1-explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "game_csv = pd.read_csv('./data/Training_dataset_Original.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "gameDf = pd.DataFrame(game_csv)\n",
    "\n",
    "dataDict = pd.read_csv('./data/Data_Dictionary.csv',na_values=\"NA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80000, 49)\n",
      "(49, 2)\n"
     ]
    }
   ],
   "source": [
    "print(gameDf.shape)\n",
    "print(dataDict.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = dict(zip(dataDict.values[:, 1], dataDict.values[:, 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar25'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['Average tenure of active revolving credit cards (in days)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['application_key', 'mvar1', 'mvar2', 'mvar3', 'mvar4', 'mvar5', 'mvar6',\n",
       "       'mvar7', 'mvar8', 'mvar9', 'mvar10', 'mvar11', 'mvar12', 'mvar13',\n",
       "       'mvar14', 'mvar15', 'mvar16', 'mvar17', 'mvar18', 'mvar19', 'mvar20',\n",
       "       'mvar21', 'mvar22', 'mvar23', 'mvar24', 'mvar25', 'mvar26', 'mvar27',\n",
       "       'mvar28', 'mvar29', 'mvar30', 'mvar31', 'mvar32', 'mvar33', 'mvar34',\n",
       "       'mvar35', 'mvar36', 'mvar37', 'mvar38', 'mvar39', 'mvar40', 'mvar41',\n",
       "       'mvar42', 'mvar43', 'mvar44', 'mvar45', 'mvar46', 'mvar47',\n",
       "       'default_ind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1696.0\n",
       "1        1846.0\n",
       "2        1745.0\n",
       "3        1739.0\n",
       "4        1787.0\n",
       "5        1579.0\n",
       "6        1818.0\n",
       "7           NaN\n",
       "8        1836.0\n",
       "9        1839.0\n",
       "10       1903.0\n",
       "11       1681.0\n",
       "12       1841.0\n",
       "13       1794.0\n",
       "14       1669.0\n",
       "15       1583.0\n",
       "16       1899.0\n",
       "17       1690.0\n",
       "18       1599.0\n",
       "19       1859.0\n",
       "20       1880.0\n",
       "21       1852.0\n",
       "22       1655.0\n",
       "23       1863.0\n",
       "24       1753.0\n",
       "25       1894.0\n",
       "26       1803.0\n",
       "27       1932.0\n",
       "28       1714.0\n",
       "29          NaN\n",
       "          ...  \n",
       "79970    1834.0\n",
       "79971    1729.0\n",
       "79972    1753.0\n",
       "79973    1748.0\n",
       "79974    1771.0\n",
       "79975    1766.0\n",
       "79976    1822.0\n",
       "79977    1824.0\n",
       "79978    1914.0\n",
       "79979    1600.0\n",
       "79980    1822.0\n",
       "79981    1693.0\n",
       "79982    1735.0\n",
       "79983    1671.0\n",
       "79984    1924.0\n",
       "79985    1670.0\n",
       "79986    1901.0\n",
       "79987    1835.0\n",
       "79988    1927.0\n",
       "79989       NaN\n",
       "79990    1941.0\n",
       "79991    1899.0\n",
       "79992    1761.0\n",
       "79993    1700.0\n",
       "79994    1910.0\n",
       "79995    1736.0\n",
       "79996    1724.0\n",
       "79997    1605.0\n",
       "79998    1780.0\n",
       "79999    1727.0\n",
       "Name: mvar1, Length: 80000, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf[dic[\"Credit worthiness score calculated on the basis of borrower's credit history\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove ID column\n",
    "del gameDf[dic['Application ID (primary key)']]\n",
    "del dic['Application ID (primary key)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Seperate target variable y (Default or not)\n",
    "#y= gameDf.pop(dic['Customer spending capacity. 0 = No data available'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mvar1', 'mvar2', 'mvar3', 'mvar4', 'mvar5', 'mvar6', 'mvar7', 'mvar8',\n",
       "       'mvar9', 'mvar10', 'mvar11', 'mvar12', 'mvar13', 'mvar14', 'mvar15',\n",
       "       'mvar16', 'mvar17', 'mvar18', 'mvar19', 'mvar20', 'mvar21', 'mvar22',\n",
       "       'mvar23', 'mvar24', 'mvar25', 'mvar26', 'mvar27', 'mvar28', 'mvar29',\n",
       "       'mvar30', 'mvar31', 'mvar32', 'mvar33', 'mvar34', 'mvar35', 'mvar36',\n",
       "       'mvar37', 'mvar38', 'mvar39', 'mvar40', 'mvar41', 'mvar42', 'mvar43',\n",
       "       'mvar44', 'mvar45', 'mvar46', 'mvar47', 'default_ind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gameDf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = list(dic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>default_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76361.000000</td>\n",
       "      <td>74447.000000</td>\n",
       "      <td>79499.000000</td>\n",
       "      <td>79499.000000</td>\n",
       "      <td>79499.00000</td>\n",
       "      <td>61036.000000</td>\n",
       "      <td>7.260600e+04</td>\n",
       "      <td>61036.00000</td>\n",
       "      <td>6.859200e+04</td>\n",
       "      <td>7.949800e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>79498.000000</td>\n",
       "      <td>73960.000000</td>\n",
       "      <td>17443.000000</td>\n",
       "      <td>25422.000000</td>\n",
       "      <td>78099.000000</td>\n",
       "      <td>79182.000000</td>\n",
       "      <td>72255.000000</td>\n",
       "      <td>35145.000000</td>\n",
       "      <td>57211.000000</td>\n",
       "      <td>80000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1745.971085</td>\n",
       "      <td>1.105629</td>\n",
       "      <td>5.280955</td>\n",
       "      <td>0.474516</td>\n",
       "      <td>1.12472</td>\n",
       "      <td>1606.702372</td>\n",
       "      <td>1.759581e+04</td>\n",
       "      <td>6773.68666</td>\n",
       "      <td>3.409744e+04</td>\n",
       "      <td>3.049237e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>4.286422</td>\n",
       "      <td>0.090819</td>\n",
       "      <td>98.497452</td>\n",
       "      <td>72.096758</td>\n",
       "      <td>0.335142</td>\n",
       "      <td>6.552777</td>\n",
       "      <td>0.705285</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.096135</td>\n",
       "      <td>0.246213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>94.537617</td>\n",
       "      <td>1.645299</td>\n",
       "      <td>10.815463</td>\n",
       "      <td>1.796261</td>\n",
       "      <td>5.99545</td>\n",
       "      <td>3703.654127</td>\n",
       "      <td>4.708164e+04</td>\n",
       "      <td>10127.74526</td>\n",
       "      <td>5.337692e+04</td>\n",
       "      <td>6.717785e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>3.964464</td>\n",
       "      <td>0.419301</td>\n",
       "      <td>29.762399</td>\n",
       "      <td>24.442156</td>\n",
       "      <td>0.375656</td>\n",
       "      <td>6.311087</td>\n",
       "      <td>0.245879</td>\n",
       "      <td>0.287365</td>\n",
       "      <td>0.346109</td>\n",
       "      <td>0.430806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1493.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1679.000000</td>\n",
       "      <td>0.140100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.756000e+03</td>\n",
       "      <td>490.00000</td>\n",
       "      <td>3.498000e+03</td>\n",
       "      <td>1.134000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.689000</td>\n",
       "      <td>58.153000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.515220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1742.000000</td>\n",
       "      <td>0.538900</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>6.967000e+03</td>\n",
       "      <td>2478.00000</td>\n",
       "      <td>1.417100e+04</td>\n",
       "      <td>9.492500e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>78.883500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.737030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1810.000000</td>\n",
       "      <td>1.445050</td>\n",
       "      <td>6.447000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1343.250000</td>\n",
       "      <td>1.805575e+04</td>\n",
       "      <td>9684.25000</td>\n",
       "      <td>4.385375e+04</td>\n",
       "      <td>3.402550e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>111.698000</td>\n",
       "      <td>91.639750</td>\n",
       "      <td>0.571430</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.943475</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>44.630100</td>\n",
       "      <td>286.858000</td>\n",
       "      <td>114.696000</td>\n",
       "      <td>226.52700</td>\n",
       "      <td>136595.000000</td>\n",
       "      <td>5.549600e+06</td>\n",
       "      <td>291810.00000</td>\n",
       "      <td>3.711880e+06</td>\n",
       "      <td>5.633320e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>430.300000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              mvar1         mvar2         mvar3         mvar4        mvar5  \\\n",
       "count  76361.000000  74447.000000  79499.000000  79499.000000  79499.00000   \n",
       "mean    1745.971085      1.105629      5.280955      0.474516      1.12472   \n",
       "std       94.537617      1.645299     10.815463      1.796261      5.99545   \n",
       "min     1493.000000      0.000000      0.000000      0.000000      0.00000   \n",
       "25%     1679.000000      0.140100      0.000000      0.000000      0.00000   \n",
       "50%     1742.000000      0.538900      0.300000      0.000000      0.00000   \n",
       "75%     1810.000000      1.445050      6.447000      0.000000      0.00000   \n",
       "max     1950.000000     44.630100    286.858000    114.696000    226.52700   \n",
       "\n",
       "               mvar6         mvar7         mvar8         mvar9        mvar10  \\\n",
       "count   61036.000000  7.260600e+04   61036.00000  6.859200e+04  7.949800e+04   \n",
       "mean     1606.702372  1.759581e+04    6773.68666  3.409744e+04  3.049237e+04   \n",
       "std      3703.654127  4.708164e+04   10127.74526  5.337692e+04  6.717785e+04   \n",
       "min         0.000000  0.000000e+00       0.00000  0.000000e+00  0.000000e+00   \n",
       "25%        36.000000  1.756000e+03     490.00000  3.498000e+03  1.134000e+03   \n",
       "50%       297.000000  6.967000e+03    2478.00000  1.417100e+04  9.492500e+03   \n",
       "75%      1343.250000  1.805575e+04    9684.25000  4.385375e+04  3.402550e+04   \n",
       "max    136595.000000  5.549600e+06  291810.00000  3.711880e+06  5.633320e+06   \n",
       "\n",
       "           ...             mvar38        mvar39        mvar40        mvar41  \\\n",
       "count      ...       79498.000000  73960.000000  17443.000000  25422.000000   \n",
       "mean       ...           4.286422      0.090819     98.497452     72.096758   \n",
       "std        ...           3.964464      0.419301     29.762399     24.442156   \n",
       "min        ...           0.000000      0.000000      0.014000      0.000000   \n",
       "25%        ...           2.000000      0.000000     90.689000     58.153000   \n",
       "50%        ...           3.000000      0.000000    100.000000     78.883500   \n",
       "75%        ...           6.000000      0.000000    111.698000     91.639750   \n",
       "max        ...          95.000000     21.000000    430.300000    170.000000   \n",
       "\n",
       "             mvar42        mvar43        mvar44        mvar45        mvar46  \\\n",
       "count  78099.000000  79182.000000  72255.000000  35145.000000  57211.000000   \n",
       "mean       0.335142      6.552777      0.705285      0.070736      0.096135   \n",
       "std        0.375656      6.311087      0.245879      0.287365      0.346109   \n",
       "min        0.000000      0.000000      0.060660      0.000000      0.000000   \n",
       "25%        0.000000      2.000000      0.515220      0.000000      0.000000   \n",
       "50%        0.200000      5.000000      0.737030      0.000000      0.000000   \n",
       "75%        0.571430     10.000000      0.943475      0.000000      0.000000   \n",
       "max        2.000000     91.000000      1.000000      5.000000     19.000000   \n",
       "\n",
       "        default_ind  \n",
       "count  80000.000000  \n",
       "mean       0.246213  \n",
       "std        0.430806  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe gameDf\n",
    "gameDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mvar1'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic[features[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mvar1   Credit worthiness score calculated on the basis of borrower's credit history\n",
      "count    76361.000000\n",
      "mean      1745.971085\n",
      "std         94.537617\n",
      "min       1493.000000\n",
      "25%       1679.000000\n",
      "50%       1742.000000\n",
      "75%       1810.000000\n",
      "max       1950.000000\n",
      "Name: mvar1, dtype: float64 \n",
      "\n",
      "mvar2   A score calculated based on the number and riskiness of credit enquiries made to any lender by a borrower\n",
      "count    74447.000000\n",
      "mean         1.105629\n",
      "std          1.645299\n",
      "min          0.000000\n",
      "25%          0.140100\n",
      "50%          0.538900\n",
      "75%          1.445050\n",
      "max         44.630100\n",
      "Name: mvar2, dtype: float64 \n",
      "\n",
      "mvar3   Severity of default by the borrower on any loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    79499.000000\n",
      "mean         5.280955\n",
      "std         10.815463\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.300000\n",
      "75%          6.447000\n",
      "max        286.858000\n",
      "Name: mvar3, dtype: float64 \n",
      "\n",
      "mvar4   Severity of default by the borrower on auto loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    79499.000000\n",
      "mean         0.474516\n",
      "std          1.796261\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max        114.696000\n",
      "Name: mvar4, dtype: float64 \n",
      "\n",
      "mvar5   Severity of default by the borrower on education loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    79499.00000\n",
      "mean         1.12472\n",
      "std          5.99545\n",
      "min          0.00000\n",
      "25%          0.00000\n",
      "50%          0.00000\n",
      "75%          0.00000\n",
      "max        226.52700\n",
      "Name: mvar5, dtype: float64 \n",
      "\n",
      "mvar6   Minimum of credit available on all revolving credit cards (in $)\n",
      "count     61036.000000\n",
      "mean       1606.702372\n",
      "std        3703.654127\n",
      "min           0.000000\n",
      "25%          36.000000\n",
      "50%         297.000000\n",
      "75%        1343.250000\n",
      "max      136595.000000\n",
      "Name: mvar6, dtype: float64 \n",
      "\n",
      "mvar7   Maximum of credit available on all active credit lines (in $)\n",
      "count    7.260600e+04\n",
      "mean     1.759581e+04\n",
      "std      4.708164e+04\n",
      "min      0.000000e+00\n",
      "25%      1.756000e+03\n",
      "50%      6.967000e+03\n",
      "75%      1.805575e+04\n",
      "max      5.549600e+06\n",
      "Name: mvar7, dtype: float64 \n",
      "\n",
      "mvar8   Maximum of credit available on all active revolving credit cards (in $)\n",
      "count     61036.00000\n",
      "mean       6773.68666\n",
      "std       10127.74526\n",
      "min           0.00000\n",
      "25%         490.00000\n",
      "50%        2478.00000\n",
      "75%        9684.25000\n",
      "max      291810.00000\n",
      "Name: mvar8, dtype: float64 \n",
      "\n",
      "mvar9   Sum of available credit on credit cards that the borrower has missed 1 payment (in $)\n",
      "count    6.859200e+04\n",
      "mean     3.409744e+04\n",
      "std      5.337692e+04\n",
      "min      0.000000e+00\n",
      "25%      3.498000e+03\n",
      "50%      1.417100e+04\n",
      "75%      4.385375e+04\n",
      "max      3.711880e+06\n",
      "Name: mvar9, dtype: float64 \n",
      "\n",
      "mvar10   Total amount of credit available on accepted credit lines (in $)\n",
      "count    7.949800e+04\n",
      "mean     3.049237e+04\n",
      "std      6.717785e+04\n",
      "min      0.000000e+00\n",
      "25%      1.134000e+03\n",
      "50%      9.492500e+03\n",
      "75%      3.402550e+04\n",
      "max      5.633320e+06\n",
      "Name: mvar10, dtype: float64 \n",
      "\n",
      "mvar11   Amount of dues collected post default where due amount was more than 500 (in $)\n",
      "count     35181.000000\n",
      "mean       5342.149740\n",
      "std       13300.872733\n",
      "min           0.000000\n",
      "25%         550.000000\n",
      "50%        1780.000000\n",
      "75%        5047.000000\n",
      "max      464264.000000\n",
      "Name: mvar11, dtype: float64 \n",
      "\n",
      "mvar12   Sum of amount due on active credit cards (in $)\n",
      "count     65922.000000\n",
      "mean       7124.405130\n",
      "std       11879.550129\n",
      "min           0.000000\n",
      "25%         706.000000\n",
      "50%        2977.500000\n",
      "75%        8660.000000\n",
      "max      413685.000000\n",
      "Name: mvar12, dtype: float64 \n",
      "\n",
      "mvar13   Annual amount paid towards all credit cards during the previous year (in $)\n",
      "count    7.164800e+04\n",
      "mean     1.542968e+04\n",
      "std      5.723149e+04\n",
      "min      0.000000e+00\n",
      "25%      4.990000e+02\n",
      "50%      4.090000e+03\n",
      "75%      1.437025e+04\n",
      "max      6.318314e+06\n",
      "Name: mvar13, dtype: float64 \n",
      "\n",
      "mvar14   Annual income (in $)\n",
      "count    8.000000e+04\n",
      "mean     1.960918e+05\n",
      "std      7.888377e+06\n",
      "min      0.000000e+00\n",
      "25%      4.261300e+04\n",
      "50%      7.234300e+04\n",
      "75%      1.238750e+05\n",
      "max      9.910000e+08\n",
      "Name: mvar14, dtype: float64 \n",
      "\n",
      "mvar15   Estimated market value of a properety owned/used by the borrower (in $)\n",
      "count    4.793300e+04\n",
      "mean     2.940801e+05\n",
      "std      2.997564e+05\n",
      "min      1.387000e+03\n",
      "25%      1.284020e+05\n",
      "50%      2.161520e+05\n",
      "75%      3.666700e+05\n",
      "max      1.001817e+07\n",
      "Name: mvar15, dtype: float64 \n",
      "\n",
      "mvar16   Number of active revolving credit cards on which full credit limit is utilized by the borrower\n",
      "count    61490.000000\n",
      "mean         0.231582\n",
      "std          0.622041\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         11.000000\n",
      "Name: mvar16, dtype: float64 \n",
      "\n",
      "mvar17   Number of active credit cards on which full credit limit is utilized by the borrower\n",
      "count    64154.000000\n",
      "mean         0.266281\n",
      "std          0.714537\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         21.000000\n",
      "Name: mvar17, dtype: float64 \n",
      "\n",
      "mvar18   Number of active credit lines on which full credit limit is utilized by the borrower\n",
      "count    65229.000000\n",
      "mean         0.272026\n",
      "std          0.719861\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         21.000000\n",
      "Name: mvar18, dtype: float64 \n",
      "\n",
      "mvar19   Number of active credit cards on which atleast 75% credit limit is utilized by the borrower\n",
      "count    79996.000000\n",
      "mean         0.945697\n",
      "std          1.727162\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         28.000000\n",
      "Name: mvar19, dtype: float64 \n",
      "\n",
      "mvar20   Number of active credit lines on which atleast 75% credit limit is utilized by the borrower\n",
      "count    79498.000000\n",
      "mean         1.028252\n",
      "std          1.776818\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         29.000000\n",
      "Name: mvar20, dtype: float64 \n",
      "\n",
      "mvar21   Average utilization of active revolving credit card loans (%)\n",
      "count    57439.000000\n",
      "mean        57.790097\n",
      "std         35.577165\n",
      "min          0.170000\n",
      "25%         26.930000\n",
      "50%         59.690000\n",
      "75%         86.620000\n",
      "max       1305.250000\n",
      "Name: mvar21, dtype: float64 \n",
      "\n",
      "mvar22   Average utilization of line on all active credit lines activated in last 2 years (%)\n",
      "count    50876.000000\n",
      "mean      4423.856509\n",
      "std       3782.048321\n",
      "min          0.000000\n",
      "25%        726.715000\n",
      "50%       3974.425000\n",
      "75%       7579.297500\n",
      "max      64800.000000\n",
      "Name: mvar22, dtype: float64 \n",
      "\n",
      "mvar23   Average utilization of line on all active credit cards activated in last 1 year (%)\n",
      "count     39752.000000\n",
      "mean       4305.929828\n",
      "std        3864.873106\n",
      "min           0.000000\n",
      "25%         541.157500\n",
      "50%        3700.000000\n",
      "75%        7520.950000\n",
      "max      124533.330000\n",
      "Name: mvar23, dtype: float64 \n",
      "\n",
      "mvar24   Average utilization of line on credit cards on which the borrower has missed 1 payment during last 6 months (%)\n",
      "count    61098.000000\n",
      "mean        69.981834\n",
      "std        300.080442\n",
      "min          0.000000\n",
      "25%          9.892500\n",
      "50%         40.440000\n",
      "75%         76.540000\n",
      "max      27867.400000\n",
      "Name: mvar24, dtype: float64 \n",
      "\n",
      "mvar25   Average tenure of active revolving credit cards (in days)\n",
      "count    72294.000000\n",
      "mean      1750.065303\n",
      "std       1306.001244\n",
      "min          0.000000\n",
      "25%        821.000000\n",
      "50%       1472.000000\n",
      "75%       2352.750000\n",
      "max      16729.000000\n",
      "Name: mvar25, dtype: float64 \n",
      "\n",
      "mvar26   Tenure of oldest credit card among all active credit cards (in days)\n",
      "count    69390.000000\n",
      "mean      3369.121848\n",
      "std       2931.242239\n",
      "min          0.000000\n",
      "25%       1125.000000\n",
      "50%       2738.000000\n",
      "75%       4715.000000\n",
      "max      24425.000000\n",
      "Name: mvar26, dtype: float64 \n",
      "\n",
      "mvar27   Tenure of oldest revolving credit card among all active revolving credit cards (in days)\n",
      "count    66791.000000\n",
      "mean      3096.580153\n",
      "std       2695.058155\n",
      "min          0.000000\n",
      "25%       1034.000000\n",
      "50%       2555.000000\n",
      "75%       4319.000000\n",
      "max      22052.000000\n",
      "Name: mvar27, dtype: float64 \n",
      "\n",
      "mvar28   Number of days since last missed payment on any credit line\n",
      "count    79498.000000\n",
      "mean      8703.352223\n",
      "std      13499.173287\n",
      "min          0.000000\n",
      "25%         30.000000\n",
      "50%        243.000000\n",
      "75%      30386.000000\n",
      "max      30386.000000\n",
      "Name: mvar28, dtype: float64 \n",
      "\n",
      "mvar29   Tenure of oldest credit line (in days)\n",
      "count    79498.000000\n",
      "mean      5505.117412\n",
      "std       3222.652954\n",
      "min          0.000000\n",
      "25%       3255.000000\n",
      "50%       5019.000000\n",
      "75%       7270.000000\n",
      "max      24638.000000\n",
      "Name: mvar29, dtype: float64 \n",
      "\n",
      "mvar30   Maximum tenure on all auto loans (in days)\n",
      "count    43583.000000\n",
      "mean      2611.880274\n",
      "std       1564.307917\n",
      "min          0.000000\n",
      "25%       1156.000000\n",
      "50%       2829.000000\n",
      "75%       3863.000000\n",
      "max      12775.000000\n",
      "Name: mvar30, dtype: float64 \n",
      "\n",
      "mvar31   Maximum tenure on all education loans (in days)\n",
      "count    23782.000000\n",
      "mean      3626.892986\n",
      "std       2028.647572\n",
      "min          0.000000\n",
      "25%       2099.000000\n",
      "50%       3528.000000\n",
      "75%       4745.000000\n",
      "max      15695.000000\n",
      "Name: mvar31, dtype: float64 \n",
      "\n",
      "mvar32   Sum of tenures (in months) of active credit cards\n",
      "count    72294.000000\n",
      "mean       470.430879\n",
      "std        524.320849\n",
      "min          0.000000\n",
      "25%         97.000000\n",
      "50%        300.000000\n",
      "75%        667.000000\n",
      "max      10653.000000\n",
      "Name: mvar32, dtype: float64 \n",
      "\n",
      "mvar33   Duration of stay at the current residential address (in years)\n",
      "count    78249.000000\n",
      "mean         6.996557\n",
      "std          7.650139\n",
      "min          0.083300\n",
      "25%          1.166700\n",
      "50%          3.916700\n",
      "75%         10.666700\n",
      "max         76.000000\n",
      "Name: mvar33, dtype: float64 \n",
      "\n",
      "mvar34   Number of active credit lines over the last 6 months on which the borrower has missed 1 payment\n",
      "count    79498.000000\n",
      "mean         0.230094\n",
      "std          0.679609\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         21.000000\n",
      "Name: mvar34, dtype: float64 \n",
      "\n",
      "mvar35   Number of revolving credit cards over the last 2 years on which the borrower has missed 1 payment\n",
      "count    46986.000000\n",
      "mean         0.198123\n",
      "std          0.569388\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         10.000000\n",
      "Name: mvar35, dtype: float64 \n",
      "\n",
      "mvar36   Number of active credit lines\n",
      "count    77102.000000\n",
      "mean         6.417265\n",
      "std          5.285543\n",
      "min          1.000000\n",
      "25%          3.000000\n",
      "50%          5.000000\n",
      "75%          8.000000\n",
      "max        100.000000\n",
      "Name: mvar36, dtype: float64 \n",
      "\n",
      "mvar37   Number of credit cards with an active tenure of at least 2 years\n",
      "count    72294.000000\n",
      "mean         4.251362\n",
      "std          4.685612\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          3.000000\n",
      "75%          6.000000\n",
      "max         60.000000\n",
      "Name: mvar37, dtype: float64 \n",
      "\n",
      "mvar38   Number of credit lines activated in last 2 years\n",
      "count    79498.000000\n",
      "mean         4.286422\n",
      "std          3.964464\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          3.000000\n",
      "75%          6.000000\n",
      "max         95.000000\n",
      "Name: mvar38, dtype: float64 \n",
      "\n",
      "mvar39   Number of credit lines on which the borrower has current delinquency\n",
      "count    73960.000000\n",
      "mean         0.090819\n",
      "std          0.419301\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         21.000000\n",
      "Name: mvar39, dtype: float64 \n",
      "\n",
      "mvar40   Utilization of line on active education loans (%)\n",
      "count    17443.000000\n",
      "mean        98.497452\n",
      "std         29.762399\n",
      "min          0.014000\n",
      "25%         90.689000\n",
      "50%        100.000000\n",
      "75%        111.698000\n",
      "max        430.300000\n",
      "Name: mvar40, dtype: float64 \n",
      "\n",
      "mvar41   Utilization of line on active auto loans (%)\n",
      "count    25422.000000\n",
      "mean        72.096758\n",
      "std         24.442156\n",
      "min          0.000000\n",
      "25%         58.153000\n",
      "50%         78.883500\n",
      "75%         91.639750\n",
      "max        170.000000\n",
      "Name: mvar41, dtype: float64 \n",
      "\n",
      "mvar42   Financial stress index of the borrower. This index is a function of collection trades, bankruptcies files, tax liens invoked, etc. \n",
      "count    78099.000000\n",
      "mean         0.335142\n",
      "std          0.375656\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.200000\n",
      "75%          0.571430\n",
      "max          2.000000\n",
      "Name: mvar42, dtype: float64 \n",
      "\n",
      "mvar43   Number of credit lines on which the borrower has never missed a payment in last 2 yrs, yet considered as high risk loans based on market prediction of economic scenario\n",
      "count    79182.000000\n",
      "mean         6.552777\n",
      "std          6.311087\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          5.000000\n",
      "75%         10.000000\n",
      "max         91.000000\n",
      "Name: mvar43, dtype: float64 \n",
      "\n",
      "mvar44   Ratio of maximum amount due on all active credit lines and sum of amounts due on all active credit lines \n",
      "count    72255.000000\n",
      "mean         0.705285\n",
      "std          0.245879\n",
      "min          0.060660\n",
      "25%          0.515220\n",
      "50%          0.737030\n",
      "75%          0.943475\n",
      "max          1.000000\n",
      "Name: mvar44, dtype: float64 \n",
      "\n",
      "mvar45   Number of mortgage loans on which the borrower has missed 2 payments\n",
      "count    35145.000000\n",
      "mean         0.070736\n",
      "std          0.287365\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          5.000000\n",
      "Name: mvar45, dtype: float64 \n",
      "\n",
      "mvar46   Number of auto loans on which the borrower has missed 2 payments\n",
      "count    57211.000000\n",
      "mean         0.096135\n",
      "std          0.346109\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         19.000000\n",
      "Name: mvar46, dtype: float64 \n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mvar47   Type of product that the applicant applied for. (C = Charge; L = Lending)\n",
      "count     80000\n",
      "unique        2\n",
      "top           C\n",
      "freq      51840\n",
      "Name: mvar47, dtype: object \n",
      "\n",
      "default_ind   Indicator for default\n",
      "count    80000.000000\n",
      "mean         0.246213\n",
      "std          0.430806\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          1.000000\n",
      "Name: default_ind, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in features:\n",
    "    print(dic[i], \" \", i)\n",
    "    print(gameDf[dic[i]].describe(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0476552166681945"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=features[0]\n",
    "(80000-gameDf[dic[i]].describe()['count'])/gameDf[dic[i]].describe()['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t\t      Count \t#mising values \t%missing values\n",
      "mvar1   Credit worthiness score calculated    76361.0 \t  3639.0  \t 4.54875\n",
      "mvar2   A score calculated based on the num   74447.0 \t  5553.0  \t 6.94125\n",
      "mvar3   Severity of default by the borrower   79499.0 \t  501.0  \t 0.62625\n",
      "mvar4   Severity of default by the borrower   79499.0 \t  501.0  \t 0.62625\n",
      "mvar5   Severity of default by the borrower   79499.0 \t  501.0  \t 0.62625\n",
      "mvar6   Minimum of credit available on all    61036.0 \t  18964.0  \t 23.705\n",
      "mvar7   Maximum of credit available on all    72606.0 \t  7394.0  \t 9.2425\n",
      "mvar8   Maximum of credit available on all    61036.0 \t  18964.0  \t 23.705\n",
      "mvar9   Sum of available credit on credit c   68592.0 \t  11408.0  \t 14.26\n",
      "mvar10   Total amount of credit available on   79498.0 \t  502.0  \t 0.6275\n",
      "mvar11   Amount of dues collected post defau   35181.0 \t  44819.0  \t 56.02375\n",
      "mvar12   Sum of amount due on active credit    65922.0 \t  14078.0  \t 17.5975\n",
      "mvar13   Annual amount paid towards all cred   71648.0 \t  8352.0  \t 10.44\n",
      "mvar14   Annual income (in $)   80000.0 \t  0.0  \t 0.0\n",
      "mvar15   Estimated market value of a propere   47933.0 \t  32067.0  \t 40.08375\n",
      "mvar16   Number of active revolving credit c   61490.0 \t  18510.0  \t 23.1375\n",
      "mvar17   Number of active credit cards on wh   64154.0 \t  15846.0  \t 19.8075\n",
      "mvar18   Number of active credit lines on wh   65229.0 \t  14771.0  \t 18.46375\n",
      "mvar19   Number of active credit cards on wh   79996.0 \t  4.0  \t 0.005\n",
      "mvar20   Number of active credit lines on wh   79498.0 \t  502.0  \t 0.6275\n",
      "mvar21   Average utilization of active revol   57439.0 \t  22561.0  \t 28.20125\n",
      "mvar22   Average utilization of line on all    50876.0 \t  29124.0  \t 36.405\n",
      "mvar23   Average utilization of line on all    39752.0 \t  40248.0  \t 50.31\n",
      "mvar24   Average utilization of line on cred   61098.0 \t  18902.0  \t 23.6275\n",
      "mvar25   Average tenure of active revolving    72294.0 \t  7706.0  \t 9.6325\n",
      "mvar26   Tenure of oldest credit card among    69390.0 \t  10610.0  \t 13.2625\n",
      "mvar27   Tenure of oldest revolving credit c   66791.0 \t  13209.0  \t 16.51125\n",
      "mvar28   Number of days since last missed pa   79498.0 \t  502.0  \t 0.6275\n",
      "mvar29   Tenure of oldest credit line (in da   79498.0 \t  502.0  \t 0.6275\n",
      "mvar30   Maximum tenure on all auto loans (i   43583.0 \t  36417.0  \t 45.52125\n",
      "mvar31   Maximum tenure on all education loa   23782.0 \t  56218.0  \t 70.2725\n",
      "mvar32   Sum of tenures (in months) of activ   72294.0 \t  7706.0  \t 9.6325\n",
      "mvar33   Duration of stay at the current res   78249.0 \t  1751.0  \t 2.18875\n",
      "mvar34   Number of active credit lines over    79498.0 \t  502.0  \t 0.6275\n",
      "mvar35   Number of revolving credit cards ov   46986.0 \t  33014.0  \t 41.2675\n",
      "mvar36   Number of active credit lines   77102.0 \t  2898.0  \t 3.6225\n",
      "mvar37   Number of credit cards with an acti   72294.0 \t  7706.0  \t 9.6325\n",
      "mvar38   Number of credit lines activated in   79498.0 \t  502.0  \t 0.6275\n",
      "mvar39   Number of credit lines on which the   73960.0 \t  6040.0  \t 7.55\n",
      "mvar40   Utilization of line on active educa   17443.0 \t  62557.0  \t 78.19625\n",
      "mvar41   Utilization of line on active auto    25422.0 \t  54578.0  \t 68.2225\n",
      "mvar42   Financial stress index of the borro   78099.0 \t  1901.0  \t 2.37625\n",
      "mvar43   Number of credit lines on which the   79182.0 \t  818.0  \t 1.0225\n",
      "mvar44   Ratio of maximum amount due on all    72255.0 \t  7745.0  \t 9.68125\n",
      "mvar45   Number of mortgage loans on which t   35145.0 \t  44855.0  \t 56.06875\n",
      "mvar46   Number of auto loans on which the b   57211.0 \t  22789.0  \t 28.48625\n",
      "mvar47   Type of product that the applicant    80000 \t  0  \t 0.0\n",
      "default_ind   Indicator for default   80000.0 \t  0.0  \t 0.0\n"
     ]
    }
   ],
   "source": [
    "#number of misssing values\n",
    "print(\"\\t\\t\\t\\t\\t      Count\", \"\\t#mising values\", \"\\t%missing values\")\n",
    "for i in features:\n",
    "#     print(i[:25])\n",
    "    print(dic[i], \" \", i[:35], \" \" ,gameDf[dic[i]].describe()['count'], \"\\t \", 80000-gameDf[dic[i]].describe()['count'], \" \\t\", 100*(80000-gameDf[dic[i]].describe()['count'])/80000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       198200, 1263525.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, 0.0833, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, 'C', 0], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.values[7, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar37</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>23828.000000</td>\n",
       "      <td>23046.000000</td>\n",
       "      <td>24830.000000</td>\n",
       "      <td>24830.000000</td>\n",
       "      <td>24830.000000</td>\n",
       "      <td>18773.000000</td>\n",
       "      <td>2.254700e+04</td>\n",
       "      <td>18760.000000</td>\n",
       "      <td>21391.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>22625.000000</td>\n",
       "      <td>24830.000000</td>\n",
       "      <td>23005.000000</td>\n",
       "      <td>5128.000000</td>\n",
       "      <td>7308.000000</td>\n",
       "      <td>24344.000000</td>\n",
       "      <td>24712.000000</td>\n",
       "      <td>22375.000000</td>\n",
       "      <td>11874.000000</td>\n",
       "      <td>17768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>582574.500000</td>\n",
       "      <td>1751.198086</td>\n",
       "      <td>0.943321</td>\n",
       "      <td>5.860158</td>\n",
       "      <td>0.411080</td>\n",
       "      <td>1.029131</td>\n",
       "      <td>1807.602088</td>\n",
       "      <td>1.818150e+04</td>\n",
       "      <td>7238.955704</td>\n",
       "      <td>35732.629050</td>\n",
       "      <td>...</td>\n",
       "      <td>4.678541</td>\n",
       "      <td>4.009907</td>\n",
       "      <td>0.087546</td>\n",
       "      <td>96.396320</td>\n",
       "      <td>72.161646</td>\n",
       "      <td>0.328102</td>\n",
       "      <td>6.431248</td>\n",
       "      <td>0.714432</td>\n",
       "      <td>0.073269</td>\n",
       "      <td>0.081833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7217.022701</td>\n",
       "      <td>97.487803</td>\n",
       "      <td>1.447225</td>\n",
       "      <td>12.262912</td>\n",
       "      <td>1.616504</td>\n",
       "      <td>5.502576</td>\n",
       "      <td>3847.006727</td>\n",
       "      <td>4.366567e+04</td>\n",
       "      <td>10398.684072</td>\n",
       "      <td>49353.703919</td>\n",
       "      <td>...</td>\n",
       "      <td>4.746114</td>\n",
       "      <td>3.792525</td>\n",
       "      <td>0.403627</td>\n",
       "      <td>30.599735</td>\n",
       "      <td>24.578127</td>\n",
       "      <td>0.378251</td>\n",
       "      <td>6.124857</td>\n",
       "      <td>0.244369</td>\n",
       "      <td>0.298549</td>\n",
       "      <td>0.304468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>570075.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>576324.750000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>0.110600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.765500e+03</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>3606.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.345000</td>\n",
       "      <td>58.333000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>582574.500000</td>\n",
       "      <td>1747.000000</td>\n",
       "      <td>0.445100</td>\n",
       "      <td>0.285000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>7.433000e+03</td>\n",
       "      <td>2890.000000</td>\n",
       "      <td>16319.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79.010500</td>\n",
       "      <td>0.181820</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.749120</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>588824.250000</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1.263550</td>\n",
       "      <td>7.049750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1632.000000</td>\n",
       "      <td>1.906950e+04</td>\n",
       "      <td>10540.000000</td>\n",
       "      <td>49154.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.886750</td>\n",
       "      <td>91.895000</td>\n",
       "      <td>0.571430</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.951965</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>595074.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>25.315300</td>\n",
       "      <td>399.334000</td>\n",
       "      <td>22.963900</td>\n",
       "      <td>134.543000</td>\n",
       "      <td>68899.000000</td>\n",
       "      <td>1.833350e+06</td>\n",
       "      <td>218131.000000</td>\n",
       "      <td>635253.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>646.893000</td>\n",
       "      <td>142.119000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key         mvar1         mvar2         mvar3  \\\n",
       "count     25000.000000  23828.000000  23046.000000  24830.000000   \n",
       "mean     582574.500000   1751.198086      0.943321      5.860158   \n",
       "std        7217.022701     97.487803      1.447225     12.262912   \n",
       "min      570075.000000   1508.000000      0.000000      0.000000   \n",
       "25%      576324.750000   1680.000000      0.110600      0.000000   \n",
       "50%      582574.500000   1747.000000      0.445100      0.285000   \n",
       "75%      588824.250000   1822.000000      1.263550      7.049750   \n",
       "max      595074.000000   1950.000000     25.315300    399.334000   \n",
       "\n",
       "              mvar4         mvar5         mvar6         mvar7          mvar8  \\\n",
       "count  24830.000000  24830.000000  18773.000000  2.254700e+04   18760.000000   \n",
       "mean       0.411080      1.029131   1807.602088  1.818150e+04    7238.955704   \n",
       "std        1.616504      5.502576   3847.006727  4.366567e+04   10398.684072   \n",
       "min        0.000000      0.000000      0.000000  0.000000e+00       0.000000   \n",
       "25%        0.000000      0.000000     47.000000  1.765500e+03     496.000000   \n",
       "50%        0.000000      0.000000    340.000000  7.433000e+03    2890.000000   \n",
       "75%        0.000000      0.000000   1632.000000  1.906950e+04   10540.000000   \n",
       "max       22.963900    134.543000  68899.000000  1.833350e+06  218131.000000   \n",
       "\n",
       "               mvar9      ...             mvar37        mvar38        mvar39  \\\n",
       "count   21391.000000      ...       22625.000000  24830.000000  23005.000000   \n",
       "mean    35732.629050      ...           4.678541      4.009907      0.087546   \n",
       "std     49353.703919      ...           4.746114      3.792525      0.403627   \n",
       "min         0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%      3606.000000      ...           1.000000      1.000000      0.000000   \n",
       "50%     16319.000000      ...           4.000000      3.000000      0.000000   \n",
       "75%     49154.000000      ...           7.000000      5.000000      0.000000   \n",
       "max    635253.000000      ...          50.000000     97.000000     17.000000   \n",
       "\n",
       "            mvar40       mvar41        mvar42        mvar43        mvar44  \\\n",
       "count  5128.000000  7308.000000  24344.000000  24712.000000  22375.000000   \n",
       "mean     96.396320    72.161646      0.328102      6.431248      0.714432   \n",
       "std      30.599735    24.578127      0.378251      6.124857      0.244369   \n",
       "min       0.076000     0.000000      0.000000      0.000000      0.075340   \n",
       "25%      88.345000    58.333000      0.000000      2.000000      0.523750   \n",
       "50%     100.000000    79.010500      0.181820      5.000000      0.749120   \n",
       "75%     109.886750    91.895000      0.571430     10.000000      0.951965   \n",
       "max     646.893000   142.119000      2.000000     52.000000      1.000000   \n",
       "\n",
       "             mvar45        mvar46  \n",
       "count  11874.000000  17768.000000  \n",
       "mean       0.073269      0.081833  \n",
       "std        0.298549      0.304468  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        7.000000      3.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dist of eval set\n",
    "eval_csv = pd.read_csv('./data/Evaluation_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "evalDf = pd.DataFrame(eval_csv)\n",
    "\n",
    "evalDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mvar1   Credit worthiness score calculated on the basis of borrower's credit history\n",
      "count    23828.000000\n",
      "mean      1751.198086\n",
      "std         97.487803\n",
      "min       1508.000000\n",
      "25%       1680.000000\n",
      "50%       1747.000000\n",
      "75%       1822.000000\n",
      "max       1950.000000\n",
      "Name: mvar1, dtype: float64 \n",
      "\n",
      "mvar2   A score calculated based on the number and riskiness of credit enquiries made to any lender by a borrower\n",
      "count    23046.000000\n",
      "mean         0.943321\n",
      "std          1.447225\n",
      "min          0.000000\n",
      "25%          0.110600\n",
      "50%          0.445100\n",
      "75%          1.263550\n",
      "max         25.315300\n",
      "Name: mvar2, dtype: float64 \n",
      "\n",
      "mvar3   Severity of default by the borrower on any loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24830.000000\n",
      "mean         5.860158\n",
      "std         12.262912\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.285000\n",
      "75%          7.049750\n",
      "max        399.334000\n",
      "Name: mvar3, dtype: float64 \n",
      "\n",
      "mvar4   Severity of default by the borrower on auto loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24830.000000\n",
      "mean         0.411080\n",
      "std          1.616504\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         22.963900\n",
      "Name: mvar4, dtype: float64 \n",
      "\n",
      "mvar5   Severity of default by the borrower on education loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24830.000000\n",
      "mean         1.029131\n",
      "std          5.502576\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max        134.543000\n",
      "Name: mvar5, dtype: float64 \n",
      "\n",
      "mvar6   Minimum of credit available on all revolving credit cards (in $)\n",
      "count    18773.000000\n",
      "mean      1807.602088\n",
      "std       3847.006727\n",
      "min          0.000000\n",
      "25%         47.000000\n",
      "50%        340.000000\n",
      "75%       1632.000000\n",
      "max      68899.000000\n",
      "Name: mvar6, dtype: float64 \n",
      "\n",
      "mvar7   Maximum of credit available on all active credit lines (in $)\n",
      "count    2.254700e+04\n",
      "mean     1.818150e+04\n",
      "std      4.366567e+04\n",
      "min      0.000000e+00\n",
      "25%      1.765500e+03\n",
      "50%      7.433000e+03\n",
      "75%      1.906950e+04\n",
      "max      1.833350e+06\n",
      "Name: mvar7, dtype: float64 \n",
      "\n",
      "mvar8   Maximum of credit available on all active revolving credit cards (in $)\n",
      "count     18760.000000\n",
      "mean       7238.955704\n",
      "std       10398.684072\n",
      "min           0.000000\n",
      "25%         496.000000\n",
      "50%        2890.000000\n",
      "75%       10540.000000\n",
      "max      218131.000000\n",
      "Name: mvar8, dtype: float64 \n",
      "\n",
      "mvar9   Sum of available credit on credit cards that the borrower has missed 1 payment (in $)\n",
      "count     21391.000000\n",
      "mean      35732.629050\n",
      "std       49353.703919\n",
      "min           0.000000\n",
      "25%        3606.000000\n",
      "50%       16319.000000\n",
      "75%       49154.000000\n",
      "max      635253.000000\n",
      "Name: mvar9, dtype: float64 \n",
      "\n",
      "mvar10   Total amount of credit available on accepted credit lines (in $)\n",
      "count    2.483000e+04\n",
      "mean     3.134997e+04\n",
      "std      6.334321e+04\n",
      "min      0.000000e+00\n",
      "25%      1.067250e+03\n",
      "50%      1.006450e+04\n",
      "75%      3.648450e+04\n",
      "max      2.225709e+06\n",
      "Name: mvar10, dtype: float64 \n",
      "\n",
      "mvar11   Amount of dues collected post default where due amount was more than 500 (in $)\n",
      "count     10610.000000\n",
      "mean       5823.687182\n",
      "std       14438.660557\n",
      "min           0.000000\n",
      "25%         575.000000\n",
      "50%        1876.500000\n",
      "75%        5338.750000\n",
      "max      389671.000000\n",
      "Name: mvar11, dtype: float64 \n",
      "\n",
      "mvar12   Sum of amount due on active credit cards (in $)\n",
      "count     20413.000000\n",
      "mean       6999.842600\n",
      "std       11551.415067\n",
      "min           0.000000\n",
      "25%         650.000000\n",
      "50%        2841.000000\n",
      "75%        8387.000000\n",
      "max      242291.000000\n",
      "Name: mvar12, dtype: float64 \n",
      "\n",
      "mvar13   Annual amount paid towards all credit cards during the previous year (in $)\n",
      "count    2.042100e+04\n",
      "mean     1.882602e+04\n",
      "std      8.358483e+04\n",
      "min      0.000000e+00\n",
      "25%      1.200000e+03\n",
      "50%      5.502000e+03\n",
      "75%      1.799900e+04\n",
      "max      9.106307e+06\n",
      "Name: mvar13, dtype: float64 \n",
      "\n",
      "mvar14   Annual income (in $)\n",
      "count    2.500000e+04\n",
      "mean     1.960816e+05\n",
      "std      7.867975e+06\n",
      "min      0.000000e+00\n",
      "25%      4.261300e+04\n",
      "50%      7.234300e+04\n",
      "75%      1.090100e+05\n",
      "max      9.910000e+08\n",
      "Name: mvar14, dtype: float64 \n",
      "\n",
      "mvar15   Estimated market value of a properety owned/used by the borrower (in $)\n",
      "count    1.476300e+04\n",
      "mean     2.910113e+05\n",
      "std      3.207931e+05\n",
      "min      2.413000e+03\n",
      "25%      1.276540e+05\n",
      "50%      2.181350e+05\n",
      "75%      3.666700e+05\n",
      "max      1.650424e+07\n",
      "Name: mvar15, dtype: float64 \n",
      "\n",
      "mvar16   Number of active revolving credit cards on which full credit limit is utilized by the borrower\n",
      "count    18910.000000\n",
      "mean         0.213802\n",
      "std          0.586421\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          8.000000\n",
      "Name: mvar16, dtype: float64 \n",
      "\n",
      "mvar17   Number of active credit cards on which full credit limit is utilized by the borrower\n",
      "count    19805.000000\n",
      "mean         0.245746\n",
      "std          0.664207\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         11.000000\n",
      "Name: mvar17, dtype: float64 \n",
      "\n",
      "mvar18   Number of active credit lines on which full credit limit is utilized by the borrower\n",
      "count    20209.000000\n",
      "mean         0.253155\n",
      "std          0.678641\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         13.000000\n",
      "Name: mvar18, dtype: float64 \n",
      "\n",
      "mvar19   Number of active credit cards on which atleast 75% credit limit is utilized by the borrower\n",
      "count    24993.000000\n",
      "mean         0.828872\n",
      "std          1.547466\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         21.000000\n",
      "Name: mvar19, dtype: float64 \n",
      "\n",
      "mvar20   Number of active credit lines on which atleast 75% credit limit is utilized by the borrower\n",
      "count    24830.000000\n",
      "mean         0.919573\n",
      "std          1.606007\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         21.000000\n",
      "Name: mvar20, dtype: float64 \n",
      "\n",
      "mvar21   Average utilization of active revolving credit card loans (%)\n",
      "count    17613.000000\n",
      "mean        56.445314\n",
      "std         35.870292\n",
      "min          0.198000\n",
      "25%         24.490000\n",
      "50%         57.396000\n",
      "75%         86.000000\n",
      "max        675.000000\n",
      "Name: mvar21, dtype: float64 \n",
      "\n",
      "mvar22   Average utilization of line on all active credit lines activated in last 2 years (%)\n",
      "count    15169.000000\n",
      "mean      4184.583659\n",
      "std       3840.085673\n",
      "min          0.000000\n",
      "25%        428.990000\n",
      "50%       3460.870000\n",
      "75%       7320.760000\n",
      "max      67500.000000\n",
      "Name: mvar22, dtype: float64 \n",
      "\n",
      "mvar23   Average utilization of line on all active credit cards activated in last 1 year (%)\n",
      "count    11601.000000\n",
      "mean      4121.398783\n",
      "std       3909.827820\n",
      "min          0.000000\n",
      "25%        311.490000\n",
      "50%       3300.000000\n",
      "75%       7260.000000\n",
      "max      67500.000000\n",
      "Name: mvar23, dtype: float64 \n",
      "\n",
      "mvar24   Average utilization of line on credit cards on which the borrower has missed 1 payment during last 6 months (%)\n",
      "count    18883.000000\n",
      "mean        68.147017\n",
      "std        361.547730\n",
      "min          0.000000\n",
      "25%          8.270000\n",
      "50%         36.220000\n",
      "75%         73.190000\n",
      "max      26034.030000\n",
      "Name: mvar24, dtype: float64 \n",
      "\n",
      "mvar25   Average tenure of active revolving credit cards (in days)\n",
      "count    22625.000000\n",
      "mean      1872.995315\n",
      "std       1346.551013\n",
      "min          0.000000\n",
      "25%        921.000000\n",
      "50%       1600.000000\n",
      "75%       2500.000000\n",
      "max      15513.000000\n",
      "Name: mvar25, dtype: float64 \n",
      "\n",
      "mvar26   Tenure of oldest credit card among all active credit cards (in days)\n",
      "count    21446.000000\n",
      "mean      3601.857829\n",
      "std       3001.727167\n",
      "min          0.000000\n",
      "25%       1278.000000\n",
      "50%       2890.000000\n",
      "75%       5019.000000\n",
      "max      20045.000000\n",
      "Name: mvar26, dtype: float64 \n",
      "\n",
      "mvar27   Tenure of oldest revolving credit card among all active revolving credit cards (in days)\n",
      "count    20601.000000\n",
      "mean      3304.800204\n",
      "std       2752.395162\n",
      "min          0.000000\n",
      "25%       1156.000000\n",
      "50%       2707.000000\n",
      "75%       4684.000000\n",
      "max      18828.000000\n",
      "Name: mvar27, dtype: float64 \n",
      "\n",
      "mvar28   Number of days since last missed payment on any credit line\n",
      "count    24830.000000\n",
      "mean      9014.615143\n",
      "std      13660.441024\n",
      "min          0.000000\n",
      "25%         30.000000\n",
      "50%        243.000000\n",
      "75%      30386.000000\n",
      "max      30386.000000\n",
      "Name: mvar28, dtype: float64 \n",
      "\n",
      "mvar29   Tenure of oldest credit line (in days)\n",
      "count    24830.000000\n",
      "mean      5738.759404\n",
      "std       3316.945929\n",
      "min          0.000000\n",
      "25%       3346.000000\n",
      "50%       5262.000000\n",
      "75%       7543.000000\n",
      "max      23634.000000\n",
      "Name: mvar29, dtype: float64 \n",
      "\n",
      "mvar30   Maximum tenure on all auto loans (in days)\n",
      "count    13403.000000\n",
      "mean      2667.380586\n",
      "std       1525.850247\n",
      "min          0.000000\n",
      "25%       1278.000000\n",
      "50%       2859.000000\n",
      "75%       3863.000000\n",
      "max       8669.000000\n",
      "Name: mvar30, dtype: float64 \n",
      "\n",
      "mvar31   Maximum tenure on all education loans (in days)\n",
      "count     7173.000000\n",
      "mean      3680.808170\n",
      "std       2073.702188\n",
      "min          0.000000\n",
      "25%       2038.000000\n",
      "50%       3650.000000\n",
      "75%       4806.000000\n",
      "max      15665.000000\n",
      "Name: mvar31, dtype: float64 \n",
      "\n",
      "mvar32   Sum of tenures (in months) of active credit cards\n",
      "count    22625.000000\n",
      "mean       503.778387\n",
      "std        530.628350\n",
      "min          0.000000\n",
      "25%        114.000000\n",
      "50%        344.000000\n",
      "75%        720.000000\n",
      "max       7696.000000\n",
      "Name: mvar32, dtype: float64 \n",
      "\n",
      "mvar33   Duration of stay at the current residential address (in years)\n",
      "count    24384.000000\n",
      "mean         7.278962\n",
      "std          7.784131\n",
      "min          0.083300\n",
      "25%          1.333300\n",
      "50%          4.333300\n",
      "75%         10.833300\n",
      "max         61.750000\n",
      "Name: mvar33, dtype: float64 \n",
      "\n",
      "mvar34   Number of active credit lines over the last 6 months on which the borrower has missed 1 payment\n",
      "count    24830.000000\n",
      "mean         0.216633\n",
      "std          0.634177\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         16.000000\n",
      "Name: mvar34, dtype: float64 \n",
      "\n",
      "mvar35   Number of revolving credit cards over the last 2 years on which the borrower has missed 1 payment\n",
      "count    13594.000000\n",
      "mean         0.166691\n",
      "std          0.507450\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          7.000000\n",
      "Name: mvar35, dtype: float64 \n",
      "\n",
      "mvar36   Number of active credit lines\n",
      "count    23988.000000\n",
      "mean         6.198766\n",
      "std          5.158101\n",
      "min          1.000000\n",
      "25%          3.000000\n",
      "50%          5.000000\n",
      "75%          8.000000\n",
      "max         90.000000\n",
      "Name: mvar36, dtype: float64 \n",
      "\n",
      "mvar37   Number of credit cards with an active tenure of at least 2 years\n",
      "count    22625.000000\n",
      "mean         4.678541\n",
      "std          4.746114\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          4.000000\n",
      "75%          7.000000\n",
      "max         50.000000\n",
      "Name: mvar37, dtype: float64 \n",
      "\n",
      "mvar38   Number of credit lines activated in last 2 years\n",
      "count    24830.000000\n",
      "mean         4.009907\n",
      "std          3.792525\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          3.000000\n",
      "75%          5.000000\n",
      "max         97.000000\n",
      "Name: mvar38, dtype: float64 \n",
      "\n",
      "mvar39   Number of credit lines on which the borrower has current delinquency\n",
      "count    23005.000000\n",
      "mean         0.087546\n",
      "std          0.403627\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         17.000000\n",
      "Name: mvar39, dtype: float64 \n",
      "\n",
      "mvar40   Utilization of line on active education loans (%)\n",
      "count    5128.000000\n",
      "mean       96.396320\n",
      "std        30.599735\n",
      "min         0.076000\n",
      "25%        88.345000\n",
      "50%       100.000000\n",
      "75%       109.886750\n",
      "max       646.893000\n",
      "Name: mvar40, dtype: float64 \n",
      "\n",
      "mvar41   Utilization of line on active auto loans (%)\n",
      "count    7308.000000\n",
      "mean       72.161646\n",
      "std        24.578127\n",
      "min         0.000000\n",
      "25%        58.333000\n",
      "50%        79.010500\n",
      "75%        91.895000\n",
      "max       142.119000\n",
      "Name: mvar41, dtype: float64 \n",
      "\n",
      "mvar42   Financial stress index of the borrower. This index is a function of collection trades, bankruptcies files, tax liens invoked, etc. \n",
      "count    24344.000000\n",
      "mean         0.328102\n",
      "std          0.378251\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.181820\n",
      "75%          0.571430\n",
      "max          2.000000\n",
      "Name: mvar42, dtype: float64 \n",
      "\n",
      "mvar43   Number of credit lines on which the borrower has never missed a payment in last 2 yrs, yet considered as high risk loans based on market prediction of economic scenario\n",
      "count    24712.000000\n",
      "mean         6.431248\n",
      "std          6.124857\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          5.000000\n",
      "75%         10.000000\n",
      "max         52.000000\n",
      "Name: mvar43, dtype: float64 \n",
      "\n",
      "mvar44   Ratio of maximum amount due on all active credit lines and sum of amounts due on all active credit lines \n",
      "count    22375.000000\n",
      "mean         0.714432\n",
      "std          0.244369\n",
      "min          0.075340\n",
      "25%          0.523750\n",
      "50%          0.749120\n",
      "75%          0.951965\n",
      "max          1.000000\n",
      "Name: mvar44, dtype: float64 \n",
      "\n",
      "mvar45   Number of mortgage loans on which the borrower has missed 2 payments\n",
      "count    11874.000000\n",
      "mean         0.073269\n",
      "std          0.298549\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          7.000000\n",
      "Name: mvar45, dtype: float64 \n",
      "\n",
      "mvar46   Number of auto loans on which the borrower has missed 2 payments\n",
      "count    17768.000000\n",
      "mean         0.081833\n",
      "std          0.304468\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          3.000000\n",
      "Name: mvar46, dtype: float64 \n",
      "\n",
      "mvar47   Type of product that the applicant applied for. (C = Charge; L = Lending)\n",
      "count     25000\n",
      "unique        2\n",
      "top           C\n",
      "freq      14513\n",
      "Name: mvar47, dtype: object \n",
      "\n",
      "default_ind   Indicator for default\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'default_ind'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2392\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2393\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-00ff130433cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevalDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2067\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2069\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2071\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1532\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1534\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1535\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2393\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2395\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2397\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'"
     ]
    }
   ],
   "source": [
    "for i in features:\n",
    "    print(dic[i], \" \", i)\n",
    "    print(evalDf[dic[i]].describe(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>application_key</th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
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       "      <th>mvar9</th>\n",
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       "      <th>mvar37</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
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       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>23850.000000</td>\n",
       "      <td>23294.000000</td>\n",
       "      <td>24842.000000</td>\n",
       "      <td>24842.000000</td>\n",
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       "      <td>2.269400e+04</td>\n",
       "      <td>19193.000000</td>\n",
       "      <td>21523.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>22621.000000</td>\n",
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       "      <td>23078.000000</td>\n",
       "      <td>5458.000000</td>\n",
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       "      <td>22538.000000</td>\n",
       "      <td>11027.000000</td>\n",
       "      <td>17851.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>1746.901551</td>\n",
       "      <td>1.118127</td>\n",
       "      <td>5.243267</td>\n",
       "      <td>0.463290</td>\n",
       "      <td>1.080183</td>\n",
       "      <td>1624.069192</td>\n",
       "      <td>1.775037e+04</td>\n",
       "      <td>6753.202313</td>\n",
       "      <td>33825.554662</td>\n",
       "      <td>...</td>\n",
       "      <td>4.237832</td>\n",
       "      <td>4.251349</td>\n",
       "      <td>0.089912</td>\n",
       "      <td>98.491140</td>\n",
       "      <td>71.990942</td>\n",
       "      <td>0.331992</td>\n",
       "      <td>6.546091</td>\n",
       "      <td>0.702558</td>\n",
       "      <td>0.071280</td>\n",
       "      <td>0.092712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7217.022701</td>\n",
       "      <td>94.088195</td>\n",
       "      <td>1.658707</td>\n",
       "      <td>10.605688</td>\n",
       "      <td>1.688155</td>\n",
       "      <td>5.813250</td>\n",
       "      <td>3588.247900</td>\n",
       "      <td>5.485979e+04</td>\n",
       "      <td>10133.583639</td>\n",
       "      <td>51781.414456</td>\n",
       "      <td>...</td>\n",
       "      <td>4.678669</td>\n",
       "      <td>3.870975</td>\n",
       "      <td>0.444250</td>\n",
       "      <td>30.488996</td>\n",
       "      <td>24.806054</td>\n",
       "      <td>0.374753</td>\n",
       "      <td>6.260583</td>\n",
       "      <td>0.246612</td>\n",
       "      <td>0.302654</td>\n",
       "      <td>0.321896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350053.000000</td>\n",
       "      <td>1477.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.072760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>356302.750000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>0.142725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.701000e+03</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>3518.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.338250</td>\n",
       "      <td>58.179500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.509132</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>362552.500000</td>\n",
       "      <td>1742.000000</td>\n",
       "      <td>0.542400</td>\n",
       "      <td>0.286000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>7.011500e+03</td>\n",
       "      <td>2503.000000</td>\n",
       "      <td>14072.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>79.123000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.732740</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>368802.250000</td>\n",
       "      <td>1811.000000</td>\n",
       "      <td>1.469375</td>\n",
       "      <td>6.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1371.000000</td>\n",
       "      <td>1.803575e+04</td>\n",
       "      <td>9595.000000</td>\n",
       "      <td>43009.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.237500</td>\n",
       "      <td>91.666000</td>\n",
       "      <td>0.561950</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.942372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375052.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>31.018100</td>\n",
       "      <td>167.740000</td>\n",
       "      <td>21.245200</td>\n",
       "      <td>156.878000</td>\n",
       "      <td>59192.000000</td>\n",
       "      <td>5.637108e+06</td>\n",
       "      <td>223123.000000</td>\n",
       "      <td>840658.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>631.360000</td>\n",
       "      <td>182.111000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       application_key         mvar1         mvar2         mvar3  \\\n",
       "count     25000.000000  23850.000000  23294.000000  24842.000000   \n",
       "mean     362552.500000   1746.901551      1.118127      5.243267   \n",
       "std        7217.022701     94.088195      1.658707     10.605688   \n",
       "min      350053.000000   1477.000000      0.000000      0.000000   \n",
       "25%      356302.750000   1680.000000      0.142725      0.000000   \n",
       "50%      362552.500000   1742.000000      0.542400      0.286000   \n",
       "75%      368802.250000   1811.000000      1.469375      6.480000   \n",
       "max      375052.000000   1950.000000     31.018100    167.740000   \n",
       "\n",
       "              mvar4         mvar5         mvar6         mvar7          mvar8  \\\n",
       "count  24842.000000  24842.000000  19193.000000  2.269400e+04   19193.000000   \n",
       "mean       0.463290      1.080183   1624.069192  1.775037e+04    6753.202313   \n",
       "std        1.688155      5.813250   3588.247900  5.485979e+04   10133.583639   \n",
       "min        0.000000      0.000000      0.000000  0.000000e+00       0.000000   \n",
       "25%        0.000000      0.000000     36.000000  1.701000e+03     494.000000   \n",
       "50%        0.000000      0.000000    297.000000  7.011500e+03    2503.000000   \n",
       "75%        0.000000      0.000000   1371.000000  1.803575e+04    9595.000000   \n",
       "max       21.245200    156.878000  59192.000000  5.637108e+06  223123.000000   \n",
       "\n",
       "               mvar9      ...             mvar37        mvar38        mvar39  \\\n",
       "count   21523.000000      ...       22621.000000  24842.000000  23078.000000   \n",
       "mean    33825.554662      ...           4.237832      4.251349      0.089912   \n",
       "std     51781.414456      ...           4.678669      3.870975      0.444250   \n",
       "min         0.000000      ...           0.000000      0.000000      0.000000   \n",
       "25%      3518.000000      ...           1.000000      2.000000      0.000000   \n",
       "50%     14072.000000      ...           3.000000      3.000000      0.000000   \n",
       "75%     43009.500000      ...           6.000000      6.000000      0.000000   \n",
       "max    840658.000000      ...          77.000000     73.000000     20.000000   \n",
       "\n",
       "            mvar40       mvar41        mvar42        mvar43        mvar44  \\\n",
       "count  5458.000000  7748.000000  24367.000000  24723.000000  22538.000000   \n",
       "mean     98.491140    71.990942      0.331992      6.546091      0.702558   \n",
       "std      30.488996    24.806054      0.374753      6.260583      0.246612   \n",
       "min       0.018000     0.000000      0.000000      0.000000      0.072760   \n",
       "25%      91.338250    58.179500      0.000000      2.000000      0.509132   \n",
       "50%     100.000000    79.123000      0.200000      5.000000      0.732740   \n",
       "75%     112.237500    91.666000      0.561950     10.000000      0.942372   \n",
       "max     631.360000   182.111000      2.000000     63.000000      1.000000   \n",
       "\n",
       "             mvar45        mvar46  \n",
       "count  11027.000000  17851.000000  \n",
       "mean       0.071280      0.092712  \n",
       "std        0.302654      0.321896  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      0.000000  \n",
       "75%        0.000000      0.000000  \n",
       "max        7.000000      3.000000  \n",
       "\n",
       "[8 rows x 47 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dist for leaderboard set\n",
    "leader_csv = pd.read_csv('./data/Leaderboard_dataset.csv',na_values=[\"NA\", \"na\", \"missing\"])\n",
    "leaderDf = pd.DataFrame(leader_csv)\n",
    "\n",
    "leaderDf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mvar1   Credit worthiness score calculated on the basis of borrower's credit history\n",
      "count    23850.000000\n",
      "mean      1746.901551\n",
      "std         94.088195\n",
      "min       1477.000000\n",
      "25%       1680.000000\n",
      "50%       1742.000000\n",
      "75%       1811.000000\n",
      "max       1950.000000\n",
      "Name: mvar1, dtype: float64 \n",
      "\n",
      "mvar2   A score calculated based on the number and riskiness of credit enquiries made to any lender by a borrower\n",
      "count    23294.000000\n",
      "mean         1.118127\n",
      "std          1.658707\n",
      "min          0.000000\n",
      "25%          0.142725\n",
      "50%          0.542400\n",
      "75%          1.469375\n",
      "max         31.018100\n",
      "Name: mvar2, dtype: float64 \n",
      "\n",
      "mvar3   Severity of default by the borrower on any loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24842.000000\n",
      "mean         5.243267\n",
      "std         10.605688\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.286000\n",
      "75%          6.480000\n",
      "max        167.740000\n",
      "Name: mvar3, dtype: float64 \n",
      "\n",
      "mvar4   Severity of default by the borrower on auto loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24842.000000\n",
      "mean         0.463290\n",
      "std          1.688155\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         21.245200\n",
      "Name: mvar4, dtype: float64 \n",
      "\n",
      "mvar5   Severity of default by the borrower on education loan(s). Severity is a function of amount, time since default and number of defaults\n",
      "count    24842.000000\n",
      "mean         1.080183\n",
      "std          5.813250\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max        156.878000\n",
      "Name: mvar5, dtype: float64 \n",
      "\n",
      "mvar6   Minimum of credit available on all revolving credit cards (in $)\n",
      "count    19193.000000\n",
      "mean      1624.069192\n",
      "std       3588.247900\n",
      "min          0.000000\n",
      "25%         36.000000\n",
      "50%        297.000000\n",
      "75%       1371.000000\n",
      "max      59192.000000\n",
      "Name: mvar6, dtype: float64 \n",
      "\n",
      "mvar7   Maximum of credit available on all active credit lines (in $)\n",
      "count    2.269400e+04\n",
      "mean     1.775037e+04\n",
      "std      5.485979e+04\n",
      "min      0.000000e+00\n",
      "25%      1.701000e+03\n",
      "50%      7.011500e+03\n",
      "75%      1.803575e+04\n",
      "max      5.637108e+06\n",
      "Name: mvar7, dtype: float64 \n",
      "\n",
      "mvar8   Maximum of credit available on all active revolving credit cards (in $)\n",
      "count     19193.000000\n",
      "mean       6753.202313\n",
      "std       10133.583639\n",
      "min           0.000000\n",
      "25%         494.000000\n",
      "50%        2503.000000\n",
      "75%        9595.000000\n",
      "max      223123.000000\n",
      "Name: mvar8, dtype: float64 \n",
      "\n",
      "mvar9   Sum of available credit on credit cards that the borrower has missed 1 payment (in $)\n",
      "count     21523.000000\n",
      "mean      33825.554662\n",
      "std       51781.414456\n",
      "min           0.000000\n",
      "25%        3518.000000\n",
      "50%       14072.000000\n",
      "75%       43009.500000\n",
      "max      840658.000000\n",
      "Name: mvar9, dtype: float64 \n",
      "\n",
      "mvar10   Total amount of credit available on accepted credit lines (in $)\n",
      "count    2.484200e+04\n",
      "mean     3.017275e+04\n",
      "std      7.003973e+04\n",
      "min      0.000000e+00\n",
      "25%      1.140000e+03\n",
      "50%      9.403500e+03\n",
      "75%      3.402075e+04\n",
      "max      5.647073e+06\n",
      "Name: mvar10, dtype: float64 \n",
      "\n",
      "mvar11   Amount of dues collected post default where due amount was more than 500 (in $)\n",
      "count    1.097400e+04\n",
      "mean     5.564579e+03\n",
      "std      2.238664e+04\n",
      "min      0.000000e+00\n",
      "25%      5.360000e+02\n",
      "50%      1.764000e+03\n",
      "75%      5.078250e+03\n",
      "max      1.847613e+06\n",
      "Name: mvar11, dtype: float64 \n",
      "\n",
      "mvar12   Sum of amount due on active credit cards (in $)\n",
      "count     20690.000000\n",
      "mean       7069.090188\n",
      "std       11431.342377\n",
      "min           0.000000\n",
      "25%         729.000000\n",
      "50%        3017.000000\n",
      "75%        8475.250000\n",
      "max      201964.000000\n",
      "Name: mvar12, dtype: float64 \n",
      "\n",
      "mvar13   Annual amount paid towards all credit cards during the previous year (in $)\n",
      "count    2.243500e+04\n",
      "mean     1.492284e+04\n",
      "std      4.402639e+04\n",
      "min      0.000000e+00\n",
      "25%      5.260000e+02\n",
      "50%      4.133000e+03\n",
      "75%      1.445650e+04\n",
      "max      1.917041e+06\n",
      "Name: mvar13, dtype: float64 \n",
      "\n",
      "mvar14   Annual income (in $)\n",
      "count    2.500000e+04\n",
      "mean     1.523549e+05\n",
      "std      6.337980e+06\n",
      "min      0.000000e+00\n",
      "25%      4.261300e+04\n",
      "50%      7.234300e+04\n",
      "75%      1.238750e+05\n",
      "max      9.910000e+08\n",
      "Name: mvar14, dtype: float64 \n",
      "\n",
      "mvar15   Estimated market value of a properety owned/used by the borrower (in $)\n",
      "count    1.494900e+04\n",
      "mean     2.936359e+05\n",
      "std      2.958244e+05\n",
      "min      1.741000e+03\n",
      "25%      1.268480e+05\n",
      "50%      2.147890e+05\n",
      "75%      3.666700e+05\n",
      "max      5.896799e+06\n",
      "Name: mvar15, dtype: float64 \n",
      "\n",
      "mvar16   Number of active revolving credit cards on which full credit limit is utilized by the borrower\n",
      "count    19324.000000\n",
      "mean         0.228886\n",
      "std          0.611633\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          9.000000\n",
      "Name: mvar16, dtype: float64 \n",
      "\n",
      "mvar17   Number of active credit cards on which full credit limit is utilized by the borrower\n",
      "count    20110.000000\n",
      "mean         0.261064\n",
      "std          0.684919\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         11.000000\n",
      "Name: mvar17, dtype: float64 \n",
      "\n",
      "mvar18   Number of active credit lines on which full credit limit is utilized by the borrower\n",
      "count    20418.000000\n",
      "mean         0.266823\n",
      "std          0.691766\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         11.000000\n",
      "Name: mvar18, dtype: float64 \n",
      "\n",
      "mvar19   Number of active credit cards on which atleast 75% credit limit is utilized by the borrower\n",
      "count    24999.000000\n",
      "mean         0.940238\n",
      "std          1.684380\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         25.000000\n",
      "Name: mvar19, dtype: float64 \n",
      "\n",
      "mvar20   Number of active credit lines on which atleast 75% credit limit is utilized by the borrower\n",
      "count    24842.000000\n",
      "mean         1.020691\n",
      "std          1.733333\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          1.000000\n",
      "max         26.000000\n",
      "Name: mvar20, dtype: float64 \n",
      "\n",
      "mvar21   Average utilization of active revolving credit card loans (%)\n",
      "count    18106.000000\n",
      "mean        57.885791\n",
      "std         35.344775\n",
      "min          0.117000\n",
      "25%         27.244750\n",
      "50%         59.569500\n",
      "75%         86.366500\n",
      "max        565.800000\n",
      "Name: mvar21, dtype: float64 \n",
      "\n",
      "mvar22   Average utilization of line on all active credit lines activated in last 2 years (%)\n",
      "count    15919.000000\n",
      "mean      4427.876770\n",
      "std       3813.637964\n",
      "min          0.000000\n",
      "25%        750.845000\n",
      "50%       3978.570000\n",
      "75%       7523.980000\n",
      "max      99800.000000\n",
      "Name: mvar22, dtype: float64 \n",
      "\n",
      "mvar23   Average utilization of line on all active credit cards activated in last 1 year (%)\n",
      "count    12359.000000\n",
      "mean      4270.918441\n",
      "std       3774.419701\n",
      "min          0.000000\n",
      "25%        532.705000\n",
      "50%       3707.500000\n",
      "75%       7405.470000\n",
      "max      41737.500000\n",
      "Name: mvar23, dtype: float64 \n",
      "\n",
      "mvar24   Average utilization of line on credit cards on which the borrower has missed 1 payment during last 6 months (%)\n",
      "count    19223.000000\n",
      "mean        72.500520\n",
      "std        305.673992\n",
      "min          0.000000\n",
      "25%         10.345000\n",
      "50%         41.220000\n",
      "75%         76.250000\n",
      "max      22621.130000\n",
      "Name: mvar24, dtype: float64 \n",
      "\n",
      "mvar25   Average tenure of active revolving credit cards (in days)\n",
      "count    22621.000000\n",
      "mean      1752.017860\n",
      "std       1329.276829\n",
      "min          0.000000\n",
      "25%        816.000000\n",
      "50%       1470.000000\n",
      "75%       2352.000000\n",
      "max      19010.000000\n",
      "Name: mvar25, dtype: float64 \n",
      "\n",
      "mvar26   Tenure of oldest credit card among all active credit cards (in days)\n",
      "count    21800.000000\n",
      "mean      3359.007982\n",
      "std       2922.767820\n",
      "min          0.000000\n",
      "25%       1125.000000\n",
      "50%       2738.000000\n",
      "75%       4684.000000\n",
      "max      23847.000000\n",
      "Name: mvar26, dtype: float64 \n",
      "\n",
      "mvar27   Tenure of oldest revolving credit card among all active revolving credit cards (in days)\n",
      "count    21007.000000\n",
      "mean      3096.124625\n",
      "std       2691.724594\n",
      "min          0.000000\n",
      "25%       1034.000000\n",
      "50%       2555.000000\n",
      "75%       4319.000000\n",
      "max      19923.000000\n",
      "Name: mvar27, dtype: float64 \n",
      "\n",
      "mvar28   Number of days since last missed payment on any credit line\n",
      "count    24842.000000\n",
      "mean      8838.908139\n",
      "std      13566.837115\n",
      "min          0.000000\n",
      "25%         30.000000\n",
      "50%        243.000000\n",
      "75%      30386.000000\n",
      "max      30386.000000\n",
      "Name: mvar28, dtype: float64 \n",
      "\n",
      "mvar29   Tenure of oldest credit line (in days)\n",
      "count    24842.000000\n",
      "mean      5493.759118\n",
      "std       3254.915936\n",
      "min          0.000000\n",
      "25%       3224.000000\n",
      "50%       5019.000000\n",
      "75%       7239.000000\n",
      "max      23847.000000\n",
      "Name: mvar29, dtype: float64 \n",
      "\n",
      "mvar30   Maximum tenure on all auto loans (in days)\n",
      "count    13551.000000\n",
      "mean      2598.012914\n",
      "std       1561.590684\n",
      "min          0.000000\n",
      "25%       1156.000000\n",
      "50%       2798.000000\n",
      "75%       3833.000000\n",
      "max       9034.000000\n",
      "Name: mvar30, dtype: float64 \n",
      "\n",
      "mvar31   Maximum tenure on all education loans (in days)\n",
      "count     7363.000000\n",
      "mean      3661.680565\n",
      "std       2025.492787\n",
      "min          0.000000\n",
      "25%       2129.000000\n",
      "50%       3620.000000\n",
      "75%       4775.000000\n",
      "max      15756.000000\n",
      "Name: mvar31, dtype: float64 \n",
      "\n",
      "mvar32   Sum of tenures (in months) of active credit cards\n",
      "count    22621.000000\n",
      "mean       466.639627\n",
      "std        512.257726\n",
      "min          0.000000\n",
      "25%         98.000000\n",
      "50%        300.000000\n",
      "75%        666.000000\n",
      "max       6965.000000\n",
      "Name: mvar32, dtype: float64 \n",
      "\n",
      "mvar33   Duration of stay at the current residential address (in years)\n",
      "count    24461.000000\n",
      "mean         6.920566\n",
      "std          7.655343\n",
      "min          0.083300\n",
      "25%          1.166700\n",
      "50%          3.833300\n",
      "75%         10.500000\n",
      "max         76.500000\n",
      "Name: mvar33, dtype: float64 \n",
      "\n",
      "mvar34   Number of active credit lines over the last 6 months on which the borrower has missed 1 payment\n",
      "count    24842.000000\n",
      "mean         0.229571\n",
      "std          0.691601\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         20.000000\n",
      "Name: mvar34, dtype: float64 \n",
      "\n",
      "mvar35   Number of revolving credit cards over the last 2 years on which the borrower has missed 1 payment\n",
      "count    14787.000000\n",
      "mean         0.200243\n",
      "std          0.567271\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          8.000000\n",
      "Name: mvar35, dtype: float64 \n",
      "\n",
      "mvar36   Number of active credit lines\n",
      "count    24021.000000\n",
      "mean         6.391657\n",
      "std          5.263733\n",
      "min          1.000000\n",
      "25%          3.000000\n",
      "50%          5.000000\n",
      "75%          8.000000\n",
      "max         99.000000\n",
      "Name: mvar36, dtype: float64 \n",
      "\n",
      "mvar37   Number of credit cards with an active tenure of at least 2 years\n",
      "count    22621.000000\n",
      "mean         4.237832\n",
      "std          4.678669\n",
      "min          0.000000\n",
      "25%          1.000000\n",
      "50%          3.000000\n",
      "75%          6.000000\n",
      "max         77.000000\n",
      "Name: mvar37, dtype: float64 \n",
      "\n",
      "mvar38   Number of credit lines activated in last 2 years\n",
      "count    24842.000000\n",
      "mean         4.251349\n",
      "std          3.870975\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          3.000000\n",
      "75%          6.000000\n",
      "max         73.000000\n",
      "Name: mvar38, dtype: float64 \n",
      "\n",
      "mvar39   Number of credit lines on which the borrower has current delinquency\n",
      "count    23078.000000\n",
      "mean         0.089912\n",
      "std          0.444250\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max         20.000000\n",
      "Name: mvar39, dtype: float64 \n",
      "\n",
      "mvar40   Utilization of line on active education loans (%)\n",
      "count    5458.000000\n",
      "mean       98.491140\n",
      "std        30.488996\n",
      "min         0.018000\n",
      "25%        91.338250\n",
      "50%       100.000000\n",
      "75%       112.237500\n",
      "max       631.360000\n",
      "Name: mvar40, dtype: float64 \n",
      "\n",
      "mvar41   Utilization of line on active auto loans (%)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    7748.000000\n",
      "mean       71.990942\n",
      "std        24.806054\n",
      "min         0.000000\n",
      "25%        58.179500\n",
      "50%        79.123000\n",
      "75%        91.666000\n",
      "max       182.111000\n",
      "Name: mvar41, dtype: float64 \n",
      "\n",
      "mvar42   Financial stress index of the borrower. This index is a function of collection trades, bankruptcies files, tax liens invoked, etc. \n",
      "count    24367.000000\n",
      "mean         0.331992\n",
      "std          0.374753\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.200000\n",
      "75%          0.561950\n",
      "max          2.000000\n",
      "Name: mvar42, dtype: float64 \n",
      "\n",
      "mvar43   Number of credit lines on which the borrower has never missed a payment in last 2 yrs, yet considered as high risk loans based on market prediction of economic scenario\n",
      "count    24723.000000\n",
      "mean         6.546091\n",
      "std          6.260583\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          5.000000\n",
      "75%         10.000000\n",
      "max         63.000000\n",
      "Name: mvar43, dtype: float64 \n",
      "\n",
      "mvar44   Ratio of maximum amount due on all active credit lines and sum of amounts due on all active credit lines \n",
      "count    22538.000000\n",
      "mean         0.702558\n",
      "std          0.246612\n",
      "min          0.072760\n",
      "25%          0.509132\n",
      "50%          0.732740\n",
      "75%          0.942372\n",
      "max          1.000000\n",
      "Name: mvar44, dtype: float64 \n",
      "\n",
      "mvar45   Number of mortgage loans on which the borrower has missed 2 payments\n",
      "count    11027.000000\n",
      "mean         0.071280\n",
      "std          0.302654\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          7.000000\n",
      "Name: mvar45, dtype: float64 \n",
      "\n",
      "mvar46   Number of auto loans on which the borrower has missed 2 payments\n",
      "count    17851.000000\n",
      "mean         0.092712\n",
      "std          0.321896\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          0.000000\n",
      "75%          0.000000\n",
      "max          3.000000\n",
      "Name: mvar46, dtype: float64 \n",
      "\n",
      "mvar47   Type of product that the applicant applied for. (C = Charge; L = Lending)\n",
      "count     25000\n",
      "unique        2\n",
      "top           C\n",
      "freq      16182\n",
      "Name: mvar47, dtype: object \n",
      "\n",
      "default_ind   Indicator for default\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'default_ind'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2392\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2393\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-cc145ecdbe90>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleaderDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2067\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2069\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2071\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1532\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1534\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1535\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2393\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2395\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2397\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'"
     ]
    }
   ],
   "source": [
    "for i in features:\n",
    "    print(dic[i], \" \", i)\n",
    "    print(leaderDf[dic[i]].describe(), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.648\n",
      "0.58052\n",
      "0.64728\n"
     ]
    }
   ],
   "source": [
    "#% of C in C vs L \n",
    "print(51840/80000) #train\n",
    "print(14513/25000) #eval\n",
    "print(16182/25000) #leader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t\t      %Train \t%eval \t%leader\n",
      "mvar1   Credit worthiness score calculated    4.54875 \t 4.688 \t 4.6\n",
      "mvar2   A score calculated based on the num   6.94125 \t 7.816 \t 6.824\n",
      "mvar3   Severity of default by the borrower   0.62625 \t 0.68 \t 0.632\n",
      "mvar4   Severity of default by the borrower   0.62625 \t 0.68 \t 0.632\n",
      "mvar5   Severity of default by the borrower   0.62625 \t 0.68 \t 0.632\n",
      "mvar6   Minimum of credit available on all    23.705 \t 24.908 \t 23.228\n",
      "mvar7   Maximum of credit available on all    9.2425 \t 9.812 \t 9.224\n",
      "mvar8   Maximum of credit available on all    23.705 \t 24.96 \t 23.228\n",
      "mvar9   Sum of available credit on credit c   14.26 \t 14.436 \t 13.908\n",
      "mvar10   Total amount of credit available on   0.6275 \t 0.68 \t 0.632\n",
      "mvar11   Amount of dues collected post defau   56.02375 \t 57.56 \t 56.104\n",
      "mvar12   Sum of amount due on active credit    17.5975 \t 18.348 \t 17.24\n",
      "mvar13   Annual amount paid towards all cred   10.44 \t 18.316 \t 10.26\n",
      "mvar14   Annual income (in $)   0.0 \t 0.0 \t 0.0\n",
      "mvar15   Estimated market value of a propere   40.08375 \t 40.948 \t 40.204\n",
      "mvar16   Number of active revolving credit c   23.1375 \t 24.36 \t 22.704\n",
      "mvar17   Number of active credit cards on wh   19.8075 \t 20.78 \t 19.56\n",
      "mvar18   Number of active credit lines on wh   18.46375 \t 19.164 \t 18.328\n",
      "mvar19   Number of active credit cards on wh   0.005 \t 0.028 \t 0.004\n",
      "mvar20   Number of active credit lines on wh   0.6275 \t 0.68 \t 0.632\n",
      "mvar21   Average utilization of active revol   28.20125 \t 29.548 \t 27.576\n",
      "mvar22   Average utilization of line on all    36.405 \t 39.324 \t 36.324\n",
      "mvar23   Average utilization of line on all    50.31 \t 53.596 \t 50.564\n",
      "mvar24   Average utilization of line on cred   23.6275 \t 24.468 \t 23.108\n",
      "mvar25   Average tenure of active revolving    9.6325 \t 9.5 \t 9.516\n",
      "mvar26   Tenure of oldest credit card among    13.2625 \t 14.216 \t 12.8\n",
      "mvar27   Tenure of oldest revolving credit c   16.51125 \t 17.596 \t 15.972\n",
      "mvar28   Number of days since last missed pa   0.6275 \t 0.68 \t 0.632\n",
      "mvar29   Tenure of oldest credit line (in da   0.6275 \t 0.68 \t 0.632\n",
      "mvar30   Maximum tenure on all auto loans (i   45.52125 \t 46.388 \t 45.796\n",
      "mvar31   Maximum tenure on all education loa   70.2725 \t 71.308 \t 70.548\n",
      "mvar32   Sum of tenures (in months) of activ   9.6325 \t 9.5 \t 9.516\n",
      "mvar33   Duration of stay at the current res   2.18875 \t 2.464 \t 2.156\n",
      "mvar34   Number of active credit lines over    0.6275 \t 0.68 \t 0.632\n",
      "mvar35   Number of revolving credit cards ov   41.2675 \t 45.624 \t 40.852\n",
      "mvar36   Number of active credit lines   3.6225 \t 4.048 \t 3.916\n",
      "mvar37   Number of credit cards with an acti   9.6325 \t 9.5 \t 9.516\n",
      "mvar38   Number of credit lines activated in   0.6275 \t 0.68 \t 0.632\n",
      "mvar39   Number of credit lines on which the   7.55 \t 7.98 \t 7.688\n",
      "mvar40   Utilization of line on active educa   78.19625 \t 79.488 \t 78.168\n",
      "mvar41   Utilization of line on active auto    68.2225 \t 70.768 \t 69.008\n",
      "mvar42   Financial stress index of the borro   2.37625 \t 2.624 \t 2.532\n",
      "mvar43   Number of credit lines on which the   1.0225 \t 1.152 \t 1.108\n",
      "mvar44   Ratio of maximum amount due on all    9.68125 \t 10.5 \t 9.848\n",
      "mvar45   Number of mortgage loans on which t   56.06875 \t 52.504 \t 55.892\n",
      "mvar46   Number of auto loans on which the b   28.48625 \t 28.928 \t 28.596\n",
      "mvar47   Type of product that the applicant    0.0 \t 0.0 \t 0.0\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'default_ind'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2392\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2393\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-9cb6bd6d3e36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#     print(i[:25])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m80000\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mgameDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m80000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25000\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mevalDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m25000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m25000\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mleaderDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m25000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2067\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2069\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2071\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1532\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1534\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1535\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3590\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3591\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\n",
      "\u001b[0;32m/home/ekka/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2393\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2394\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2395\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2397\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20405)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item (pandas/_libs/hashtable.c:20359)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'default_ind'"
     ]
    }
   ],
   "source": [
    "#number of misssing values for all 3 sets\n",
    "print(\"\\t\\t\\t\\t\\t      %Train\", \"\\t%eval\", \"\\t%leader\")\n",
    "for i in features:\n",
    "#     print(i[:25])\n",
    "    print(dic[i], \" \", i[:35], \" \" ,100*(80000-gameDf[dic[i]].describe()['count'])/80000, \"\\t\", 100*(25000-evalDf[dic[i]].describe()['count'])/25000, \"\\t\", 100*(25000-leaderDf[dic[i]].describe()['count'])/25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+F/ZZ3cX/OLv07GFlagLY8Nani2UNf6Hceh9eX+YPtbPXddfM7cb6F5b73Nnv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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a55ddef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8a55b5c080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation plots\n",
    "#gameDf.corr()\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.matshow(gameDf.corr(), fignum=1)\n",
    "plt.xticks(range(len(gameDf.columns)), gameDf.columns, rotation=\"vertical\")\n",
    "plt.yticks(range(len(gameDf.columns)), gameDf.columns)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mvar1</th>\n",
       "      <th>mvar2</th>\n",
       "      <th>mvar3</th>\n",
       "      <th>mvar4</th>\n",
       "      <th>mvar5</th>\n",
       "      <th>mvar6</th>\n",
       "      <th>mvar7</th>\n",
       "      <th>mvar8</th>\n",
       "      <th>mvar9</th>\n",
       "      <th>mvar10</th>\n",
       "      <th>...</th>\n",
       "      <th>mvar38</th>\n",
       "      <th>mvar39</th>\n",
       "      <th>mvar40</th>\n",
       "      <th>mvar41</th>\n",
       "      <th>mvar42</th>\n",
       "      <th>mvar43</th>\n",
       "      <th>mvar44</th>\n",
       "      <th>mvar45</th>\n",
       "      <th>mvar46</th>\n",
       "      <th>default_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mvar1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.265793</td>\n",
       "      <td>-0.502089</td>\n",
       "      <td>-0.268229</td>\n",
       "      <td>-0.240259</td>\n",
       "      <td>0.405196</td>\n",
       "      <td>0.267627</td>\n",
       "      <td>0.588904</td>\n",
       "      <td>0.506039</td>\n",
       "      <td>0.415873</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174035</td>\n",
       "      <td>-0.250907</td>\n",
       "      <td>-0.363777</td>\n",
       "      <td>-0.208557</td>\n",
       "      <td>-0.771688</td>\n",
       "      <td>0.440592</td>\n",
       "      <td>0.021616</td>\n",
       "      <td>-0.307051</td>\n",
       "      <td>-0.321960</td>\n",
       "      <td>-0.417302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar2</th>\n",
       "      <td>-0.265793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119156</td>\n",
       "      <td>0.086873</td>\n",
       "      <td>0.039322</td>\n",
       "      <td>-0.168128</td>\n",
       "      <td>-0.096857</td>\n",
       "      <td>-0.203014</td>\n",
       "      <td>-0.158317</td>\n",
       "      <td>-0.122271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.285102</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.100970</td>\n",
       "      <td>0.159704</td>\n",
       "      <td>0.137295</td>\n",
       "      <td>0.014375</td>\n",
       "      <td>-0.089405</td>\n",
       "      <td>0.056462</td>\n",
       "      <td>0.100936</td>\n",
       "      <td>0.227953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar3</th>\n",
       "      <td>-0.502089</td>\n",
       "      <td>0.119156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.368939</td>\n",
       "      <td>0.658109</td>\n",
       "      <td>-0.117079</td>\n",
       "      <td>-0.084982</td>\n",
       "      <td>-0.202675</td>\n",
       "      <td>-0.200606</td>\n",
       "      <td>-0.146626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114511</td>\n",
       "      <td>0.128314</td>\n",
       "      <td>0.213647</td>\n",
       "      <td>0.033008</td>\n",
       "      <td>0.432163</td>\n",
       "      <td>-0.245018</td>\n",
       "      <td>-0.018925</td>\n",
       "      <td>0.267140</td>\n",
       "      <td>0.392322</td>\n",
       "      <td>0.194814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar4</th>\n",
       "      <td>-0.268229</td>\n",
       "      <td>0.086873</td>\n",
       "      <td>0.368939</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091933</td>\n",
       "      <td>-0.067449</td>\n",
       "      <td>-0.045899</td>\n",
       "      <td>-0.111535</td>\n",
       "      <td>-0.118342</td>\n",
       "      <td>-0.074592</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064709</td>\n",
       "      <td>0.123631</td>\n",
       "      <td>0.107712</td>\n",
       "      <td>-0.003572</td>\n",
       "      <td>0.209824</td>\n",
       "      <td>-0.130205</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>0.135537</td>\n",
       "      <td>0.892075</td>\n",
       "      <td>0.141313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar5</th>\n",
       "      <td>-0.240259</td>\n",
       "      <td>0.039322</td>\n",
       "      <td>0.658109</td>\n",
       "      <td>0.091933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040019</td>\n",
       "      <td>-0.048359</td>\n",
       "      <td>-0.072506</td>\n",
       "      <td>-0.086020</td>\n",
       "      <td>-0.072473</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034388</td>\n",
       "      <td>0.058855</td>\n",
       "      <td>0.155475</td>\n",
       "      <td>0.036596</td>\n",
       "      <td>0.235934</td>\n",
       "      <td>-0.132246</td>\n",
       "      <td>-0.067406</td>\n",
       "      <td>0.041785</td>\n",
       "      <td>0.117824</td>\n",
       "      <td>0.088442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar6</th>\n",
       "      <td>0.405196</td>\n",
       "      <td>-0.168128</td>\n",
       "      <td>-0.117079</td>\n",
       "      <td>-0.067449</td>\n",
       "      <td>-0.040019</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192671</td>\n",
       "      <td>0.490946</td>\n",
       "      <td>0.243674</td>\n",
       "      <td>0.230486</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.205386</td>\n",
       "      <td>-0.076016</td>\n",
       "      <td>-0.154404</td>\n",
       "      <td>-0.102246</td>\n",
       "      <td>-0.201271</td>\n",
       "      <td>-0.054085</td>\n",
       "      <td>0.152521</td>\n",
       "      <td>-0.072196</td>\n",
       "      <td>-0.062485</td>\n",
       "      <td>-0.163856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar7</th>\n",
       "      <td>0.267627</td>\n",
       "      <td>-0.096857</td>\n",
       "      <td>-0.084982</td>\n",
       "      <td>-0.045899</td>\n",
       "      <td>-0.048359</td>\n",
       "      <td>0.192671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.345828</td>\n",
       "      <td>0.308526</td>\n",
       "      <td>0.891959</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.080942</td>\n",
       "      <td>-0.014592</td>\n",
       "      <td>-0.165445</td>\n",
       "      <td>-0.187826</td>\n",
       "      <td>-0.181921</td>\n",
       "      <td>0.115480</td>\n",
       "      <td>0.036661</td>\n",
       "      <td>-0.027935</td>\n",
       "      <td>-0.062547</td>\n",
       "      <td>-0.114585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar8</th>\n",
       "      <td>0.588904</td>\n",
       "      <td>-0.203014</td>\n",
       "      <td>-0.202675</td>\n",
       "      <td>-0.111535</td>\n",
       "      <td>-0.072506</td>\n",
       "      <td>0.490946</td>\n",
       "      <td>0.345828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.560884</td>\n",
       "      <td>0.455136</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129942</td>\n",
       "      <td>-0.103041</td>\n",
       "      <td>-0.235222</td>\n",
       "      <td>-0.154867</td>\n",
       "      <td>-0.356205</td>\n",
       "      <td>0.186343</td>\n",
       "      <td>0.089321</td>\n",
       "      <td>-0.121916</td>\n",
       "      <td>-0.109787</td>\n",
       "      <td>-0.228546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar9</th>\n",
       "      <td>0.506039</td>\n",
       "      <td>-0.158317</td>\n",
       "      <td>-0.200606</td>\n",
       "      <td>-0.118342</td>\n",
       "      <td>-0.086020</td>\n",
       "      <td>0.243674</td>\n",
       "      <td>0.308526</td>\n",
       "      <td>0.560884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022249</td>\n",
       "      <td>-0.074108</td>\n",
       "      <td>-0.278261</td>\n",
       "      <td>-0.179703</td>\n",
       "      <td>-0.400814</td>\n",
       "      <td>0.451355</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>-0.112344</td>\n",
       "      <td>-0.127856</td>\n",
       "      <td>-0.198013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar10</th>\n",
       "      <td>0.415873</td>\n",
       "      <td>-0.122271</td>\n",
       "      <td>-0.146626</td>\n",
       "      <td>-0.074592</td>\n",
       "      <td>-0.072473</td>\n",
       "      <td>0.230486</td>\n",
       "      <td>0.891959</td>\n",
       "      <td>0.455136</td>\n",
       "      <td>0.498627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035861</td>\n",
       "      <td>-0.029197</td>\n",
       "      <td>-0.268487</td>\n",
       "      <td>-0.232596</td>\n",
       "      <td>-0.300776</td>\n",
       "      <td>0.312105</td>\n",
       "      <td>-0.011159</td>\n",
       "      <td>-0.069100</td>\n",
       "      <td>-0.102893</td>\n",
       "      <td>-0.172239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar11</th>\n",
       "      <td>-0.151720</td>\n",
       "      <td>0.037108</td>\n",
       "      <td>0.263732</td>\n",
       "      <td>0.042771</td>\n",
       "      <td>0.198875</td>\n",
       "      <td>0.012817</td>\n",
       "      <td>-0.005264</td>\n",
       "      <td>-0.017762</td>\n",
       "      <td>-0.020190</td>\n",
       "      <td>-0.035276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120647</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.068417</td>\n",
       "      <td>0.003614</td>\n",
       "      <td>0.124874</td>\n",
       "      <td>-0.095187</td>\n",
       "      <td>0.026682</td>\n",
       "      <td>0.054385</td>\n",
       "      <td>0.056944</td>\n",
       "      <td>0.032217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar12</th>\n",
       "      <td>0.114491</td>\n",
       "      <td>-0.022040</td>\n",
       "      <td>-0.143968</td>\n",
       "      <td>-0.073101</td>\n",
       "      <td>-0.062461</td>\n",
       "      <td>-0.061367</td>\n",
       "      <td>0.118573</td>\n",
       "      <td>0.176645</td>\n",
       "      <td>0.316054</td>\n",
       "      <td>0.184174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059018</td>\n",
       "      <td>-0.008107</td>\n",
       "      <td>-0.157485</td>\n",
       "      <td>-0.097831</td>\n",
       "      <td>-0.265870</td>\n",
       "      <td>0.339212</td>\n",
       "      <td>-0.224707</td>\n",
       "      <td>-0.064317</td>\n",
       "      <td>-0.084580</td>\n",
       "      <td>-0.045913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar13</th>\n",
       "      <td>0.226078</td>\n",
       "      <td>-0.067167</td>\n",
       "      <td>-0.101471</td>\n",
       "      <td>-0.059466</td>\n",
       "      <td>-0.043350</td>\n",
       "      <td>0.165493</td>\n",
       "      <td>0.311260</td>\n",
       "      <td>0.318104</td>\n",
       "      <td>0.247445</td>\n",
       "      <td>0.411443</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029275</td>\n",
       "      <td>-0.034482</td>\n",
       "      <td>-0.184726</td>\n",
       "      <td>-0.072611</td>\n",
       "      <td>-0.176592</td>\n",
       "      <td>0.142769</td>\n",
       "      <td>-0.029014</td>\n",
       "      <td>-0.054273</td>\n",
       "      <td>-0.061139</td>\n",
       "      <td>-0.094533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar14</th>\n",
       "      <td>0.000311</td>\n",
       "      <td>-0.003943</td>\n",
       "      <td>-0.000579</td>\n",
       "      <td>-0.000843</td>\n",
       "      <td>-0.002412</td>\n",
       "      <td>0.004034</td>\n",
       "      <td>0.007045</td>\n",
       "      <td>0.006918</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004368</td>\n",
       "      <td>-0.001575</td>\n",
       "      <td>-0.000228</td>\n",
       "      <td>-0.016236</td>\n",
       "      <td>0.004082</td>\n",
       "      <td>-0.006044</td>\n",
       "      <td>0.006475</td>\n",
       "      <td>-0.002961</td>\n",
       "      <td>-0.000188</td>\n",
       "      <td>0.006686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar15</th>\n",
       "      <td>0.224650</td>\n",
       "      <td>-0.081434</td>\n",
       "      <td>-0.113164</td>\n",
       "      <td>-0.066300</td>\n",
       "      <td>-0.064214</td>\n",
       "      <td>0.159119</td>\n",
       "      <td>0.249501</td>\n",
       "      <td>0.267815</td>\n",
       "      <td>0.181071</td>\n",
       "      <td>0.251234</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.104280</td>\n",
       "      <td>-0.045294</td>\n",
       "      <td>-0.113195</td>\n",
       "      <td>-0.113639</td>\n",
       "      <td>-0.183063</td>\n",
       "      <td>0.047383</td>\n",
       "      <td>0.058867</td>\n",
       "      <td>-0.025062</td>\n",
       "      <td>-0.079753</td>\n",
       "      <td>-0.126203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar16</th>\n",
       "      <td>-0.361808</td>\n",
       "      <td>0.152664</td>\n",
       "      <td>0.109098</td>\n",
       "      <td>0.066766</td>\n",
       "      <td>0.037814</td>\n",
       "      <td>-0.162081</td>\n",
       "      <td>-0.065034</td>\n",
       "      <td>-0.181698</td>\n",
       "      <td>-0.113054</td>\n",
       "      <td>-0.099276</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159043</td>\n",
       "      <td>0.278723</td>\n",
       "      <td>0.079718</td>\n",
       "      <td>0.049261</td>\n",
       "      <td>0.117762</td>\n",
       "      <td>0.018004</td>\n",
       "      <td>-0.162050</td>\n",
       "      <td>0.072838</td>\n",
       "      <td>0.073739</td>\n",
       "      <td>0.281395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar17</th>\n",
       "      <td>-0.341212</td>\n",
       "      <td>0.156583</td>\n",
       "      <td>0.097016</td>\n",
       "      <td>0.055887</td>\n",
       "      <td>0.030054</td>\n",
       "      <td>-0.161177</td>\n",
       "      <td>-0.063427</td>\n",
       "      <td>-0.182310</td>\n",
       "      <td>-0.106396</td>\n",
       "      <td>-0.094469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193091</td>\n",
       "      <td>0.303510</td>\n",
       "      <td>0.072720</td>\n",
       "      <td>0.047579</td>\n",
       "      <td>0.091665</td>\n",
       "      <td>0.054576</td>\n",
       "      <td>-0.174534</td>\n",
       "      <td>0.063661</td>\n",
       "      <td>0.061961</td>\n",
       "      <td>0.275107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar18</th>\n",
       "      <td>-0.332663</td>\n",
       "      <td>0.156218</td>\n",
       "      <td>0.092714</td>\n",
       "      <td>0.053787</td>\n",
       "      <td>0.027300</td>\n",
       "      <td>-0.160091</td>\n",
       "      <td>-0.060014</td>\n",
       "      <td>-0.179703</td>\n",
       "      <td>-0.101027</td>\n",
       "      <td>-0.088743</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191886</td>\n",
       "      <td>0.297221</td>\n",
       "      <td>0.070923</td>\n",
       "      <td>0.045495</td>\n",
       "      <td>0.081426</td>\n",
       "      <td>0.060466</td>\n",
       "      <td>-0.177650</td>\n",
       "      <td>0.061590</td>\n",
       "      <td>0.059507</td>\n",
       "      <td>0.271068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar19</th>\n",
       "      <td>-0.134761</td>\n",
       "      <td>0.171004</td>\n",
       "      <td>-0.058973</td>\n",
       "      <td>-0.031962</td>\n",
       "      <td>-0.049836</td>\n",
       "      <td>-0.255631</td>\n",
       "      <td>-0.063363</td>\n",
       "      <td>-0.258723</td>\n",
       "      <td>-0.055813</td>\n",
       "      <td>-0.055514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295977</td>\n",
       "      <td>0.139971</td>\n",
       "      <td>-0.005558</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>0.369830</td>\n",
       "      <td>-0.308354</td>\n",
       "      <td>-0.004733</td>\n",
       "      <td>-0.055694</td>\n",
       "      <td>0.133321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar20</th>\n",
       "      <td>-0.122177</td>\n",
       "      <td>0.166147</td>\n",
       "      <td>-0.064237</td>\n",
       "      <td>-0.035606</td>\n",
       "      <td>-0.053587</td>\n",
       "      <td>-0.251206</td>\n",
       "      <td>-0.051552</td>\n",
       "      <td>-0.247576</td>\n",
       "      <td>-0.040542</td>\n",
       "      <td>-0.036412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290001</td>\n",
       "      <td>0.139956</td>\n",
       "      <td>-0.010292</td>\n",
       "      <td>0.018963</td>\n",
       "      <td>-0.194927</td>\n",
       "      <td>0.380885</td>\n",
       "      <td>-0.313551</td>\n",
       "      <td>-0.007426</td>\n",
       "      <td>-0.060846</td>\n",
       "      <td>0.126824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar21</th>\n",
       "      <td>-0.666369</td>\n",
       "      <td>0.231543</td>\n",
       "      <td>0.229255</td>\n",
       "      <td>0.141211</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>-0.416424</td>\n",
       "      <td>-0.181757</td>\n",
       "      <td>-0.529806</td>\n",
       "      <td>-0.338294</td>\n",
       "      <td>-0.272215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142191</td>\n",
       "      <td>0.184047</td>\n",
       "      <td>0.172877</td>\n",
       "      <td>0.109420</td>\n",
       "      <td>0.347396</td>\n",
       "      <td>-0.123897</td>\n",
       "      <td>-0.180785</td>\n",
       "      <td>0.138357</td>\n",
       "      <td>0.146216</td>\n",
       "      <td>0.330834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar22</th>\n",
       "      <td>-0.575690</td>\n",
       "      <td>0.193542</td>\n",
       "      <td>0.206792</td>\n",
       "      <td>0.118664</td>\n",
       "      <td>0.095217</td>\n",
       "      <td>-0.316494</td>\n",
       "      <td>-0.144844</td>\n",
       "      <td>-0.385159</td>\n",
       "      <td>-0.297200</td>\n",
       "      <td>-0.223707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110026</td>\n",
       "      <td>0.179315</td>\n",
       "      <td>0.150463</td>\n",
       "      <td>0.118846</td>\n",
       "      <td>0.323390</td>\n",
       "      <td>-0.145111</td>\n",
       "      <td>-0.170510</td>\n",
       "      <td>0.093241</td>\n",
       "      <td>0.130798</td>\n",
       "      <td>0.323623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar23</th>\n",
       "      <td>-0.560481</td>\n",
       "      <td>0.188093</td>\n",
       "      <td>0.197830</td>\n",
       "      <td>0.108777</td>\n",
       "      <td>0.093450</td>\n",
       "      <td>-0.316466</td>\n",
       "      <td>-0.135664</td>\n",
       "      <td>-0.367580</td>\n",
       "      <td>-0.286801</td>\n",
       "      <td>-0.231971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072157</td>\n",
       "      <td>0.173209</td>\n",
       "      <td>0.137164</td>\n",
       "      <td>0.122903</td>\n",
       "      <td>0.310287</td>\n",
       "      <td>-0.156018</td>\n",
       "      <td>-0.155629</td>\n",
       "      <td>0.087695</td>\n",
       "      <td>0.119293</td>\n",
       "      <td>0.316376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar24</th>\n",
       "      <td>-0.193534</td>\n",
       "      <td>0.039911</td>\n",
       "      <td>0.250980</td>\n",
       "      <td>0.062327</td>\n",
       "      <td>0.039031</td>\n",
       "      <td>-0.068289</td>\n",
       "      <td>-0.030940</td>\n",
       "      <td>-0.098712</td>\n",
       "      <td>-0.068702</td>\n",
       "      <td>-0.054737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>0.034270</td>\n",
       "      <td>0.075392</td>\n",
       "      <td>0.004057</td>\n",
       "      <td>0.152038</td>\n",
       "      <td>-0.077826</td>\n",
       "      <td>0.007133</td>\n",
       "      <td>0.080577</td>\n",
       "      <td>0.072396</td>\n",
       "      <td>0.063625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar25</th>\n",
       "      <td>0.365264</td>\n",
       "      <td>-0.227627</td>\n",
       "      <td>-0.086893</td>\n",
       "      <td>-0.072512</td>\n",
       "      <td>-0.062436</td>\n",
       "      <td>0.313181</td>\n",
       "      <td>0.231366</td>\n",
       "      <td>0.376077</td>\n",
       "      <td>0.286920</td>\n",
       "      <td>0.288192</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.327948</td>\n",
       "      <td>-0.037246</td>\n",
       "      <td>-0.166366</td>\n",
       "      <td>-0.211160</td>\n",
       "      <td>-0.242985</td>\n",
       "      <td>0.056631</td>\n",
       "      <td>0.082116</td>\n",
       "      <td>-0.005701</td>\n",
       "      <td>-0.098313</td>\n",
       "      <td>-0.218988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar26</th>\n",
       "      <td>0.415031</td>\n",
       "      <td>-0.195480</td>\n",
       "      <td>-0.085930</td>\n",
       "      <td>-0.081192</td>\n",
       "      <td>-0.071138</td>\n",
       "      <td>0.235002</td>\n",
       "      <td>0.244502</td>\n",
       "      <td>0.391905</td>\n",
       "      <td>0.479852</td>\n",
       "      <td>0.364529</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.193949</td>\n",
       "      <td>-0.036009</td>\n",
       "      <td>-0.185643</td>\n",
       "      <td>-0.208283</td>\n",
       "      <td>-0.325402</td>\n",
       "      <td>0.267862</td>\n",
       "      <td>0.024721</td>\n",
       "      <td>-0.030971</td>\n",
       "      <td>-0.105280</td>\n",
       "      <td>-0.219277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar27</th>\n",
       "      <td>0.412648</td>\n",
       "      <td>-0.201733</td>\n",
       "      <td>-0.083170</td>\n",
       "      <td>-0.079417</td>\n",
       "      <td>-0.068945</td>\n",
       "      <td>0.249592</td>\n",
       "      <td>0.247927</td>\n",
       "      <td>0.413473</td>\n",
       "      <td>0.483454</td>\n",
       "      <td>0.371335</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.212647</td>\n",
       "      <td>-0.040877</td>\n",
       "      <td>-0.194064</td>\n",
       "      <td>-0.214341</td>\n",
       "      <td>-0.316558</td>\n",
       "      <td>0.247080</td>\n",
       "      <td>0.033432</td>\n",
       "      <td>-0.029814</td>\n",
       "      <td>-0.101934</td>\n",
       "      <td>-0.220136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar28</th>\n",
       "      <td>0.673919</td>\n",
       "      <td>-0.150974</td>\n",
       "      <td>-0.314099</td>\n",
       "      <td>-0.169822</td>\n",
       "      <td>-0.120682</td>\n",
       "      <td>0.292475</td>\n",
       "      <td>0.137557</td>\n",
       "      <td>0.391186</td>\n",
       "      <td>0.329365</td>\n",
       "      <td>0.236237</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.123639</td>\n",
       "      <td>-0.143653</td>\n",
       "      <td>-0.228827</td>\n",
       "      <td>-0.087454</td>\n",
       "      <td>-0.477560</td>\n",
       "      <td>0.213584</td>\n",
       "      <td>0.048737</td>\n",
       "      <td>-0.177092</td>\n",
       "      <td>-0.168777</td>\n",
       "      <td>-0.221452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar29</th>\n",
       "      <td>0.382671</td>\n",
       "      <td>-0.153044</td>\n",
       "      <td>-0.069683</td>\n",
       "      <td>-0.058800</td>\n",
       "      <td>-0.052801</td>\n",
       "      <td>0.214465</td>\n",
       "      <td>0.233995</td>\n",
       "      <td>0.351297</td>\n",
       "      <td>0.412731</td>\n",
       "      <td>0.325602</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.112679</td>\n",
       "      <td>-0.023103</td>\n",
       "      <td>-0.074388</td>\n",
       "      <td>-0.167391</td>\n",
       "      <td>-0.323853</td>\n",
       "      <td>0.235834</td>\n",
       "      <td>0.022396</td>\n",
       "      <td>-0.035804</td>\n",
       "      <td>-0.111774</td>\n",
       "      <td>-0.210200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar30</th>\n",
       "      <td>0.136741</td>\n",
       "      <td>-0.110667</td>\n",
       "      <td>-0.004692</td>\n",
       "      <td>-0.027125</td>\n",
       "      <td>-0.030984</td>\n",
       "      <td>0.078209</td>\n",
       "      <td>0.123040</td>\n",
       "      <td>0.110805</td>\n",
       "      <td>0.159287</td>\n",
       "      <td>0.147833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.143794</td>\n",
       "      <td>-0.023986</td>\n",
       "      <td>-0.042715</td>\n",
       "      <td>-0.197577</td>\n",
       "      <td>-0.105380</td>\n",
       "      <td>0.039656</td>\n",
       "      <td>0.028044</td>\n",
       "      <td>0.036143</td>\n",
       "      <td>-0.043547</td>\n",
       "      <td>-0.131391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar31</th>\n",
       "      <td>0.158508</td>\n",
       "      <td>-0.048130</td>\n",
       "      <td>-0.008486</td>\n",
       "      <td>-0.006269</td>\n",
       "      <td>0.008258</td>\n",
       "      <td>0.081774</td>\n",
       "      <td>0.130180</td>\n",
       "      <td>0.103642</td>\n",
       "      <td>0.125953</td>\n",
       "      <td>0.151423</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103953</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.014167</td>\n",
       "      <td>-0.057643</td>\n",
       "      <td>-0.134142</td>\n",
       "      <td>0.055211</td>\n",
       "      <td>0.116473</td>\n",
       "      <td>0.010183</td>\n",
       "      <td>-0.049013</td>\n",
       "      <td>-0.117606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar32</th>\n",
       "      <td>0.436865</td>\n",
       "      <td>-0.160136</td>\n",
       "      <td>-0.114251</td>\n",
       "      <td>-0.104995</td>\n",
       "      <td>-0.091445</td>\n",
       "      <td>0.131582</td>\n",
       "      <td>0.250328</td>\n",
       "      <td>0.439533</td>\n",
       "      <td>0.691605</td>\n",
       "      <td>0.407414</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074090</td>\n",
       "      <td>-0.045930</td>\n",
       "      <td>-0.217330</td>\n",
       "      <td>-0.192949</td>\n",
       "      <td>-0.365768</td>\n",
       "      <td>0.421262</td>\n",
       "      <td>-0.025267</td>\n",
       "      <td>-0.069798</td>\n",
       "      <td>-0.130551</td>\n",
       "      <td>-0.220256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar33</th>\n",
       "      <td>0.279234</td>\n",
       "      <td>-0.124796</td>\n",
       "      <td>-0.105729</td>\n",
       "      <td>-0.074273</td>\n",
       "      <td>-0.066537</td>\n",
       "      <td>0.155762</td>\n",
       "      <td>0.185914</td>\n",
       "      <td>0.209211</td>\n",
       "      <td>0.250093</td>\n",
       "      <td>0.240739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125932</td>\n",
       "      <td>-0.020419</td>\n",
       "      <td>-0.051997</td>\n",
       "      <td>-0.119161</td>\n",
       "      <td>-0.203269</td>\n",
       "      <td>0.116927</td>\n",
       "      <td>0.054383</td>\n",
       "      <td>0.004539</td>\n",
       "      <td>-0.096904</td>\n",
       "      <td>-0.141373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar34</th>\n",
       "      <td>-0.243017</td>\n",
       "      <td>0.064890</td>\n",
       "      <td>0.095425</td>\n",
       "      <td>0.098152</td>\n",
       "      <td>0.017418</td>\n",
       "      <td>-0.110985</td>\n",
       "      <td>-0.013385</td>\n",
       "      <td>-0.142275</td>\n",
       "      <td>-0.080365</td>\n",
       "      <td>-0.017163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095345</td>\n",
       "      <td>0.639352</td>\n",
       "      <td>0.045742</td>\n",
       "      <td>-0.006317</td>\n",
       "      <td>-0.011954</td>\n",
       "      <td>-0.013144</td>\n",
       "      <td>-0.088141</td>\n",
       "      <td>0.092700</td>\n",
       "      <td>0.037126</td>\n",
       "      <td>0.152899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar35</th>\n",
       "      <td>-0.449950</td>\n",
       "      <td>0.087430</td>\n",
       "      <td>0.281362</td>\n",
       "      <td>0.124362</td>\n",
       "      <td>0.085240</td>\n",
       "      <td>-0.099387</td>\n",
       "      <td>-0.063852</td>\n",
       "      <td>-0.137949</td>\n",
       "      <td>-0.137180</td>\n",
       "      <td>-0.110392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118035</td>\n",
       "      <td>0.295919</td>\n",
       "      <td>0.085858</td>\n",
       "      <td>0.033107</td>\n",
       "      <td>0.303944</td>\n",
       "      <td>-0.178063</td>\n",
       "      <td>-0.030277</td>\n",
       "      <td>0.108424</td>\n",
       "      <td>0.175592</td>\n",
       "      <td>0.258219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar36</th>\n",
       "      <td>-0.253283</td>\n",
       "      <td>0.155159</td>\n",
       "      <td>0.368575</td>\n",
       "      <td>0.142885</td>\n",
       "      <td>0.237066</td>\n",
       "      <td>-0.199645</td>\n",
       "      <td>-0.046840</td>\n",
       "      <td>-0.174552</td>\n",
       "      <td>-0.036135</td>\n",
       "      <td>-0.008577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.570781</td>\n",
       "      <td>0.130226</td>\n",
       "      <td>0.199724</td>\n",
       "      <td>0.009492</td>\n",
       "      <td>0.081990</td>\n",
       "      <td>0.433243</td>\n",
       "      <td>-0.461160</td>\n",
       "      <td>0.073373</td>\n",
       "      <td>0.136080</td>\n",
       "      <td>0.168372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar37</th>\n",
       "      <td>0.357138</td>\n",
       "      <td>-0.141626</td>\n",
       "      <td>-0.059202</td>\n",
       "      <td>-0.085890</td>\n",
       "      <td>-0.074232</td>\n",
       "      <td>0.099031</td>\n",
       "      <td>0.200983</td>\n",
       "      <td>0.348712</td>\n",
       "      <td>0.671694</td>\n",
       "      <td>0.335644</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052935</td>\n",
       "      <td>-0.041615</td>\n",
       "      <td>-0.198810</td>\n",
       "      <td>-0.168566</td>\n",
       "      <td>-0.310006</td>\n",
       "      <td>0.378256</td>\n",
       "      <td>-0.004895</td>\n",
       "      <td>-0.053145</td>\n",
       "      <td>-0.114972</td>\n",
       "      <td>-0.212687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar38</th>\n",
       "      <td>-0.174035</td>\n",
       "      <td>0.285102</td>\n",
       "      <td>0.114511</td>\n",
       "      <td>0.064709</td>\n",
       "      <td>0.034388</td>\n",
       "      <td>-0.205386</td>\n",
       "      <td>-0.080942</td>\n",
       "      <td>-0.129942</td>\n",
       "      <td>0.022249</td>\n",
       "      <td>-0.035861</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060322</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>0.221525</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.432626</td>\n",
       "      <td>-0.277987</td>\n",
       "      <td>-0.038233</td>\n",
       "      <td>0.060412</td>\n",
       "      <td>0.151684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar39</th>\n",
       "      <td>-0.250907</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.128314</td>\n",
       "      <td>0.123631</td>\n",
       "      <td>0.058855</td>\n",
       "      <td>-0.076016</td>\n",
       "      <td>-0.014592</td>\n",
       "      <td>-0.103041</td>\n",
       "      <td>-0.074108</td>\n",
       "      <td>-0.029197</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060618</td>\n",
       "      <td>0.009543</td>\n",
       "      <td>0.091068</td>\n",
       "      <td>-0.065416</td>\n",
       "      <td>-0.062565</td>\n",
       "      <td>0.171391</td>\n",
       "      <td>0.102376</td>\n",
       "      <td>0.150155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar40</th>\n",
       "      <td>-0.363777</td>\n",
       "      <td>0.100970</td>\n",
       "      <td>0.213647</td>\n",
       "      <td>0.107712</td>\n",
       "      <td>0.155475</td>\n",
       "      <td>-0.154404</td>\n",
       "      <td>-0.165445</td>\n",
       "      <td>-0.235222</td>\n",
       "      <td>-0.278261</td>\n",
       "      <td>-0.268487</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>0.060618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103737</td>\n",
       "      <td>0.277048</td>\n",
       "      <td>-0.078789</td>\n",
       "      <td>-0.145938</td>\n",
       "      <td>0.090760</td>\n",
       "      <td>0.127500</td>\n",
       "      <td>0.166993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar41</th>\n",
       "      <td>-0.208557</td>\n",
       "      <td>0.159704</td>\n",
       "      <td>0.033008</td>\n",
       "      <td>-0.003572</td>\n",
       "      <td>0.036596</td>\n",
       "      <td>-0.102246</td>\n",
       "      <td>-0.187826</td>\n",
       "      <td>-0.154867</td>\n",
       "      <td>-0.179703</td>\n",
       "      <td>-0.232596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221525</td>\n",
       "      <td>0.009543</td>\n",
       "      <td>0.103737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139761</td>\n",
       "      <td>-0.048498</td>\n",
       "      <td>0.032476</td>\n",
       "      <td>-0.010876</td>\n",
       "      <td>0.009986</td>\n",
       "      <td>0.126180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar42</th>\n",
       "      <td>-0.771688</td>\n",
       "      <td>0.137295</td>\n",
       "      <td>0.432163</td>\n",
       "      <td>0.209824</td>\n",
       "      <td>0.235934</td>\n",
       "      <td>-0.201271</td>\n",
       "      <td>-0.181921</td>\n",
       "      <td>-0.356205</td>\n",
       "      <td>-0.400814</td>\n",
       "      <td>-0.300776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.091068</td>\n",
       "      <td>0.277048</td>\n",
       "      <td>0.139761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.548109</td>\n",
       "      <td>0.138304</td>\n",
       "      <td>0.265403</td>\n",
       "      <td>0.333598</td>\n",
       "      <td>0.330032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar43</th>\n",
       "      <td>0.440592</td>\n",
       "      <td>0.014375</td>\n",
       "      <td>-0.245018</td>\n",
       "      <td>-0.130205</td>\n",
       "      <td>-0.132246</td>\n",
       "      <td>-0.054085</td>\n",
       "      <td>0.115480</td>\n",
       "      <td>0.186343</td>\n",
       "      <td>0.451355</td>\n",
       "      <td>0.312105</td>\n",
       "      <td>...</td>\n",
       "      <td>0.432626</td>\n",
       "      <td>-0.065416</td>\n",
       "      <td>-0.078789</td>\n",
       "      <td>-0.048498</td>\n",
       "      <td>-0.548109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377501</td>\n",
       "      <td>-0.167379</td>\n",
       "      <td>-0.178940</td>\n",
       "      <td>-0.168915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar44</th>\n",
       "      <td>0.021616</td>\n",
       "      <td>-0.089405</td>\n",
       "      <td>-0.018925</td>\n",
       "      <td>0.001213</td>\n",
       "      <td>-0.067406</td>\n",
       "      <td>0.152521</td>\n",
       "      <td>0.036661</td>\n",
       "      <td>0.089321</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>-0.011159</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.277987</td>\n",
       "      <td>-0.062565</td>\n",
       "      <td>-0.145938</td>\n",
       "      <td>0.032476</td>\n",
       "      <td>0.138304</td>\n",
       "      <td>-0.377501</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040535</td>\n",
       "      <td>0.008622</td>\n",
       "      <td>-0.065298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar45</th>\n",
       "      <td>-0.307051</td>\n",
       "      <td>0.056462</td>\n",
       "      <td>0.267140</td>\n",
       "      <td>0.135537</td>\n",
       "      <td>0.041785</td>\n",
       "      <td>-0.072196</td>\n",
       "      <td>-0.027935</td>\n",
       "      <td>-0.121916</td>\n",
       "      <td>-0.112344</td>\n",
       "      <td>-0.069100</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038233</td>\n",
       "      <td>0.171391</td>\n",
       "      <td>0.090760</td>\n",
       "      <td>-0.010876</td>\n",
       "      <td>0.265403</td>\n",
       "      <td>-0.167379</td>\n",
       "      <td>0.040535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140546</td>\n",
       "      <td>0.154632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mvar46</th>\n",
       "      <td>-0.321960</td>\n",
       "      <td>0.100936</td>\n",
       "      <td>0.392322</td>\n",
       "      <td>0.892075</td>\n",
       "      <td>0.117824</td>\n",
       "      <td>-0.062485</td>\n",
       "      <td>-0.062547</td>\n",
       "      <td>-0.109787</td>\n",
       "      <td>-0.127856</td>\n",
       "      <td>-0.102893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060412</td>\n",
       "      <td>0.102376</td>\n",
       "      <td>0.127500</td>\n",
       "      <td>0.009986</td>\n",
       "      <td>0.333598</td>\n",
       "      <td>-0.178940</td>\n",
       "      <td>0.008622</td>\n",
       "      <td>0.140546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_ind</th>\n",
       "      <td>-0.417302</td>\n",
       "      <td>0.227953</td>\n",
       "      <td>0.194814</td>\n",
       "      <td>0.141313</td>\n",
       "      <td>0.088442</td>\n",
       "      <td>-0.163856</td>\n",
       "      <td>-0.114585</td>\n",
       "      <td>-0.228546</td>\n",
       "      <td>-0.198013</td>\n",
       "      <td>-0.172239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151684</td>\n",
       "      <td>0.150155</td>\n",
       "      <td>0.166993</td>\n",
       "      <td>0.126180</td>\n",
       "      <td>0.330032</td>\n",
       "      <td>-0.168915</td>\n",
       "      <td>-0.065298</td>\n",
       "      <td>0.154632</td>\n",
       "      <td>0.187228</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mvar1     mvar2     mvar3     mvar4     mvar5     mvar6  \\\n",
       "mvar1        1.000000 -0.265793 -0.502089 -0.268229 -0.240259  0.405196   \n",
       "mvar2       -0.265793  1.000000  0.119156  0.086873  0.039322 -0.168128   \n",
       "mvar3       -0.502089  0.119156  1.000000  0.368939  0.658109 -0.117079   \n",
       "mvar4       -0.268229  0.086873  0.368939  1.000000  0.091933 -0.067449   \n",
       "mvar5       -0.240259  0.039322  0.658109  0.091933  1.000000 -0.040019   \n",
       "mvar6        0.405196 -0.168128 -0.117079 -0.067449 -0.040019  1.000000   \n",
       "mvar7        0.267627 -0.096857 -0.084982 -0.045899 -0.048359  0.192671   \n",
       "mvar8        0.588904 -0.203014 -0.202675 -0.111535 -0.072506  0.490946   \n",
       "mvar9        0.506039 -0.158317 -0.200606 -0.118342 -0.086020  0.243674   \n",
       "mvar10       0.415873 -0.122271 -0.146626 -0.074592 -0.072473  0.230486   \n",
       "mvar11      -0.151720  0.037108  0.263732  0.042771  0.198875  0.012817   \n",
       "mvar12       0.114491 -0.022040 -0.143968 -0.073101 -0.062461 -0.061367   \n",
       "mvar13       0.226078 -0.067167 -0.101471 -0.059466 -0.043350  0.165493   \n",
       "mvar14       0.000311 -0.003943 -0.000579 -0.000843 -0.002412  0.004034   \n",
       "mvar15       0.224650 -0.081434 -0.113164 -0.066300 -0.064214  0.159119   \n",
       "mvar16      -0.361808  0.152664  0.109098  0.066766  0.037814 -0.162081   \n",
       "mvar17      -0.341212  0.156583  0.097016  0.055887  0.030054 -0.161177   \n",
       "mvar18      -0.332663  0.156218  0.092714  0.053787  0.027300 -0.160091   \n",
       "mvar19      -0.134761  0.171004 -0.058973 -0.031962 -0.049836 -0.255631   \n",
       "mvar20      -0.122177  0.166147 -0.064237 -0.035606 -0.053587 -0.251206   \n",
       "mvar21      -0.666369  0.231543  0.229255  0.141211  0.092308 -0.416424   \n",
       "mvar22      -0.575690  0.193542  0.206792  0.118664  0.095217 -0.316494   \n",
       "mvar23      -0.560481  0.188093  0.197830  0.108777  0.093450 -0.316466   \n",
       "mvar24      -0.193534  0.039911  0.250980  0.062327  0.039031 -0.068289   \n",
       "mvar25       0.365264 -0.227627 -0.086893 -0.072512 -0.062436  0.313181   \n",
       "mvar26       0.415031 -0.195480 -0.085930 -0.081192 -0.071138  0.235002   \n",
       "mvar27       0.412648 -0.201733 -0.083170 -0.079417 -0.068945  0.249592   \n",
       "mvar28       0.673919 -0.150974 -0.314099 -0.169822 -0.120682  0.292475   \n",
       "mvar29       0.382671 -0.153044 -0.069683 -0.058800 -0.052801  0.214465   \n",
       "mvar30       0.136741 -0.110667 -0.004692 -0.027125 -0.030984  0.078209   \n",
       "mvar31       0.158508 -0.048130 -0.008486 -0.006269  0.008258  0.081774   \n",
       "mvar32       0.436865 -0.160136 -0.114251 -0.104995 -0.091445  0.131582   \n",
       "mvar33       0.279234 -0.124796 -0.105729 -0.074273 -0.066537  0.155762   \n",
       "mvar34      -0.243017  0.064890  0.095425  0.098152  0.017418 -0.110985   \n",
       "mvar35      -0.449950  0.087430  0.281362  0.124362  0.085240 -0.099387   \n",
       "mvar36      -0.253283  0.155159  0.368575  0.142885  0.237066 -0.199645   \n",
       "mvar37       0.357138 -0.141626 -0.059202 -0.085890 -0.074232  0.099031   \n",
       "mvar38      -0.174035  0.285102  0.114511  0.064709  0.034388 -0.205386   \n",
       "mvar39      -0.250907  0.053952  0.128314  0.123631  0.058855 -0.076016   \n",
       "mvar40      -0.363777  0.100970  0.213647  0.107712  0.155475 -0.154404   \n",
       "mvar41      -0.208557  0.159704  0.033008 -0.003572  0.036596 -0.102246   \n",
       "mvar42      -0.771688  0.137295  0.432163  0.209824  0.235934 -0.201271   \n",
       "mvar43       0.440592  0.014375 -0.245018 -0.130205 -0.132246 -0.054085   \n",
       "mvar44       0.021616 -0.089405 -0.018925  0.001213 -0.067406  0.152521   \n",
       "mvar45      -0.307051  0.056462  0.267140  0.135537  0.041785 -0.072196   \n",
       "mvar46      -0.321960  0.100936  0.392322  0.892075  0.117824 -0.062485   \n",
       "default_ind -0.417302  0.227953  0.194814  0.141313  0.088442 -0.163856   \n",
       "\n",
       "                mvar7     mvar8     mvar9    mvar10     ...         mvar38  \\\n",
       "mvar1        0.267627  0.588904  0.506039  0.415873     ...      -0.174035   \n",
       "mvar2       -0.096857 -0.203014 -0.158317 -0.122271     ...       0.285102   \n",
       "mvar3       -0.084982 -0.202675 -0.200606 -0.146626     ...       0.114511   \n",
       "mvar4       -0.045899 -0.111535 -0.118342 -0.074592     ...       0.064709   \n",
       "mvar5       -0.048359 -0.072506 -0.086020 -0.072473     ...       0.034388   \n",
       "mvar6        0.192671  0.490946  0.243674  0.230486     ...      -0.205386   \n",
       "mvar7        1.000000  0.345828  0.308526  0.891959     ...      -0.080942   \n",
       "mvar8        0.345828  1.000000  0.560884  0.455136     ...      -0.129942   \n",
       "mvar9        0.308526  0.560884  1.000000  0.498627     ...       0.022249   \n",
       "mvar10       0.891959  0.455136  0.498627  1.000000     ...      -0.035861   \n",
       "mvar11      -0.005264 -0.017762 -0.020190 -0.035276     ...       0.120647   \n",
       "mvar12       0.118573  0.176645  0.316054  0.184174     ...       0.059018   \n",
       "mvar13       0.311260  0.318104  0.247445  0.411443     ...      -0.029275   \n",
       "mvar14       0.007045  0.006918  0.000446  0.002404     ...      -0.004368   \n",
       "mvar15       0.249501  0.267815  0.181071  0.251234     ...      -0.104280   \n",
       "mvar16      -0.065034 -0.181698 -0.113054 -0.099276     ...       0.159043   \n",
       "mvar17      -0.063427 -0.182310 -0.106396 -0.094469     ...       0.193091   \n",
       "mvar18      -0.060014 -0.179703 -0.101027 -0.088743     ...       0.191886   \n",
       "mvar19      -0.063363 -0.258723 -0.055813 -0.055514     ...       0.295977   \n",
       "mvar20      -0.051552 -0.247576 -0.040542 -0.036412     ...       0.290001   \n",
       "mvar21      -0.181757 -0.529806 -0.338294 -0.272215     ...       0.142191   \n",
       "mvar22      -0.144844 -0.385159 -0.297200 -0.223707     ...       0.110026   \n",
       "mvar23      -0.135664 -0.367580 -0.286801 -0.231971     ...       0.072157   \n",
       "mvar24      -0.030940 -0.098712 -0.068702 -0.054737     ...       0.009477   \n",
       "mvar25       0.231366  0.376077  0.286920  0.288192     ...      -0.327948   \n",
       "mvar26       0.244502  0.391905  0.479852  0.364529     ...      -0.193949   \n",
       "mvar27       0.247927  0.413473  0.483454  0.371335     ...      -0.212647   \n",
       "mvar28       0.137557  0.391186  0.329365  0.236237     ...      -0.123639   \n",
       "mvar29       0.233995  0.351297  0.412731  0.325602     ...      -0.112679   \n",
       "mvar30       0.123040  0.110805  0.159287  0.147833     ...      -0.143794   \n",
       "mvar31       0.130180  0.103642  0.125953  0.151423     ...      -0.103953   \n",
       "mvar32       0.250328  0.439533  0.691605  0.407414     ...      -0.074090   \n",
       "mvar33       0.185914  0.209211  0.250093  0.240739     ...      -0.125932   \n",
       "mvar34      -0.013385 -0.142275 -0.080365 -0.017163     ...       0.095345   \n",
       "mvar35      -0.063852 -0.137949 -0.137180 -0.110392     ...       0.118035   \n",
       "mvar36      -0.046840 -0.174552 -0.036135 -0.008577     ...       0.570781   \n",
       "mvar37       0.200983  0.348712  0.671694  0.335644     ...      -0.052935   \n",
       "mvar38      -0.080942 -0.129942  0.022249 -0.035861     ...       1.000000   \n",
       "mvar39      -0.014592 -0.103041 -0.074108 -0.029197     ...       0.060322   \n",
       "mvar40      -0.165445 -0.235222 -0.278261 -0.268487     ...       0.098393   \n",
       "mvar41      -0.187826 -0.154867 -0.179703 -0.232596     ...       0.221525   \n",
       "mvar42      -0.181921 -0.356205 -0.400814 -0.300776     ...       0.002891   \n",
       "mvar43       0.115480  0.186343  0.451355  0.312105     ...       0.432626   \n",
       "mvar44       0.036661  0.089321  0.001032 -0.011159     ...      -0.277987   \n",
       "mvar45      -0.027935 -0.121916 -0.112344 -0.069100     ...      -0.038233   \n",
       "mvar46      -0.062547 -0.109787 -0.127856 -0.102893     ...       0.060412   \n",
       "default_ind -0.114585 -0.228546 -0.198013 -0.172239     ...       0.151684   \n",
       "\n",
       "               mvar39    mvar40    mvar41    mvar42    mvar43    mvar44  \\\n",
       "mvar1       -0.250907 -0.363777 -0.208557 -0.771688  0.440592  0.021616   \n",
       "mvar2        0.053952  0.100970  0.159704  0.137295  0.014375 -0.089405   \n",
       "mvar3        0.128314  0.213647  0.033008  0.432163 -0.245018 -0.018925   \n",
       "mvar4        0.123631  0.107712 -0.003572  0.209824 -0.130205  0.001213   \n",
       "mvar5        0.058855  0.155475  0.036596  0.235934 -0.132246 -0.067406   \n",
       "mvar6       -0.076016 -0.154404 -0.102246 -0.201271 -0.054085  0.152521   \n",
       "mvar7       -0.014592 -0.165445 -0.187826 -0.181921  0.115480  0.036661   \n",
       "mvar8       -0.103041 -0.235222 -0.154867 -0.356205  0.186343  0.089321   \n",
       "mvar9       -0.074108 -0.278261 -0.179703 -0.400814  0.451355  0.001032   \n",
       "mvar10      -0.029197 -0.268487 -0.232596 -0.300776  0.312105 -0.011159   \n",
       "mvar11       0.000881  0.068417  0.003614  0.124874 -0.095187  0.026682   \n",
       "mvar12      -0.008107 -0.157485 -0.097831 -0.265870  0.339212 -0.224707   \n",
       "mvar13      -0.034482 -0.184726 -0.072611 -0.176592  0.142769 -0.029014   \n",
       "mvar14      -0.001575 -0.000228 -0.016236  0.004082 -0.006044  0.006475   \n",
       "mvar15      -0.045294 -0.113195 -0.113639 -0.183063  0.047383  0.058867   \n",
       "mvar16       0.278723  0.079718  0.049261  0.117762  0.018004 -0.162050   \n",
       "mvar17       0.303510  0.072720  0.047579  0.091665  0.054576 -0.174534   \n",
       "mvar18       0.297221  0.070923  0.045495  0.081426  0.060466 -0.177650   \n",
       "mvar19       0.139971 -0.005558  0.024793 -0.182333  0.369830 -0.308354   \n",
       "mvar20       0.139956 -0.010292  0.018963 -0.194927  0.380885 -0.313551   \n",
       "mvar21       0.184047  0.172877  0.109420  0.347396 -0.123897 -0.180785   \n",
       "mvar22       0.179315  0.150463  0.118846  0.323390 -0.145111 -0.170510   \n",
       "mvar23       0.173209  0.137164  0.122903  0.310287 -0.156018 -0.155629   \n",
       "mvar24       0.034270  0.075392  0.004057  0.152038 -0.077826  0.007133   \n",
       "mvar25      -0.037246 -0.166366 -0.211160 -0.242985  0.056631  0.082116   \n",
       "mvar26      -0.036009 -0.185643 -0.208283 -0.325402  0.267862  0.024721   \n",
       "mvar27      -0.040877 -0.194064 -0.214341 -0.316558  0.247080  0.033432   \n",
       "mvar28      -0.143653 -0.228827 -0.087454 -0.477560  0.213584  0.048737   \n",
       "mvar29      -0.023103 -0.074388 -0.167391 -0.323853  0.235834  0.022396   \n",
       "mvar30      -0.023986 -0.042715 -0.197577 -0.105380  0.039656  0.028044   \n",
       "mvar31       0.002227  0.014167 -0.057643 -0.134142  0.055211  0.116473   \n",
       "mvar32      -0.045930 -0.217330 -0.192949 -0.365768  0.421262 -0.025267   \n",
       "mvar33      -0.020419 -0.051997 -0.119161 -0.203269  0.116927  0.054383   \n",
       "mvar34       0.639352  0.045742 -0.006317 -0.011954 -0.013144 -0.088141   \n",
       "mvar35       0.295919  0.085858  0.033107  0.303944 -0.178063 -0.030277   \n",
       "mvar36       0.130226  0.199724  0.009492  0.081990  0.433243 -0.461160   \n",
       "mvar37      -0.041615 -0.198810 -0.168566 -0.310006  0.378256 -0.004895   \n",
       "mvar38       0.060322  0.098393  0.221525  0.002891  0.432626 -0.277987   \n",
       "mvar39       1.000000  0.060618  0.009543  0.091068 -0.065416 -0.062565   \n",
       "mvar40       0.060618  1.000000  0.103737  0.277048 -0.078789 -0.145938   \n",
       "mvar41       0.009543  0.103737  1.000000  0.139761 -0.048498  0.032476   \n",
       "mvar42       0.091068  0.277048  0.139761  1.000000 -0.548109  0.138304   \n",
       "mvar43      -0.065416 -0.078789 -0.048498 -0.548109  1.000000 -0.377501   \n",
       "mvar44      -0.062565 -0.145938  0.032476  0.138304 -0.377501  1.000000   \n",
       "mvar45       0.171391  0.090760 -0.010876  0.265403 -0.167379  0.040535   \n",
       "mvar46       0.102376  0.127500  0.009986  0.333598 -0.178940  0.008622   \n",
       "default_ind  0.150155  0.166993  0.126180  0.330032 -0.168915 -0.065298   \n",
       "\n",
       "               mvar45    mvar46  default_ind  \n",
       "mvar1       -0.307051 -0.321960    -0.417302  \n",
       "mvar2        0.056462  0.100936     0.227953  \n",
       "mvar3        0.267140  0.392322     0.194814  \n",
       "mvar4        0.135537  0.892075     0.141313  \n",
       "mvar5        0.041785  0.117824     0.088442  \n",
       "mvar6       -0.072196 -0.062485    -0.163856  \n",
       "mvar7       -0.027935 -0.062547    -0.114585  \n",
       "mvar8       -0.121916 -0.109787    -0.228546  \n",
       "mvar9       -0.112344 -0.127856    -0.198013  \n",
       "mvar10      -0.069100 -0.102893    -0.172239  \n",
       "mvar11       0.054385  0.056944     0.032217  \n",
       "mvar12      -0.064317 -0.084580    -0.045913  \n",
       "mvar13      -0.054273 -0.061139    -0.094533  \n",
       "mvar14      -0.002961 -0.000188     0.006686  \n",
       "mvar15      -0.025062 -0.079753    -0.126203  \n",
       "mvar16       0.072838  0.073739     0.281395  \n",
       "mvar17       0.063661  0.061961     0.275107  \n",
       "mvar18       0.061590  0.059507     0.271068  \n",
       "mvar19      -0.004733 -0.055694     0.133321  \n",
       "mvar20      -0.007426 -0.060846     0.126824  \n",
       "mvar21       0.138357  0.146216     0.330834  \n",
       "mvar22       0.093241  0.130798     0.323623  \n",
       "mvar23       0.087695  0.119293     0.316376  \n",
       "mvar24       0.080577  0.072396     0.063625  \n",
       "mvar25      -0.005701 -0.098313    -0.218988  \n",
       "mvar26      -0.030971 -0.105280    -0.219277  \n",
       "mvar27      -0.029814 -0.101934    -0.220136  \n",
       "mvar28      -0.177092 -0.168777    -0.221452  \n",
       "mvar29      -0.035804 -0.111774    -0.210200  \n",
       "mvar30       0.036143 -0.043547    -0.131391  \n",
       "mvar31       0.010183 -0.049013    -0.117606  \n",
       "mvar32      -0.069798 -0.130551    -0.220256  \n",
       "mvar33       0.004539 -0.096904    -0.141373  \n",
       "mvar34       0.092700  0.037126     0.152899  \n",
       "mvar35       0.108424  0.175592     0.258219  \n",
       "mvar36       0.073373  0.136080     0.168372  \n",
       "mvar37      -0.053145 -0.114972    -0.212687  \n",
       "mvar38      -0.038233  0.060412     0.151684  \n",
       "mvar39       0.171391  0.102376     0.150155  \n",
       "mvar40       0.090760  0.127500     0.166993  \n",
       "mvar41      -0.010876  0.009986     0.126180  \n",
       "mvar42       0.265403  0.333598     0.330032  \n",
       "mvar43      -0.167379 -0.178940    -0.168915  \n",
       "mvar44       0.040535  0.008622    -0.065298  \n",
       "mvar45       1.000000  0.140546     0.154632  \n",
       "mvar46       0.140546  1.000000     0.187228  \n",
       "default_ind  0.154632  0.187228     1.000000  \n",
       "\n",
       "[47 rows x 47 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.corr()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56590499267508465"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = 17\n",
    "v2 = 19\n",
    "gameDf.corr().iloc[v1-1,v2-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mvar1          float64\n",
       "mvar2          float64\n",
       "mvar3          float64\n",
       "mvar4          float64\n",
       "mvar5          float64\n",
       "mvar6          float64\n",
       "mvar7          float64\n",
       "mvar8          float64\n",
       "mvar9          float64\n",
       "mvar10         float64\n",
       "mvar11         float64\n",
       "mvar12         float64\n",
       "mvar13         float64\n",
       "mvar14           int64\n",
       "mvar15         float64\n",
       "mvar16         float64\n",
       "mvar17         float64\n",
       "mvar18         float64\n",
       "mvar19         float64\n",
       "mvar20         float64\n",
       "mvar21         float64\n",
       "mvar22         float64\n",
       "mvar23         float64\n",
       "mvar24         float64\n",
       "mvar25         float64\n",
       "mvar26         float64\n",
       "mvar27         float64\n",
       "mvar28         float64\n",
       "mvar29         float64\n",
       "mvar30         float64\n",
       "mvar31         float64\n",
       "mvar32         float64\n",
       "mvar33         float64\n",
       "mvar34         float64\n",
       "mvar35         float64\n",
       "mvar36         float64\n",
       "mvar37         float64\n",
       "mvar38         float64\n",
       "mvar39         float64\n",
       "mvar40         float64\n",
       "mvar41         float64\n",
       "mvar42         float64\n",
       "mvar43         float64\n",
       "mvar44         float64\n",
       "mvar45         float64\n",
       "mvar46         float64\n",
       "mvar47          object\n",
       "default_ind      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.26579277101410331"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gameDf[dic[features[0]]].corr(gameDf[dic[features[1]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mvar 17\n",
      "mvar 1   -0.341212182504\n",
      "mvar 2   0.156583130951\n",
      "mvar 3   0.0970161291969\n",
      "mvar 4   0.0558871993865\n",
      "mvar 5   0.0300544467312\n",
      "mvar 6   -0.161176759935\n",
      "mvar 7   -0.0634266241671\n",
      "mvar 8   -0.182309675805\n",
      "mvar 9   -0.106395930995\n",
      "mvar 10   -0.0944689880399\n",
      "mvar 11   -0.0140860906317\n",
      "mvar 12   0.124437289436\n",
      "mvar 13   -0.0363065913385\n",
      "mvar 14   -0.000815168591843\n",
      "mvar 15   -0.063306595563\n",
      "mvar 16   0.938673320267\n",
      "mvar 17   1.0\n",
      "mvar 18   0.990621368887\n",
      "mvar 19   0.565904992676\n",
      "mvar 20   0.556615139286\n",
      "mvar 21   0.413508167447\n",
      "mvar 22   0.398416599497\n",
      "mvar 23   0.372883097247\n",
      "mvar 24   0.0502349426206\n",
      "mvar 25   -0.116993422186\n",
      "mvar 26   -0.085647729406\n",
      "mvar 27   -0.0949733815718\n",
      "mvar 28   -0.161869979541\n",
      "mvar 29   -0.076108077298\n",
      "mvar 30   -0.0554043758055\n",
      "mvar 31   -0.0352534208436\n",
      "mvar 32   -0.0654522263447\n",
      "mvar 33   -0.0575577305641\n",
      "mvar 34   0.344029913148\n",
      "mvar 35   0.308606980702\n",
      "mvar 36   0.267143251641\n",
      "mvar 37   -0.0711228182491\n",
      "mvar 38   0.193091145208\n",
      "mvar 39   0.303510319532\n",
      "mvar 40   0.0727203176745\n",
      "mvar 41   0.0475787703241\n",
      "mvar 42   0.0916649347011\n",
      "mvar 43   0.0545764496645\n",
      "mvar 44   -0.174534161372\n",
      "mvar 45   0.0636606992491\n",
      "mvar 46   0.0619606537948\n"
     ]
    }
   ],
   "source": [
    "v1 = 17\n",
    "print(\"mvar\", v1)\n",
    "for i in range(0, 46):\n",
    "    print(\"mvar\", i+1, \" \", gameDf.iloc[:, v1-1].corr(gameDf.iloc[:,i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ekka/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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LeV1VDbEJiYyYrAghCPDzYUJIAMUVeiZOSiDebdzU1tZiZ8pZhaC5dxSD2YLvUKdD7gp9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2ckt3xj57fevZbWgq3RmTNnZs45s/eznye7sIXS5n4yjzXzeW4NZouV1j49aw/Vs6eygx9cqiOF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hIX0QiDg2PV071nH3p3i3bUv3FzNlHdexKqFFHhobS2acJCFUV5uxjmmq8geDHx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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa133a35358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pd.scatter_matrix(gameDf, figsize=(5,5))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}