWZKkiSpgDNTkiRJBSxTkiRJBSxT0nksIjIi/uOo7ZaIGIiIr1Xbr4uIr0XEDyPiiYj4RjU+JyIeHOcs11V5lo4aW1CN3XWOx1s4avt3IuJnEfFY9V5uHa/sY8hyc0S8edT2f4iIp6vf659HxJci4vIxHOdbEeEtHqQGY5mSzm9DwPyIeFW1/evAz0Y9/xlgR2a+JTPfDKwEyMz+zPzABOTZA9wyavuDwA/P8VjXAQtPG7s3M98K3ATcFxEzzvHYr9TNwJtPG/tUZr4F+BXgUaAnIi6YpDySJpFlSjr//TdgUfX4VmDLqOcuA/pGNjLzcYCImBsRe6vHH4mIhyLijyPiyYj43Mj+EfG+iPhBNQOzsxprj4gvRsT3I+LRiLhp1Pl+CrRVM2IBvK/KN3K8t0bEroh4PCL+S0TMqsY/Vs02PR4RX4mIucBy4BPVTNTfGf2GM/NJ4JfAyOvfUOXfHRHfiYg3VeOvq87zw+rPwmr8H0fE/6yOfV9ETK/G/29ErKn23VW9fiGwGLin2v8Np2XJzLwX+Dnw/uo4742I71W/u60R8erT/9Ii4jeqWa1vR8QfRMS/PePfrqS6s0xJ57+vAB+MiDbgWuCRUc/9PrA5InoiYnVEzHmJY7yV2ozSNcAtEfH6iJgN/AHwj6oZmCXVvquBhzPzV4HrqZWM9lHHerDadyHwA+CFUc99CejOzGupzWJ9uhpfCSyoxpdn5o+BjVQzUZn5ndFhI+JtwJOZ+Ww1tAnoysy3A3cB/64a/zfAt6v8bwP2RcS86r2+q5rlOg58qNq/HdhV7f+nwLLM/DNgO7WZqLdm5v95id/hD4A3RcSlwD8HbszMtwG9wJ2n5b8M+F3gXdRmE0+f9ZI0hbTUO4CkiZWZj1czObcC3zjtuW9GxF+nNkP0fuDRiJh/hsPszMxDABHxBHAltVmfP83Mp6tjDVb7vhdYPGodVBvw10Yd66vAA8CbqM2SjcwGXQR0ZOa3q/3uB7ZWjx8HvhwRfwT80cu83U9ExDJg5D1RzfosBLbWJsMAaK1+vgf4J1X+48ChiPgw8Hbg+9X+rwJGStlR4GvV493Uis5YjZz8ndTK0f+ojn8B8L3T9v1bwLcyc6B6Dw8Af/MVnEvSJLJMSc1hO7CO2jqjS0Y/UZWgPwT+sFqY/mvUisJoo2ePjlP7b0cAZ7pRXVCbrfrfpwxGvK46388j4hi1IvJxXrzu6UwWVbkWA/8iIq5+if3uzcx1EfEPgS9Vl9ymAQerWaaxCOD+zPztMzx3LP//zflGfg9jtQDYWR1/R2aebYG8NwGUGoSX+aTm8EXgM5m5Z/RgRLwnIi6sHr8GeAO1dU1j8T3g70bEVdXrL67Gvwl0VWuiiIgFZ3jtv6R2Oe/4yEA183Vg1PqnDwPfjohpwOszswf4LaADeDXwHPCaMwXLzIeoXT67LTMPA09HxJIqT0TEW6pddwIrqvHpETGzGvtARLx25H1FxJVn+V28ZJbqfB+jtj7tj4FdwLsi4m9Uz18YEafPOj0CXBcRl0RtEf0SJE1ZlimpCWRmX2Z+/gxPvR3ojYjHqZWjL2Tm98d4zAHgduChiPghtUt3AL8HzAAej9oi9t87w2v/LDPPdLnuNmprrB6ntk7rM8B04D9FxB5qn4q7NzMPAv8V+AdnWoBe+QxwZ1XGPgQsrXLuo/ZpP6jNjF1fHXs3cHVmPkFtTdOfVDl2UCtCL+crwKeqBfcjC9Dvqc7358CvAtdn5tHq9/YRYEt1/F3ULnmO/v08A/wOtb+T/05tvZWkKcqvk5GkKS4iPgJ0ZuZv1juLpBdzZkqSJKmAM1OSJEkFnJmSJEkqYJmSJEkqYJmSJEkqYJmSJEkqYJmSJEkq8P8AfUMYdHDzb2MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAEyCAYAAADJI8VDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAE9ZJREFUeJzt3X2wpnV93/HP131gkRBQWQFBXDKhumSjYldj1DpscDIxJGE71URM223cKbVjFkzSiZtuM2hntoPTTJSAtd26tJvWWVaJBRppROkyutMJuiiRlaWFyoMrT5tpIJYCi/jrH+di5wTOssu5z2/v8/B6zeyc+77O776u79x/nH3Pdd0P1VoLAAAz6yXjHgAAYD4SWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADpYPO4BkuSkk05qK1asGPcYAACHdeutt/5la2354dbNishasWJFdu/ePe4xAAAOq6ruO5J1LhcCAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0MFhI6uqrqqqR6pqz6RtL6+qL1fVXcPPlw3bq6r+qKrurqpvV9Wbeg4PMJVly5alqg7+W7Zs2bhHAhagIzmT9R+T/MJztm1MclNr7awkNw33k+TdSc4a/l2U5NMzMybAkVm2bFmeeuqpnHzyydm7d29OPvnkPPXUU0ILOOoOG1mtta8m+T/P2XxBkm3D7W1J1k7a/sdtwp8nObGqTp2pYQEO59nAeuihh/K6170uDz300MHQAjiapvuarJNbaw8myfDzlcP205J8b9K6fcO256mqi6pqd1Xt3r9//zTHAHi+m2+++QXvAxwNM/3C95piW5tqYWttS2ttdWtt9fLlh/2ORYAjdu65577gfYCjYbqR9fCzlwGHn48M2/clefWkdacneWD64wG8OMccc0wefvjhnHLKKbnzzjtzyimn5OGHH84xxxwz7tGABWa6kXV9knXD7XVJrpu0/R8O7zJ8a5LHnr2sCHA0PPnkkwdDa+XKlQcD68knnxz3aMACs/hwC6pqe5Jzk5xUVfuSXJrksiSfq6r1Se5P8t5h+Q1JfjHJ3Un+X5Lf6DAzwAsSVMBscNjIaq1deIhfnTfF2pbkQ6MOBQAw1/nEdwCADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBcw727dvz6pVq7Jo0aKsWrUq27dvH/dIwAK0eNwDAMyk7du3Z9OmTdm6dWve8Y53ZNeuXVm/fn2S5MILLxzzdMBCUq21cc+Q1atXt927d497DGAeWLVqVdauXZtrr702e/fuzcqVKw/e37Nnz7jHA+aBqrq1tbb6cOucyQLmlTvuuCOPP/54rrrqqoNnsj7wgQ/kvvvuG/dowALjNVnAvLJ06dJs2LAha9asyZIlS7JmzZps2LAhS5cuHfdowAIjsoB55cCBA7nyyiuzc+fOPP3009m5c2euvPLKHDhwYNyjAQuMy4XAvHL22Wdn7dq12bBhw8HXZL3//e/PtddeO+7RgAVGZAHzyqZNm3LJJZfkuOOOS5I8/vjj2bJlSy6//PIxTwYsNC4XAvPWbHj3NLBwiSxgXtm8eXN27NiRe+65Jz/60Y9yzz33ZMeOHdm8efO4RwMWGJ+TBcwrixYtypNPPpklS5Yc3Pb0009n2bJleeaZZ8Y4GTBfHOnnZDmTBcwrK1euzK5du/7Gtl27dmXlypVjmghYqEQWMK9s2rQp69ev/xsf4bB+/fps2rRp3KMBC4x3FwLzyrPfTzj5Ixw2b97sewuBo86ZLACADpzJAuaV7du3Z9OmTdm6devB7y5cv359kjibBRxV3l0IzCurVq3KFVdckTVr1hzctnPnzmzYsCF79uwZ42TAfHGk7y4UWcC84iMcgN58hAOwIPkIB2C2EFnAvOIjHIDZwuVCYFaoqnGPcMRmw99NYHyOyuXCqvqtqvpOVe2pqu1VtayqzqyqW6rqrqraUVVLRzkGsDC01mb832s+8qdd9gtwJKYdWVV1WpKLk6xura1KsijJ+5J8PMknWmtnJfmrJOtnYlAAgLlk1NdkLU5ybFUtTvLSJA8m+bkk1wy/35Zk7YjHAACYc6YdWa217yf5gyT3ZyKuHktya5JHW2s/HJbtS3LaqEMCAMw1o1wufFmSC5KcmeRVSY5L8u4plk75AoaquqiqdlfV7v379093DACAWWmUy4XvSnJPa21/a+3pJF9I8rYkJw6XD5Pk9CQPTPXg1tqW1trq1trq5cuXjzAGAMDsM0pk3Z/krVX10pp47/V5Se5IsjPJe4Y165JcN9qIAABzzyivybolEy9w/2aS24d9bUnykSS/XVV3J3lFkq0zMCcAwJyy+PBLDq21dmmSS5+z+btJ3jLKfgEA5jpfqwMA0IHIAgDoQGQBAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0IHIAgDoQGQBAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0IHIAgDoQGQBAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0IHIAgDoQGQBAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0IHIAgDoQGQBAHQgsgAAOhBZAAAdiCwAgA5EFgBAByILAKADkQUA0MFIkVVVJ1bVNVV1Z1XtraqfraqXV9WXq+qu4efLZmpYAIC5YtQzWZcn+bPW2uuSvCHJ3iQbk9zUWjsryU3DfQCABWXakVVVP57knUm2Jklr7UBr7dEkFyTZNizblmTtqEMCAMw1o5zJ+okk+5P8h6r6VlV9pqqOS3Jya+3BJBl+vnKqB1fVRVW1u6p279+/f4QxAABmn1Eia3GSNyX5dGvtnCSP50VcGmytbWmtrW6trV6+fPkIYwAAzD6jRNa+JPtaa7cM96/JRHQ9XFWnJsnw85HRRgQAmHumHVmttYeSfK+qXjtsOi/JHUmuT7Ju2LYuyXUjTQgAMActHvHxG5J8tqqWJvlukt/IRLh9rqrWJ7k/yXtHPAYAwJwzUmS11m5LsnqKX503yn4BAOY6n/gOANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHSwe9wDA3POGj92Yx554etxjHJEVG7847hEO64Rjl+QvLv35cY8BzDCRBbxojz3xdO697PxxjzFvzIUQBF48lwsBADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoYObKqalFVfauq/nS4f2ZV3VJVd1XVjqpaOvqYAABzy0ycybokyd5J9z+e5BOttbOS/FWS9TNwDACAOWWkyKqq05Ocn+Qzw/1K8nNJrhmWbEuydpRjAADMRaOeyfpkkt9N8qPh/iuSPNpa++Fwf1+S00Y8BgDAnDPtyKqqX0rySGvt1smbp1jaDvH4i6pqd1Xt3r9//3THAACYlUY5k/X2JL9SVfcmuToTlwk/meTEqlo8rDk9yQNTPbi1tqW1trq1tnr58uUjjAEAMPtMO7Jaa7/XWju9tbYiyfuS/PfW2q8n2ZnkPcOydUmuG3lKAIA5psfnZH0kyW9X1d2ZeI3W1g7HAACY1RYffsnhtdZuTnLzcPu7Sd4yE/sFAJirfOI7AEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhgRr4gGlhYjl+5MT+9beO4x5g3jl+ZJOePewxghoks4EX7wd7Lcu9lomCmrNj4xXGPAHTgciEAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6EBkAQB0ILIAADoQWQAAHYgsAIAORBYAQAeLxz0AMDet2PjFcY8wb5xw7JJxjwB0ILKAF+3ey84f9whHZMXGL86ZWYH5Z9qXC6vq1VW1s6r2VtV3quqSYfvLq+rLVXXX8PNlMzcuAMDcMMprsn6Y5HdaayuTvDXJh6rq7CQbk9zUWjsryU3DfQCABWXakdVae7C19s3h9g+S7E1yWpILkmwblm1LsnbUIQEA5poZeXdhVa1Ick6SW5Kc3Fp7MJkIsSSvPMRjLqqq3VW1e//+/TMxBgDArDFyZFXVjyX5kyQfbq399ZE+rrW2pbW2urW2evny5aOOAQAwq4wUWVW1JBOB9dnW2heGzQ9X1anD709N8shoIwIAzD2jvLuwkmxNsre19oeTfnV9knXD7XVJrpv+eAAAc9Mon5P19iT/IMntVXXbsO2fJ7ksyeeqan2S+5O8d7QRAQDmnmlHVmttV5I6xK/Pm+5+AQDmA99dCADQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB2ILACADkQWAEAHIgsAoIMukVVVv1BV/7Oq7q6qjT2OAQAwm814ZFXVoiSfSvLuJGcnubCqzp7p4wAAzGY9zmS9JcndrbXvttYOJLk6yQUdjgMAMGst7rDP05J8b9L9fUl+5rmLquqiJBclyRlnnNFhDGAuqao++/34zO+ztTbzOwXmnR6RNdVfyuf9RWqtbUmyJUlWr17tLxYscMIFmG96XC7cl+TVk+6fnuSBDscBAJi1ekTWN5KcVVVnVtXSJO9Lcn2H4wAAzFozfrmwtfbDqvrNJF9KsijJVa2178z0cQAAZrMer8lKa+2GJDf02DcAwFzgE98BADoQWQAAHYgsAIAORBYAQAciCwCgA5EFANCByAIA6KBmw/eFVdX+JPeNew5g3jkpyV+Oewhg3nlNa2354RbNisgC6KGqdrfWVo97DmBhcrkQAKADkQUA0IHIAuazLeMeAFi4vCYLAKADZ7IAADoQWQAAHYgs4Hmq6pmquq2q9lTV56vqpWOa48OTj11V91bVSdPYz7lV9bYjWPePqurK4fZHq+qfVdWnhufijqp6Yrh9W1W958XOMek4+6rqxOk+HpgbRBYwlSdaa29sra1KciDJB4/0gVW1aAbn+HCSmQi8c5McNrKm0lr7UGvtjUl+Mcn/Hp6XN7bWrpm8rqoWjz4mMJ+ILOBwvpbkJ5Okqv5+VX19OJPz754Nqqr6v1X1L6vqliQ/W1Vvrqr/UVV/Maw/vqoWVdW/rqpvVNW3q+qfDI89t6purqprqurOqvpsTbg4yauS7KyqnZMHqqoVVbW3qv59VX2nqm6sqmOH3108nHX6dlVdXVUrMhGJvzXM/Xeq6per6paq+lZVfaWqTp7OE1NVu6pqc1V9NclvVtUFk/Z7Y1W9cli3vKq+XFXfrKpPJ6lJ+1g36Tn9N1X1kqpaXFX/qapuH84mXjyd+YDxElnAIQ1nZ96d5PaqWpnk15K8fTiz80ySXx+WHpdkT2vtZ5J8PcmOJJe01t6Q5F1JnkiyPsljrbU3J3lzkn9cVWcOjz8nE2etzk7yE8Mx/ijJA0nWtNbWTDHeWUk+1Vr7qSSPJvl7w/aNSc5prb0+yQdba/cm+bdJPjGcgfpakl1J3tpaOyfJ1Ul+d4Sn6cdba+9srX0yyVcn7fcLSX5nWPOxJDtba29K8meZiMdU1aokfzfJ24bndHGS9yX520lOaq399HA28Y9HmA8YE6e3gakcW1W3Dbe/lmRrkosy8Z//N6oqSY5N8siw5pkkfzLcfm2SB1tr30iS1tpfJ0lV/XyS1096LdMJmQilA0m+3lrbN6y7LcmKTITQC7mntfbsjLcOj0mSbyf5bFVdm+TaQzz29CQ7qurUJEuT3HOYY72QqyfdPiPJ56rqlCTHJPlfw/Z3ZuJyY1pr11XVD4bt78pEcO6e9Jx+L8mXkry2qi5PckOSG0eYDxgTkQVM5YnhzMpBNVEB21prvzfF+idba888uzTJVB/AV0k2tNa+9Jz9npvkqUmbnsmR/W167mOOHW6fn4mo+ZUkv19VPzXFY69I8oetteuH43/0CI53KI9Puv2pJP+qtXZDVb0rE2fVnnWo5+Sq1trvP+8XVa/PxFnEizNxlu6iEWYExsDlQuBI3ZTkPZNeZ/TyqnrNFOvuTPKqqnrzsO744bLjl5L806paMmz/W1V13GGO+YMkxx/pgFX1kiSvbq3tzMQlwBOT/NgU+zkhyfeH2+uOdP9H4IQk3x+CdPJ+v5rh0mpV/fKkWb6S5FeffcdkVb2iqs6oquWZ+LDozye5NMmbZnBG4ChxJgs4Iq21O6rqXyS5cYiZp5N8KMl9z1l3oKp+LckVw4vRn8jEZbHPZOKS3jeHCNmfZO1hDrslyX+rqgcP8bqs51qU5D9X1QmZOEv0idbao1X1X5NcU1UXJNmQiTNXn6+q7yf58yRnHmqHL9JHk/yXJPsy8dq0U4ftlybZXlW/mmRnhsBrrd1eVR9L8pVJz+kHM3FmbuvwPLUkH5mh+YCjyNfqAAB04HIhAEAHIgsAoAORBQDQgcgCAOhAZAEAdCCyAAA6EFkAAB38f19q72RpbIbWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAEyCAYAAADTHyXNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAF+dJREFUeJzt3X+U5lddH/D3J7NrFhAhnl01hxJTLdrR3SXFbUTc1qwRSwSz0tY2ORSpHYk56lYETxMzp4Xabg6iEtv4Yxs6KPToCFQ4uw0/AsZN4iKIGwu7wa3ijyBCDtkcEDEQ3Z29/WO+E2c3+2M2czPPPjuv1zlznud7n/vc7+d5SIZ37r3z/VZrLQAALM8Foy4AAOB8IFQBAHQgVAEAdCBUAQB0IFQBAHQgVAEAdCBUAQB0IFQBAHQgVAEAdLBmFCddv359u/TSS0dxagCAs3Lvvfc+1FrbcKZ+IwlVl156afbv3z+KUwMAnJWq+vhS+ln+AwDoQKgCAOhAqAIA6ECoAgDoQKgCAOhAqAIA6ECoAgDoYMmhqqqeWVV7q+pQVX20qn50aP/yqnpfVX1seLzoiSsX4LFmZ2ezcePGTExMZOPGjZmdnR11ScAqdDYzVUeTvKq1NpnkuUl+uKq+IcmNSe5srT0ryZ3DMcCKmJ2dzfT0dG699dY88sgjufXWWzM9PS1YAStuyaGqtfZAa+33h+efT3IoyTOSbE/ypqHbm5J8T+8iAU5l586dmZmZybZt27J27dps27YtMzMz2blz56hLA1aZaq2d/ZuqLk1yT5KNSf68tfb0Ra99trX2mCXAqrouyXVJcskll3zTxz++pCu+A5zWxMREHnnkkaxdu/bRtiNHjmTdunWZm5sbYWXA+aKq7m2tbTlTv7PeqF5VX5rkN5K8orX2V0t9X2vtttbaltbalg0bznhPQoAlmZyczL59+45r27dvXyYnJ0dUEbBanVWoqqq1mQ9Uv9pae/vQ/Omqunh4/eIkD/YtEeDUpqenMzU1lb179+bIkSPZu3dvpqamMj09PerSgFVmzVI7VlUlmUlyqLX2+kUv7UnysiSvHR53d60Q4DSuvfbaJMmOHTty6NChTE5OZufOnY+2A6yUJe+pqqqtSX47ycEkx4bmm5L8bpK3JrkkyZ8n+d7W2mdON9aWLVva/v37H2/NAAArZql7qpY8U9Va25ekTvHylUsdBwDgfOSK6gAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVcDYm52dzcaNGzMxMZGNGzdmdnZ21CUBq9CSb1MDcC6anZ3N9PR0ZmZmsnXr1uzbty9TU1NJ4qbKwIpa8g2Ve3JDZaCXjRs35tZbb822bdsebdu7d2927NiR++67b4SVAeeLpd5QWagCxtrExEQeeeSRrF279tG2I0eOZN26dZmbmxthZcD5Yqmhyp4qYKxNTk5m3759x7Xt27cvk5OTI6oIWK2EKmCsTU9PZ2pqKnv37s2RI0eyd+/eTE1NZXp6etSlAauMjerAWFvYjL5jx44cOnQok5OT2blzp03qwIqzpwoA4DTsqQIAWEFCFTD2XPwTOBfYUwWMNRf/BM4V9lQBY83FP4Enmot/AquCi38CTzQb1YFVwcU/gXOFUAWMNRf/BM4VNqoDY83FP4FzhT1VAACnYU8VAMAKEqqAsbdjx46sW7cuVZV169Zlx44doy4JWIWEKmCs7dixI7t27crNN9+chx9+ODfffHN27dolWAErzp4qYKytW7cuN998c175ylc+2vb6178+N910Ux555JERVgacL1z8E1gVqioPP/xwnvzkJz/a9oUvfCFPecpTMorfb8D5x0Z1YFW48MILs2vXruPadu3alQsvvHBEFQGrletUAWPt5S9/eW644YYkyfXXX59du3blhhtuyPXXXz/iyoDVRqgCxtqtt96aJLnpppvyqle9KhdeeGGuv/76R9sBVoo9VQAAp9F9T1VVvbGqHqyq+xa1vaaqPllVHx5+vuvxFgzweM3Ozmbjxo2ZmJjIxo0bMzs7O+qSgFXobJb/fiXJzyd58wntt7TWfqZbRQBnYXZ2NtPT05mZmcnWrVuzb9++TE1NJYn7/wEraskzVa21e5J85gmsBeCs7dy5MzMzM9m2bVvWrl2bbdu2ZWZmJjt37hx1acAq0+OSCj9SVQeG5cGLTtWpqq6rqv1Vtf/w4cMdTguQHDp0KFu3bj2ubevWrTl06NCIKgJWq+WGql9K8rVJLkvyQJKfPVXH1tptrbUtrbUtGzZsWOZpAeZNTk5m3759x7Xt27cvk5OTI6oIWK2WFapaa59urc211o4leUOSy/uUBbA009PTmZqayt69e3PkyJHs3bs3U1NTmZ6eHnVpwCqzrOtUVdXFrbUHhsMXJ7nvdP0BelvYjL5jx44cOnQok5OT2blzp03qwIpb8nWqqmo2yRVJ1if5dJJXD8eXJWlJ7k/yg4tC1im5ThUAMC6Wep2qJc9UtdZO9p99M2dVFQDAecoNlYGxt3nz5lTVoz+bN28edUnAKiRUAWNt8+bNOXjwYK6++uocPnw4V199dQ4ePChYAStOqALG2kKg2r17d9avX5/du3c/GqwAVpJQBYy9mZmZ0x4DrAShChh7C/f6O9UxwEoQqoCxtmnTpuzZsyfbt2/PQw89lO3bt2fPnj3ZtGnTqEsDVpllXfwTYNQOHDiQzZs3Z8+ePVm4BdamTZty4MCBEVcGrDZCFTD2BCjgXGD5DwCgAzNVwNirqse0LfUWXAC9mKkCxtpCoJqYmMhdd92ViYmJ49oBVoqZKmDsTUxM5OjRo0mSo0ePZs2aNZmbmxtxVcBqY6YKGHt33nnnaY8BVoJQBYy9K6+88rTHACtBqALG3tzcXNasWZO7777b0h8wMvZUAWOttZaqytzcXK644orj2gFWklAFjD0BCjgXWP4DAOhAqALG3ubNm1NVj/5s3rx51CUBq5BQBYy1zZs35+DBg7n66qtz+PDhXH311Tl48KBgBaw4oQoYawuBavfu3Vm/fn127979aLACWElCFTD2ZmZmTnsMsBKEKmDsTU1NnfYYYCUIVcBY27RpU/bs2ZPt27fnoYceyvbt27Nnz55s2rRp1KUBq4zrVAFj7cCBA9m8eXP27NmTDRs2JJkPWgcOHBhxZcBqI1QBY0+AAs4Flv8AADowUwWMvap6TJtb1wArzUwVMNYWAtXExETuuuuuTExMHNcOsFLMVAFjb2JiIkePHk2SHD16NGvWrMnc3NyIqwJWGzNVwNi78847T3sMsBKEKmDsXXnllac9BlgJQhUw9ubm5rJmzZrcfffdlv6AkbGnChhrrbVUVebm5nLFFVcc1w6wkoQqYOwJUMC5YMnLf1X1xqp6sKruW9T25VX1vqr62PB40RNTJgDAue1s9lT9SpIXnNB2Y5I7W2vPSnLncAywoqrqMT8AK23Joaq1dk+Sz5zQvD3Jm4bnb0ryPZ3qAliShQBVVXnPe95z3DHASlrunqqvbK09kCSttQeq6is61ARwVqoqx44dS5IcO3YsF1xwgX1WwIpbsUsqVNV1VbW/qvYfPnx4pU4LrALvfve7T3sMsBKWG6o+XVUXJ8nw+OCpOrbWbmutbWmtbdmwYcMyTwvwd6666qrTHgOshOWGqj1JXjY8f1mS3cscD+CstdZywQUX5I477rD0B4zM2VxSYTbJB5J8fVX9RVVNJXltkudX1ceSPH84BlgxCwGqtZYXvOAFxx0DrKQlb1RvrV17ipfcZAsYKQEKOBe49x8AQAdCFQBAB0IVAEAHbqgMjMQ4XfHcni1gKcxUASPRWuv+89U33P6EjAuwFEIVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB0IVAEAHQhUAQAdCFQBAB2t6DFJV9yf5fJK5JEdba1t6jAsAMC66hKrBttbaQx3HAwAYG5b/AAA66BWqWpL3VtW9VXVdpzEBAMZGr+W/b22tfaqqviLJ+6rq/7XW7lncYQhb1yXJJZdc0um0wEp49n9+bz73xSOjLmNJLr3xnaMu4Yye9qS1+cirv3PUZQCddQlVrbVPDY8PVtU7klye5J4T+tyW5LYk2bJlS+txXmBlfO6LR3L/a1846jLOG+MQ/ICzt+zlv6p6SlU9deF5ku9Mct9yxwUAGCc9Zqq+Msk7qmphvF9rrb2nw7gAAGNj2aGqtfanSZ7doRYAgLHlkgoAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdLPuGysD576mTN2bTm24cdRnnjadOJskLR10G0JlQBZzR5w+9Nve/Vgjo5dIb3znqEoAngOU/AIAOhCoAgA6EKgCADoQqAIAOhCoAgA6EKgCADoQqAIAOXKcKWBLXVurnaU9aO+oSgCeAUAWc0bhc+PPSG985NrUC5x/LfwAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHQhVAAAdCFUAAB0IVQAAHXQJVVX1gqr6w6r646q6sceYAADjZNmhqqomkvxCkquSfEOSa6vqG5Y7LgDAOOkxU3V5kj9urf1pa+1vk/x6ku0dxgUAGBtrOozxjCSfWHT8F0m++cROVXVdkuuS5JJLLulwWmCcVdUTM+5P9R+ztdZ/UOC80yNUnew342N+A7XWbktyW5Js2bLFbyhY5QQV4HzTY/nvL5I8c9Hx30vyqQ7jAgCMjR6h6veSPKuq/n5VfUmSa5Ls6TAuAMDYWPbyX2vtaFX9SJI7kkwkeWNr7aPLrgwAYIz02FOV1tq7kryrx1gAAOPIFdUBADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOlhWqKqq11TVJ6vqw8PPd/UqDABgnKzpMMYtrbWf6TAOAMDYsvwHANBBj1D1I1V1oKreWFUXnapTVV1XVfurav/hw4c7nBYA4NxRrbXTd6j6zSRfdZKXppN8MMlDSVqS/5Lk4tbavzvTSbds2dL2799/9tUCAKywqrq3tbblTP3OuKeqtfYdSzzhG5LcvpS+AADnm+X+9d/Fiw5fnOS+5ZUDADCelvvXf6+rqssyv/x3f5IfXHZFAABjaFmhqrX20l6FAACMM5dUAADoQKgCAOhAqAIA6ECoAgDoQKgCAOhAqAIA6ECoAgDoQKgCAOhAqAIA6ECoAgDoQKgCAOhAqAIA6ECoAgDoQKgCAOhAqAIA6ECoAgDoQKgCAOhgzagLAFiuqnpMW2ttBJUAq5mZKmCsLQ5Ut99++0nbAVaCmSrgvLAwM9VaE6iAkTBTBYy9xTNUJzsGWAk1in0HW7Zsafv371/x8wLnn4VZqcW/y07WBvB4VdW9rbUtZ+pnpgo4L1RV3vnOd1r6A0ZGqALG2uLZqBe96EUnbQdYCTaqA2NPgALOBWaqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoQqgAAOhCqAAA6EKoAADoYyb3/qupwko+v+ImB8936JA+NugjgvPPVrbUNZ+o0klAF8ESoqv1LuekpwBPB8h8AQAdCFQBAB0IVcD65bdQFAKuXPVUAAB2YqQIA6ECoAgDoQKiCc1RVtar62UXHP15Vr+k09muq6pNV9eGq+oOqurbHuCec41eq6l+eoc+7qurpj2PsfzbU/uGq+uuq+sPh+ZuXUe9/rapXPI73HayqjcPztVX1xaq6ZtHrH6mqzVX1A1V1eKjzo1X11qp60hnG/oGq+rmz/zTAKAhVcO76myT/vKrWP0Hj39JauyzJ9iT/o6rWPkHnOaXW2ne11v7ycbzvjtbaZUP9+5O8ZDj+vsX9qmpNr1pP43eSPG94/pwkhxaOq+qpSZ6Z5L7h9V8d6vzG4fi0oRMYL0IVnLuOZv6v2X7sxBdOnAWqqr8eHq+oqruHWZA/qqrXVtVLqupDw4zK1544VmvtY0m+kOSiYYyvrar3VNW9VfXbVfUPq+ppVXV/VV0w9HlyVX1imJm5rKo+WFUHquodVXXRCbVeVVVvXXR8RVX9n+H5/VW1vqourapDVfWGYRbnvQuzOFX1j4exP1BVP11V9+U0htmdX6+q25O8u6q+rKp+q6p+fxjnRYv6/qdhlut9SZ61qP1ZVXXH8B3cU1VfN7RfU1X3DbNPe4fu78/fharnJfnFzIerJHlukt9rrR07ocY1SZ6c5LPD8faq+t2q+r/DZ/+Kk3yuk/YZZthmhv/d/7SqfnjRe75/+MwfqapfHtq+sqreXlX7h38unnu67xNYOqEKzm2/kOQlVfW0s3jPs5P8aJJNSV6a5Otaa5cn+Z9JdpzYuaqek+RjrbUHh6bbkuxorX1Tkh9P8outtc8l+UiSbxv6fHeSO1prR5K8OckNrbXNSQ4mefUJp3hfkudW1VOG43+d5C0nqftZSX5hmMX5yyT/Ymj/5STXt9a+JcncEr+Db0ny0tba85N8Mcn21tpzknxHkluGz335cI7LMj9jdPmi99+W5IeG7+Ankvz80P7qJFe21p6d5MVD2+KZqucluSvJsap68nD8/kXjvqSqPpzkU0mekuRdQ/s9SZ7bWvtHSd6e5FUn+Uyn6/N1SZ6f+RD3k1U1UVXPTnJDkiuGehf6//ckrxuuPP+vMv/PBdDBSkyNA49Ta+2vhn1C/z7z4WApfq+19kCSVNWfJHnv0H4wybZF/X6sql6e5GuSvGDo/6WZDwJvq6qFfhcOj2/JfCDam+SaJL84hL2nt9buHvq8KcnbTvgMR6vqPUm+u6r+d5IXJvkPJ6n7z1prHx6e35vk0prfb/XU1trvDO2/luRFJ3nvid7bWvvs8LyS/FRVbU1yLMkzhyXVf5rkN1prX0zyxUWzZ0/PfDj5jUXfwcLvyvcneXNVvS3zwSattT8eZsPWJ/kHw/H+zIe05yX56UV1/Wpr7RXDjN8vJXllkp9JckmSt1bVV2X++/6jk3ym0/W5vbX2t0kerKrPJNmQ5NuTvKW19pmhzs8Mfb8jydcv+mwXVdWThu8BWAYzVXDu+7kkU5mf2VhwNMO/vzX//45fsui1v1n0/Nii42M5/j+kbmmtfX3mg9Kbq2rdMOZfLuxXGn4mh/57klxVVV+e5JuS/NZZfIa3ZH5W5NszH/o+f5I+i+ueG2qtk/RbiocXPf++JE9L8pxhD9ZDSdYNr53sQn2V5KETvoONw2svz/xs1aVJPrJoqfMDSa5N8onh+INJtibZkuR3TzzBsBx4e+aDXTI/I3lLa21Tkh9aVN9ip+tzqu/uVJ/v8kWf7RkCFfQhVME5bphheGvmg9WC+zMfbJL5jeaPe5N5a+3tmd/s/bLW2l8l+bOq+t5kPrANy0hprf11kg8l+W+ZnxmZG5YFP1tV/2QY7qVJ7n7MSeaXxJ6T+VBysqW/U9X22SSfX7Tv55rT9T+FpyV5cJgxe36SZwzt92T+DwHWVdWXZZgBG875QFW9OEmq6oKF7yDJ17TWPpjkP2Z+P9TCWO9P8orMh6sMj9+f5M9PESCT+dD1J4tq/OQQkF92ms9xpj6L/WaSa4YQnIXHoX3xvqvLljAWsARCFYyHn02y+K8A35Dk26rqQ0m+OcfPzDweP5nklcOy1EuSTFXVR5J8NPOhbcFbkvybHB+MXpbkp6vqQOb3J/3kiYO31uYyPzNz1fB4NqaS3FZVH8j8LMvnzvL9/yvJ84Ylue9N8rGhpg8leUfm94q9LfMha8E1Sa5f9B0sLDneUlUHM7+U+puttYVN8+/P/DLqB4axP5H5JbrfyfFeUvOXVDiY5BuT7BzaXzPUcneST5/icyylz6NaaweSvC7JPcM+roVlyB9O8q3DBvY/yHzQBTpwmxrgnFZVXzrMkqWqbkxycWvtR0dcFsBj2KgOnOteWFU/kfnfVx9P8m9HWw7AyZmpAgDowJ4qAIAOhCoAgA6EKgCADoQqAIAOhCoAgA7+P/MVAFaQwNkrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp = PdfPages('continuous_boxplots_1-1.pdf')\n",
    "\n",
    "for col in continuous_columns:\n",
    "    f = df_clean[col].plot(kind='box', figsize=(10,5))\n",
    "    pp.savefig(f.get_figure())\n",
    "    plt.show()\n",
    "\n",
    "pp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For categorical features we plot bar charts and save to a pdf file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp = PdfPages('categorical_barcharts_1-1.pdf')\n",
    "\n",
    "for column in categorical_columns:\n",
    "    f = df_clean[column].value_counts().plot(kind='bar', title=column, figsize=(12,10))\n",
    "    pp.savefig(f.get_figure())\n",
    "    plt.show()\n",
    "\n",
    "pp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial findings discussion\n",
    "The discussion is provided in the pdf file **dqreport.pdf**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Quality Plan\n",
    "### Data quality issues\n",
    "\n",
    "An initial overview of the reported issues is contained in the **dqreport.pdf**:\n",
    "\n",
    "- **MSinceOldestTradeOpen**, **MSinceMostRecentDelq**, **MSinceMostRecentInqexcl7days**, **NetFractionRevolvingBurden**, **NetFractionInstallBurden**, **NumRevolvingTradesWBalance**, **NumInstallTradesWBalance**, **NumBank2NatlTradesWHighUtilization** have negative minimum values\n",
    "- **NumTrades60Ever2DerogPubRec** and **NumTrades90Ever2DerogPubRec** have zeros in more than 75% observations\n",
    "- **NumTradesOpeninLast12M** has values below 1 in 50% of observations\n",
    "- **NumInqLast6M** and **NumInqLast6Mexcl7days** have 25% of zeros and seem to be identical features.\n",
    "- the outliers in the box plots should be further investigated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further standard checks\n",
    "#### Irregular cardinality \n",
    "Since categorical features could be affected by same values spelled differently or inconsistencies, we check for irregular cardinality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values for:\n",
      "- RiskPerformance: ['Good' 'Bad']\n",
      "Unique values for:\n",
      "- MaxDelq2PublicRecLast12M: [6 4 7 0 5 3 2 1]\n",
      "Unique values for:\n",
      "- MaxDelqEver: [6 8 5 3 4 7 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique values for:\\n- RiskPerformance:\", pd.unique(df_clean.RiskPerformance.ravel()))\n",
    "print(\"Unique values for:\\n- MaxDelq2PublicRecLast12M:\", pd.unique(df_clean.MaxDelq2PublicRecLast12M.ravel()))\n",
    "print(\"Unique values for:\\n- MaxDelqEver:\", pd.unique(df_clean.MaxDelqEver.ravel()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null values\n",
    "No inconsistencies nor wrong spelled values are found in categorical features.\n",
    "We now check for the presence of 'null' values in each feature column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RiskPerformance                       0\n",
       "ExternalRiskEstimate                  0\n",
       "MSinceOldestTradeOpen                 0\n",
       "MSinceMostRecentTradeOpen             0\n",
       "AverageMInFile                        0\n",
       "NumSatisfactoryTrades                 0\n",
       "NumTrades60Ever2DerogPubRec           0\n",
       "NumTrades90Ever2DerogPubRec           0\n",
       "PercentTradesNeverDelq                0\n",
       "MSinceMostRecentDelq                  0\n",
       "MaxDelq2PublicRecLast12M              0\n",
       "MaxDelqEver                           0\n",
       "NumTotalTrades                        0\n",
       "NumTradesOpeninLast12M                0\n",
       "PercentInstallTrades                  0\n",
       "MSinceMostRecentInqexcl7days          0\n",
       "NumInqLast6M                          0\n",
       "NumInqLast6Mexcl7days                 0\n",
       "NetFractionRevolvingBurden            0\n",
       "NetFractionInstallBurden              0\n",
       "NumRevolvingTradesWBalance            0\n",
       "NumInstallTradesWBalance              0\n",
       "NumBank2NatlTradesWHighUtilization    0\n",
       "PercentTradesWBalance                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check whether there are null values in the data where values would be expected\n",
    "df_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No null values are present in any feature column.\n",
    "\n",
    "No other problems are identified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negative special values\n",
    "We now analyze the frequencies of negative values in each feature affected by the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of negative values for:\n",
      "\n",
      "- MSinceOldestTradeOpen: 2.442%\n",
      "- MSinceMostRecentDelq: 47.346%\n",
      "- MSinceMostRecentInqexcl7days: 25.265%\n",
      "- NetFractionRevolvingBurden: 2.229%\n",
      "- NetFractionInstallBurden: 35.138%\n",
      "- NumRevolvingTradesWBalance: 1.699%\n",
      "- NumInstallTradesWBalance: 9.554%\n",
      "- NumBank2NatlTradesWHighUtilization: 5.096%\n"
     ]
    }
   ],
   "source": [
    "# Print all relative frequencies for features affected by special values\n",
    "# re-assign temp_df each time\n",
    "# use format method to show percentages\n",
    "\n",
    "print(\"Frequency of negative values for:\\n\")\n",
    "temp_df = df_clean[df_clean['MSinceOldestTradeOpen'] < 0]\n",
    "freq = temp_df.MSinceOldestTradeOpen.count()/df_clean.MSinceOldestTradeOpen.count()*100\n",
    "print(\"- MSinceOldestTradeOpen:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['MSinceMostRecentDelq'] < 0]\n",
    "freq = temp_df.MSinceMostRecentDelq.count()/df_clean.MSinceMostRecentDelq.count()*100\n",
    "print(\"- MSinceMostRecentDelq:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['MSinceMostRecentInqexcl7days'] < 0]\n",
    "freq = temp_df.MSinceMostRecentInqexcl7days.count()/df_clean.MSinceMostRecentInqexcl7days.count()*100\n",
    "print(\"- MSinceMostRecentInqexcl7days:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['NetFractionRevolvingBurden'] < 0]\n",
    "freq = temp_df.NetFractionRevolvingBurden.count()/df_clean.NetFractionRevolvingBurden.count()*100\n",
    "print(\"- NetFractionRevolvingBurden:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['NetFractionInstallBurden'] < 0]\n",
    "freq = temp_df.NetFractionInstallBurden.count()/df_clean.NetFractionInstallBurden.count()*100\n",
    "print(\"- NetFractionInstallBurden:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['NumRevolvingTradesWBalance'] < 0]\n",
    "freq = temp_df.NumRevolvingTradesWBalance.count()/df_clean.NumRevolvingTradesWBalance.count()*100\n",
    "print(\"- NumRevolvingTradesWBalance:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['NumInstallTradesWBalance'] < 0]\n",
    "freq = temp_df.NumInstallTradesWBalance.count()/df_clean.NumInstallTradesWBalance.count()*100\n",
    "print(\"- NumInstallTradesWBalance:\", \"{0:.03f}%\".format(freq))\n",
    "\n",
    "temp_df = df_clean[df_clean['NumBank2NatlTradesWHighUtilization'] < 0]\n",
    "freq = temp_df.NumBank2NatlTradesWHighUtilization.count()/df_clean.NumBank2NatlTradesWHighUtilization.count()*100\n",
    "print(\"- NumBank2NatlTradesWHighUtilization:\", \"{0:.03f}%\".format(freq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features showing a high frequency of negative values should be dropped as they are not useful for our analysis. In particular, one feature is affected by a very high level of negative/special values:\n",
    "\n",
    "- **MSinceMostRecentDelq**\n",
    "\n",
    "The features **NetFractionInstallBurden** and **MSinceMostRecentInqexcl7days** contain more than a quarter of observations that are not applicable/not available. We decide not to drop them as we would lost the other 75% of data in that case.\n",
    "Therefore, we perform imputation: we set their negative values to zeros in order to \"neuter\" the possible distortion effect of unavailable information.\n",
    "\n",
    "For all the other features affected by a relatively low frequency of negative values, we proceed to impute zeros as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['MSinceMostRecentDelq'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-b30d54ba76b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Drop selected feature\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# create updated dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf_clean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MSinceMostRecentDelq'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf_clean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/comp47350py37/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3928\u001b[0m                                            \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3929\u001b[0m                                            \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3930\u001b[0;31m                                            errors=errors)\n\u001b[0m\u001b[1;32m   3931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3932\u001b[0m     @rewrite_axis_style_signature('mapper', [('copy', True),\n",
      "\u001b[0;32m~/anaconda3/envs/comp47350py37/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3768\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3769\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3770\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3771\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3772\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/comp47350py37/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3800\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/comp47350py37/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   4908\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4909\u001b[0m                 raise KeyError(\n\u001b[0;32m-> 4910\u001b[0;31m                     '{} not found in axis'.format(labels[mask]))\n\u001b[0m\u001b[1;32m   4911\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['MSinceMostRecentDelq'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# Drop selected feature\n",
    "# create updated dataframe\n",
    "df_clean = df_clean.drop('MSinceMostRecentDelq', 1)\n",
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MSinceOldestTradeOpen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['MSinceOldestTradeOpen'] = df_clean['MSinceOldestTradeOpen'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['MSinceOldestTradeOpen'] < 0]\n",
    "if temp_df.MSinceOldestTradeOpen.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MSinceMostRecentInqexcl7days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "# This feature has -7 and -8 negative levels\n",
    "df_clean['MSinceMostRecentInqexcl7days'] = df_clean['MSinceMostRecentInqexcl7days'].replace(-8, 0)\n",
    "df_clean['MSinceMostRecentInqexcl7days'] = df_clean['MSinceMostRecentInqexcl7days'].replace(-7, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['MSinceMostRecentInqexcl7days'] < 0]\n",
    "if temp_df.MSinceMostRecentInqexcl7days.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NetFractionRevolvingBurden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['NetFractionRevolvingBurden'] = df_clean['NetFractionRevolvingBurden'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['NetFractionRevolvingBurden'] < 0]\n",
    "if temp_df.NetFractionRevolvingBurden.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NetFractionInstallBurden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['NetFractionInstallBurden'] = df_clean['NetFractionInstallBurden'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['NetFractionInstallBurden'] < 0]\n",
    "if temp_df.NetFractionInstallBurden.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumRevolvingTradesWBalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['NumRevolvingTradesWBalance'] = df_clean['NumRevolvingTradesWBalance'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['NumRevolvingTradesWBalance'] < 0]\n",
    "if temp_df.NumRevolvingTradesWBalance.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumInstallTradesWBalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['NumInstallTradesWBalance'] = df_clean['NumInstallTradesWBalance'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['NumInstallTradesWBalance'] < 0]\n",
    "if temp_df.NumInstallTradesWBalance.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumBank2NatlTradesWHighUtilization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputation successful.\n"
     ]
    }
   ],
   "source": [
    "# Replace negative values with zeros\n",
    "df_clean['NumBank2NatlTradesWHighUtilization'] = df_clean['NumBank2NatlTradesWHighUtilization'].replace(-8, 0)\n",
    "\n",
    "# Check if imputation has been successful\n",
    "temp_df = df_clean[df_clean['NumBank2NatlTradesWHighUtilization'] < 0]\n",
    "if temp_df.NumBank2NatlTradesWHighUtilization.count() == 0:\n",
    "    print(\"Imputation successful.\")\n",
    "else:\n",
    "    print(\"Error! Negative values still present.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Other features\n",
    "We decide to leave the other features as they are, as we have no further information available; we opt for a conservative approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Data Quality Plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Feature                                    | Data Quality Issue    | Handling Strategy |\n",
    "|--------------------------------------------|-----------------------|-------------------|\n",
    "| **MSinceOldestTradeOpen**                  | negative values (2%)  | imputation        |\n",
    "| **MSinceMostRecentInqexcl7days**           | negative values (25 %)| imputation        |\n",
    "| **NetFractionRevolvingBurden**             | negative values (2%)  | imputation        |\n",
    "| **NetFractionInstallBurden**               | negative values (35%) | imputation        |\n",
    "| **NumRevolvingTradesWBalance**             | negative values (2%)  | imputation        |\n",
    "| **NumInstallTradesWBalance**               | negative values (10%) | imputation        |\n",
    "| **NumBank2NatlTradesWHighUtilization**     | negative values (5%)  | imputation        |\n",
    "| **MSinceMostRecentDelq**                   | negative values (47%) | drop feature      |\n",
    "| **NumTrades60Ever2DerogPubRec**            | many 0s               | do nothing        |\n",
    "| **NumTrades90Ever2DerogPubRec**            | many 0s               | do nothing        |\n",
    "| **NumTradesOpeninLast12M**                 | many values in [0-1]  | do nothing        |\n",
    "| **NumInqLast6M**                           | many 0s               | do nothing        |\n",
    "| **NumInqLast6Mexcl7days**                  | many 0s               | do nothing        |\n",
    "| **MSinceMostRecentTradeOpen**              | outliers              | do nothing        |\n",
    "| **AverageMInFile**                         | outliers              | do nothing        |\n",
    "| **NumSatisfactoryTrades**                  | outliers              | do nothing        |\n",
    "| **NumTotalTrades**                         | outliers              | do nothing        |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now check again the descriptive statistics of our dataset:\n",
    "- continuous features have been affected by our adjustments.\n",
    "- categorical features have not been adjusted as there are no critical issues.\n",
    "\n",
    "We notice that negative levels have been successfully imputed to zeros (**min** column) and that **MSinceMostRecentDelq** has been removed from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <td>942.0</td>\n",
       "      <td>72.150743</td>\n",
       "      <td>10.039486</td>\n",
       "      <td>37.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>196.823779</td>\n",
       "      <td>101.320551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>183.5</td>\n",
       "      <td>252.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <td>942.0</td>\n",
       "      <td>9.325902</td>\n",
       "      <td>11.255838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AverageMInFile</th>\n",
       "      <td>942.0</td>\n",
       "      <td>78.936306</td>\n",
       "      <td>32.789643</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>21.399151</td>\n",
       "      <td>11.398674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.587049</td>\n",
       "      <td>1.202354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <td>942.0</td>\n",
       "      <td>0.384289</td>\n",
       "      <td>0.905109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <td>942.0</td>\n",
       "      <td>91.898089</td>\n",
       "      <td>12.294483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTotalTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>23.289809</td>\n",
       "      <td>13.362273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumTradesOpeninLast12M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.817410</td>\n",
       "      <td>1.731313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentInstallTrades</th>\n",
       "      <td>942.0</td>\n",
       "      <td>35.298301</td>\n",
       "      <td>17.510804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSinceMostRecentInqexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.829087</td>\n",
       "      <td>4.286435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.450106</td>\n",
       "      <td>1.871299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.397028</td>\n",
       "      <td>1.834697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>33.378981</td>\n",
       "      <td>28.939596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <td>942.0</td>\n",
       "      <td>43.814225</td>\n",
       "      <td>38.054613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>79.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>3.968153</td>\n",
       "      <td>3.000008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>2.317410</td>\n",
       "      <td>1.800370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <td>942.0</td>\n",
       "      <td>1.046709</td>\n",
       "      <td>1.574216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <td>942.0</td>\n",
       "      <td>66.690021</td>\n",
       "      <td>22.626372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count        mean         std   min  \\\n",
       "ExternalRiskEstimate                942.0   72.150743   10.039486  37.0   \n",
       "MSinceOldestTradeOpen               942.0  196.823779  101.320551   0.0   \n",
       "MSinceMostRecentTradeOpen           942.0    9.325902   11.255838   0.0   \n",
       "AverageMInFile                      942.0   78.936306   32.789643   5.0   \n",
       "NumSatisfactoryTrades               942.0   21.399151   11.398674   1.0   \n",
       "NumTrades60Ever2DerogPubRec         942.0    0.587049    1.202354   0.0   \n",
       "NumTrades90Ever2DerogPubRec         942.0    0.384289    0.905109   0.0   \n",
       "PercentTradesNeverDelq              942.0   91.898089   12.294483   0.0   \n",
       "NumTotalTrades                      942.0   23.289809   13.362273   0.0   \n",
       "NumTradesOpeninLast12M              942.0    1.817410    1.731313   0.0   \n",
       "PercentInstallTrades                942.0   35.298301   17.510804   0.0   \n",
       "MSinceMostRecentInqexcl7days        942.0    1.829087    4.286435   0.0   \n",
       "NumInqLast6M                        942.0    1.450106    1.871299   0.0   \n",
       "NumInqLast6Mexcl7days               942.0    1.397028    1.834697   0.0   \n",
       "NetFractionRevolvingBurden          942.0   33.378981   28.939596   0.0   \n",
       "NetFractionInstallBurden            942.0   43.814225   38.054613   0.0   \n",
       "NumRevolvingTradesWBalance          942.0    3.968153    3.000008   0.0   \n",
       "NumInstallTradesWBalance            942.0    2.317410    1.800370   0.0   \n",
       "NumBank2NatlTradesWHighUtilization  942.0    1.046709    1.574216   0.0   \n",
       "PercentTradesWBalance               942.0   66.690021   22.626372   0.0   \n",
       "\n",
       "                                      25%    50%    75%    max  \n",
       "ExternalRiskEstimate                 65.0   72.0   80.0   93.0  \n",
       "MSinceOldestTradeOpen               134.0  183.5  252.0  528.0  \n",
       "MSinceMostRecentTradeOpen             3.0    6.0   12.0  145.0  \n",
       "AverageMInFile                       58.0   75.5   98.0  256.0  \n",
       "NumSatisfactoryTrades                13.0   20.0   28.0   70.0  \n",
       "NumTrades60Ever2DerogPubRec           0.0    0.0    1.0   12.0  \n",
       "NumTrades90Ever2DerogPubRec           0.0    0.0    0.0    9.0  \n",
       "PercentTradesNeverDelq               88.0   97.0  100.0  100.0  \n",
       "NumTotalTrades                       14.0   21.0   31.0   85.0  \n",
       "NumTradesOpeninLast12M                0.0    1.0    3.0   11.0  \n",
       "PercentInstallTrades                 22.0   33.0   46.0  100.0  \n",
       "MSinceMostRecentInqexcl7days          0.0    0.0    1.0   24.0  \n",
       "NumInqLast6M                          0.0    1.0    2.0   16.0  \n",
       "NumInqLast6Mexcl7days                 0.0    1.0    2.0   16.0  \n",
       "NetFractionRevolvingBurden            7.0   27.0   55.0  112.0  \n",
       "NetFractionInstallBurden              0.0   49.5   79.0  142.0  \n",
       "NumRevolvingTradesWBalance            2.0    3.0    5.0   20.0  \n",
       "NumInstallTradesWBalance              1.0    2.0    3.0   14.0  \n",
       "NumBank2NatlTradesWHighUtilization    0.0    0.5    2.0   18.0  \n",
       "PercentTradesWBalance                50.0   67.0   83.0  100.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.select_dtypes(['float64', 'int64']).describe().T"
   ]
  },
  {
   "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",
       "    }\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RiskPerformance</th>\n",
       "      <td>942</td>\n",
       "      <td>2</td>\n",
       "      <td>Bad</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <td>942</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDelqEver</th>\n",
       "      <td>942</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count unique  top freq\n",
       "RiskPerformance            942      2  Bad  490\n",
       "MaxDelq2PublicRecLast12M   942      8    7  388\n",
       "MaxDelqEver                942      7    8  416"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.select_dtypes(['category']).describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then proceed to write our adjusted dataframe to a new csv file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.to_csv('CreditRisk-18202941_cleaned_updated.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring relationships between feature pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset selection\n",
    "We select a subset of features that we consider promising and then we proceed to plot pairwise feature interactions.\n",
    "We choose to analyze the following features:\n",
    "- **continuous**\n",
    "    - AverageMInFile\n",
    "    - ExternalRiskEstimate\n",
    "    - MSinceMostRecentTradeOpen\n",
    "    - MSinceOldestTradeOpen\n",
    "    - NumberSatisfactoryTrades\n",
    "    - NumTotalTrades\n",
    "    - NumTrades90Ever2DerogPubRec\n",
    "    - NumTradesOpeninLast12M\n",
    "    - PercentTradesNeverDelq\n",
    "\n",
    "- **categorical**\n",
    "    - RiskPerformance\n",
    "    - MaxDelq2PublicRecLast12M\n",
    "    - MaxDelqEver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous-continuous plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]),\n",
       " <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix using code found on https://stanford.edu/~mwaskom/software/seaborn/examples/many_pairwise_correlations.html\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Select columns containing continuous data\n",
    "continuous_columns = df_clean[['AverageMInFile',\\\n",
    "                               'ExternalRiskEstimate',\\\n",
    "                               'MSinceMostRecentTradeOpen',\\\n",
    "                              'MSinceOldestTradeOpen',\\\n",
    "                              'NumSatisfactoryTrades',\\\n",
    "                              'NumTotalTrades',\\\n",
    "                              'NumTrades90Ever2DerogPubRec',\\\n",
    "                              'NumTradesOpeninLast12M',\\\n",
    "                              'PercentTradesNeverDelq']].columns\n",
    "\n",
    "# Calculate correlation of all pairs of continuous features\n",
    "corr = df_clean[continuous_columns].corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom colormap - blue and red\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, annot=True, mask=mask, cmap=cmap, vmax=1, vmin=-1,\n",
    "            square=True, xticklabels=True, yticklabels=True,\n",
    "            linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n",
    "plt.yticks(rotation = 0)\n",
    "plt.xticks(rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to the previous correlation matrix, we are able to notice how strong is the correlation between each pair of selected features. We decide to draw scatter plots for the pairs showing correlation greater than (absolute value) 40%:\n",
    "- **AverageMInFile** and **MSinceOldestTradeOpen** (+0.68)\n",
    "- **ExternalRiskEstimate** and **PercentTradesNeverDelq** (+0.51)\n",
    "- **MSinceMostRecentTradeOpen** and **NumTradesOpeninLast12M** (-0.49)\n",
    "- **NumSatisfactoryTrades** and **NumTotalTrades** (+0.84)\n",
    "- **NumTrades90Ever2DerogPubRec** and **PercentTradesNeverDelq** (-0.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16fd0518>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean.plot(kind='scatter', x='AverageMInFile', y='MSinceOldestTradeOpen')\n",
    "df_clean.plot(kind='scatter', x='ExternalRiskEstimate', y='PercentTradesNeverDelq')\n",
    "df_clean.plot(kind='scatter', x='MSinceMostRecentTradeOpen', y='NumTradesOpeninLast12M')\n",
    "df_clean.plot(kind='scatter', x='NumSatisfactoryTrades', y='NumTotalTrades')\n",
    "df_clean.plot(kind='scatter', x='NumTrades90Ever2DerogPubRec', y='PercentTradesNeverDelq')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plots, we can notice that:\n",
    "- the correlation between **AverageMInFile** and **MSinceOldestTradeOpen** (first plot) is close to linear and positive, as we would expect: the longer a trade line is in the credit bureau file, the larger is the number of months passed since the oldest trade was opened.\n",
    "- the correlation between **NumSatisfactoryTrades** and **NumTotalTrades** (fourth plot) is quite clearly linear and positive. A higher count of trades for each credit applicant is likely to imply a high portion of satisfactory trade lines.\n",
    "- the remaining scatter plots show some form of correlation, but we cannot say much in terms of linearity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical-categorical plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using code from the module lab\n",
    "maxdelq = pd.unique(df_clean.MaxDelqEver.ravel())\n",
    "\n",
    "df_clean['percent'] = 0\n",
    "\n",
    "for i in maxdelq:\n",
    "    count = 1 / df_clean[df_clean.MaxDelqEver == i].count()['RiskPerformance']\n",
    "    index_list = df_clean[df_clean['MaxDelqEver'] == i].index.tolist()\n",
    "    for ind in index_list:\n",
    "        df_clean.loc[ind, 'percent'] = count * 100\n",
    "        \n",
    "group = df_clean[['percent','MaxDelqEver','RiskPerformance']].groupby(['MaxDelqEver','RiskPerformance']).sum()\n",
    "\n",
    "my_plot = group.unstack().plot(kind='bar', stacked=True, title=\"Risk performance based on max delinquecies ever\", figsize=(15,7))\n",
    "\n",
    "red_patch = mpatches.Patch(color='orange', label='good')\n",
    "blue_patch = mpatches.Patch(color='blue', label='bad')\n",
    "my_plot.legend(handles=[red_patch, blue_patch], frameon = True)\n",
    "\n",
    "my_plot.set_xlabel(\"Max delinquencies ever\")\n",
    "my_plot.set_ylabel(\"RiskPerformance (%good/bad)\")\n",
    "my_plot.set_ylim([0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4MAAAG2CAYAAADFv1PNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XmcjfXj///nObOhEYYZlCVLKIUiso7dMAYhtAzeFWUJvSXDMPYskZLUW9u7t4931kGjTIUWmqGFN9GQYmwxRjGGGbOd1/cPP+dn4jhizpl0Pe5/ua5zzvV6nuuacfN0XdfrshljjAAAAAAAlmIv7AAAAAAAAO+jDAIAAACABVEGAQAAAMCCKIMAAAAAYEGUQQAAAACwIMogAAAAAFgQZRBAgahZs6YiIiLUtWtXdevWTR06dFCPHj30ww8/SJI++OADLVy48KrbaN26tfP9nvbGG2+oZcuWGjNmjFfG84bIyEjFx8d7fdzOnTtr69atXh/XU3744Qe1bt3a7fsu7u+UlBT16dPHC8muTXR0tBISEgo7xg17+umnFRsb+6c+Ex8fr8jIyMvW//DDDxo2bNif2lZUVJTeeeedP/WZi9LT09W3b9+rvufYsWNq3ry5fv/9d+e6nTt3qk+fPuratasiIiK0Zs2a6xr/RuzcuVMxMTGSpK1bt6pz585ezwDAe3wLOwCAv4/3339fQUFBzuV33nlHU6dO1dKlS/XII48UYrLLrVixQrNnz1aDBg0KOwpucmXLltWSJUsKO4bTtGnTCjvCX869996refPmeW28tLS0q/7H1urVqzVv3jydOHHCuc4Yo2HDhunFF19UkyZNdPz4cT300EOqW7eu7rjjDi+kvuDnn39WSkqK18YDULgogwA8Ijc3V8eOHVOJEiUkSa+99ppOnTqlmJgY/fe//9WSJUvk5+engIAATZ48WdWrV3d+9ty5cxo4cKDq1aunUaNG5dvua6+9poMHD+r48eNKTU1VrVq1NG3aNAUGBiolJUWTJ0/WsWPHlJOTo/DwcD3zzDM6cuSIHnvsMVWrVk1Hjx5VhQoVlJKSoujoaA0fPlz333+/Jk6cqKNHj8oYo27duumpp5667HMzZszQ888/r6ZNm2rXrl3Ky8vTsGHDtHTpUu3fv1/33HOPXn75Zdntdr355pvasGGDzp8/r8zMTI0ePVrt2rXTa6+9pqNHjyo1NVVHjx5V2bJl9dJLLykkJEQHDhxQTEyMfv/9d9ntdg0aNEidOnVy+b2u5LPPPtPChQt1/vx5RUREaNCgQZLkMs8vv/yi6OhoZWdnyxijnj176rHHHpN04ezpp59+KofDodtvv10TJkxQ2bJl9fPPP2vs2LHKzMxU1apVlZGRccUsx48fd7lf+/fvr9DQUO3YsUNnzpzRqFGj1K5du3yfP3LkiPr163dD+3vMmDHKyMjQq6++qn379qlv3776v//7P1WrVi3fWP/973/1/vvvKzAwUDVq1Mj3mqv9cGnOiIgIbd++/arH99tvv9XUqVNls9lUt25dbdq0Sf/5z3909OhRTZkyRWvXrpV04WzMpcuuxk9NTdWECRO0f/9+2e129enTR3379lVkZKQee+wxhYWFadu2bZo9e7YyMzNlt9s1dOhQtWrVSqmpqRo9erROnTolSQoNDdWIESMuO4b33HOP2rRpoz179mj27NkqVqyYpk2bptOnTysvL0+RkZHq2bOnpAv/wfLee+/JbrerVKlSmjlzpsqXL6+lS5dq0aJFstvtKlOmjMaPH68qVaooKipKp0+f1uHDh9WyZUv17dtXUVFROnHihG677Tb99ttvzhy//PKLy3FfffVVxcXFqWTJkqpcufIVfxYv3adRUVEKDAzU3r17dfz4cdWsWVMzZ87ULbfccsXPXvxuS5cuVU5OjtLS0jRgwAA9+uijLvfjmDFjdP78eXXt2lWxsbHy8fFxbislJUXr16/XO++8o7CwMOf67OxsDRkyRE2aNJEklStXTkFBQTp+/PhlZbB169bq3LmztmzZorS0ND311FPatm2bdu/eLV9fX73xxhsqW7as9u3bp8mTJ+v06dOy2Wx64okn1K1bN23dulVz585VxYoVtW/fPuXm5mrSpEm67bbbNG/ePKWnp2vMmDHq1q2bMjIy9Nxzz2n//v3KysrS1KlT1aBBA3333XeaMWOGHA6HpAtncjt06OByHwL4izIAUABq1KhhOnfubDp37myaNm1qWrdubaZMmWJOnjxpjDFm3rx5ZtKkSSY3N9fUrl3bpKSkGGOMWbVqlVmyZIkxxphWrVqZhIQE07t3b/Ovf/3riuPMmzfPtGjRwqSmppq8vDzzz3/+08yYMcMYY0xkZKTZsGGDMcaY8+fPm8jISPPRRx+Zw4cPmxo1aphvv/3WuZ1WrVqZnTt3GmOMeeyxx8y7775rjDHmzJkzJiIiwqxdu/ayz11cXr9+vTHGmJiYGNOqVSuTnp5uzp8/b5o2bWq+//57c+TIERMZGWkyMzONMcasXbvWdO7c2Zm/TZs2Jj093RhjzNNPP21effVVY4wx3bp1M//3f/9njDHm119/db7P1ff6o8cff9w8/fTTJicnx6Snp5uwsDDzxRdfXDXPmDFjnPv6xIkTZsSIESYvL8+sWrXKjBgxwuTk5BhjjFmyZIl56qmnjDHGdO3a1SxbtswYY8x3331natasabZs2XJZHnf7dePGjcYYY+Lj403Lli0v+3xB7O9z586Z9u3bm9jYWBMeHm7WrFlz2Tg//vijady4sTlx4oQxxpjx48ebVq1aGWPMVffD448/btatW2cOHz5s6tWrd9Xjm5WVZR588EHz9ddfG2OMWbNmjalRo4Y5fPiw2bJliwkPD3fmuXT5auMPGTLEzJw507l/w8PDTXJysjPX6dOnTfv27c3hw4eNMcYcP37ctGjRwhw9etTMnz/fjB8/3rmPRowYYc6cOXPZvqlRo4ZZtWqVMcaYnJwc06lTJ7Nr1y7nmB07djTbt283SUlJplGjRubXX381xhjz3nvvmfHjx5uEhATTtm1b89tvvxljjFm5cqXp2LGjcTgcZvTo0aZfv37OsQYPHmzmzp1rjDEmOTnZ1KtXz6xcufKq43722WemU6dOJj093eTk5JiBAweaxx9//LLvcek+HT16tOndu7fJysoy2dnZplu3bmbFihWXfWb06NHm7bffNmfPnjW9evUyv//+uzHGmO3btzuPt6v9eOnPxNXUqFHDuW/+aMmSJSY0NNT5c32pVq1amRdffNEYY8xHH31katWqZZKSkpz78Y033jA5OTmmTZs25pNPPjHGXDj+zZs3N9u2bTNbtmwxd911l/nxxx+NMca888475rHHHjPGXDhGAwcOdO63u+66y/zvf/8zxlw4rn379jXGGNO3b1+zdu1aY4wxSUlJZuLEiW6/L4C/Hs4MAigwFy8T3b17twYOHKhGjRqpdOnS+d7j4+OjsLAw9enTRy1btlSzZs0UGhrqfH3UqFHy9fW96v02YWFhKlOmjCSpZ8+eevHFF/Xss8/q22+/VVpaml599VVJUkZGhvbs2aM6derI19dX9erVu2xbGRkZ2rZtm959911JUvHixdW9e3d99dVXqlu37mWf8/Pzc95PVqlSJd13330KDAyUJIWEhCgtLU3333+/Zs2apbi4OB08eFA7duzQuXPnnNto2LCh8zN333230tLSdPr0ae3Zs0cPP/ywJKl8+fJav369MjIyXH6vTp06XfZ9evbsKV9fXwUGBqpDhw5KSEhQaGioyzzt2rXT6NGjtXPnTjVu3Fjjxo2T3W7X559/rh9++EE9evSQJDkcDmVmZurUqVPau3evunXrJkmqX7++7rzzzj+9X/38/JzH/e6779bp06eveKxvdH8XK1ZMc+fOVa9evdSlSxd16dLlsjESExPVtGlTBQcHS5J69+6tzZs3S5LL/XA1Vzq+e/fulb+/v/OsT5cuXTRlypSrbsfd+AkJCc4z58WLF3eeSbzof//7n1JTUzVkyBDnOpvNpr1796p58+YaOHCgjh07piZNmmjkyJEqXrz4FTNcvJQ6OTlZhw4d0tixY52vnT9/Xj/++KOysrLUrFkzlS9fXpLUv39/SdKsWbPUqVMn5+Xj3bt317Rp03TkyBFJF35+LkpISNDo0aMlSZUrV1ajRo3cjvvLL7+oXbt2zv3do0cPLVq0yO1+bd68ufz9/SVJNWrUUFpamsv33nLLLXrzzTf15ZdfKjk5WXv27HGeDXe1H6+2vWuxcOFC/ec//9Hbb7+tIkWKXPE97du3lyRVrFhRZcqUUa1atSRd+D1JS0tTcnKysrKynO8rW7as2rdvr02bNqlRo0a67bbbdNddd0m68HO6atWqK45TsWJF1a1bV5JUq1YtrVy5UpLUsWNHTZ48WRs3blSTJk30z3/+84a+M4DCQRkEUOBq166tMWPGKCoqSnfddZcqVKiQ7/XZs2frp59+UkJCghYuXKg1a9Y4i86gQYO0detWvfTSSxo/fvwVt3/pJVcOh0N2u10Oh0PGGC1ZskRFixaVJP3+++8KCAjQqVOn5O/vL1/fy//Ku/i5P67Lzc2VpMs+5+fnJ5vNlm/5j3bv3q3Bgwerf//+atq0qR544AFNmjTJ+fql/7iz2WwyxjjHuHTb+/fvV3BwsMvv5W7fXNzu1fK0atVKn3zyiRISEpSYmKjXX39dsbGxcjgceuqpp/Too49KunAJ26X/wL10n13PfvXz85Pdbr/sO/9RQezvAwcOqGTJkkpKSlJ2drazBFzq0qx//Pm62n64kisd3yJFily2Py495pe+lpOTc03j+/r65ts3hw8fVqlSpZzLeXl5qlatmpYvX+5cl5KSoqCgIPn5+WnDhg1KTEzUli1b9PDDD+utt97SPffcc9n3KVasmHN7xYsXzzepycmTJ1W8eHEtXbo0X5bz58/r6NGjzksIL2WMcf4cXNz2lfbDxf1ztXFnzZrl8thdzZWOkSvHjx9X79691atXL9WvX19hYWH6/PPPJUl16tS54n4sWbLkNeX4o+zsbEVFRennn3/WkiVLLvu781KX/hxf6fciLy/vst+tS/f9te6DS7d96fv69OmjVq1a6euvv9amTZs0f/58xcfHu/y7CcBfE7OJAvCIzp07q06dOpo+fXq+9b///rtCQ0NVsmRJ9e/fXyNGjMg30UKdOnU0ceJExcfHO8/O/NGGDRuUnp4uh8OhZcuWqVWrVgoMDFS9evX03nvvSZLOnDmjRx55RBs2bLhqzsDAQNWtW1eLFy+WdGEWwNWrVzvP4FyPb7/9Vvfcc4/+8Y9/qGHDhtqwYYPy8vLc5qhdu7ZWr14t6cJMg4888ojOnz//p77X6tWrZYxRWlqa1q1bp+bNm181z8iRI/Xxxx8rPDxcEyZMUGBgoA4dOqRmzZppxYoVOnv2rKQL92W98MILKlWqlGrXru0sGLt379ZPP/10xe9T0PvVlat9vyNHjmjatGl69913VbVqVc2ePfuyzzdt2lRff/21jh8/Lkn5zpC42g9/VpUqVVSkSBFt3LhRkvTll186Z5EMCgrSr7/+qt9++03GGH300UfXNH7jxo2dZ2nS09PVr18/JScnOz9br149HTx4UN9++60kKSkpSR06dFBKSopmz56tBQsWqG3btoqOjlb16tW1b9++a/oOF0vZsWPH1LlzZ+3atUuNGjVSYmKic0KUJUuW6KWXXlLz5s318ccfO7/rypUrXd7b17x5cy1dulSS9OuvvzpnqL3auC1atFB8fLzOnDkjh8Phkdk3d+3apaCgIA0ePFjNmjVzFsG8vDyX+9HX11d5eXlXLZlX8vzzz+vs2bNui+C1qFq1qnx9ffXpp59KuvAfAZ988onb30EfHx9nYbyaPn36KCkpSd27d9eUKVN05swZpaam3lBmAN7HmUEAHjN+/Hh16dJFmzZtcq4LCgrSoEGD1L9/fxUpUkQ+Pj6aOnVqvs8FBQVpwoQJGjt2rOLi4pyT0FxUpkwZDRgwQKdOndIDDzzgnExl9uzZmjJliiIiIpSdna3OnTurS5cuzkvSXJk9e7YmT56s2NhYZWdnKyIiQt27d9fRo0ev63t37txZn376qTp27CiHw6FWrVopLS3N+Q96V+bMmaNJkyZp0aJFstlsmjZtmoKDg11+ryu5eDnm+fPn9fjjj+vBBx9U9erVXeYZPHiwoqOjtXTpUvn4+Kht27Z64IEH1KBBA6WkpKhXr16y2WwqX768ZsyYIUl6+eWXNWbMGC1ZskSVKlVS1apVvbJfXbna/h45cqSefPJJ1ahRQzExMYqIiFCTJk3UsmVL5+dr1qypUaNGqV+/frrllltUp04d52sPP/ywy/3wZ/j6+uq1117TpEmTNG/ePNWoUcN5BqV69erq06ePevTooeDgYLVs2dL5HyRXGz8mJkYTJ05URESEjDF6+umn853ZCwoK0rx58zRr1ixlZWXJGKNZs2apQoUK6tevn6KiotS5c2f5+/urZs2aCg8Pv+p38Pf314IFCzRt2jS9/fbbys3N1fDhw52Xeo4aNUpPPfWUJCk4OFgvvviiypYtq/79+6tfv35yOBwKCgrSv/71L+dZ4UtNmDBBY8aMUceOHVWuXDnnZY/uxt27d6969OihW2+9VbVq1XJO5lJQmjZtqhUrVigsLEw2m00NGzZUUFCQDh486HI/+vj4qE6dOgoPD9fixYvznbF1Zfv27frkk090xx135Jt9+fnnn1fz5s3/dG4/Pz8tWLBAU6dO1Wuvvaa8vDwNGTJEDz744FUfBVOvXj29/vrrGjp06BUf03FprhdffFGvvPKKbDabhg4desMFFoD32cyf/W8rAChEl85KCtzM7rvvPsXFxfEPaABAoeEyUQAAAACwII+eGTx79qz69OmjN998UxUqVFBCQoKmT5+urKwsdezYUc8995ykC/cxREdH69y5c2rQoIEmTZp0xQkJAAAAAAAFw2NnBnfs2KFHHnnEeTP7+fPnNXbsWC1YsEAff/yxdu3apS+//FLShfsMYmJi9Mknn8gYo2XLlnkqFgAAAABAHpxAZtmyZZowYYJz1rOdO3eqcuXKqlixoiQpIiJC8fHxql69unO2POnCM4jmzZvnnEbbnfPnz2vXrl0KDg6+5imlAQAAAODvIi8vT6mpqbrnnntcPp/0SjxWBqdNm5Zv+cSJE84H+koXHhackpJy2frg4GClpKRc8zi7du3SY489duOBAQAAAOAmtnjxYjVo0OCa3++1G/McDke+h58aY2Sz2Vyuv1YXi+TixYtVrly5ggvswuHXB3l8DG+qOOSNwo5Q4J6a9llhRygwb0e3K+wIBY7fob++v9Mx+jseH4lj9Ff3dzo+Esfor+7veHwkjtGfdfz4cT322GP5TrJdC6+VwXLlyuV7GGlqaqpCQkIuW3/y5EmFhIRc83YvXhparlw5r0zPnR3o7/ExvOnvOKW5X7Ggwo5QYP6Ox4ffob++v9Mx+jseH4lj9Ff3dzo+Esfor+7veHwkjtH1+rO3zXnt0RJ169bVgQMHdPDgQeXl5Wnt2rVq0aKFbr/9dgUEBOj777+XJK1Zs0YtWrTwViwAAAAAsCSvnRkMCAjQjBkz9OyzzyorK0uhoaEKCwuTJM2ePVvjxo3T2bNnVbt2bfXt29dbsQAAAADAkjxeBjdu3Oj8c+PGjfXhhx9e9p5atWppxYoVno4CAAAAAPj/8GR3AAAAADcNY7Mru3ID5VW+T8Y3QNK1Tz5ZGJKSkgpsWz4+PipZsqTKlCkju/3G7/ijDAIAAAC4aZyvG6GiFe9WSNCt8rXb/tSTCApDQPnqBbIdY4xycnKUkpKiI0eOqFKlSje8Ta9NIAMAAAAAN8oRVEnly5SQn4/9L18EC5LNZpO/v79uv/12nTt3rkC2SRkEAAAAcPOw2WW3UAn8o4K4PNS5rQLbEgAAAADgpsE9gwAAAABuav6bG8uWc7LAt2v8yii7WWKBb9edrVu3av78+Vq0aJFHx+HMIAAAAICbmieKoCe3+1fBmUEAAAAAuAGvLvy3Pvvya5UscauCS5dSyyaNZCS9v3SVfPwDVLt2bY0fP1633HKLPv/8c73yyityOByqWLGiJk+erDJlymjz5s2aPn26AgICVKVKFa/k5swgAAAAAFynLxK2atsPP2rVvxdowcyJStq3X+cyM/XWoqV679UZiouLU9GiRTV//nz99ttviomJ0euvv664uDjdf//9mjx5srKzsxUVFaV58+YpNjZWRYoU8Up2yiAAAAAAXKct321Xh1bN5Ofnp1uLF1frZg/KJptCmzRUyRK3SpJ69+6tLVu2aOfOnapTp44qVKiQb/3evXsVEhKiatWqSZIeeughr2SnDAIAAADAdbLb7XI4TL51DpN/2Rij3NxcORyOK6632Wwyl3zGx8fHc4EvQRkEAAAAgOv0YP16Wv/V18rJydHZcxn6KvFbnUk/qy8StirtTLokadmyZWrUqJHq1q2rHTt26MiRI5KkpUuXqlGjRqpZs6ZOnjypPXv2SJI++ugjr2RnAhkAAAAANzXjV8Zjj5Zwp0Xjhtqxe496DRimW4sXV3CZIFWtXFFPPtpL/xg+Wnmyq3bt2po0aZICAwM1efJkDR06VDk5Obrttts0bdo0+fn56eWXX9aoUaPk6+uru+++u8C/y5VQBgEAAADc1ArjWYAX7didpEoVbtOqf7+hnNxcRQ4eqSqVKqhGtSrq0bmDAspXz/f+1q1bq3Xr1pdt54EHHlBcXJy3YkuiDAIAAADAdbujYgW98e//atHy1XI4HOrSoY1qVPPOoyFuFGUQAAAAAK5TiVuL682XphR2jOvCBDIAAAAAYEGUQQAAAACwIMogAAAAAFgQZRAAAAAALIgJZAAAAADc1CrXvUMpqQVfbcoG5+rgjuQC3+5fBWcGAQAAANzUPFEEr3W7327fqSeGR13X9rdu3arIyMjr+mxBoAwCAAAAgAVxmSgAAAAA3IBTaWf0zKjxOnHyN917V01FjxislWvjFffZRmXlGvn5+WnOnDmqWrWqNm/erOnTpysgIEBVqhTuw+k5MwgAAAAAN+Do8eMaM/wZrXz3dWVkZmrZhx9r4+ZEvfvKDK1du1YtW7bU4sWLlZ2draioKM2bN0+xsbEqUqRIoeamDAIAAADADahf5x5VrnC7bDabOrVtqe/+94NmjH9B8Ru/0pw5c/T5558rIyNDe/fuVUhIiKpVqyZJeuihhwo1N2UQAAAAAG6Aj4+P88/GYZR+9qwih4xU+tmzatGihR566CEZY2Sz2WSMueLnCgNlEAAAAABuwPYfdutYygk5HA7FfbpBTRs1UKXbb1Pkww/p3nvv1fr165WXl6eaNWvq5MmT2rNnjyTpo48+KtTcTCADAAAA4KZWNjjXY88ZvBbV76ismJmv6OTvp9Twvjp6OKKjtny3Xd36PSP5+OmBBx7Qvn375Ofnp5dfflmjRo2Sr6+v7r777gLP/GdQBgEAAADc1ArzwfAP3FdH789/6bL1C+dMkyQFlK+e//0PPKC4uDivZHOHy0QBAAAAwIIogwAAAABgQZRBAAAAADcP45Djkhk5rcbhcBTYtiiDAAAAAG4a9t8P6djJNOXkOfI9puHvzhij7OxsHT16VLfcckuBbJMJZAAAAADcNIrsiFPW6WNKrnSfjG+AZLMVdqSr8j2dU3Db8vVViRIlVKZMmYLZXoFsBQAAAAC8wGYcCkj+Rkr+prCjXJOq0SsLO4JLXCYKAAAAABZEGQQAAAAAC6IMAgAAAIAFUQYBAAAAwIIogwAAAABgQZRBAAAAALAgyiAAAAAAWBBlEAAAAAAsiDIIAAAAABZEGQQAAAAAC6IMAgAAAIAFUQYBAAAAwIIogwAAAABgQb6FHeBm4sjJVtXolYUdo0A5crJl9/Mv7BgAAAAAvIwy+CfY/fwVMXJNYccoUHFzuhZ2BAAAAACFgMtEAQAAAMCCKIMAAAAAYEGUQQAAAACwIMogAAAAAFgQZRAAAAAALIgyCAAAAAAWRBkEAAAAAAviOYMAACdHTraqRq8s7BgFxpGTLbuff2HHAADgL4kyCABwsvv5K2LkmsKOUWDi5nQt7AgAAPxlcZkoAAAAAFgQZRAAAAAALIgyCAAAAAAWRBkEAAAAAAuiDAIAAACABVEGAQAAAMCCKIMAAAAAYEGFUgbXrFmj8PBwhYeHa+bMmZKkpKQkde/eXR06dFB0dLRyc3MLIxoAAAAAWILXy2BmZqamTZumRYsWac2aNfruu++UkJCgUaNGKSYmRp988omMMVq2bJm3owEAAACAZXi9DObl5cnhcCgzM1O5ubnKzc2Vr6+vzp8/r3r16kmSunfvrvj4eG9HAwAAAADL8PX2gIGBgRo+fLg6duyookWL6oEHHpCfn5+Cg4Od7wkODlZKSoq3owEAAACAZXj9zOCePXu0cuVKff7559q0aZPsdru+/vpr2Ww253uMMfmWAQAAAAAFy+tlcPPmzWrcuLFKly4tf39/de/eXVu3blVqaqrzPSdPnlRISIi3owEAAACAZXi9DNaqVUsJCQnKyMiQMUYbN25Uw4YNFRAQoO+//17ShdlGW7Ro4e1oAAAAAGAZXr9nsFmzZvrxxx/VvXt3+fn56d5779XAgQPVrl07jRs3TmfPnlXt2rXVt29fb0cDAAAAAMvwehmUpIEDB2rgwIH51tWqVUsrVqwojDgAAAAAYDmF8tB5AAAAAEDhogwCAAAAgAVRBgEAAADAggrlnkEA1uTIyVbV6JWFHaNAOXKyZffzL+wYAAAAfxplEIDX2P38FTFyTWHHKFBxc7oWdgQAAIDrwmWiAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAABX7Y9pAAAgAElEQVQAFkQZBAAAAAALogwCAAAAgAX5untDbm6utm3bpgMHDshut6tq1aqqX7++N7IBAAAAADzkqmVw8eLF+te//qWSJUuqQoUK8vX11QcffKBTp05pwIABeuSRR2Sz2byVFQAAAABQQFyWwWeffVZ33XWXVqxYoZCQkHyv/fbbb/rggw80ZMgQLViwwOMhAQAAAAAFy2UZjI6OVrly5a74WunSpTV06FAdP37cY8EAAABuNo6cbFWNXlnYMQqUIydbdj//wo4BwANclkFXRfDPvgcAAMAq7H7+ihi5prBjFKi4OV0LOwIAD3FZBmvVqnXV+wGTkpI8EggAAAAA4Hkuy2BiYqKMMXr11Vd1++23q3fv3vLx8VFsbKx+/fVXb2YEAAAAABQwl88ZLFWqlIKCgrRr1y4NHDhQJUqUUGBgoPr27atvvvnGmxkBAAAAAAXM7XMGMzMztX//flWtWlWStHfvXuXk5Hg8GAAAuNzfbYISJicBgMLjtgyOGDFCvXv3Vs2aNeVwOPTLL79o9uzZ3sgGAAD+4O82QQmTkwBA4XFbBtu3b6/69evr+++/lyQ1aNBAQUFBHg8GAAAAAPAcl/cMXpSdna1t27YpIyNDGRkZ+vzzzzV37lxvZAMAAAAAeIjbM4PPPfecDh8+rNTUVN19993asWOHGjZs6I1sAAAAAAAPcXtmMCkpSbGxsWrTpo3Gjh2rDz74QGlpad7IBgAAAADwELdlMCQkRL6+vrrjjjv0008/6c4771R6ero3sgEAAAAAPMRtGSxWrJji4uJUq1YtrVu3Tnv37lVGRoY3sgEAAAAAPMRtGYyJiVFSUpKaNm0qu92uxx9/XE8++aQ3sgEAAAAAPMTtBDJ33HGHXnjhBZ09e1YzZ85UQECAN3IBAAAAADzI7ZnB5ORk9erVSw0bNlT9+vXVr18/HTt2zBvZAAAAAAAeck2Xifbs2VM7duzQtm3b1LZtW40bN84b2QAAAAAAHuK2DJ45c0a9evWSn5+f/P39FRkZqZMnT3ojGwAAAADAQ9yWwUqVKmnHjh3O5T179qhSpUoeDQUAAAAA8CyXE8hERERIks6dO6dHH31UNWvWlN1u1549e1StWjWvBQQAAAAAFDyXZXD8+PHezAEAAAAA8CKXZfDll19Wy5Yt1bZtW1WvXt2bmQAAAAAAHuayDL711lv6/PPP9dprr+nQoUN68MEH1aZNG9WvX182m82bGQEAAAAABcxlGSxevLi6dOmiLl26KDs7W4mJifrwww81adIk3XvvvXrxxRe9mRMAAAAAUIBclsFL+fv7KzQ0VKGhoZKUb3ZRAAAAAMDNx2UZjIyMvOrloP/5z388EggAAAAA4Hkuy+Djjz8uSfrss8909uxZ9ejRQz4+PlqzZo1uvfVWrwUEAAAAABQ8l2WwQ4cOkqR33nlHS5Yskd1+4fn0LVu2VO/evb2TDgAAAADgEXZ3bzh16pSysrKcy+fOnVNaWppHQwEAAAAAPMvtBDKdO3dWr1691K5dOxljFB8fr169enkjGwAAAADAQ9yWweHDh+uee+5RYmKiJCkqKso5qygAAAAA4OZ0TY+WKFOmjEqUKKHc3FwVLVrU05kAAAAAAB7m9p7B1atXa9iwYUpPT9e5c+c0cuRILVu2zBvZAAAAAAAe4vbM4L///W8tX75cISEhkqQBAwboySef5L5BAAAAALiJuT0z6HA4nEVQksqWLet8zAQAAAAA4ObkttWVLFlS69evdy6vX79eJUqU8GgoAAAAAIBnub1MdPz48Ro8eLCmTJkiSfLz89Prr79+Q4Nu3LhR8+fPV2Zmppo2bapx48YpISFB06dPV1ZWljp27KjnnnvuhsYAAAAAALjmtgzeeeedio+PV3JyshwOh6pUqSJf32uahPSKDh8+rAkTJmj58uUqXbq0+vXrpy+//FITJkzQokWLVL58eT399NP68ssveYQFAAAAAHiI21aXkZGhWbNm6auvvlJubq6aNm2q6OhoBQYGXteAn332mTp16qRy5cpJkubOnauDBw+qcuXKqlixoiQpIiJC8fHxlEEAAAAA8BC39wxOnz5d2dnZev3117VgwQLZbDbnJaPX4+DBg8rLy9Mzzzyjrl276r///a9OnDih4OBg53tCQkKUkpJy3WMAAAAAAK7O7ZnBHTt26MMPP3QuT506VeHh4dc9YF5enr777jstWrRIxYoV06BBg1SkSBHZbDbne4wx+ZYBAAAAAAXLbRnMy8uTw+FwPk7C4XDIx8fnugcsU6aMGjdurKCgIElS27ZtFR8fn2+bqamp+R5nAQAAAAAoWG4vE23cuLFGjBihxMREJSYm6p///KcaNWp03QO2atVKmzdv1pkzZ5SXl6dNmzYpLCxMBw4ccF5CunbtWrVo0eK6xwAAAAAAXJ3bM4NRUVFasGCBXn75ZeXl5al58+YaPHjwdQ9Yt25dPfXUU3r00UeVk5Ojpk2b6pFHHlHVqlX17LPPKisrS6GhoQoLC7vuMQAAAAAAV+e2DPr6+mrYsGEaNmxYgQ3as2dP9ezZM9+6xo0b57s3EQAAAADgOW7LYO3ateVwOPKtK1KkiGrUqKEXX3xR1apV81g4AAAAAIBnuC2Dffv21S233KLIyEjZ7XYtX75c+/fvV2hoqCZOnKhFixZ5IycAAAAAoAC5nUBm69atGjp0qEqUKKHixYvriSee0J49e9SuXTudOXPGGxkBAAAAAAXMbRnMzMzUiRMnnMsnTpxQVlaWpAuPnQAAAAAA3HzcXiY6YMAAPfTQQ2rWrJkcDocSExM1ZswYzZ8/X/fff783MgIAAAAACpjbMti9e3fVrVtXmzZtkq+vr4YMGaI77rhDR44cUbly5byREQAAAABQwNyWQUmqVq2aqlWrpi1btig5OVkVK1ZUhQoVPJ0NAAAAAOAh11QGJWnOnDk6deqUc0bR119/3ZO5AAAAAAAe5LIMJiYmqnHjxs7lvXv3auHChZKk9u3bez4ZAAAAAMBjXJbBjz76SMuWLVNUVJTKli2r+++/X//4xz/k4+Oj++67z5sZAQAAAAAFzGUZnDp1qnbu3Knnn39eTZo00ZNPPqmwsDBlZ2erRo0a3swIAAAAAChgV33OYJ06dbRo0SKVKVNGkZGRSk5OpggCAAAAwN+AyzK4e/duDRs2TM8//7waNGigt956S1999ZUGDRqkgwcPejMjAAAAAKCAuSyDUVFRevLJJ9W1a1eNGzdOt956q2JiYjR8+HBNmjTJmxkBAAAAAAXM5T2D2dnZqlChgs6ePavMzEzn+lq1aundd9/1SjgAAACgIDlyslU1emVhxygwjpxs2f38CzsGblIuy+Do0aM1YMAA+fv7a+zYsd7MBAAAAHiE3c9fESPXFHaMAhM3p2thR8BNzGUZbN26tVq3bu3NLAAAAAAAL3F5z+DYsWOVmprq8oMnTpzQmDFjPBIKAAAAAOBZLs8MRkZG6umnn1bFihXVqlUrVapUSQ6HQ4cPH9ZXX32l5ORkTZkyxZtZAQAAAAAFxGUZvOuuu7Ry5UqtW7dO8fHxOnDggCSpSpUqCgsLU1hYmOz2qz6mEAAAAADwF+WyDEqSzWZTp06d1KlTJ2/lAQAAAAB4Aaf2AAAAAMCCKIMAAAAAYEGUQQAAAACwoGsqgzt37tSSJUuUnZ2t7du3ezoTAAAAAMDD3JbB2NhYjRkzRm+//bbS09M1ePBgLVu2zBvZAAAAAAAe4rYMLlq0SEuXLlVgYKBKly6t2NhYvf/++97IBgAAAADwELdl0G63KzAw0Llcvnx5+fj4eDQUAAAAAMCz3JbBkiVLKikpSTabTZL04YcfqkSJEh4PBgAAAADwnKs+dF6Sxo4dq+HDh+vQoUNq1qyZAgICtGDBAm9kAwAAAAB4iNsyWK1aNa1atUqHDh1SXl6eqlatKl9ftx8DAAAAAPyFub1MdMuWLerRo4eqVasmm82m0NBQHi8BAAAAADc5t2Vw1qxZmj59uiTpzjvv1MKFC53LAAAAAICbk9symJOTo9q1azuXa9eurezsbI+GAgAAAAB4ltsyWLRoUX311VfO5cTERBUrVsyjoQAAAAAAnuV2Jpjo6GgNGTLEOWmM3W7Xa6+95vFgAAAAAADPcVsG69atqy+++EI//fSTfHx8VKVKFfn7+3sjGwAAAADAQ9yWwczMTMXHxystLU3GGCUkJEiS/vGPf3g8HAAAAADAM9yWwRdeeEFHjx5VjRo1ZLPZvJEJAAAAAOBhbsvg3r179fHHH/OgeQAAAAD4G3E7m2i5cuW8kQMAAAAA4EVuT/fVqFFDffv2VfPmzVWkSBHneu4ZBAAAAICbl9syeO7cOVWuXFmHDh3yRh4AAAAAgBe4LYPTp0/3Rg4AAAAAgBe5LYPbt2/XwoULlZGRIWOMHA6Hjhw5oi+++MIL8QAAAAAAnuB2Aplx48bpvvvu09mzZxUREaHAwEC1b9/eG9kAAAAAAB7i9sygzWbTwIEDderUKVWtWlURERHq0aOHN7IBAAAAADzE7ZnBW265RZJUqVIl7du3T0WKFJHd7vZjAAAAAIC/MLdnBuvUqaMRI0Zo+PDhevrpp5WcnMwD6AEAAADgJuf2FN/YsWPVv39/ValSRWPHjpXD4dCcOXO8kQ0AAAAA4CFuy6DNZtPtt9+un3/+WRUqVFCvXr3kcDi8kQ0AAAAA4CHX9JzBxYsXKzAw0LnOZrMpMTHRo8EAAAAAAJ7jtgx+9tln2rRpk0qVKuWNPAAAAAAAL3B7megdd9yhW2+91RtZAAAAAABe4vbMYGRkpB5//HE1atQo3yyiQ4cO9WgwAAAAAIDnuC2DCxcuVGBgoNLT072RBwAAAADgBW7LYGZmpj744ANvZAEAAAAAeInbewarVKmiPXv2eCMLAAAAAMBL3J4ZPHbsmHr27Knbb79d/v7+zvVxcXEeDQYAAAAA8By3ZXDo0KH5SiAAAAAA4Obntgy+9NJLWrNmjTeyAAAAAAC8xO09g0WLFtXx48e9kQUAAAAA4CXXNJtomzZtVK5cORUrVsy5nnsGAQAAAODm5bYMRkdHeyMHAAAAAMCL3F4m2rBhQwUEBOibb77R119/7VwHAAAAALh5uS2Dq1ev1rBhw5SWlqZz585p5MiRWrZsmTeyAQAAAAA8xO1lov/+97+1fPlyhYSESJIGDBigJ598Ur169fJ4OAAAAACAZ7g9M+hwOJxFUJLKli0ru93txwAAAAAAf2FuW13JkiW1fv165/L69etVokSJGx545syZioqKkiQlJSWpe/fu6tChg6Kjo5Wbm3vD2wcAAAAAuOa2DMbExGjmzJkKDQ1Vy5YtNWPGDI0bN+6GBk1MTNSqVaucy6NGjVJMTIw++eQTGWO4JxEAAAAAPMxlGXzllVckSWlpaYqPj9e7776rhQsXKj4+XjVr1rzuAU+fPq25c+fqmWeekSQdPXpU58+fV7169SRJ3bt3V3x8/HVvHwAAAADgnssyuHbtWqWkpGjSpEk6e/asSpcurZCQEJ09e1anT5++7gFjYmL03HPP6dZbb5UknThxQsHBwc7Xg4ODlZKSct3bBwAAAAC453I20aZNm6ply5YyxqhRo0aSJJvNJmOMbDabkpKS/vRgy5cvV/ny5dW4cWPFxsZKujBBjc1mc77n4vYBAAAAAJ7jsgxOmjRJkyZNUp8+fbRkyZICGezjjz9WamqqunbtqrS0NGVkZMhmsyk1NdX5npMnT+abvRQAAAAAUPDcPmcwMzOzwAZ77733nH+OjY3VN998o+nTp6tz5876/vvvVb9+fa1Zs0YtWrQosDEBAAAAAJdzO5to0aJFdfz4cY+GmD17tqZPn66wsDBlZGSob9++Hh0PAAAAAKzums4MtmnTRuXKlVOxYsWc6+Pi4m5o4O7du6t79+6SpFq1amnFihU3tD0AAAAAwLVzWwajo6O9kQMAAAAA4EVuLxNt2LChihQpov3796tevXry8/NTw4YNvZENAAAAAOAhbstgbGysxowZo7ffflvp6ekaPHiwli1b5o1sAAAAAAAPcVsGFy1apKVLlyowMFClS5dWbGys3n//fW9kAwAAAAB4iNsyaLfbFRgY6FwuX768fHx8PBoKAAAAAOBZbstgyZIllZSUJJvNJkn68MMPVaJECY8HAwAAAAB4jtvZRMeOHavhw4fr0KFDatasmQICArRgwQJvZAMAAAAAeIjbMlitWjWtWbNGycnJysvLU5UqVeTn5+eNbAAAAAAAD3FZBrOysvTqq69q//79evDBB9W3b1/Z7W6vKgUAAAAA3ARclsGJEycqIyNDLVq00Lp165SWlqbhw4d7MxsAAAAAi3HkZKtq9MrCjlFgHDnZsvv5F3aMK3JZBnft2qW4uDhJUnh4uPr160cZBAAAAOBRdj9/RYxcU9gxCkzcnK6FHcEll9d9+vr+/z2xRIkSMsZ4JRAAAAAAwPOu+SZA7hcEAAAAgL8Pl5eJnjlzRp9++qlzOT09Pd9y+/btPZsMAAAAAOAxLsvgbbfdpkWLFjmXy5cv71y22WyUQQAAAAC4ibksg5cWwT9KT0/3SBgAAAAAgHe4vRFwzpw5+ZYTEhIUERHhsUAAAAAAAM9zWwa3b9+ud955R9nZ2Zo6dapeeOEFRUdHeyMbAAAAAMBD3JbBhQsXav369QoLC9PJkycVFxendu3aeSMbAAAAAMBDXJbB3bt3a/fu3Tpw4ICGDRumnJwcNWrUSL/++qt2797tzYwAAAAAgALmcgKZZ599Nt+yn5+f3nrrLUkXZhPdsGGDZ5MBAAAAADzGZRncuHGjN3MAAAAAALzI7T2Dv/zyi5YvXy5jjJ577jm1bdtWW7Zs8UY2AAAAAICHuC2DEyZMUEBAgL744gsdP35c06ZN09y5c72RDQAAAADgIW7LYFZWlrp06aLNmzerY8eOatSokXJycryRDQAAAADgIW7LYHZ2tk6ePKkvvvhCTZo00cmTJ5WVleWNbAAAAAAAD3FbBnv37q1WrVqpfv36ql69unr27Kl+/fp5IxsAAAAAwENcziZ60aOPPqo+ffrIbr/QG1etWqVSpUp5PBgAAAAAwHNclsG33npLAwYM0NSpU6/4+rhx4zwWCgAAAADgWS7LYPHixSVJJUuW9FoYAAAAAIB3uCyDffr0kSQNHTr0ste+/vprzyUCAAAAAHicywlkdu/erT59+uiZZ57R77//Lkn69ddfNXToUA0aNMhrAQEAAAAABc9lGZw4caLat2+vChUq6I033tD69evVpUsXZWZmas2aNd7MCAAAAAAoYC4vE01PT9cTTzyhvLw8dejQQevWrdOkSZMUHh7uzXwAAAAAAA9wWQaLFi0qSfLx8VFWVpYWLlyou+++22vBAAAAAACe4/IyUWOM88+lSpWiCAIAAADA34jLM4MOh0NpaWnOUnjpnyUeOQEAAAAANzOXZfCnn37Sgw8+6CyAjRo1cr5ms9mUlJTk+XQAAAAAAI9wWQb37NnjzRwAAAAAAC9yec8gAAAAAODvizIIAAAAABZEGQQAAAAAC6IMAgAAAIAFUQYBAAAAwIIogwAAAABgQZRBAAAAALAgyiAAAAAAWBBlEAAAAAAsiDIIAAAAABZEGQQAAAAAC6IMAgAAAIAFUQYBAAAAwIIogwAAAABgQZRBAAAAALAgyiAAAAAAWBBlEAAAAAAsiDIIAAAAABZEGQQAAAAAC6IMAgAAAIAFUQYBAAAAwIIogwAAAABgQZRBAAAAALAgyiAAAAAAWBBlEAAAAAAsiDIIAAAAABZEGQQAAAAAC6IMAgAAAIAFFUoZnD9/vsLDwxUeHq5Zs2ZJkhISEhQREaH27dtr7ty5hRELAAAAACzD62UwISFBmzdv1qpVq7R69Wrt3r1ba9eu1dixY7VgwQJ9/PHH2rVrl7788ktvRwMAAAAAy/B6GQwODlZUVJT8/f3l5+enatWqKTk5WZUrV1bFihXl6+uriIgIxcfHezsaAAAAAFiG18vgnXfeqXr16kmSkpOTtW7dOtlsNgUHBzvfExISopSUFG9HAwAAAADLKLQJZPbt26cnnnhCL7zwgipWrCibzeZ8zRiTbxkAAAAAULAKpQx+//336t+/v0aOHKmHHnpI5cqVU2pqqvP11NRUhYSEFEY0AAAAALAEr5fBY8eOaciQIZo9e7bCw8MlSXXr1tWBAwd08OBB5eXlae3atWrRooW3owEAAACAZfh6e8B33nlHWVlZmjFjhnNdnz59NGPGDD377LPKyspSaGiowsLCvB0NAAAAACzD62Vw3LhxGjdu3BVf+/DDD72cBgAAAACsqdAmkAEAAAAAFB7KIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAAAAFkQZBAAAAAALogwCAAAAgAVRBgEAAADAgiiDAAAAAGBBlEEAAAAAsCDKIAAAAABYEGUQAAAAACyIMggAAID/196dB1VV/38cf16hC5kmmAKZM4pYtpDalBGYpDiZIHBFwdRywWwwKUuywlKxL2iO2m42pjnJQLEEoiYp0cKkLFNmoeNoWZJgKJJTmSTL5f7+8OcdUZZs8dy8r8dfnO1zXudzzoz37ecsIuKEVAyKiIiIiIg4IRWDIiIiIiIiTkjFoIiIiIiIiBNSMSgiIiIiIuKEVAyKiIiIiIg4IRWDIiIiIiIiTkjFoIiIiIiIiBNSMSgiIiIiIuKEVAyKiIiIiIg4IRWDIiIiIiIiTkjFoIiIiIiIiBNSMSgiIiIiIuKEVAyKiIiIiIg4IRWDIiIiIiIiTkjFoIiIiIiIiBNyqGJwy5YthIWFMWrUKNLT042OIyIiIiIictlyNTrAWceOHePll18mNzcXs9nMxIkTCQgIoH///kZHExERERERuew4TDFYXFzMXXfdhYeHBwD33Xcf27Zt49FHH213O6vVCsDRo0f/9YwAjXUnLsl+LpWqqiqjI/zjLqdzpPPj+HSOHNvleH5A58jRXU7nB3SOHN3leH5A5+hina2FztZGf5bJZrPZ/o1AF2vNmjXU1dUxd+5cALKzsykvLyc5Obnd7b788kseeOCBSxFRRERERETEYaWnp3PHHXf86fUdZmSwubkZk8lkn7bZbC2m2+Lv7096ejo9e/bExcXl34woIiIiIiLicKxWK8ePH8ff3/+itnOYYtDHx4cvv/zSPn38+HG8vLw63M7d3f2iql8REREREZHLTZ8+fS56G4d5m2hQUBAlJSWcOHGCP/74g4KCAoKDg42OJSIiIiIicllymJFBb29v5s6dy9SpU2lsbCQ6OpqBAwcaHUtEREREROSy5DAvkBEREREREZFLx2FuExUREREREZFLR8WgiIiIiIiIE1IxKCIiIiIi4oRUDIqIiIiIiDghFYMiIiIiIiJOyGXx4sWLjQ7h7L7//nsyMjLYsmULRUVF7Nu3D3d3d7y9vY2OJvKfUFhYyM6dO/H09KRbt272+ZmZmfj7+xuYTM6qqKigvr6eLl26kJ2dzcaNG/n999+5/vrrjY4m4vDKy8vtvwlKSkpIT0+ntLSUq666Ch8fH4PTyVmff/45np6euLm5kZeXR1ZWFrW1tdx8881GR3N6KSkpDBo0CHd3d6OjOByNDBosPT2dhIQEAG699VZuueUWABYuXMj69euNjCbyn7By5UrS0tKoqKhg0qRJbNq0yb4sIyPDwGRy1jvvvMNDDz3ExIkTmT9/Plu3bsXX15ecnBzeeOMNo+NJK5YtW2Z0BDlHUlIScOY3w9KlS/Hx8aFHjx4sWrSItLQ0g9MJwJIlS1izZg319fW88sorbN68mf79+/PRRx+RkpJidDynl5eXx4QJEygoKDA6isNxmI/OO6vU1FTy8vK48sorW8yPjY0lKiqKGTNmGJRMzvrpp5/aXd6rV69LlERaU1RUxMaNG3F1dWXKlCnMmDEDs9lMaGgo+oyqY8jJySE/P5/a2lrCw8MpLS3Fzc2NmJgYoqOjiY+PNzqiU5s/f/4F8z755BN+/fVXAF544YVLHUnakJWVRWpqKp6engBER0cTHR3Ngw8+aHAyKS4uZvPmzbi4uFBUVERmZiZms5n777+f8PBwo+M5vd69e7Ny5UoWL17M2rVriY2NJSQkRCOFqBg0nKurK01NTRfMP336NFdccYUBieR8cXFxVFRU4OXldUFxYTKZ+Pjjjw1KJgA2mw2TyQRA3759WbNmDbGxsXTv3t0+X4zV3NyM2WzmuuuuY8aMGbi5udmXWa1WA5MJgIeHB3l5ecyaNYurr74agNLSUu68806Dk8lZTU1NNDc34+Hhgdlsts83m8106qSbvByBu7s7P//8M15eXvj4+FBXV4fZbOaPP/7A1VU/t41mMpno378/aWlpFBcXk5mZyZIlS+jbty8+Pj68+OKLRkc0jK5Og82aNYuxY8cSGBhIz549MZlM1NTUUFpayty5c42OJ8B7773H5MmTSUpK4vbbbzc6jpxn9OjRTJkyhcTERAYOHMj111/Pq8uY/s4AAAooSURBVK++yqOPPkpDQ4PR8QQYNWoUDz74IKmpqTz22GMA7N+/nwULFhAaGmpwOnnmmWcIDg7mlVdeISEhgYCAADZs2EBUVJTR0eT/eXh4MHz4cACSk5NZtmwZJSUlrFixgtGjRxsbTgCIj48nOjqaMWPG0Lt3b6ZMmUJgYCA7duxg5syZRsdzeuf+Z35QUBBBQUE0NjZy4MABKisrDUxmPJNN91EZ7tixY5SUlFBTU0NzczM+Pj4EBgbqBTIOpLy8nOzsbJKTk42OIq0oKSnBy8sLPz8/+7zq6mrWr1/Pc889Z2AyOeuLL75gyJAh9ukffviByspK7rnnHgNTybl++eUXkpKS6NWrFzt27GDLli1GR5Lz/PDDD/z2228MHjyYXbt2cfLkSXuRKMarrKyksLCQH3/8EavVSo8ePRgxYgQDBw40OprTy87OJiYmxugYDknFoIiIiNhlZ2fz4Ycf6iVmIiJOQMWgiIiIiIiIE9JTxyIiIiIiIk5IxaCIiIiIiIgTUjEoIiIXqKqqYsCAAa1+vywxMZEBAwZw4sSJi2739ddf56677sJisWCxWBgzZgwJCQlUVFR0uG1ZWdmf+l5XeXk5EydOxGKxEBERwaZNm+zHdNNNN9n3bbFYiIyM5P333++wzcTERN5+++1Wl53ti48//rjDj0uff/yRkZGEhITwwgsv/OXvYlZVVXHbbbf9pW1bs2rVKgoLC1vMq66uZtiwYS3OeXv9/G9cOyIi8s/TpyVERKRVbm5uHDp0iCNHjnDdddcBUFdXx1dfffW32g0LC2PRokX26by8PKZNm8bWrVvp0qXL32rbZrMxZ84cli5dSlBQEEePHiUqKopBgwbh6uqKu7u7vWiBM29zDg8Px9/fnxtvvPFv7XvkyJGMHDmyw/XOP/5ff/2VyMhI7r77boYNG/a3MvwTysrK6N+/v306Ly+P1157jZqaGvu8jvr537p2RETkn6WRQRERaZWLiwuhoaEtPjFQUFBgL3hsNhspKSnExMQQFhZGaGgou3btorm5mWnTprF8+XIAiouLCQ4Opra2ttX9jB07Fj8/P/t+vv/+e2bMmMG4ceOwWCytjtwdO3aM2NhYxowZw8MPP8ysWbPIzc2loaGB+Ph4goKCAPDx8aF79+4cPXq01X17e3vTp08fKioqyM3NJS4uzr7s/Oldu3YxYcIEwsLCWLJkCU1NTS3aOnf948ePM3v2bEaPHk1YWBipqalt9nNtbS2nT5+mW7duHR7/+++/z5gxY4iIiGDq1KlUV1e32S7AoUOHiI2NZcKECYwYMYJHHnmE+vp6AF577TUiIiIYN24cDz30EDU1NaSnp7N3716WL1/ORx99xLFjxygsLLxgVLSjfu7o2hEREcegYlBERNo0duzYFiNpeXl59o+RHzp0iJqaGjIzM8nPzycqKoq1a9fSqVMnVqxYwaZNmygsLCQxMZEXX3yRHj16tLmfAQMG8O2339LU1MScOXN48sknyc3NJS0tjfXr1/P111+3WD8pKYlBgwaxdetWnn32WUpLS4Ezo5nnfksqMzOTU6dOMXjw4Fb3u3v3bg4fPsygQYM67IujR4/yzjvvkJeXx/79+8nKympz3eeff56+ffuybds2MjMzycrK4scffwQgPz8fi8XCqFGjCAgIICUlheeff56BAwe2e/z79+9n5cqVrFu3ji1bthASEsKbb77ZbuasrCzGjh1LVlYWBQUFVFVV8dlnn1FdXc2GDRvIyckhNzeXoUOHUl5ezgMPPIC/vz9PP/009957L97e3qxatQpfX98W7f6Zfm7v2hEREceg20RFRKRN/v7+uLi4sHfvXq655hpOnTrFDTfcAEC/fv144oknyMjIoLKykrKyMq666ioAvLy8SE5OZvbs2Tz22GMtPjjfGpPJhLu7OxUVFRw+fJhnn33Wvuz06dPs27cPPz8/+7yysjL7Or6+vvYRqnO99dZbpKamsm7dOtzd3e1tWSwWAKxWK56enqxYsYJrr722w76wWCx07twZgMjISIqKipg8eXKr6xYXF/PUU08B0LVrVz744AP7srO3iTY0NJCcnMzBgwcJCQkBaPf46+vrufvuu+1Zp0+fDpx5Rq8tTz31FDt37mTt2rVUVFRQU1NDXV0d3t7e3HjjjURFRREcHExwcDCBgYEd9kFrWutnaP/aERERx6BiUERE2hUZGcnmzZvp3r27vZACKCoqYvXq1cTGxjJy5Ej69evH5s2b7csPHjxIjx49KC8v73Afe/bsYfz48VitVrp27dpiRKm2tpauXbu2GB10c3Nr8cKVK664wv53Q0MDiYmJHDx4kIyMDHr37m1fdv4zg+cymUwt2mxsbGyx3MXFxf63zWbD1bXtf0JdXV0xmUz26crKSjw9PVusYzabWbhwIePHj2f58uUsWLCg3ePPzMxs0ebp06c5cuQIbm5ubeZISEjAarUSGhrK8OHDqa6uxmaz0alTJ9LS0tizZw8lJSUsXbqUYcOG8fTTT7fZ1vna6+ez2rp2RETEMeg2URERaZfFYmHbtm3k5+e3eJvnnj17GDFiBJMnT8bf35/CwkKsVitw5k2Tqamp5OTkcPLkSTZs2NBm+9nZ2VRVVREaGoqvr2+Lgq26uprw8HD27t3bYpvhw4eTkZEBnLl9s6SkxL5s3rx5/P77720WKG3p3r073333HfX19TQ2NrJ9+/YWy7du3UpDQwP19fVs3LiR4ODgNtsKDAwkJycHgJMnTzJt2rRW35hqNptJSkri3XffZd++fe0ef0BAACUlJfYXuWRkZLBixYp2j2nHjh3Ex8cTFhYGwDfffIPVamX//v2Eh4fj5+dHXFwc06dPZ8+ePcCZovf85yFb82f6ua1rR0REHINGBkVEpF3e3t74+fnRtWtXPDw87PPDwsJISUkhIiKCpqYmhg4dSkFBASdPniQhIYEFCxbg7e3NsmXLiImJsd8qmp+fz65duzCZTDQ3N+Pr60tqaqp9hGv16tUsWbKEdevW0dTUxOOPP87tt99OWVmZfd/z588nKSmJiIgIrrnmGvutk7t372b79u307duXSZMm2defN2/eBc+9nW/o0KEMGTKE0NBQevbsSUBAAAcOHLAv7927N5MnT+bUqVPce++97T7/tmjRIhYvXkxERAQ2m424uDj8/f359NNPL1j3jjvuICIigv/973+89957bR4/nLntc+bMmQD07NmTpUuX0tjYSF1d3QWfl8jIyGDu3LnEx8fTuXNnunTpwpAhQzh8+DAxMTGEhoYyfvx4OnfujLu7OwsWLAAgJCSEl156icbGxjaP8c/2c1vXjoiIOAaT7a9+2EhERMRBxMXFcd999zFu3Dijo4iIiPxn6DZRERERERERJ6SRQRERERERESekkUEREREREREnpGJQRERERETECakYFBERERERcUIqBkVERERERJyQikEREREREREn9H9U+psBY9gb7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using code from the module lab\n",
    "maxdelq = pd.unique(df_clean.MaxDelq2PublicRecLast12M.ravel())\n",
    "\n",
    "df_clean['percent'] = 0\n",
    "\n",
    "for i in maxdelq:\n",
    "    count = 1 / df_clean[df_clean.MaxDelq2PublicRecLast12M == i].count()['RiskPerformance']\n",
    "    index_list = df_clean[df_clean['MaxDelq2PublicRecLast12M'] == i].index.tolist()\n",
    "    for ind in index_list:\n",
    "        df_clean.loc[ind, 'percent'] = count * 100\n",
    "        \n",
    "group = df_clean[['percent','MaxDelq2PublicRecLast12M','RiskPerformance']].groupby(['MaxDelq2PublicRecLast12M','RiskPerformance']).sum()\n",
    "\n",
    "my_plot = group.unstack().plot(kind='bar', stacked=True, title=\"Risk performance based on max delinquecies recorded in last 12 months\", figsize=(15,7))\n",
    "\n",
    "red_patch = mpatches.Patch(color='orange', label='good')\n",
    "blue_patch = mpatches.Patch(color='blue', label='bad')\n",
    "my_plot.legend(handles=[red_patch, blue_patch], frameon = True)\n",
    "\n",
    "my_plot.set_xlabel(\"MaxDelq2PublicRecLast12M\")\n",
    "my_plot.set_ylabel(\"RiskPerformance (%good/bad)\")\n",
    "my_plot.set_ylim([0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Previous cell has created a new 'percent' column\n",
    "df_clean.head(5)\n",
    "\n",
    "# Remove 'percent' column created in previous cell\n",
    "df_clean = df_clean.drop('percent', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two categorical-categorical bar-plots show the percentage of 'good' and 'bad' credit applicants for each level of maximum delinquency recorded.\n",
    "We briefly discuss our findings in the following points:\n",
    "\n",
    "- **Risk performance based on max delinquency ever**\n",
    "    - In presence of a 'derogatory comment' (level 2 on x-axis), most of the credit applicants fall under the 'bad' category (around 80%)\n",
    "    - We find perplexing that for level 3 (120+ delinquency) the percentage of 'good' and 'bad' are almost equal\n",
    "    - As we might expect, the percentage of 'bad' applicants decreases as the days of recorded delinquency decrease as well (levels 4,5,6)\n",
    "    - We cannot say anything about level 7 (unknown delinquency) but portion of 'good' and 'bad' applicants are almost equal\n",
    "    - It is puzzling that at level 8 (never delinquent) there is still a portion of 'bad' applicants (~35%) <br><br>\n",
    "- **Risk performance based on max delinquency over last 12 months**\n",
    "    - **Considerations exposed above apply also here**. We notice that in this case, for delinquencies of 120+ days, all applicants are labeled as 'bad' (as we would expect). It is somewhat puzzling that portion of 'bad' is still higher at level 3 (60 days) than at level 2 (90 days) - we would expect the opposite."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous-categorical plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1712f630>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "flierprops = dict(marker='o', markerfacecolor='green', markersize=6,\n",
    "                  linestyle='none')\n",
    "df_clean.boxplot(column=['NumTotalTrades'], by=['RiskPerformance'], flierprops=flierprops, figsize=(10,7))\n",
    "df_clean.boxplot(column=['ExternalRiskEstimate'], by=['RiskPerformance'], flierprops=flierprops, figsize=(10,7))\n",
    "df_clean.boxplot(column=['MSinceMostRecentTradeOpen'], by=['RiskPerformance'], flierprops=flierprops, figsize=(10,7))\n",
    "df_clean.boxplot(column=['NumSatisfactoryTrades'], by=['RiskPerformance'], flierprops=flierprops, figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a14bda7b8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean[df_clean['RiskPerformance'] == 'Good'][['NumTotalTrades']].hist(figsize=(7,7), bins=40)\n",
    "df_clean[df_clean['RiskPerformance'] == 'Bad'][['NumTotalTrades']].hist(figsize=(7,7), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a149a5be0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean[df_clean['RiskPerformance'] == 'Good'][['ExternalRiskEstimate']].hist(figsize=(7,7), bins=40)\n",
    "df_clean[df_clean['RiskPerformance'] == 'Bad'][['ExternalRiskEstimate']].hist(figsize=(7,7), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a14997240>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbYAAAGsCAYAAACxRIKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xt0lPWdx/HPkBsqtBadACfhcNRy6Um47KKyEQyCCAlJjA5UQSC4mkU4yk0BuZl0K+Gi2NiWYrUi20V6BAEN5GDQU9RC4o1sC0RoRSFIIISJFyCG3Ca//YPDHMMlZCYDmfx4v/7KPHmeeX7fTODNMxkmDmOMEQAAlmjT0gsAACCQCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgQcCUlJerRo4fGjRt33ufmzJmjHj166Ntvv9U//vEPjR8/XikpKUpOTlZ6err2798vSdqzZ4+mTp3aYmvzx+7du5WRkeG93aNHD6WkpCg1NVX33Xefhg8frpEjR2rPnj1+r98fp06dUlpamiTp7bffVmpqqlJTU3X77bfrzjvv9N7euXOn3+fYs2ePhgwZ0qR9Dx8+rBkzZmjo0KG69957NWrUKK1fv97vcwPnCm3pBcBOEREROnjwoI4cOaKoqChJUmVlpf7v//5PklRfX6/HHntMr732mmJiYiRJOTk5+q//+i/99a9/Va9evfS73/2uRdbmry+//FJlZWUNtv35z39Whw4dvLdXrlyphQsXau3atc06ly9OnDjhjel9992n++67T9KZkHfr1k2PPvroFVtLSUmJxo0bp+nTp+s3v/mNHA6HysrKNHPmTB06dEhPPfXUFVsL7EXYcFmEhIQoMTFRmzdv1qRJkyRJ7777ru6++2699tpratOmjU6dOqXKykrvMffee6/atWsnj8ejnTt36tlnn1Vubq7mzJmjdu3a6V//+peOHTumHj16aOnSpbruuuu0a9cuLVy4UKdPn1ZYWJhmz56tuLg4ffXVV8rKytL3338vj8ej8ePHa9SoUU1a21lr167V6tWr1aZNG91444165plndNNNN2nnzp1asmSJ6uvrJUmPPfaYevfurd/97nc6deqU5s6dq8WLF5/3Namrq1Npaal++tOfere99NJLevfdd1VfX6+oqChlZmaqY8eOcrvdyszM1IEDB9SmTRuNHj1aaWlpOnXqlLKysvTFF1+otrZWcXFxmj17tkJDQ9WrVy9NnDhR+fn5On78uNLT0/XQQw9p7ty5qqqqUmpqqjZu3KiQkJCLPm7jx4/XT3/6Ux04cEBjxoxRr1699Pzzz6umpkZut1t33HGHFi1aJEn6y1/+oj//+c9q166dunfv3uB+LjbXK6+8ouTkZN1///3efTt27Kjs7GzdfffdGj9+vHbs2KG8vDzV19fr6NGj6tixo5YsWaKOHTv6NT+uQgYIsMOHD5u+ffuaPXv2mISEBO/2CRMmmH/961+me/fu5ptvvjGvvfaa6d27txkyZIiZOXOmefPNN01lZaUxxpiPP/7YJCUlGWOMefrpp82DDz5oqqurTU1NjbnvvvvM+vXrTU1NjRkwYIB5//33jTHG7NmzxyQnJ5vq6mozYsQIU1RUZIwx5uTJkyYxMdH8/e9/b/LaCgoKzNChQ80333xjjDFmw4YNJjEx0dTX15u0tDSTm5trjDFm37595le/+pV3n4kTJ3rvs3v37iY5OdkkJyebAQMGmCFDhphnn33WlJeXG2OMeeutt8z06dNNbW2tMcaYN954w6SnpxtjjHn88cfN0qVLvetPSkoyxcXFZs6cOeZ///d/jTHG1NXVmZkzZ5pXXnnFe77Vq1d7vxaxsbGmqqrKO/O5nn76afPqq6822DZu3Dgzd+5c7+0ZM2aYjz/+2BhjTEVFhenfv7/Zs2eP2bt3r4mLizPHjx83xhjzzDPPmMGDB19yruTkZO/jda7777/fvPvuu2bDhg2mb9++5sCBA8YYY55//nkzZcoUY4zxa35cfbhiw2UTGxurkJAQFRUV6YYbbtAPP/zQ4F/2//mf/6lf/vKX+uyzz/TZZ5/pT3/6k/70pz9d8Octd955p8LDwyVJ3bt314kTJ/TFF1+oTZs2uuuuu7zn27x5s7788kt9/fXXmjdvnvf4qqoq7d27V/Hx8U1a2/bt2zVixAjv04gul0tZWVkqKSlRYmKifv3rX2vbtm2644479OSTT170a3D2qcjPP/9cEydOVP/+/XXDDTdIkt5//33t2bNHI0eOlHTm6dnTp09LkgoKCjRr1ixJUvv27ZWbmytJ+uCDD7Rnzx7v16iqqqrB+e6++25JUkxMjGpqahpcETfVrbfe6v14yZIl+tvf/qY//vGPOnDggKqrq1VZWamioiINGDBATqdTkvTggw9qx44dl5xLOnPleiE1NTVyOBySpAEDBuimm26SJD3wwANKTU31e/6IiAifvwZo3QgbLqt7771XmzZtUocOHbx/OUlnfh61e/dupaena/DgwRo8eLCefPJJJScnKz8/Xz/72c8a3E/btm29HzscDhljFBIS4v2L8KwvvvhCxhi1b99eOTk53u3l5eVq37693G73Jdcmyfs0448ZY1RXV6fRo0dr8ODBys/P1/bt27V8+XLl5eU1+nWIiYnR3LlzNWfOHP3iF79QdHS06uvrGzxdVlNToxMnTkiSQkNDG8x2+PBh/exnP1N9fb1++9vf6pZbbpEknTx5ssF+Z/8SP7vN+PFWsNdee63343HjxqlHjx668847lZiYqF27dnnv88f3/eOnNxub69/+7d/0ySefaOjQoQ3OWVZWppKSEvXq1Uv5+fnn3d/Z21difrR+vCoSl1Vqaqry8vK0ZcsWJScne7e3bdtWL730UoNX4rndblVUVJz385qLufnmm+VwOJSfny9J+vzzzzVhwgTddNNNatu2rTdspaWlSk5OVlFRUZPWJp25QtyyZYv3FZIbNmzQ9ddfr65du2r06NHat2+fXC6Xnn32WZ08eVJut1shISEXvRqRpOTkZPXu3dv787eBAwdq/fr1qqiokCT99re/1ezZsyVJcXFx2rBhg6Qzr2qcMGGCiouLNXDgQP3P//yPjDGqqanR5MmT9frrrzf6dQoNDZXH4/H5L/mTJ09qz549mjlzpoYNG6Zjx47p66+/Vn19vQYMGKD8/HwdO3ZMkvTWW295j2tsrsmTJ+udd95psP+xY8f05JNPasyYMerYsaMk6eOPP/a+EOeNN97Q4MGDvfft6/y4+nDFhsuqY8eOuuWWW9S+fXtdf/313u3R0dH6wx/+oOzsbB07dkwRERFq3769Fi1apJtvvrnBldXFhIeH6/e//70WLVqk5557TmFhYfr973+v8PBwrVixQllZWXr11VdVV1enadOmqV+/fiopKbnk2qQzT4U9/PDDmjBhgurr69WhQwe9/PLLatOmjWbOnKlFixbpxRdflMPh0BNPPKHo6Gh5PB794Q9/0BNPPKHly5dfcM3PPPOM7r33Xm3fvl2//OUvVVZWpgceeEAOh0OdO3fWkiVLJEkZGRn61a9+pZSUFBlj9Nhjjyk2Nlbz589XVlaWUlJSVFtbqzvuuEPp6emNfp2cTqd69+6tpKQkrVmz5ryr4Yv5yU9+ookTJ+r+++/Xtddeq44dO+rf//3fdejQIcXFxWnWrFmaMGGCrrvuOvXu3dt7XGNzde7cWWvXrtWLL76oP/7xjwoNDVVERIQeeOABPfjggw0em1mzZsntduvnP/+5fv3rX0uSX/Pj6uMwXKsDCCIbN27U1q1b9fLLL7f0UtBK8VQkAMAqXLEBAKzCFRsAwCqEDQBglaB5VWRVVZWKiorkdDobfcsfAMDVw+PxyO12KzY2tsH/Z21M0IStqKhIY8eObellAACC0Jo1axq8K05jgiZsZ9+aZ82aNerUqVOz7quoqEixsbGBWFaLYYbgwAzBgRmCQ0vMcOzYMY0dO9bbiKYImrCdffqxU6dOio6ObtZ9lZWVNfs+WhozBAdmCA7MEBxacgZffkTFi0cAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWMXKsPX8Rcwl96mp9VyBlQAArrSg+UWjgXTdtW2V8lROo/tsfiH1Cq0GAHAlWXnFBgC4ehE2AIBVCBsAwCpXbdia8uIRXmACAK2PlS8eaYrwsBBeYAIAFrpqr9gAAHYibAAAqxA2AIBVCBsAwCpNCltFRYWSk5NVUlLSYPvrr7+u8ePHe28fPXpUY8eOVUJCgiZPnqwffvghsKsFAOASLhm2Xbt2acyYMSouLm6w/csvv9Qrr7zSYNt///d/66GHHlJeXp5iY2O1YsWKgC4WAIBLuWTY1q1bp8zMTEVGRnq31dTUKCMjQ1OnTvVuq62t1Weffabhw4dLklwul/Ly8i7DkgEAuLhL/j+2rKys87a98MILGjlypKKjo73bvvvuO7Vr106hoWfu0ul0qqysLIBLBQDg0nz+D9r5+fkqLS3V3Llz9cknn3i3G2PkcDga7Hvu7aYoKipqdhD79evXrON/rLCwMGD31ZrOHSjMEByYITgwg+/cbrfPx/gcttzcXO3fv1+pqamqrKxUeXm5pk+frueff16nTp2Sx+NRSEiI3G53g6cvmyo2NrbBlWBLC2QkfVFYWNhi5w4UZggOzBAcmME/575osSl8DtvixYu9H3/yySdavny5XnzxRUnSrbfeqi1btiglJUVvv/224uPjfV4QAADNEdD/x5aZmal169ZpxIgR2rlzp6ZPnx7IuwcA4JKafMW2bdu287b1799f/fv3996OiorS6tWrA7MyAAD8wDuPAACsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCs0uSwVVRUKDk5WSUlJZKktWvXKjk5WSkpKZo7d65qamokSfv27ZPL5dLw4cM1f/581dXVXZ6VAwBwAU0K265duzRmzBgVFxdLkg4ePKiVK1fqjTfe0KZNm1RfX6+//OUvkqRZs2YpIyNDW7dulTFG69atu2yLBwDgXE0K27p165SZmanIyEhJUnh4uDIzM9WuXTs5HA51795dR48e1ZEjR1RVVaW+fftKklwul/Ly8i7f6gEAOEdoU3bKyspqcDsqKkpRUVGSpG+//VZr1qzR4sWLdfz4cTmdTu9+TqdTZWVlPi2oqKjI52PO1a9fv2Yd/2OFhYUBu6/WdO5AYYbgwAzBgRl853a7fT6mSWG7mLKyMqWnp2vkyJHq37+/CgsL5XA4vJ83xjS43RSxsbGKjo5uzrICKpCR9EVhYWGLnTtQmCE4MENwYAb/nH1dhy/8flXkV199pdGjR+v+++/X448/Lknq1KlTg7qWl5d7n74EAOBK8CtsFRUVevTRRzVt2jQ98sgj3u1RUVGKiIjwXqrm5OQoPj4+MCsFAKAJ/Hoqcv369SovL9eqVau0atUqSdKQIUM0bdo0LVu2TAsWLFBFRYViYmKUlpYW0AUDANAYn8K2bds2SdLDDz+shx9++IL79OzZU+vXr2/2wgAA8AfvPAIAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsEqTw1ZRUaHk5GSVlJRIkgoKCpSSkqJhw4YpOzvbu9++ffvkcrk0fPhwzZ8/X3V1dYFfNQAAF9GksO3atUtjxoxRcXGxJKmqqkrz5s3TihUrtGXLFhUVFenDDz+UJM2aNUsZGRnaunWrjDFat27dZVs8AADnalLY1q1bp8zMTEVGRkqSdu/era5du6pLly4KDQ1VSkqK8vLydOTIEVVVValv376SJJfLpby8vMu3egAAzhHalJ2ysrIa3D5+/LicTqf3dmRkpMrKys7b7nQ6VVZW5tOCioqKfD7mXP369WvW8T9WWFgYsPtqTecOFGYIDswQHJjBd2632+djmhS2c9XX18vhcHhvG2PkcDguut0XsbGxio6O9mdZl0UgI+mLwsLCFjt3oDBDcGCG4MAM/jn7ug5f+PWqyE6dOjWoqNvtVmRk5Hnby8vLvU9fAgBwJfgVtj59+ujgwYM6dOiQPB6PcnNzFR8fr6ioKEVERHgvVXNychQfHx/QBQMA0Bi/noqMiIjQkiVLNGXKFFVXV2vQoEFKSEiQJC1btkwLFixQRUWFYmJilJaWFtAFAwDQGJ/Ctm3bNu/HcXFx2rRp03n79OzZU+vXr2/+ygAA8APvPAIAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGCVZoUtJydHSUlJSkpK0tKlSyVJ+/btk8vl0vDhwzV//nzV1dUFZKEAADSF32E7ffq0srKytHr1auXk5Gjnzp0qKCjQrFmzlJGRoa1bt8oYo3Xr1gVyvQAANMrvsHk8HtXX1+v06dOqq6tTXV2dQkNDVVVVpb59+0qSXC6X8vLyArZYAAAuJdTfA9u1a6dp06YpMTFR11xzjW677TaFhYXJ6XR693E6nSorKwvIQgEAaAq/w/bPf/5TGzZs0Pvvv6/27dtr5syZys/Pl8Ph8O5jjGlwuymKioqaHcN+/fo16/gfKywsDNh9taZzBwozBAdmCA7M4Du32+3zMX6HbceOHYqLi9MNN9wg6czTjitXrmywiPLyckVGRvp0v7GxsYqOjvZ3WQEXyEj6orCwsMXOHSjMEByYITgwg39KSkp8Psbvn7H17NlTBQUFqqyslDFG27Zt0+23366IiAhv0XNychQfH+/vKQAA8JnfV2wDBw7U3r175XK5FBYWpl69emnixIm65557tGDBAlVUVCgmJkZpaWmBXC8AAI3yO2ySNHHiRE2cOLHBtp49e2r9+vXNWhQAAP7inUcAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFZpVti2bdsml8ulxMRELVy4UJJUUFCglJQUDRs2TNnZ2QFZJAAATeV32A4fPqzMzEytWLFCmzZt0t69e/Xhhx9q3rx5WrFihbZs2aKioiJ9+OGHgVwvAACN8jts7733nkaMGKFOnTopLCxM2dnZuuaaa9S1a1d16dJFoaGhSklJUV5eXiDXCwBAo0L9PfDQoUMKCwvTpEmTVFpaqrvuukvdunWT0+n07hMZGamysjKf7reoqMjnY87Vr1+/Zh3/Y4WFhQG7r9Z07kBhhuDADMGBGXzndrt9PsbvsHk8Hu3cuVOrV6/Wtddeq8mTJ6tt27ZyOBzefYwxDW43RWxsrKKjo/1dVsAFMpK+KCwsbLFzBwozBAdmCA7M4J+SkhKfj/E7bDfeeKPi4uLUoUMHSdLQoUOVl5enkJAQ7z5ut1uRkZH+ngIAAJ/5/TO2wYMHa8eOHTp58qQ8Ho+2b9+uhIQEHTx4UIcOHZLH41Fubq7i4+MDuV4AABrl9xVbnz59lJ6eroceeki1tbUaMGCAxowZo5tvvllTpkxRdXW1Bg0apISEhECuFwCARvkdNkkaNWqURo0a1WBbXFycNm3a1KxFAQDgL955BABgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCFsjamo9AdkHAHDlNOs/aNsuPCxEKU/lNLrP5hdSr9BqAABNwRUbAMAqhA0AYBXCBgCwCmEDAFiFsAEArELYAABWIWwAAKsQNgCAVQgbAMAqhA0AYBXCBgCwCmEDAFiFsAEArELYmulSv7aGX2sDAFcWv7ammS71q234tTYAcGVxxQYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGCVgIRt6dKlmjNnjiRp3759crlcGj58uObPn6+6urpAnAIAgCZpdtg++ugjvfXWW97bs2bNUkZGhrZu3SpjjNatW9fcUwAA0GTNCtv333+v7OxsTZo0SZJ05MgRVVVVqW/fvpIkl8ulvLy85q8SAIAmalbYMjIyNGPGDP3kJz+RJB0/flxOp9P7eafTqbKysuatEAAAH4T6e+Cbb76pzp07Ky4uThs3bpQk1dfXy+FwePcxxjS43RRFRUXNjmG/fv2adXygFRYWXtHjggkzBAdmCA7M4Du32+3zMX6HbcuWLXK73UpNTdWJEydUWVkph8PRYBHl5eWKjIz06X5jY2MVHR3t77KCkj+hLSwsDLpA+4oZggMzBAdm8E9JSYnPx/gdtlWrVnk/3rhxoz799FMtXrxYycnJ3uFzcnIUHx/v7ykAAPCZ32G7mGXLlmnBggWqqKhQTEyM0tLSAn0KAAAuKiBhc7lccrlckqSePXtq/fr1gbhbAAB8xjuPAACsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYbvMamo9AdkHANA0AX/nETQUHhailKdyGt1n8wupV2g1AGA/rtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCsQtgAAFYhbAAAqxA2AIBVCBsAwCqEDQBgFcIGALAKYQMAWIWwAQCs0qywLV++XElJSUpKStJzzz0nSSooKFBKSoqGDRum7OzsgCwSAICm8jtsBQUF2rFjh9566y29/fbb+vzzz5Wbm6t58+ZpxYoV2rJli4qKivThhx8Gcr0AADTK77A5nU7NmTNH4eHhCgsL0y233KLi4mJ17dpVXbp0UWhoqFJSUpSXlxfI9QIA0Ci/w9atWzf17dtXklRcXKx33nlHDodDTqfTu09kZKTKysqav0oAAJootLl3sH//fj322GOaPXu2QkJCVFxc7P2cMUYOh8On+ysqKmp2DPv169es41tCYWFhk7a1NswQHJghODCD79xut8/HNCtshYWFmjp1qubNm6ekpCR9+umnDRbhdrsVGRnp033GxsYqOjq6Octqlc6NcWFhYasM9I8xQ3BghuDADP4pKSnx+Ri/n4osLS3V448/rmXLlikpKUmS1KdPHx08eFCHDh2Sx+NRbm6u4uPj/T0FAAA+8/uKbeXKlaqurtaSJUu820aPHq0lS5ZoypQpqq6u1qBBg5SQkBCQhQIA0BR+h23BggVasGDBBT+3adMmvxcEAEBz8M4jQaCm1nPetnOfx77QPgCA8zX7VZFovvCwEKU8ldPoPptfSL1CqwGA1o0rNgCAVQgbAMAqhA0AYBXCBgCwCmEDAFiFsAEArELYAABWIWwAAKsQtlaiKe88wruTAADvPNJq8O4kANA0XLEBAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKoQNAGAVwgYAsAphAwBYhbABAKxC2CxSU+sJyD4A0JqFtvQCEDjhYSFKeSqn0X02v5B6hVYDAC2DKzYAgFUI21WGpysB2I6nIq8yPF0JwHZcseE8wXRVF0xrAdA6cMWG8wTTVV0wrQVA68AVGwDAKoQNVwWe0gSuHjwViasCT2kCVw+u2OCXplzd9PxFzBVYCVdaABriig1+CaYroGBaC4CWd1mu2DZv3qwRI0Zo2LBhWrNmzeU4BQAAFxTwK7aysjJlZ2dr48aNCg8P1+jRo9W/f3/9/Oc/D/Sp0MrV1HoUHhbS0stoEU2Z/Wr++iD4BfP3cMDDVlBQoP/4j//Q9ddfL0kaPny48vLy9MQTTzR6nMdz5uckx44dC8g6aiu/bfTzJSUlV2SfK3WeYN3nUtKz3mv086/Ov6dF1+t2u5s0hz+aMnsgXM4ZrhRmCA7nznAlvofPNuFsI5rCYYwxzT7zj7z88suqrKzUjBkzJElvvvmmdu/erWeffbbR43bu3KmxY8cGcikAAEusWbNGt956a5P2DfgVW319vRwOh/e2MabB7YuJjY3VmjVr5HQ6FRLC0y8AgDNXam63W7GxsU0+JuBh69Spk3bu3Om97Xa7FRkZecnj2rZt2+QaAwCuHl27dvVp/4C/KvKOO+7QRx99pG+//VanT5/Wu+++q/j4+ECfBgCACwr4FVvHjh01Y8YMpaWlqba2VqNGjVLv3r0DfRoAAC4o4C8eAQCgJfGWWgAAqxA2AIBVCBsAwCrNxE4HAAAGPklEQVSEDQBgFavC1lrffHn58uVKSkpSUlKSnnvuOUln3posJSVFw4YNU3Z2dguvsOmWLl2qOXPmSJL27dsnl8ul4cOHa/78+aqrq2vh1TVu27ZtcrlcSkxM1MKFCyW1vschJyfH+720dOlSSa3ncaioqFBycrL3LZsu9rUP5nnOnWHt2rVKTk5WSkqK5s6dq5qaGkmta4azXn/9dY0fP957++jRoxo7dqwSEhI0efJk/fDDD1d6qRdnLHHs2DEzePBg891335kffvjBpKSkmP3797f0si4pPz/fPPjgg6a6utrU1NSYtLQ0s3nzZjNo0CDz9ddfm9raWvPII4+YDz74oKWXekkFBQWmf//+5umnnzbGGJOUlGT+/ve/G2OMmTt3rlmzZk1LLq9RX3/9tRk4cKApLS01NTU1ZsyYMeaDDz5oVY9DZWWlue2228w333xjamtrzahRo0x+fn6reBz+8Y9/mOTkZBMTE2MOHz5sTp8+fdGvfbDOc+4MBw4cMPfcc485deqUqa+vN7NnzzarVq0yxrSeGc7av3+/ufPOO824ceO82yZOnGhyc3ONMcYsX77cPPfcc1d8vRdjzRXbj998+dprr/W++XKwczqdmjNnjsLDwxUWFqZbbrlFxcXF6tq1q7p06aLQ0FClpKQE/Szff/+9srOzNWnSJEnSkSNHVFVVpb59+0qSXC5XUM/w3nvvacSIEerUqZPCwsKUnZ2ta665plU9Dh6PR/X19Tp9+rTq6upUV1en0NDQVvE4rFu3TpmZmd53Kdq9e/cFv/bB/H117gzh4eHKzMxUu3bt5HA41L17dx09erRVzSBJNTU1ysjI0NSpU73bamtr9dlnn2n48OGSgmsGyaJfNHr8+HE5nU7v7cjISO3evbsFV9Q03bp1835cXFysd955R+PGjTtvlrKyspZYXpNlZGRoxowZKi0tlXT+4+F0OoN6hkOHDiksLEyTJk1SaWmp7rrrLnXr1q1VPQ7t2rXTtGnTlJiYqGuuuUa33XabwsLCWsXjkJWV1eD2hf48l5WVBfX31bkzREVFKSoqSpL07bffas2aNVq8eHGrmkGSXnjhBY0cOVLR0dHebd99953atWun0NAzCQmmGSSLfsbm75svB4v9+/frkUce0ezZs9WlS5dWNcubb76pzp07Ky4uzruttT0eHo9HH330kRYtWqS1a9dq9+7dOnz4cKua4Z///Kc2bNig999/X9u3b1ebNm2Un5/fqmY462LfP63t+0o68zsqJ0yYoJEjR6p///6taob8/HyVlpZq5MiRDbZfaM3BNIM1V2z+vvlyMCgsLNTUqVM1b948JSUl6dNPP5Xb7fZ+Pthn2bJli9xut1JTU3XixAlVVlbK4XA0mKG8vDyoZ7jxxhsVFxenDh06SJKGDh2qvLy8Br9pItgfhx07diguLk433HCDpDNPD61cubJVPQ5nderU6YJ/Bs7dHuzzfPXVV0pPT9f48eP1yCOPSDp/tmCeITc3V/v371dqaqoqKytVXl6u6dOn6/nnn9epU6fk8XgUEhISdH82rLlia61vvlxaWqrHH39cy5YtU1JSkiSpT58+OnjwoA4dOiSPx6Pc3NygnmXVqlXKzc1VTk6Opk6dqiFDhmjx4sWKiIhQYWGhpDOv1gvmGQYPHqwdO3bo5MmT8ng82r59uxISElrV49CzZ08VFBSosrJSxhht27ZNt99+e6t6HM662J+BqKioVjNPRUWFHn30UU2bNs0bNUmtaobFixfrnXfeUU5OjhYuXKjY2Fi9+OKLCgsL06233qotW7ZIkt5+++2gmsGaK7bW+ubLK1euVHV1tZYsWeLdNnr0aC1ZskRTpkxRdXW1Bg0apISEhBZcpX+WLVumBQsWqKKiQjExMUpLS2vpJV1Unz59lJ6eroceeki1tbUaMGCAxowZo5tvvrnVPA4DBw7U3r175XK5FBYWpl69emnixIm65557Ws3jcFZERMRF/wy0lu+r9evXq7y8XKtWrdKqVaskSUOGDNG0adNazQyNyczM1Jw5c/TSSy+pc+fO+s1vftPSS/LiTZABAFax5qlIAAAkwgYAsAxhAwBYhbABAKxC2AAAViFsAACrEDYAgFUIGwDAKv8P53rKFGzrch8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean[df_clean['RiskPerformance'] == 'Good'][['MSinceMostRecentTradeOpen']].hist(figsize=(7,7), bins=40)\n",
    "df_clean[df_clean['RiskPerformance'] == 'Bad'][['MSinceMostRecentTradeOpen']].hist(figsize=(7,7), bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x105193358>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean[df_clean['RiskPerformance'] == 'Good'][['NumSatisfactoryTrades']].hist(figsize=(7,7), bins=40)\n",
    "df_clean[df_clean['RiskPerformance'] == 'Bad'][['NumSatisfactoryTrades']].hist(figsize=(7,7), bins=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entire dataset presents some difficulties in order to assess the probability of default (i.e. 'bad' category) of a single applicant.\n",
    "\n",
    "Nevertheless, the analyzed data have some degree of consistency:\n",
    "- the number of satisfactory trade lines (**NumSatisfactoryTrades**) is directly proportional to the number of total trades (**NumTotalTrades**), as expected\n",
    "- the average number of monts in file (**AverageMInFile**) is directly proportional to the trade line seniority (**MSinceOldestTradeOpen**), as expected\n",
    "- the older the most recent trade line (**MSinceMostRecentTradeOpen**), the higher the change of multiple trade lines opened in the last year (**NumTradesOpeninLast12M**), as expected\n",
    "- a better external credit rating (**ExternalRiskEstimate**) means a higher portion of trade lines that have never been delinquent (**PercentTradesNeverDelq**), as expected\n",
    "\n",
    "Categorical features show some flaws, though. In general, we can gather the evidence that the deeper a delinquency has resulted (in terms of days), the higher the default probability; this conclusion should be further verified because of the aforementioned categorical features' incosistencies.\n",
    "\n",
    "In perspective of a predictive machine learning model, we suggest to consider the following features:\n",
    "- ExternalRiskEstimate\n",
    "- AverageMInFile\n",
    "- PercentTradesNeverDelq \n",
    "- MaxDelq2PublicRecLast12M\n",
    "- NumSatisfactoryTrades\n",
    "- MSinceMostRecentTradeOpen\n",
    "- MSinceOldestTradeOpen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation 1 - binning\n",
    "**ExternalRiskEstimate** is a feature that can split in equal-width bins. The range of such feature spans between 37 and 93, so our bins will represent a value of 10 each.\n",
    "The frequency in each bin will be variable.\n",
    "\n",
    "With such transformation we are able to better compare different levels of credit risk estimates by grouping them in classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExternalRiskEstimate\n",
      "(35, 45]      4\n",
      "(45, 55]     22\n",
      "(55, 65]    247\n",
      "(65, 75]    319\n",
      "(75, 85]    240\n",
      "(85, 95]    110\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Define bins, we consider the feature range\n",
    "bins = [35, 45, 55, 65, 75, 85, 95]\n",
    "\n",
    "# Give each bin a proper label\n",
    "labels = ['35<score>=45', '45<score>=55',\\\n",
    "          '55<score>=65', '65<score>=75', '75<score>=85',\\\n",
    "          '85<score>=95']\n",
    "\n",
    "# Create 'binned' feature\n",
    "df_clean['ExternalRiskEstimate-binned'] = pd.cut(df_clean['ExternalRiskEstimate'], bins = bins, labels = labels)\n",
    "\n",
    "# Count levels in each bin\n",
    "s = df_clean.groupby(pd.cut(df_clean['ExternalRiskEstimate'], bins=bins)).size()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation 2 - ratio\n",
    "Features **NumSatisfactoryTrades** and  **NumTotalTrades** are related, as the first is a subset of the second. We can compute a ratio, indicating the portion of trade lines that have been satisfactory so far for each credit applicant.\n",
    "\n",
    "In this way, we build a synthetic indicator that catches the portion of satisfactory trade lines over the total count of trade lines ever opened by the credit applicant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>RiskPerformance</th>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <th>...</th>\n",
       "      <th>NumInqLast6M</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <th>ExternalRiskEstimate-binned</th>\n",
       "      <th>SatisfactoryTradesRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "      <td>308</td>\n",
       "      <td>7</td>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good</td>\n",
       "      <td>75</td>\n",
       "      <td>381</td>\n",
       "      <td>15</td>\n",
       "      <td>108</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>77.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bad</td>\n",
       "      <td>58</td>\n",
       "      <td>216</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55&lt;score&gt;=65</td>\n",
       "      <td>0.433962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bad</td>\n",
       "      <td>73</td>\n",
       "      <td>191</td>\n",
       "      <td>17</td>\n",
       "      <td>53</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>0.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Good</td>\n",
       "      <td>89</td>\n",
       "      <td>280</td>\n",
       "      <td>23</td>\n",
       "      <td>107</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85&lt;score&gt;=95</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
       "0            Good                    71                    308   \n",
       "1            Good                    75                    381   \n",
       "2             Bad                    58                    216   \n",
       "3             Bad                    73                    191   \n",
       "4            Good                    89                    280   \n",
       "\n",
       "   MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
       "0                          7             104                     39   \n",
       "1                         15             108                     20   \n",
       "2                         12              86                     23   \n",
       "3                         17              53                     19   \n",
       "4                         23             107                     16   \n",
       "\n",
       "   NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   PercentTradesNeverDelq MaxDelq2PublicRecLast12M  ... NumInqLast6M  \\\n",
       "0                    98.0                        6  ...            0   \n",
       "1                    95.0                        6  ...            2   \n",
       "2                    88.0                        4  ...            1   \n",
       "3                   100.0                        7  ...            0   \n",
       "4                   100.0                        7  ...            0   \n",
       "\n",
       "   NumInqLast6Mexcl7days  NetFractionRevolvingBurden  \\\n",
       "0                      0                        75.0   \n",
       "1                      2                        32.0   \n",
       "2                      1                        84.0   \n",
       "3                      0                        31.0   \n",
       "4                      0                         7.0   \n",
       "\n",
       "   NetFractionInstallBurden  NumRevolvingTradesWBalance  \\\n",
       "0                      99.0                          11   \n",
       "1                      64.0                           6   \n",
       "2                       0.0                           7   \n",
       "3                      78.0                           4   \n",
       "4                      38.0                           1   \n",
       "\n",
       "   NumInstallTradesWBalance  NumBank2NatlTradesWHighUtilization  \\\n",
       "0                         1                                   5   \n",
       "1                         4                                   3   \n",
       "2                         1                                   5   \n",
       "3                         3                                   1   \n",
       "4                         2                                   0   \n",
       "\n",
       "   PercentTradesWBalance  ExternalRiskEstimate-binned  SatisfactoryTradesRatio  \n",
       "0                   81.0                 65<score>=75                 0.975000  \n",
       "1                   77.0                 65<score>=75                 1.000000  \n",
       "2                   75.0                 55<score>=65                 0.433962  \n",
       "3                   54.0                 65<score>=75                 0.950000  \n",
       "4                   60.0                 85<score>=95                 1.000000  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform division between columns\n",
    "z = df_clean['NumSatisfactoryTrades']/ df_clean['NumTotalTrades']\n",
    "\n",
    "# Add new column to dataframe\n",
    "df_clean['SatisfactoryTradesRatio']= z\n",
    "\n",
    "# Check result\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation 3 - range normalization\n",
    "The feature **NumTotalTrades** can be range-normalized in order to make it work better in a prospect machine learning model.\n",
    "The ultimate aim of normalization is to make feature units comparable between each other.\n",
    "\n",
    "NB: this is a mere example, as other feature (e.g. *NumSatisfactoryTrades*) can be normalized as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RiskPerformance</th>\n",
       "      <th>ExternalRiskEstimate</th>\n",
       "      <th>MSinceOldestTradeOpen</th>\n",
       "      <th>MSinceMostRecentTradeOpen</th>\n",
       "      <th>AverageMInFile</th>\n",
       "      <th>NumSatisfactoryTrades</th>\n",
       "      <th>NumTrades60Ever2DerogPubRec</th>\n",
       "      <th>NumTrades90Ever2DerogPubRec</th>\n",
       "      <th>PercentTradesNeverDelq</th>\n",
       "      <th>MaxDelq2PublicRecLast12M</th>\n",
       "      <th>...</th>\n",
       "      <th>NumInqLast6Mexcl7days</th>\n",
       "      <th>NetFractionRevolvingBurden</th>\n",
       "      <th>NetFractionInstallBurden</th>\n",
       "      <th>NumRevolvingTradesWBalance</th>\n",
       "      <th>NumInstallTradesWBalance</th>\n",
       "      <th>NumBank2NatlTradesWHighUtilization</th>\n",
       "      <th>PercentTradesWBalance</th>\n",
       "      <th>ExternalRiskEstimate-binned</th>\n",
       "      <th>SatisfactoryTradesRatio</th>\n",
       "      <th>NumTotalTrades_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good</td>\n",
       "      <td>71</td>\n",
       "      <td>308</td>\n",
       "      <td>7</td>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good</td>\n",
       "      <td>75</td>\n",
       "      <td>381</td>\n",
       "      <td>15</td>\n",
       "      <td>108</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>77.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bad</td>\n",
       "      <td>58</td>\n",
       "      <td>216</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>75.0</td>\n",
       "      <td>55&lt;score&gt;=65</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.623529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bad</td>\n",
       "      <td>73</td>\n",
       "      <td>191</td>\n",
       "      <td>17</td>\n",
       "      <td>53</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>65&lt;score&gt;=75</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Good</td>\n",
       "      <td>89</td>\n",
       "      <td>280</td>\n",
       "      <td>23</td>\n",
       "      <td>107</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85&lt;score&gt;=95</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.188235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RiskPerformance  ExternalRiskEstimate  MSinceOldestTradeOpen  \\\n",
       "0            Good                    71                    308   \n",
       "1            Good                    75                    381   \n",
       "2             Bad                    58                    216   \n",
       "3             Bad                    73                    191   \n",
       "4            Good                    89                    280   \n",
       "\n",
       "   MSinceMostRecentTradeOpen  AverageMInFile  NumSatisfactoryTrades  \\\n",
       "0                          7             104                     39   \n",
       "1                         15             108                     20   \n",
       "2                         12              86                     23   \n",
       "3                         17              53                     19   \n",
       "4                         23             107                     16   \n",
       "\n",
       "   NumTrades60Ever2DerogPubRec  NumTrades90Ever2DerogPubRec  \\\n",
       "0                            0                            0   \n",
       "1                            0                            0   \n",
       "2                            0                            0   \n",
       "3                            0                            0   \n",
       "4                            0                            0   \n",
       "\n",
       "   PercentTradesNeverDelq MaxDelq2PublicRecLast12M  ... NumInqLast6Mexcl7days  \\\n",
       "0                    98.0                        6  ...                     0   \n",
       "1                    95.0                        6  ...                     2   \n",
       "2                    88.0                        4  ...                     1   \n",
       "3                   100.0                        7  ...                     0   \n",
       "4                   100.0                        7  ...                     0   \n",
       "\n",
       "   NetFractionRevolvingBurden  NetFractionInstallBurden  \\\n",
       "0                        75.0                      99.0   \n",
       "1                        32.0                      64.0   \n",
       "2                        84.0                       0.0   \n",
       "3                        31.0                      78.0   \n",
       "4                         7.0                      38.0   \n",
       "\n",
       "   NumRevolvingTradesWBalance  NumInstallTradesWBalance  \\\n",
       "0                          11                         1   \n",
       "1                           6                         4   \n",
       "2                           7                         1   \n",
       "3                           4                         3   \n",
       "4                           1                         2   \n",
       "\n",
       "   NumBank2NatlTradesWHighUtilization  PercentTradesWBalance  \\\n",
       "0                                   5                   81.0   \n",
       "1                                   3                   77.0   \n",
       "2                                   5                   75.0   \n",
       "3                                   1                   54.0   \n",
       "4                                   0                   60.0   \n",
       "\n",
       "   ExternalRiskEstimate-binned  SatisfactoryTradesRatio  NumTotalTrades_norm  \n",
       "0                 65<score>=75                 0.975000             0.470588  \n",
       "1                 65<score>=75                 1.000000             0.235294  \n",
       "2                 55<score>=65                 0.433962             0.623529  \n",
       "3                 65<score>=75                 0.950000             0.235294  \n",
       "4                 85<score>=95                 1.000000             0.188235  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute normalized values with max-min method\n",
    "norm =(df_clean['NumTotalTrades']-df_clean['NumTotalTrades'].min())/(df_clean['NumTotalTrades'].max()-df_clean['NumTotalTrades'].min())\n",
    "\n",
    "# Add new column to dataframe\n",
    "df_clean['NumTotalTrades_norm'] = norm\n",
    "\n",
    "# Check result\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write final version of dataframe to new csv file with proper name\n",
    "df_clean.to_csv('CreditRisk-18202941_cleaned_updated_transformed.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "298.7315979003906px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}