main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295553</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771249</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     150000.000000                         150000.000000  150000.000000   \n",
       "mean           0.066840                              6.048438      52.295553   \n",
       "std            0.249746                            249.755371      14.771249   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154181      52.000000   \n",
       "75%            0.000000                              0.559046      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         150000.000000  150000.000000   1.202690e+05   \n",
       "mean                               0.421033     353.005076   6.670221e+03   \n",
       "std                                4.192781    2037.818523   1.438467e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.400000e+03   \n",
       "50%                                0.000000       0.366508   5.400000e+03   \n",
       "75%                                0.000000       0.868254   8.249000e+03   \n",
       "max                               98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    150000.000000            150000.000000   \n",
       "mean                          8.452760                 0.265973   \n",
       "std                           5.145951                 4.169304   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 150000.000000                         150000.000000   \n",
       "mean                       1.018240                              0.240387   \n",
       "std                        1.129771                              4.155179   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             98.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       146076.000000  \n",
       "mean             0.757222  \n",
       "std              1.115086  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv('kredit.csv')\n",
    "\n",
    "df.drop(df.columns[0], axis=1, inplace=True)\n",
    "df.describe()\n",
    "#MonthlyIncome and NumberOfDependents have missing values, there is a 0 in ages.\n",
    "df.age.plot.box()\n",
    "\n",
    "#replace 0 age with median\n",
    "\n",
    "def replaceValuesWithMedian(data, values = []):\n",
    "    med = np.median(data)\n",
    "    return pd.Series(map(lambda val: med if val in values else val, data))\n",
    "\n",
    "df['age'] = replaceValuesWithMedian(df['age'],[0])\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class balance:\n",
      "93.316 %\n",
      "6.683999999999999 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outcome variable mean is 0.066, not a good sign\n",
    "plot = sns.countplot(x=df['SeriousDlqin2yrs'])\n",
    "#here we see that we have a class disbalance problem\n",
    "print('Class balance:')\n",
    "for bin in plot.patches:\n",
    "    print(bin.get_bbox().get_points()[1,1]/len(df)*100, '%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     126018\n",
       "1      16033\n",
       "2       4598\n",
       "3       1754\n",
       "4        747\n",
       "5        342\n",
       "98       264\n",
       "6        140\n",
       "7         54\n",
       "8         25\n",
       "9         12\n",
       "96         5\n",
       "10         4\n",
       "12         2\n",
       "13         1\n",
       "11         1\n",
       "Name: NumberOfTime30-59DaysPastDueNotWorse, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#notice the strange value 98 appearing as max in data description for all of 3 NumberOfTime... variables\n",
    "np.unique(df['NumberOfTime30-59DaysPastDueNotWorse'])\n",
    "df['NumberOfTime30-59DaysPastDueNotWorse'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     141662\n",
       "1       5243\n",
       "2       1555\n",
       "3        667\n",
       "4        291\n",
       "98       264\n",
       "5        131\n",
       "6         80\n",
       "7         38\n",
       "8         21\n",
       "9         19\n",
       "10         8\n",
       "11         5\n",
       "96         5\n",
       "13         4\n",
       "12         2\n",
       "14         2\n",
       "15         2\n",
       "17         1\n",
       "Name: NumberOfTimes90DaysLate, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NumberOfTimes90DaysLate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     142396\n",
       "1       5731\n",
       "2       1118\n",
       "3        318\n",
       "98       264\n",
       "4        105\n",
       "5         34\n",
       "6         16\n",
       "7          9\n",
       "96         5\n",
       "8          2\n",
       "11         1\n",
       "9          1\n",
       "Name: NumberOfTime60-89DaysPastDueNotWorse, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NumberOfTime60-89DaysPastDueNotWorse'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace 98 and 96 with median\n",
    "df['NumberOfTime30-59DaysPastDueNotWorse'] = replaceValuesWithMedian(df['NumberOfTime30-59DaysPastDueNotWorse'],[96,98])\n",
    "df['NumberOfTimes90DaysLate'] = replaceValuesWithMedian(df['NumberOfTimes90DaysLate'],[96,98])\n",
    "df['NumberOfTime60-89DaysPastDueNotWorse'] = replaceValuesWithMedian(df['NumberOfTime60-89DaysPastDueNotWorse'],[96,98])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.500000e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295553</td>\n",
       "      <td>0.245353</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.418455e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.090293</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.064707</td>\n",
       "      <td>0.737413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771249</td>\n",
       "      <td>0.697231</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.289040e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>0.485107</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>0.329788</td>\n",
       "      <td>1.107021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.903000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>7.400000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     150000.000000                         150000.000000  150000.000000   \n",
       "mean           0.066840                              6.048438      52.295553   \n",
       "std            0.249746                            249.755371      14.771249   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154181      52.000000   \n",
       "75%            0.000000                              0.559046      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         150000.000000  150000.000000   1.500000e+05   \n",
       "mean                               0.245353     353.005076   6.418455e+03   \n",
       "std                                0.697231    2037.818523   1.289040e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.903000e+03   \n",
       "50%                                0.000000       0.366508   5.400000e+03   \n",
       "75%                                0.000000       0.868254   7.400000e+03   \n",
       "max                               13.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    150000.000000            150000.000000   \n",
       "mean                          8.452760                 0.090293   \n",
       "std                           5.145951                 0.485107   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                17.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 150000.000000                         150000.000000   \n",
       "mean                       1.018240                              0.064707   \n",
       "std                        1.129771                              0.329788   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             11.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       150000.000000  \n",
       "mean             0.737413  \n",
       "std              1.107021  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MonthlyIncome and NumberOfDependents have missing values(their count is not 150000 in data description)\n",
    "#replace NA's with median\n",
    "fill_values = {}\n",
    "for i in [5,10]:\n",
    "    fill_values[df.columns[i]] = np.nanmedian(df[df.columns[i]])\n",
    "df.fillna(fill_values, inplace=True)\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines   age  \\\n",
       "0                 1                              0.766127  45.0   \n",
       "1                 0                              0.957151  40.0   \n",
       "2                 0                              0.658180  38.0   \n",
       "3                 0                              0.233810  30.0   \n",
       "4                 0                              0.907239  49.0   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                   2.0   0.802982         9120.0   \n",
       "1                                   0.0   0.121876         2600.0   \n",
       "2                                   1.0   0.085113         3042.0   \n",
       "3                                   0.0   0.036050         3300.0   \n",
       "4                                   1.0   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                      0.0   \n",
       "1                                4                      0.0   \n",
       "2                                2                      1.0   \n",
       "3                                5                      0.0   \n",
       "4                                7                      0.0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                   0.0   \n",
       "1                             0                                   0.0   \n",
       "2                             0                                   0.0   \n",
       "3                             0                                   0.0   \n",
       "4                             1                                   0.0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f829978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y4A5gR3lMAp+C5TABHgDeCtwCPHAhUMqcyZ7jLv1a0lD40pe+dNH4i1/8YkOdSBu3Zjhk5peAM5eU9wDTZXsauLOnfiCXfQ24NiLeCNwOzGbmmcx8HpgF2mXfazPzq5mZwIGe15KGyujo6KpjaZis95rDGzLzewDl+fWlvg14tmfeQqmtVl9YoS4NnR/+8IerjqVhMugL0itdL8h11Fd+8YjJiJiLiLnFxcV1tij9eLzmNa9ZdSwNk/WGw+lySojy/FypLwA39MwbB06tUR9fob6izJzKzInMnBgbG1tn69KPx4svvrjqWBom6w2Hg8CFO446wGM99b3lrqWdwAvltNNh4LaIuK5ciL4NOFz2/SAidpa7lPb2vJY0VLzmoJeTNf/1RsTfAO8Ero+IBZbvOvpT4NGIuBf4DnDh4ycPAe8G5oGzwPsAMvNMRHwUOFbmfSQzL1zkfj/Ld0S9CvhMeUhD5+zZs6uOpWGyZjhk5j2X2XXrCnMTuO8yr/Mw8PAK9TngF9fqQ5J09fgOaUlSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFUMB0lSxXCQJFU2TThERDsivh0R8xFxf9P9SNIr2aYIh4gYAT4J3AHcBNwTETc125UkvXJtinAAbgHmM/PpzHwReATY03BPkvSKNdp0A8U24Nme8QLw1oZ66csnPvEJZmZmmm5jUzh79iyZ2XQbm9I73/nOpltoVETw6le/uuk2NoV2u80HP/jBptvo22ZZOcQKtep/m4iYjIi5iJhbXFy8Cm1J0itTbIbf+CLiV4A/zMzby/jDAJn5J5c7ZmJiIufm5q5Sh9LaVlolfOELX7jqfUiXExHHM3Oin7mbZeVwDNgRETdGxFbgbuBgwz1J0ivWpgiHzFwCPgAcBp4EHs3MJ5rtSroyl64SXDVomG2WC9Jk5iHgUNN9SJI2UThILweuFvRysSlOK0mSNhfDQZJUMRwkSRXDQZJU2RRvgluPiFgE/rPpPqQVXA/8V9NNSCv42cwc62fi0IaDtFlFxFy/70KVNitPK0mSKoaDJKliOEiDN9V0A9JGec1BklRx5SBJqhgO0oBERDsivh0R8xFxf9P9SBvhaSVpACJiBPg3YDfLf+b2GHBPZn6r0cakdXLlIA3GLcB8Zj6dmS8CjwB7Gu5JWjfDQRqMbcCzPeOFUpOGkuEgDUasUPOcrYaW4SANxgJwQ894HDjVUC/ShhkO0mAcA3ZExI0RsRW4GzjYcE/SuvlnQqUByMyliPgAcBgYAR7OzCcabktaN29llSRVPK0kSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkiuEgSaoYDpKkyv8HmAhL+UxGt4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize numeric variables\n",
    "print(df.columns[1])\n",
    "sns.boxplot(data=df[df.columns[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebtRatio\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f8c9f28>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uMvbEdq0ljiGNlFNPR3l6SqNsydDIzH8Ajp5S3gx0yusOcH1ffVf2/AK4MCIuBa4DpjLzaGYeA6aAVln3gcz8eWYmsOuUfS10DGmk9P7Tls4Ng17TuCQzXwAozx8u9VXAc33jZkptsfrMAvXFjiGNlJ/97GcnLXt6SqPsnb4QvtAVvxygfmYHjdgWEdMRMX3kyJEz3VwaqvXr1zM2NgbA2NgYGzZsqLkjaXCDhsaL5dQS5fmlUp8BLusbtxp4fon66gXqix3jbTJzZ2Y2M7M5MTEx4FuShqPdbr8VGuPj42zdurXmjqTBDRoae4ATd0C1gQf66lvLXVTrgFfKqaW9wMaIuKhcAN8I7C3rXo2IdeWuqa2n7GuhY0gjpdFo0Gq1iAharRaNRqPulqSBjS81ICL+BrgGuDgiZujdBfV14L6IuBn4V+DGMvxB4PNAF3gd+GOAzDwaEV8FHi/jvpKZJy6uf5HeHVrvAx4qDxY5hjRy2u02hw8fdpahkRfn2p0dzWYzp6en625DkkZKROzPzOZS4/xEuCSpMkNDklSZoSFJqszQkCRVZmhIkiozNCRJlRkakqTKDA1JUmWGhiSpMkNDklSZoSFJqszQkCRVZmhIkiozNCRJlRka0grodrt84QtfoNvt1t2KtCyGhrQC7rzzTl577TXuvPPOuluRlsXQkIas2+0yMzMDwMzMjLMNjTRDQxqyU2cXzjY0ygwNachOzDJOtyyNEkNDGrKIWHRZGiWGhjRkn/3sZxddlkbJskIjIg5HxIGIeCIipkvtQxExFRGHyvNFpR4RsSMiuhHxZER8vG8/7TL+UES0++qfKPvvlm39E00j57bbblt0WRol78RM479m5lWZ2SzLtwMPZ+Za4OGyDLAJWFse24C7oRcywB3AJ4GrgTtOBE0Zs61vu9Y70K8kaUDDOD21GeiU1x3g+r76ruz5BXBhRFwKXAdMZebRzDwGTAGtsu4DmfnzzExgV9++pJHxne9856TlnTt31tSJtHzLDY0EfhQR+yNiW6ldkpkvAJTnD5f6KuC5vm1nSm2x+swCdWmkPPzwwyct79u3r6ZOpOUbX+b2n8rM5yPiw8BURPyfRcYudD0iB6i/fce9wNoGcPnlly/esbTCvHtK55JlzTQy8/ny/BLw9/SuSbxYTi1Rnl8qw2eAy/o2Xw08v0R99QL1hfrYmZnNzGxOTEws5y1J77hrr7120WVplAwcGhHxHyLiP554DWwEfgXsAU7cAdUGHiiv9wBby11U64BXyumrvcDGiLioXADfCOwt616NiHXlrqmtffuSRsaNN9646LI0SpYz07gE+FlE/BPwGPC/M3MS+DqwISIOARvKMsCDwLNAF/gu8N8AMvMo8FXg8fL4SqkBfBH4n2WbfwYeWka/Ui327Nlz0vIPf/jDmjqRli96NyadO5rNZk5PT9fdhvSWVqvFb3/727eW3/ve9zI5OVljR9LbRcT+vo9OnJafCJeGbGxsbNFlaZQYGtKQvfbaa4suS6PE0JAkVWZoSJIqMzQkSZUZGpKkygwNSVJlhoYkqTJDQ5JUmaEhSarM0JAkVWZoSJIqMzQkSZUZGpKkygwNSVJlhoYkqTJDQ5JUmaEhSarM0JAkVWZoSJIqMzQkSZWd9aEREa2IeCYiuhFxe939SNK72VkdGhExBnwL2ARcCdwUEVfW25UkvXud1aEBXA10M/PZzHwD2A1srrknSXrXGq+7gSWsAp7rW54BPllTL5V885vfZHJysu42zgqvv/46mVl3G2ela665pu4WahURvP/976+7jbNCq9Xitttuq7uNys72mUYsUHvb/4UiYltETEfE9JEjR1agLUl6d4qz+S/BiPgvwJ2ZeV1Z/jJAZv6P023TbDZzenp6hTqUlrbQrOInP/nJivchLSYi9mdmc6lxZ/tM43FgbURcEREXAFuAPTX3JEnvWmd1aGTmHHArsBd4GrgvMw/W25V0Zk6dVTjL0Cg72y+Ek5kPAg/W3YckaQRCQzoXOLvQueKsPj0lSTq7GBqSpMoMDUlSZYaGJKmys/rDfYOIiCPAv9Tdh7SAi4GX625COo3/lJkTSw0650JDOltFxHSVT9xKZzNPT0mSKjM0JEmVGRrSytlZdwPScnlNQ5JUmTMNSVJlhoa0AiKiFRHPREQ3Im6vux9pUJ6ekoYsIsaA/wtsoPeTxY8DN2XmU7U2Jg3AmYY0fFcD3cx8NjPfAHYDm2vuSRqIoSEN3yrgub7lmVKTRo6hIQ1fLFDzvLBGkqEhDd8McFnf8mrg+Zp6kZbF0JCG73FgbURcEREXAFuAPTX3JA3En3uVhiwz5yLiVmAvMAbcm5kHa25LGoi33EqSKvP0lCSpMkNDklSZoSFJqszQkCRVZmhIkiozNCRJlRkakqTKDA1JUmX/HyGoi3nKcF4pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.columns[4])\n",
    "sns.boxplot(data=df[df.columns[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f7a2240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.columns[5])\n",
    "sns.boxplot(data=df[df.columns[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines 172\n",
      "DebtRatio 281\n",
      "MonthlyIncome 237\n"
     ]
    }
   ],
   "source": [
    "#we see that numerics have some upper outliers, see how many are there and remove them\n",
    "#take 4*standard deviation for limit\n",
    "for i in [1,4,5]:\n",
    "    print(df.columns[i],len(df[~(np.abs(df[df.columns[i]]-df[df.columns[i]].mean())<=(4*df[df.columns[i]].std()))]))\n",
    "    df = df[np.abs(df[df.columns[i]]-df[df.columns[i]].mean())<=(i*df[df.columns[i]].std())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f77b128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.columns[1])\n",
    "sns.boxplot(data=df[df.columns[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebtRatio\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f6ca080>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.columns[4])\n",
    "sns.boxplot(data=df[df.columns[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f6b1320>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df.columns[5])\n",
    "sns.boxplot(data=df[df.columns[5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f60f7f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize categoric variables\n",
    "import matplotlib.pyplot as plt\n",
    "categoric = [2,3,6,7,8,9,10]\n",
    "_, axes = plt.subplots(1, 1, figsize=(30, 8))\n",
    "sns.countplot(x=df.columns[2], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f5f0d30>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.columns[3], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f395be0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TAQAAAGBu3YOoJGmtnZPknH0WfzJrXPWutfbPSR60Tp1nJHnGyhsEAAAAYOU246N5AAAAAGxBgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6mCuIqqq3zrMMAAAAANazbdaDVXXLJIcluX1VHZGkhoduk+R7Nrg3AAAAAA4iM4OoJL+W5HGZhE5X5NtB1NeS/PkG9gUAAADAQWZmENVae26S51bVb7bW/qxTTwAAAAAchPZ3RFSSpLX2Z1X100mOmd6mtfaSDeoLAAAAgIPMXEFUVb00yfcn+WCSbw2LWxJBFAAAAABzmSuISrIjyd1aa20jmwEAAADg4HWzOde7MskdN7IRAAAAAA5u8x4RdfskH6uq9yb55t6FrbVTNqQrAAAAAA468wZRT93IJgAAAAA4+M171by/3uhGAAAAADi4zXvVvK9ncpW8JDk0yc2T/FNr7TYb1RgAAAAAB5d5j4j6zun7VXVakuM2pCMAAAAADkrzXjXvBlprr01y/Ip7AQAAAOAgNu9H8x4wdfdmSXbk2x/VAwAAAID9mveqeb84dfv6JNckOXXl3QAAAABw0Jr3HFEP2+hGAJLkohedPHrbX3rYm1fYCQAAAKs21zmiquqoqnpNVV1bVV+sqldV1VEb3RwAAAAAB495T1b+oiSXJPmeJEcmef2wDAAAAADmMm8Qtb219qLW2vXD14uTbN/AvgAAAAA4yMwbRH2pqn6lqg4Zvn4lyf/eyMYAAAAAOLjMG0Q9PMkvJflCks8nOT2JE5gDAAAAMLe5rpqX5OlJzmytfTlJquq2Sf4ok4AKAAAAAPZr3iOifmRvCJUkrbXrkvz4xrQEAAAAwMFo3iDqZlV1xN47wxFR8x5NBQAAAABzh0l/nOR/VdXFSVom54t6xoZ1BQAAAMBBZ64gqrX2kqrameT4JJXkAa21j21oZ1vUtS947lLb3+HXH7uiTgAAAABWa+6P1w3Bk/AJAAAAgFHmPUcUAAAAACxFEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHSxKUFUVR1eVRdX1d9V1VVV9VNVdduquryqrh6+HzGsW1X1vKraVVUfrqq7T9U5c1j/6qo6czOeCwAAAADz2awjop6b5M2ttf+Y5EeTXJXkiUne2lo7Nslbh/tJcu8kxw5fZyc5N0mq6rZJzknyk0mOS3LO3vAKAAAAgANP9yCqqm6T5J5Jzk+S1tq/tNa+kuTUJBcOq12Y5LTh9qlJXtIm3p3k8Kq6U5KTklzeWruutfblJJcnObnjUwEAAABgAZtxRNT3JdmT5EVV9YGqemFV3TrJd7fWPp8kw/c7DOsfmeQzU9vvHpattxwAAACAA9BmBFHbktw9ybmttR9P8k/59sfw1lJrLGszlt+4QNXZVbWzqnbu2bNn0X4BAAAAWIHNCKJ2J9ndWnvPcP/iTIKpLw4fucvw/dqp9Y+e2v6oJJ+bsfxGWmvntdZ2tNZ2bN++fWVPBAAAAID5dQ+iWmtfSPKZqrrrsOiEJB9LckmSvVe+OzPJ64bblyR56HD1vHsk+erw0b3LkpxYVUcMJyk/cVgGAAAAwAFo2yb9u7+Z5GVVdWiSTyZ5WCah2EVVdVaSTyd50LDupUnuk2RXkm8M66a1dl1VPT3J+4b1ntZau67fUwAAAABgEZsSRLXWPphkxxoPnbDGui3Jo9apc0GSC1bbHQAAAAAbYTPOEQUAAADAFiSIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF5ty1TyAHl764pNGb/ur//WyFXYCAABA4ogoAAAAADoRRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANDFts1uAOCm4IUvOWn0to946GUr7AQAAOCmyxFRAAAAAHQhiAIAAACgCx/NW4E9L3jh6G23//ojVtgJAAAAwIHLEVEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC42LYiqqkOq6gNV9Ybh/p2r6j1VdXVVvbKqDh2W32K4v2t4/JipGk8aln+8qk7anGcCAAAAwDw284ioxya5aur+s5I8p7V2bJIvJzlrWH5Wki+31n4gyXOG9VJVd0tyRpIfSnJykudX1SGdegcAAABgQZsSRFXVUUnum+SFw/1KcnySi4dVLkxy2nD71OF+hsdPGNY/NckrWmvfbK19KsmuJMf1eQYAAAAALGqzjoj60yS/k+Tfhvu3S/KV1tr1w/3dSY4cbh+Z5DNJMjz+1WH9f1++xjYAAAAAHGC6B1FVdb8k17bWrphevMaqbT+Pzdpm33/z7KraWVU79+zZs1C/AAAAAKzGZhwR9TNJTqmqa5K8IpOP5P1pksOratuwzlFJPjfc3p3k6CQZHv+uJNdNL19jmxtorZ3XWtvRWtuxffv21T4bAAAAAObSPYhqrT2ptXZUa+2YTE42/rbW2kOSvD3J6cNqZyZ53XD7kuF+hsff1lprw/Izhqvq3TnJsUne2+lpAAAAALCgbftfpZvfTfKKqvr9JB9Icv6w/PwkL62qXZkcCXVGkrTWPlpVFyX5WJLrkzyqtfat/m0DAAAAMI9NDaJaa+9I8o7h9iezxlXvWmv/nORB62z/jCTP2LgOAQAAAFiVzbpqHgAAAABbjCAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6GLbZjcAsNX8+V+ctNT2j/qVy1bUCQAAQF+OiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6GLbZjcAwHh/8pcnLbX9b/2fl62oEwAAgP1zRBQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALoQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoYttmNwDAgeP3X3nS6G1/75cvW2EnAADAwcgRUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuugeRFXV0VX19qq6qqo+WlWPHZbftqour6qrh+9HDMurqp5XVbuq6sNVdfepWmcO619dVWf2fi4AAAAAzG8zjoi6Psn/1Vr7wST3SPKoqrpbkicmeWtr7dgkbx3uJ8m9kxw7fJ2d5NxkElwlOSfJTyY5Lsk5e8MrAAAAAA483YOo1trnW2vvH25/PclVSY5McmqSC4fVLkxy2nD71CQvaRPvTnJ4Vd0pyUlJLm+tXdda+3KSy5Oc3PGpAAAAALCATT1HVFUdk+THk7wnyXe31j6fTMKqJHcYVjsyyWemNts9LFtvOQAAAAAHoE0LoqrqO5K8KsnjWmtfm7XqGsvajOVr/VtnV9XOqtq5Z8+exZsFAAAAYGmbEkRV1c0zCaFe1lp79bD4i8NH7jJ8v3ZYvjvJ0VObH5XkczOW30hr7bzW2o7W2o7t27ev7okAAAAAMLfNuGpeJTk/yVWttT+ZeuiSJHuvfHdmktdNLX/ocPW8eyT56vDRvcuSnFhVRwwnKT9xWAYAAADAAWjbJvybP5PkV5N8pKo+OCz7v5M8M8lFVXVWkk8nedDw2KVJ7pNkV5JvJHlYkrTWrquqpyd537De01pr183bxJ5z/2L0E9j+yF8ZvS0AAADAVtU9iGqt/c+sfX6nJDlhjfVbkketU+uCJBesrjsAVuVJfzX+QqZ/8KA3r7ATAADgQLGpV80DAAAAYOsQRAEAAADQhSAKAAAAgC4EUQAAAAB0IYgCAAAAoAtBFAAAAABdCKIAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAAAAAHQhiAIAAACgi22b3QAA7M+vv/rkpbZ/wQPevKJOAACAZTgiCgAAAIAuBFEAAAAAdCGIAgAAAKALQRQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALrYttkNAEBP973k5KW2f+Mpb15RJwAAsPU4IgoAAACALgRRAAAAAHQhiAIAAACgC0EUAAAAAF0IogAAAADoQhAFAAAAQBeCKAAAAAC6EEQBAAAA0MW2zW4AAG7K7v26s0dv+6ZTz1thJwAAcOBzRBQAAAAAXQiiAAAAAOhCEAUAAABAF4IoAAAAALpwsnIAOEDc57W/O3rbS0971go7AQCAjeGIKAAAAAC6EEQBAAAA0IUgCgAAAIAuBFEAAAAAdOFk5QBwELrPa5621PaX3v8pK+oEAAC+zRFRAAAAAHQhiAIAAACgCx/NAwBmuu9r/nCp7d94/99ZUScAANzUOSIKAAAAgC4EUQAAAAB04aN5AEBX9331c0dv+8YHPHaFnQAA0JsjogAAAADoQhAFAAAAQBc+mgcA3GTd99Xnjt72jQ945Ao7AQBgHoIoAIAk933VC5fa/o0PfMSKOgEAOHgJogAAVux+r7pwqe3f8MAzV9QJAMCBRRAFAHCAu9/FLxu97RtOf8gKOwEAWM5NPoiqqpOTPDfJIUle2Fp75ia3BABwwLrfxa8cve0bTv/lFXYCAGxFN+kgqqoOSfLnSX4hye4k76uqS1prH9vczgAADn6/ePGrR2/7+tMfcIP7p1z8+qV6ueT0X7zB/VMvvmx0rdedftJSvQAA67tJB1FJjkuyq7X2ySSpqlckOTWJIAoAgJU47VVvG73tax94/A3uP+BVfzu61qsf+DM3uP/AV+0cXStJXvXAHUttP8sZr75m9LaveMAxK+tjX89+zReW2v6373/HFXUCsHVVa22zexitqk5PcnJr7RHD/V9N8pOttUfvs97ZSc4e7t41ycf3U/r2Sb60wlZXWW8r1Fp1PbU2t95WqLXqeluh1qrrbYVaq663FWqtut5WqLXqeluh1qrrqbW59bZCrVXX2wq1Vl1vK9Radb2tUGvV9Taj1ve21rbvb6Wb+hFRtcayGyVrrbXzkpw3d9Gqna21lf2JaJX1tkKtVddTa3PrbYVaq663FWqtut5WqLXqeluh1qrrbYVaq663FWqtup5am1tvK9Radb2tUGvV9bZCrVXX2wq1Vl3vQK2VJDdbVaFNsjvJ0VP3j0ryuU3qBQAAAIAZbupB1PuSHFtVd66qQ5OckeSSTe4JAAAAgDXcpD+a11q7vqoeneSyJIckuaC19tEVlJ77Y3ybUG8r1Fp1PbU2t95WqLXqeluh1qrrbYVaq663FWqtut5WqLXqeluh1qrrqbW59bZCrVXX2wq1Vl1vK9Radb2tUGvV9Q7UWjftk5UDAAAAcNNxU/9oHgAAAAA3EYIoAAAAALoQRO2jqk6uqo9X1a6qeuISdS6oqmur6soV9HR0Vb29qq6qqo9W1WOXrHfLqnpvVX1oqPffVtDjIVX1gap6w5J1rqmqj1TVB6tq55K1Dq+qi6vq74bX7qeWqHXXoae9X1+rqsctUe/xw2t/ZVW9vKpuuUStxw51Pjqmp7XGalXdtqour6qrh+9HLFHrQUNmzCiHAAAXL0lEQVRv/1ZVc1/yc51azx5+nh+uqtdU1eFL1Hr6UOeDVfWWqvqeZXqbeuwJVdWq6vZL9PbUqvrs1Hi7zzJ9VdVvDr/XPlpVf7hEX6+c6umaqvrgErV+rKrevXeuV9Vx89SaUe9Hq+pdw++P11fVbeastebv1zFzYEathefAjFoLz4EZtUbNgfXqTT0+9xyY0dvCc2BWX4vOgRl9jZ0D69VbeB7MqLXwHKh19gdqchGY9wzj/5U1uSDM2FqPrsk+1SK/F9er9bLh53hlTX4P3HzJeucPyz5ck/2F7xhba+rxP6uqf1yyrxdX1aemxtqPLVGrquoZVfWJYcw8Zsne/maqr89V1WuXqHVCVb1/qPU/q+oHlqh1/FDryqq6sKrmPg9u7bMPO2b8z6i18PjfT71Rc2CdWguP//VqTS2fe/zP6Gvh8b+feqPmwDq1Fh7/M2otPP5n1Fpm/N/ovVeNfx+wVq2x7wPWqjXqfcCMemP3g9Z9v1qLvw9Ys1aN23df6zmO2m+ZUW/0/vuNtNZ8DV+ZnPD875N8X5JDk3woyd1G1rpnkrsnuXIFfd0pyd2H29+Z5BNj+xpqVJLvGG7fPMl7ktxjyR5/K8lfJnnDknWuSXL7Ff08L0zyiOH2oUkOX+E4+UKS7x25/ZFJPpXkVsP9i5L815G1fjjJlUkOy+TiA/8jybEL1rjRWE3yh0meONx+YpJnLVHrB5PcNck7kuxYsq8Tk2wbbj9ryb5uM3X7MUlesExvw/KjM7l4wj/MO47X6e2pSZ4wYjysVeu/DOPiFsP9OyzzHKce/+MkT1mir7ckufdw+z5J3rHk83xfkp8bbj88ydPnrLXm79cxc2BGrYXnwIxaC8+BGbVGzYH16o2ZAzN6W3gOzKi18ByY9RxHzoH1elt4HsyotfAcyDr7A5n8v3TGsPwFSR65RK0fT3JMFvj/fUat+wyPVZKXz9PXfupNz4E/yTDnx9Qa7u9I8tIk/7hkXy9OcvqC43+9Wg9L8pIkN5t3/O/veU6t86okD12it08k+cFh+W8kefHIWj+d5DNJ7jIsf1qSsxZ47W6wDztm/M+otfD430+9UXNgnVoLj//1ao0Z/zP6Wnj876feqDmw3vNcdPzP6Gvh8b9WrUwOLFlm/N9obGb8+4C1ao19H7BWrVHvA2bUG7sftOZ8zrj3AWv1NXbffc2+ph6fe79lRm+j99/3/XJE1A0dl2RXa+2TrbV/SfKKJKeOKdRae2eS61bRVGvt86219w+3v57kqkzCjLH1Wmtt718rbj58tbH1quqoJPdN8sKxNVatJn8JvmeS85OktfYvrbWvrKj8CUn+vrX2D0vU2JbkVsNfLA5L8rmRdX4wybtba99orV2f5K+T3H+RAuuM1VMzCfIyfD9tbK3W2lWttY8v0tOMWm8ZnmeSvDvJUUvU+trU3VtngTkwY34/J8nvrKjWwtap9cgkz2ytfXNY59pl+6qqSvJLmewEj63Vkuw9YuO7ssAcWKfeXZO8c7h9eZIHzllrvd+vC8+B9WqNmQMzai08B2bUGjUH9vN/0kJzYJX/v82otfAc2F9fI+bAevUWngczai08B2bsDxyf5OJh+bzjf81arbUPtNau2d/2c9a6dHisJXlv5v8/YL16X0v+/ed5q8wxbterVVWHJHl2JuN/LqvcH5tR65FJntZa+7dhvXn/D5jZW1V9ZybjZL9HhMyoNWb8r1XrW0m+2Vr7xLB87v8D9t2HHcbCwuN/rVpDvwuP//3UGzUH1qm18Phfr9aY8b9erWWsU2/UHJjV2yLjf0atUftBa9S6XUaO/xlGvQ9Yy5h9oBm1Rr0PmFFv9HuBdSz8PmAdo/bdZ1l0v2WG0fvv+xJE3dCRmSTKe+3OEoHPRqiqYzL568p7lqxzyHBo3rVJLm+tLVPvTzOZdP+2TE+DluQtVXVFVZ29RJ3vS7InyYuGQ1dfWFW3XkF/SXJGlpjErbXPJvmjJJ9O8vkkX22tvWVkuSuT3LOqbldVh2WSTB89trcp391a+/zQ7+eT3GEFNVft4UnetEyBmhyq/ZkkD0nylCVrnZLks621Dy1TZ8qjh8OFL5j3kOh13CXJz9bkYwZ/XVX/aQW9/WySL7bWrl6ixuOSPHt4/f8oyZOW7OnKJKcMtx+UEfNgn9+vS82BVf2u3k+thefAvrWWnQPT9ZadA2s8z9FzYJ9aS82BdV7/0XNgn3pLzYN9ao2aA/vuD2RyZPhXpnb2594XWuW+xaxaNfk40q8mefOy9arqRZkc5fwfk/zZErUeneSSvb83lu0ryTOG8f+cqrrFErW+P8kvDx+heFNVHbuC3pLJH73eus8buUVrPSLJpVW1O5Of5zPH1MokkLl5fftjP6dn/v8D9t2HvV1Gjv81ai1r3Xoj5sCatcaM/3VqjRr/6/WVEeN/Rr2xc2DWz3Oh8b9OrVHjf41aX8r48Z+s/d5r7D7Qqt7HzVNr0X2gNeuN3A+6Ua0l9oHW6mvsfsus12zMfsta9Va2/y6IuqFaY9myiebK1OSz269K8rgFfvGtqbX2rdbaj2WSJB9XVT88sqf7Jbm2tXbFMv1M+ZnW2t2T3DvJo6rqniPrbMvkozvnttZ+PMk/ZXJo6VJqcp6AU5L81RI1jsjkLw13TvI9SW5dVb8yplZr7apMDk29PJOdkQ8luX7mRgeBqnpyJs/zZcvUaa09ubV29FDn0Uv0c1iSJ2fJMGvKuZnsOP1YJmHlHy9Ra1uSIzL5GMRvJ7lo+KvIMh6c5f+i8sgkjx9e/8dnOHpxCQ/P5HfGFZl8XOlfFtl4lb9fe9QaMwfWqrXMHJiuN/Qyeg6s0dvoObBGrdFzYMbPctQcWKPe6HmwRq1Rc2Df/YFMjrS90Wpjao3dt5ij1vOTvLO19jfL1mutPSyT/4uvSvLLI2vdM5Pwb9438vvr60mZBAP/Kcltk/zuErVukeSfW2s7kvz3JBcs2dteC82BdWo9Psl9WmtHJXlRJh8PW7hWkh/K5I+Ez6mq9yb5eubYF1pnH3bUe4FV7w/PUW/uOTCr1qLjf61aNTmvzsLjf0Zfo8b/jHoLz4E5Xv+5x/+MWguP/7VqDUfHLTz+p6zqvVe3WiPfB6xZb+R+0Fq1xu4DrVVr7H7LrNd/zH7LWvVWt//eRn6m72D8SvJTSS6buv+kJE9aot4xWcE5ooZaN8/kM6e/tQHP+5yMOB/NsO0fZPLXomsy+YvKN5L8xYr6euoSfd0xyTVT9382yRtX0NOpSd6yZI0HJTl/6v5Dkzx/Ra/Z/5vkN0Zsd4OxmuTjSe403L5Tko+PrTW1/B1Z4LPh69VKcmaSdyU5bNlaU49976Jzdbpekv8jk7/MXjN8XZ/JEW93XEFvC/0eWeNn+eYk95q6//dJti/x+m9L8sUkRy05xr6apIbbleRrK/x53iXJexeodaPfr2PnwFq1ph5baA6sV2vMHJjV1/D4QnNg33rLzIE5ept7Dqzzsxw1B2a8/mPnwFq9jZoHc7xmC82Bqe3OyWSn90v59nk4brBvtGCtJ0zdvyYjzwE5XWu4/doM53tZtt7Usp/LiPNcDrXOyWQfaO/4/7dMTvWwir7utURfT0jyd0mOmRpjX13Bz+B2Sf53klsuUeu3MznNwd5l/yHJx1b0mp2Y5KI5tl1rH/ZlY8b/OrX+Yurxhcb/rHqLzoH99bbI+F+n1pfHjP85+5p7/K9Xb8wc2M/rv9D4X6fWG8eM/zlfs7nG/zr1n5rJ743R7wP2rTV1/x1Z8H3AWrUy8n3ArN6GZQu/F5iq9f9kifcBa7z+o/fd13nNRu23rNPbUvvv01+OiLqh9yU5tiZXyzg0k3T5kk3uae9nOs9PclVrba6/Fu2n3vYarjJQVbdK8vOZ/JJeWGvtSa21o1prx2Tyer2ttTbq6J6qunVNPnOdmnyM7sRMPmowpq8vJPlMVd11WHRCko+NqbWPVRwJ8ukk96iqw4af7QmZ/CVqlKq6w/D9PyR5wAr6Sybj/szh9plJXreCmkurqpMz+cvYKa21byxZa/rQ7FMycg4kSWvtI621O7TWjhnmwu5MTib8hZG93Wnq7v0zch4MXpvJeQxSVXfJ5MT9X1qi3s8n+bvW2u4laiSTz5T/3HD7+CTLfMxveh7cLMnvZXKC2Xm2W+/368JzYJW/q9erNWYOzKg1ag6sVW/sHJjR28JzYMbrv/Ac2M/PcuE5MKPewvNgxmu28BxYZ3/gqiRvz+TjHcn8439l+xbr1aqqRyQ5KcmD23C+lyXqfbyGq1QNr+kvztPvOrWuaK3dcWr8f6O1Ns8V4NZ7nnea6uu0zDf+13v9/338ZzLWPrF2hbnrJZM/qL2htfbPS9S6Ksl3DXMySX4hc+wLzXjN9o7/W2TyO3K/43+dfdiHZMT4X+X+8Kx6Y+bAWrWS/OqY8b9OX0eMGf8znuPC439WvYyYA/v5eS40/td5/U/NiPE/4zVbePwP66/33mvMPtDK3setV2vs+4AZ9RbeD1qn1vtG7gOt95qN2W+Z9fqP2W9Zr97q9t/HJlgH61cm59j5RCbJ45OXqPPyTD5O8K+ZDMa5r16wRq3/nMlhwR9O8sHh6z5L1PuRJB8Y6l2ZBc6ev5+698oSV83L5LxOHxq+PrrM6z/U+7EkO4fn+dokRyxZ77BM/gLyXSt4rf5bJr/srszkCiO3WKLW32QSsn0oyQkjtr/RWM3krz1vHX65vDXJbZeodf/h9jczSePn+sv6OrV2ZXIet73zYN6rW6xV61XD6//hJK/P5OTNo1+zfR6/JvNfLWOt3l6a5CNDb5dk+KvUyFqHZvIXwSuTvD/J8cs8x0yuZvPrKxhj/znJFcO4fU+Sn1iy3mMz+d39iUzOs1Bz1lrz9+uYOTCj1sJzYEathefAjFqj5sB69cbMgRm9LTwHZtRaeA7Meo4j58B6vS08D2bUWngOZJ39gUz+P37vMN7+KnP8HzWj1mOG8X99JjuwL1yi1vWZ7J/tfd7zXrXwRvUyOT3F3w7j7MpMjoS5zdje9lln3qvmrfc83zbV119kuErcyFqHZ3L0xUcyOYrgR5fpbXjsHUlOXmD8r9fb/Ye+PjTU/L4laj07kzfyH8/k46pzz89h+3vl21czW3j8z6i18PjfT71Rc2DfWmPH/3p9jRn/M57jwuN/P/VGzYH1nuei439GXwuP/xm1Ro3/rPPeK+P2gdarNWYfaL1aY98HrFdv4f2g9Wrts841mW8faL2+xuy3rNtXxu23rNfb6P33fb/2HlYFAAAAABvKR/MAAAAA6EIQBQAAAEAXgigAAAAAuhBEAQAAANCFIAoAAACALgRRAMBCqqpV1R9P3X9CVT11RbVfXFWnL1njqKp6XVVdXVV/X1XPrapDpx5/eVV9uKoeXxO/N6z7iap6e1X90PLPZM2+7lJVl1bVrqq6qqouqqrvXqLeU6vqCcPtp1XVzw+3H1dVh02td01V3X6fbU+pqieO/bdn9LS9qv61qn5t5Pb/OHw/pqquXG13AMCBQBAFACzqm0kesG+4sdmq6pCqqiSvTvLa1tqxSe6S5DuSPGNY545Jfrq19iOtteckeVSSn07yo621uyT5gySXVNUtV9zbLZO8Mcm5rbUfaK39YJJzk2zfZ71tY+q31p7SWvsfw93HJTlsP+tf0lp75ph/az8elOTdSR68AbUBgIOAIAoAWNT1Sc5L8vh9H9j3iKapI1zuVVV/PRwF9ImqemZVPaSq3ltVH6mq758q8/NV9TfDevcbtj+kqp5dVe8bjmb6tam6b6+qv0zykSTHJ/nn1tqLkqS19q2hz4cPRwm9JckdquqDVfWzSX43yW+21r4xrP+WJP8ryUP29l9Vf1xV76+qt1bV9mH591fVm///9u4/1O+qjuP482VY6N1S/9HckqZo05zetcuEflAzakir1ESazV8RA4V0RUmQSfQLkWmsVVpqpVOGbugqTZ0Zw1nNZpttzmLIZkW4KKhmLsvlffXHOd929vX7vfd6Hd+gXg8YfH6ccz7v8/l+/ri8eZ8zSZtqrCc2818u6WeSdjbv4sPABtv3dCZpe53tbZIulrRa0j01PiRd0cz18837vFLSdkkPATO737uky4FpwDpJ6/r9gPWZXx8n5p5xSBqS9ENJWyRtk/ShZujzgE8Cr5c0vf0OJH259nm0Uwkm6VhJG+ozvtgv3mac2bX/VklrJB1Rry+uY2yRdFenIqzf3CQdLWl9/Q621W8hIiIiBiCJqIiIiJiMbwCLJB32MvoMA0uAU4ALgDfaPg24GbisaTcDeCewAPhmrSb6KLDb9lxgLrBY0rG1/WnAlbbfBJwMbGofavtZ4HfA8cAHgB22ZwNbgCHbO7ri/EUdB2AI2Gx7DvAw8Ll6/UZKAmsE+BRwfdP/aODtwPuATtXRrO64urwFuMj2uyTNB06o85oNjEh6h6QRYCHwZuCD9T3sx/Zy4BngdNunj/G8bi+JuV8cwBnAM7aHbc8CHqjtjwFeZ3sjsApoE1RDwKO2h4H1wOJ6/auUKrG5wB8mEOcK4NO2T6UkHju/x92259bxf035XvrOjZIYXFu/g2HglxN4dkRERBwAkyr/joiIiP9vtp+VtAK4HHh+gt0es70LQNIOavUPJaHQJk1W2R4FnpK0EzgRmA+c2lTrHEZJkrwAbLT9dL0uwD2e3e96L23bUeDOenw7cLekKZTlfKvLSkAAXtP0/16N/1ea+B5QP7L953o8v/57vJ5Pocx1KrCmU70l6QcTHHsiesXcL45HgGslXQPca/uRen8hJQEFcAfwbeAr9fwF4N56vAl4Tz1+G3BOPb4NuKZfgDXpebjth+ulW4HV9XiWpC8Bh9c4144zt8eA70g6uN5PIioiImJAkoiKiIiIyVoGbAa+21z7F7XiWiVL8+rm3j+b49HmfJT9/ybpThiZkhy6zHabYEDSPGBPc+lJ9iU2Om1eCxwD7ACO/M+gJZm2R9Jxtnc2XTrVT724zu+vtZqml3aenUzVk5Qqr37aOQi42va3uubxcSaeTHu5esXcM44aywjwXuBqSQ/a/gJlWd5RkhbVZtMknWD7KWCv7U7sLzL27z0ZtwBn2d4i6WJgXnPvJXOzvb5Wdy0AbpO01PaKAxBHREREjCNL8yIiImJSagXPKvZfBvUbYKQenwkcPImhz5V0kMq+UccB2ykVLpfWCpbO/0A31KPvj4FDJV1Y270KuA64pVNJ1GUpsFzSIbX9uynLuFbW+wcB7T5PP6lL/Z6WdG7tI0nD48xpJfBWSQs6FySdIemUHm3XUva0mlLbTZd0JGVJ29mSDpE0FXh/n2f9jVI99Ur1jEPSNODvtm8HrgXmSJpJWeY43fYM2zMoG78vHOcZP23aLBqroe3dwF+a/ZwuYF/CcCqwq34fY45T5/IG4I+2b6JUbs0Zr09EREQcGKmIioiIiFfiOuBjzflNwPclbaQkhfb07DW27ZQEw1HAJbb/Ielmyt5Rm2ul1Z+As7o72raks4HrJV1FSSTdB3ymz7O+BhwBPCHpRco+RWfa7iw33AOcLGkTsJt9+x4tAm6Q9FlKsu0Oyp5TPdl+XmXj9WWSlgF7ga2UPbO62z4o6SRgQ1369xxwvu3Nku6k7Gf0W8oSuV5uBO6XtKvZJ2qrpNF6vKo+e0z94qDstbW0jrcXuJRSDbWma4i7KO9lrE3IlwArJS2p7VszJf2+Of8EcBFl37BDgZ3AR+q9q4CfU97LE4yfiJsHXCFpb53XheO0j4iIiANE+6qkIyIiIqIl6TnbU/7bcURERET8r8jSvIiIiIiIiIiIGIhUREVERERERERExECkIioiIiIiIiIiIgYiiaiIiIiIiIiIiBiIJKIiIiIiIiIiImIgkoiKiIiIiIiIiIiBSCIqIiIiIiIiIiIG4t+4rIfrR+UDFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axes = plt.subplots(1, 1, figsize=(20, 8))\n",
    "sns.countplot(x=df.columns[6], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f273dd8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.columns[7], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f1a1320>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEKCAYAAADaa8itAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHj9JREFUeJzt3XvUHVWZ5/Hvj4S7QAIEGpM4QYwXmqURMhBvtIIdAtoEbWPDAgmCHWWCgj10C6MjKLoG70qr2FxiAC8YuUikoyGDIMwMlyRAIFyUAFEiSKLhKgoGnvlj7xcqL3VO1Zvsk5eX/D5rnXWq9nlqn11V59Rz6raPIgIzM7MSNhnsBpiZ2UuHk4qZmRXjpGJmZsU4qZiZWTFOKmZmVoyTipmZFeOkYmZmxTipmJlZMU4qZmZWzPDBbsCGtuOOO8a4ceMGuxlmZkPG4sWL/xARo9rEbnRJZdy4cSxatGiwm2FmNmRI+k3bWB/+MjOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMyvGScXMzIpxUjEzs2I2ujvqAVad+b3GmFHHHrEBWmJm9tLiPRUzMyvGScXMzIpxUjEzs2KcVMzMrBgnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMytmo+yleKBWfufrjTE7feSEDdASM7MXN++pmJlZMU4qZmZWjJOKmZkV46RiZmbF9DSpSFou6TZJt0halMu2l7RA0t35eWQul6QzJC2TdKukPSv1TM/xd0uaXinfK9e/LE+rXs6PmZl1tyH2VN4RERMiYmIePwm4MiLGA1fmcYADgfH5MQM4E1ISAk4B9gH2Bk7pS0Q5ZkZluim9nx0zM+tkMA5/TQXOy8PnAYdUys+P5HpghKRdgAOABRGxOiIeBhYAU/Jr20bEdRERwPmVuszMbBD0OqkEcIWkxZJm5LKdI+JBgPy8Uy4fDdxfmXZFLutWvqKm/AUkzZC0SNKiVatWrecsmZlZJ72++fEtEfGApJ2ABZLu6hJbdz4k1qH8hYURZwFnAUycOLE2xszM1l9P91Qi4oH8vBK4lHRO5KF86Ir8vDKHrwDGViYfAzzQUD6mptzMzAZJz5KKpK0lbdM3DEwGlgJzgb4ruKYDl+XhucCR+SqwScCj+fDYfGCypJH5BP1kYH5+7XFJk/JVX0dW6jIzs0HQy8NfOwOX5qt8hwM/iIifS1oIzJF0DPBbYFqOnwccBCwDngQ+CBARqyWdBizMcZ+NiNV5+FhgNrAl8LP8MDOzQdKzpBIR9wJvqCn/I7B/TXkAMzvUNQuYVVO+CNhjvRtrZmZF+I56MzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMyvGScXMzIpxUjEzs2KcVMzMrBgnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMyvGScXMzIpxUjEzs2KcVMzMrBgnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7Niep5UJA2TdLOky/P4rpJukHS3pB9J2iyXb57Hl+XXx1XqODmX/0rSAZXyKblsmaSTej0vZmbW3YbYUzkeuLMy/gXgaxExHngYOCaXHwM8HBGvAr6W45C0O3Ao8LfAFODbOVENA74FHAjsDhyWY83MbJD0NKlIGgO8CzgnjwvYD7goh5wHHJKHp+Zx8uv75/ipwIUR8VRE3AcsA/bOj2URcW9EPA1cmGPNzGyQ9HpP5evAvwHP5vEdgEciYk0eXwGMzsOjgfsB8uuP5vjnyvtN06nczMwGSc+SiqR3AysjYnG1uCY0Gl4baHldW2ZIWiRp0apVq7q02szM1kcv91TeAhwsaTnp0NR+pD2XEZKG55gxwAN5eAUwFiC/vh2wulreb5pO5S8QEWdFxMSImDhq1Kj1nzMzM6vVs6QSESdHxJiIGEc60f6LiDgcuAp4Xw6bDlyWh+fmcfLrv4iIyOWH5qvDdgXGAzcCC4Hx+WqyzfJ7zO3V/JiZWbPhzSHFfQK4UNLngJuBc3P5ucAFkpaR9lAOBYiI2yXNAe4A1gAzI+IZAEnHAfOBYcCsiLh9g86JmZmtZYMklYi4Grg6D99LunKrf8xfgGkdpv888Pma8nnAvIJNNTOz9eA76s3MrBgnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMyvGScXMzIpxUjEzs2KcVMzMrBgnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVMzMrxknFzMyKcVIxM7NinFTMzKwYJxUzMyumVVKRdGWbMjMz27gN7/aipC2ArYAdJY0ElF/aFnh5j9tmZmZDTNekAnwYOIGUQBbzfFJ5DPhWD9tlZmZDUNekEhHfAL4h6aMR8e8bqE1mZjZENe2pABAR/y7pzcC46jQRcX6P2mVmZkNQq6Qi6QJgN+AW4JlcHICTSo3ffWtmY8zomT56aGYvPa2SCjAR2D0iom3F+ST/NcDm+X0uiohTJO0KXAhsD9wEfCAinpa0OSlJ7QX8EfiniFie6zoZOIaU0D4WEfNz+RTgG8Aw4JyIOL1t+8zMrLy296ksBf5mgHU/BewXEW8AJgBTJE0CvgB8LSLGAw+TkgX5+eGIeBXwtRyHpN2BQ4G/BaYA35Y0TNIw0sUCBwK7A4flWDMzGyRtk8qOwB2S5kua2/foNkEkT+TRTfMjgP2Ai3L5ecAheXhqHie/vr8k5fILI+KpiLgPWAbsnR/LIuLeiHiatPczteX8mJlZD7Q9/HXqulSe9yYWA68i7VXcAzwSEWtyyApgdB4eDdwPEBFrJD0K7JDLr69UW53m/n7l+6xLO83MrIy2V3/9cl0qj4hngAmSRgCXAq+rC8vP6vBap/K6vazacz6SZgAzAF7xilc0tNrMzNZV225aHpf0WH78RdIzkh5r+yYR8QhwNTAJGCGpL5mNAR7IwyuAsfn9hgPbAaur5f2m6VRe9/5nRcTEiJg4atSots02M7MBapVUImKbiNg2P7YA/hH4ZrdpJI3KeyhI2hJ4J3AncBXwvhw2HbgsD8/N4+TXf5GvNpsLHCpp83zl2HjgRmAhMF7SrpI2I53M73qex8zMeqvtOZW1RMRPJJ3UELYLcF4+r7IJMCciLpd0B3ChpM8BNwPn5vhzgQskLSPtoRya3+t2SXOAO4A1wMx8WA1JxwHzSZcUz4qI29dlfszMrIy2Nz++tzK6Cem+la73rETErcAba8rvJV251b/8L8C0DnV9Hvh8Tfk8YF63dpiZ2YbTdk/lHyrDa4Dl+PJdMzPrp+3VXx/sdUPMzGzoa3v11xhJl0paKekhSRdLGtPrxpmZ2dDS9o7675KurHo56cbDn+YyMzOz57RNKqMi4rsRsSY/ZgO+4cPMzNbSNqn8QdIRfR05SjqC1JOwmZnZc9omlaOB9wO/Bx4k3Zzok/dmZraWtpcUnwZMj4iHASRtD3yZlGzMzMyA9nsqr+9LKAARsZqaGxvNzGzj1japbCJpZN9I3lNZpy5ezMzspattYvgK8P8kXUTqnuX91HSbYmZmG7e2d9SfL2kR6V8bBbw3Iu7oacvMzGzIaX0IKycRJxIzM+uo7TkVMzOzRk4qZmZWjJOKmZkV46RiZmbFOKmYmVkxTipmZlaMk4qZmRXjpGJmZsU4qZiZWTFOKmZmVoyTipmZFeOkYmZmxTipmJlZMU4qZmZWjJOKmZkV07OkImmspKsk3SnpdknH5/LtJS2QdHd+HpnLJekMScsk3Sppz0pd03P83ZKmV8r3knRbnuYMSerV/JiZWbNe7qmsAf57RLwOmATMlLQ7cBJwZUSMB67M4wAHAuPzYwZwJqQkBJwC7APsDZzSl4hyzIzKdFN6OD9mZtagZ0klIh6MiJvy8OPAncBoYCpwXg47DzgkD08Fzo/kemCEpF2AA4AFEbE6Ih4GFgBT8mvbRsR1ERHA+ZW6zMxsEGyQcyqSxgFvBG4Ado6IByElHmCnHDYauL8y2Ypc1q18RU25mZkNkp4nFUkvAy4GToiIx7qF1pTFOpTXtWGGpEWSFq1ataqpyWZmto56mlQkbUpKKN+PiEty8UP50BX5eWUuXwGMrUw+BnigoXxMTfkLRMRZETExIiaOGjVq/WbKzMw66uXVXwLOBe6MiK9WXpoL9F3BNR24rFJ+ZL4KbBLwaD48Nh+YLGlkPkE/GZifX3tc0qT8XkdW6jIzs0EwvId1vwX4AHCbpFty2f8ATgfmSDoG+C0wLb82DzgIWAY8CXwQICJWSzoNWJjjPhsRq/PwscBsYEvgZ/lhZmaDpGdJJSL+D/XnPQD2r4kPYGaHumYBs2rKFwF7rEczzcysIN9Rb2ZmxTipmJlZMU4qZmZWjJOKmZkV46RiZmbFOKmYmVkxTipmZlaMk4qZmRXjpGJmZsU4qZiZWTFOKmZmVoyTipmZFeOkYmZmxTipmJlZMU4qZmZWjJOKmZkV46RiZmbFOKmYmVkxTipmZlaMk4qZmRXjpGJmZsU4qZiZWTFOKmZmVoyTipmZFeOkYmZmxTipmJlZMU4qZmZWjJOKmZkV07OkImmWpJWSllbKtpe0QNLd+XlkLpekMyQtk3SrpD0r00zP8XdLml4p30vSbXmaMySpV/NiZmbt9HJPZTYwpV/ZScCVETEeuDKPAxwIjM+PGcCZkJIQcAqwD7A3cEpfIsoxMyrT9X8vMzPbwHqWVCLiGmB1v+KpwHl5+DzgkEr5+ZFcD4yQtAtwALAgIlZHxMPAAmBKfm3biLguIgI4v1KXmZkNkg19TmXniHgQID/vlMtHA/dX4lbksm7lK2rKzcxsEL1YTtTXnQ+JdSivr1yaIWmRpEWrVq1axyaamVmTDZ1UHsqHrsjPK3P5CmBsJW4M8EBD+Zia8loRcVZETIyIiaNGjVrvmTAzs3obOqnMBfqu4JoOXFYpPzJfBTYJeDQfHpsPTJY0Mp+gnwzMz689LmlSvurryEpdZmY2SIb3qmJJPwTeDuwoaQXpKq7TgTmSjgF+C0zL4fOAg4BlwJPABwEiYrWk04CFOe6zEdF38v9Y0hVmWwI/yw8zMxtEPUsqEXFYh5f2r4kNYGaHemYBs2rKFwF7rE8bzcysrBfLiXozM3sJcFIxM7NinFTMzKwYJxUzMyvGScXMzIpxUjEzs2KcVMzMrBgnFTMzK8ZJxczMiunZHfXW3tJvH9wYs8d/m7sBWmJmtn68p2JmZsU4qZiZWTFOKmZmVoyTipmZFeOkYmZmxTipmJlZMU4qZmZWjJOKmZkV46RiZmbFOKmYmVkxTipmZlaMk4qZmRXjpGJmZsU4qZiZWTHu+n4IuvbsdzfGvO2fL98ALTEzW5v3VMzMrBgnFTMzK8ZJxczMihnySUXSFEm/krRM0kmD3R4zs43ZkD5RL2kY8C3g74EVwEJJcyPijsFt2YvLf846sDHmXUf/bAO0xMxe6ob6nsrewLKIuDcingYuBKYOcpvMzDZaQ3pPBRgN3F8ZXwHsM0htecn44ewDGmMOO2o+AP9xQXMswIc/kOK/cGG7+E8cOr9VnJm9uCgiBrsN60zSNOCAiPhQHv8AsHdEfLRf3AxgRh59DfCrmup2BP4wgLcfSHwv63a84x2/8cQPVlv+S0SMalVDRAzZB/AmYH5l/GTg5HWsa1Gv4ntZt+Md7/iNJ/7F1JZOj6F+TmUhMF7SrpI2Aw4F5g5ym8zMNlpD+pxKRKyRdBwwHxgGzIqI2we5WWZmG60hnVQAImIeMK9AVWf1ML6XdTve8Y7feOJfTG2pNaRP1JuZ2YvLUD+nYmZmLyIbfVIZSDcvkmZJWilpacu6x0q6StKdkm6XdHxD/BaSbpS0JMd/puX7DJN0s6TG/u4lLZd0m6RbJC1qET9C0kWS7srz8aYusa/J9fY9HpN0QkP9H8/zulTSDyVt0RB/fI69va7uunUkaXtJCyTdnZ9HNsRPy/U/K2lii/q/lJfPrZIulTSiIf60HHuLpCskvbxbfOW1EyWFpB0b6j9V0u8q6+GgpvolfTR/D26X9MWG+n9UqXu5pFsa4idIur7vMydp7y6xb5B0Xf6M/lTStpXXar9PndZvl/ja9dslvnZ9dYmvXf6d4jst/y711y4jddh+SPp+rntpXuabVt634/ag7vPWyvpePjaUH6ST+/cArwQ2A5YAu3eJ3xfYE1jasv5dgD3z8DbArxvqF/CyPLwpcAMwqcX7/AvwA+DyFrHLgR0HsIzOAz6UhzcDRgxg2f6edH17p5jRwH3Alnl8DnBUl/g9gKXAVqTzgf8bGN+0joAvAifl4ZOALzTEv450P9PVwMQW9U8GhufhL7Sof9vK8MeA7zR9xoCxpAtSflNdfx3qPxU4se1nGHhHXpab5/Gd2n7mga8An26o/wrgwDx8EHB1l9iFwN/l4aOB05q+T53Wb5f42vXbJb52fXWJr13+XeJrl3+X+NplRIftR17myo8fAsc2bQ/o8Hlr89jY91QG1M1LRFwDrG5beUQ8GBE35eHHgTtJG9JO8RERT+TRTfOj60kvSWOAdwHntG1XW/kX0L7Aubl9T0fEIy0n3x+4JyJ+0xA3HNhS0nBSsnigS+zrgOsj4smIWAP8EnhPNaDDOppKSo7k50O6xUfEnRFRd4Nsp/grcnsArgfGNMQ/Vhndmso67vIZ+xrwb/T7PKzDZ7Iu/ljg9Ih4KsesbFO/JAHvJ22ousUH0LfHsR15HXeIfQ1wTR5eAPxjpe5O36fa9dspvtP67RJfu77W4fvdKb52+XeJr11GnbYfETEvvxbAjVQ+n13Uft7a2NiTSl03Lx0/FOtD0jjgjaRfD93ihuXDCSuBBRHRNR74OmnlP9uyKQFcIWmxUk8D3bwSWAV8V+nw2jmStm75PodS2djUNiTid8CXgd8CDwKPRsQVXSZZCuwraQdJW5F+gY1t0ZadI+LB/J4PAju1mGZdHQ009s4p6fOS7gcOBz7dEHsw8LuIWDKAdhyXD9nMUuVwXwevBt4m6QZJv5T0X1u+x9uAhyLi7oa4E4Av5fn9Mukm5U6WAgfn4Wl0WL/9vk+N67ft969TfNP6qqm/6/LvF9+4/PvFd1xG3bYf+bDXB4CfV6p+wfZgHT9vz9nYk4pqyopfDifpZcDFwAn9fvW88M0jnomICaRfE3tL2qNLve8GVkbE4gE05y0RsSdwIDBT0r5dYoeTDk+cGRFvBP5EOrzQldKNqAcDP26IG0n6lbkr8HJga0lHdIqPiDtJh5cWkL4YS4A1neI3NEmfJLXn+02xEfHJiBibY4/rUudWwCdpSDz9nAnsBkwgJeuvNMQPB0aSDpX8KzAn74U0OYyGHw7ZscDH8/x+nLzn28HRpM/lYtIhn6f7Bwzk+1Qqvtv6qonvuvxr4rsu/5r4jsuoYfvxbeCaiLi2Ula3PRjo520tG3tSWcHav4TG0P3wy4DlXwcXA9+PiEvaTpcPM10NTOkS9hbgYEnLSYfu9pP0vYZ6+w49rAQuJR0C7GQFsKLya+ciUpJpciBwU0Q81BD3TuC+iFgVEX8FLgHe3ND+cyNiz4jYl3TopOlXMsBDknYByM8rG+IHTNJ04N3A4fkwQ1s/oHKIp8ZupKS7JK/nMcBNkv6m0wQR8VDeuDwLnE33dQxpPV+Sj5DcSNrr7XpyNh+ufC/wo4a6AaaT1i2kHxod2xMRd0XE5IjYi5Sw7un3vnXfp47rd6Dfvxbxa62vuvhuy79D/R2Xf4f6uy6jHLPW9kPSKcAo0vnXalz/7cHfMcDPW38be1LpaTcv+dfGucCdEfHVFvGjlK8ckrQlaaN7V6f4iDg5IsZExDhS238RER1/6UvaWtI2fcOkE8wdr2SLiN8D90t6TS7aH2jzXzVtf8H+Fpgkaau8rPYnHTfuSNJO+fkVpI1am/eZS9qwkZ8vazFNa5KmAJ8ADo6IJ1vEj6+MHkz3dXxbROwUEePyel5BOnn7+y7171IZfQ9d1nH2E2C/PO2rSRdkNHVC+E7grohY0RAH6Yfa3+Xh/ejyQ6CyfjcBPgV8p/Jap+9T7fpdh+9fbXyn9dUlvnb5d2lP7fLvUn/tMuq0/ZD0IeAA4LCc6PrqqdseLBzo5+0FYgBn9V+KD9Jx+V+Tsv0nG2J/SNqd/Wte2Mc0xL+VdDjtVuCW/DioS/zrgZtz/FIqV9W0mI+303D1F+kcyZL8uL1pfvM0E4BFuU0/AUY2xG8F/BHYrmW7P0P6ki4FLiBfAdMl/lpSYlsC7N9mHQE7AFeSNmZXAts3xL8nDz8FPMTanZbWxS8jnZvrW8ffaYi/OM/vrcBPSSeDW33G6He1Tof6LwBuy/XPBXZpiN8M+F5u003Afk3tAWYDH2m5/N8KLM7r7AZgry6xx5O+j78GTiffoN3t+9Rp/XaJr12/XeJr11eX+Nrl3yW+dvl3ia9dRnTYfpAOyd5TqaOvvHF7wACvFo0I31FvZmblbOyHv8zMrCAnFTMzK8ZJxczMinFSMTOzYpxUzMysGCcVK0qpV9OvVMZPlHRqobpnS3rfetbxjFKPrEuVengd0TxVx7qWK/fgWqm379Gx5wFJh0javUX9beNOlXTiwFq//iS9ValX3Lvyo2O3P5KOkvTNmvJ567MO7MXHScVKewp4rwbaXXaPSRqWB/8cERMiYg/SHfkzC71FX719j9O7xB5C6m22Sdu4DS7fYf0D0r0qryXdU/FhSe+qie34D7MRcVC076TUhgAnFSttDekvST/e/4X+exqSnsjPb1fqSG+OpF9LOl3S4flX8G2SdqtU805J1+a4d+fphyn9p8lCpU78Plyp9ypJPyDdjNbfdVQ6EJX0r5U6PlMp/4lSh3u3d/s1XifPyx25zi9LejPpruwv5T2a3ST9c37fJZIuzj0M1MXtJunnuS3XSnptw3v/S94jW6rKf890mh9JTyh1nLhE6f9Pds7l03IdSyT19Y47E5gdz/ei+wdSx6Yn5WlmS/qqpKtI/bV1auNySTtKGqf0vyFn53Zdke8Kp9N8d2iXDbaB3Cnphx9ND+AJUjfny0ndnJ8InJpfmw28rxqbn98OPEL6/4jNgd8Bn8mvHQ98vTL9z0k/hsaT7oreApgBfCrHbE7qAWDXXO+fgF1r3nMYqR+qKXl8MikZKtd/ObBvfq3vDu0tSXcq75DHl5PvNgae4fk7lm8B/gnYHvgVz9/xPKLDctihMvw54KMd4q4k/38MsA+pWx6o+f8OYC9SIt0aeBnpjuk3NsxPAP+Qh79YWaa38fxd5H3zcAkwtd97bgesrrT9cmBYHj8K+GbN52U5qZ+rcaQfJBNy+RzgiIb5fkG7/Bj8R8fdUrN1FRGPSTqf9IdGf2452cLI3ZdLuof0x06QNhzvqMTNidR/0d2S7gVeS0oIr6/sBW1HSjpPAzdGxH2V6bdU6hp8HKnrkAW5fHJ+3JzHX5bruAb4mKS+/20Zm8v/2K/9f47UO+xz8mGfvwDnSPpP0ka2zh6SPgeMyO87v3+AUk+1bwZ+rOc7sN28Q32QDkddGhF/ytNfQuqq/uYu8/N0pY2Lgb/Pw/8XmC1pDs93DCnqe/Sulv04Ip7p0sb+7ouIvn+RXAyMa5jvunbZIHNSsV75Oqkfo+9WytaQD7kqbSE2q7z2VGX42cr4s6z9Oe2/IQvSBu6jEbHWxljS20l7KlV/jogJkrYjbUBnAmfkOv5XRPxHTR3vBN4UEU9Kupq0d9QoItYo/XXu/qQOP48jdxzYz2zgkIhYIuko0h5Wf5sAj/RPXF3Udl3fMD9/jfyzn7TnNTzPx0ck7UP6M7hbJE0g7flMZO0OWPdi7Q5H+y/7JtXPwDOkPamO813Xrojon+xtA/M5FeuJiFhNOoRxTKV4OWnDA+l/VDZl4KZJ2iSfZ3kl6fDSfOBY5f/elvRqNfyZWEQ8StqTOjFPNx84Ov8yRtJopd5gtwMezhvg15L+86KVXNd2ETGP9EdVfRvGx0n/g9FnG+DB3I7DK+XPxUX6H437JE3LdUvSG7q8/TXAIfn8zNakThSvXZf5kbRbRNwQEZ8m9V48FvgWcFROMEjagXTu5Iudaxq4bvPdoV02yLynYr30Fdb+Q6Ozgcsk3Ug6Tj7QX7KQksgvgZ1JVx79RdI5pMNZN+U9oFVU/jK4k4i4WdIS4NCIuEDS64Dr8mGWJ4AjSOdwPiLp1vze13eoru+wWp+fA98gze8WpD2HvosXLgTOlvQx4H3A/yT13vsb0uG+bTrEHQ6cKelTpIR8IamHWYBPVU/GR8QYSbNJfx8LcE6e3ztazk/Vl5S6fxdpvS2JiFD6Q7WzlbpPF+nc10+71HOUpOp6aZugO833C9rVsj7rIfdSbGZmxfjwl5mZFeOkYmZmxTipmJlZMU4qZmZWjJOKmZkV46RiZmbFOKmYmVkxTipmZlbM/wdhnZHDv9WjwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.columns[8], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35f143cf8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.columns[9], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     90414\n",
       "1.0     26216\n",
       "2.0     19434\n",
       "3.0      9445\n",
       "4.0      2849\n",
       "5.0       744\n",
       "6.0       158\n",
       "7.0        51\n",
       "8.0        24\n",
       "9.0         5\n",
       "10.0        5\n",
       "13.0        1\n",
       "20.0        1\n",
       "Name: NumberOfDependents, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df.columns[10], data=df)\n",
    "df['NumberOfDependents'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe35eff0c88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generally speaking, categoric variables are ok\n",
    "#maybe we can remove those with more than 5 dependents\n",
    "df = df[df['NumberOfDependents'] <= 5]\n",
    "df['NumberOfDependents'].value_counts()\n",
    "sns.countplot(x=df.columns[10], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We now have 149102 rows, which means we deleted 898 rows, which is 0.005986666666666667 %. Not much.\n"
     ]
    }
   ],
   "source": [
    "print('We now have', len(df), 'rows, which means we deleted',150000 - len(df), 'rows, which is', (150000-len(df))/150000, '%. Not much.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age is not affecting the outcome too much since the plots are nearly the same,\n",
      " but we need to keep in mind that we have class imbalance\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets see how our categoric variables behave with respect to the outcome\n",
    "df[df.columns[2]].hist(by=df['SeriousDlqin2yrs'])\n",
    "print(df.columns[2], 'is not affecting the outcome too much since the plots are nearly the same,\\n',\n",
    "     'but we need to keep in mind that we have class imbalance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime30-59DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[3]].hist(by=df['SeriousDlqin2yrs'],bins=range(0,10))\n",
    "print(df.columns[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfOpenCreditLinesAndLoans\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[6]].hist(by=df['SeriousDlqin2yrs'], bins=range(0,30))\n",
    "print(df.columns[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTimes90DaysLate\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEDCAYAAAAImhLVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHCxJREFUeJzt3X+QXWWd5/H3ZxJBRCFBAosJO8E1owIlGlKQGWpdh0gIYBlqS6qC7pB1U5taDYrr7EoYpza1IFOwOyUjW0pVhChYSGQyWqQ0yGT5sZZb/Gp+EyOmJyK0QdJOQkRRWPCzf5yn5dLcdKe779P3dvJ5VXXdc77nOfc8V/vJ5zk/biPbREREdNofdbsDERGxf0rAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMAcgSUdI+o6k30j6maSPdrtPEd0g6UJJfZJelPT1bvdnfzO92x2Irvgy8BJwNPBe4HuSHrG9pbvdiph0O4AvAGcCh3S5L/sd5Zv8BxZJhwK7gRNt/6TUvgH83PbqrnYuokskfQGYY/vfd7sv+5NcIjvw/AnwylC4FI8AJ3SpPxGxn0rAHHjeDOwZVtsDvKULfYmI/VgC5sDza+CwYbXDgOe70JeI2I8lYA48PwGmS5rXUjsJyA3+iOioBMwBxvZvgG8Dl0o6VNJpwFLgG93tWcTkkzRd0huBacA0SW+UlKdrOyQBc2D6JM0jmTuBm4BP5BHlOED9NfBbYDXw78ryX3e1R/uRPKYcERFV5AwmIiKqSMBEREQVCZiIiKhi1ICRtE7STkmPt9n2XyRZ0pFlXZKultQv6VFJ81vaLpe0rfwsb6mfLOmxss/VklTqR0jaXNpvljSzMx85IiImw6g3+SW9n+bLeTfYPrGlfixwLfAu4GTbv5R0NvAp4GzgVOBLtk+VdATQBywADDxQ9tkt6T7gIuAeYBNwte1bJf0PYJftKyStBmbavni0D3TkkUd67ty5Y/tfIWIUDzzwwC9tz+p2P8YiYyFq2dfxMOrz3rZ/IGlum01XAZ8DbmmpLaUJIgP3SJoh6RjgA8Bm27sAJG0Glki6CzjM9t2lfgNwLnBrea8PlPe9HrgLGDVg5s6dS19f32jNIsZE0s+63YexyliIWvZ1PIzrHoykD9P89d1Hhm2aDTzdsj5QaiPVB9rUAY62/QxAeT1qhP6sLP9Nh77BwcFxfKKIiOi0MQeMpDcBnwf+W7vNbWoeR31MbK+1vcD2glmzptRVjIiI/dZ4zmD+FXAc8IikJ4E5wIOS/gXNGcixLW3n0PwHfUaqz2lTB3i2XF6jvO4cR18jIqJLxhwwth+zfZTtubbn0oTEfNu/ADYCF5SnyRYCe8rlrduAxZJmlqfBFgO3lW3PS1pYnh67gFfv6WwEhp42W85r7/VERESP25fHlG8C7gbeKWlA0ooRmm8CtgP9wFdp/uYV5eb+ZcD95efSoRv+wCdonkbrB/6J5gY/wBXAGZK2AWeU9YiImCL25Smy80fZPrdl2cCqvbRbB6xrU+8DTmxT/2dg0Wj9i4iI3pRv8kdERBUJmIiIqCIBExERVRxQ/+W2uau/N+59n7zinA72JKK7MhZiMuQMJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJqIySTMkbZD0Y0lbJf2ppCMkbZa0rbzOLG0l6WpJ/ZIelTS/5X2Wl/bbJC3v3ieK2DcJmIj6vgR83/a7gJOArcBq4Hbb84DbyzrAWcC88rMSuAZA0hHAGuBU4BRgzVAoRfSqUQNG0jpJOyU93lL7n2U29qik70ia0bLtkjL7ekLSmS31JaXWL2l1S/04SfeWWdm3JB1U6geX9f6yfW6nPnTEZJF0GPB+4DoA2y/Zfg5YClxfml0PnFuWlwI3uHEPMEPSMcCZwGbbu2zvBjYDSybxo0SM2b6cwXyd1/8ibwZOtP0e4CfAJQCSjgeWASeUfb4iaZqkacCXaWZnxwPnl7YAVwJXlZncbmBFqa8Adtt+B3BVaRcx1bwdGAS+JukhSddKOhQ42vYzAOX1qNJ+NvB0y/4Dpba3+mtIWimpT1Lf4OBg5z9NxBiMGjC2fwDsGlb7R9svl9V7gDlleSmw3vaLtn8K9NOczp8C9NvebvslYD2wVJKA04ENZf/hM7mhGd4GYFFpHzGVTAfmA9fYfh/wG169HNZOu99xj1B/bcFea3uB7QWzZs0aT38jOqYT92D+A3BrWR7r7OutwHMtYdU6K/vDPmX7ntL+dTJrix42AAzYvresb6AJnGfLpS/K686W9se27D8H2DFCPaJnTShgJH0eeBm4cajUptlIs6+RZmX7NGODzNqid9n+BfC0pHeW0iLgR8BGYOhJsOXALWV5I3BBeZpsIbCnXEK7DVgsaWa5ub+41CJ61vTx7lgek/wQsMj20D/8I82y2tV/SXMTc3o5S2ltP/ReA5KmA4cz7FJdxBTxKeDG8gDLduDjNJO7myWtAJ4CzittNwFn01xefqG0xfYuSZcB95d2l9rOeIieNq6AkbQEuBj4N7ZfaNm0EfimpC8Cb6N51PI+mrOReZKOA35O8yDAR21b0p3AR2juywyfyS0H7i7b72gJsogpw/bDwII2mxa1aWtg1V7eZx2wrrO9i6hn1ICRdBPwAeBISQM0z+JfAhwMbC733e+x/Z9sb5F0M80lgJeBVbZfKe9zIc0p/TRgne0t5RAXA+slfQF4iPI4Z3n9hqR+mjOXZR34vBERMUlGDRjb57cpX9emNtT+cuDyNvVNNKf/w+vbaZ4yG17/Ha9eNoiIiCkm3+SPiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERUJulJSY9JelhSX6kdIWmzpG3ldWapS9LVkvolPSppfsv7LC/tt0la3q3PE7GvEjARk+PPbb/X9oKyvhq43fY84PayDnAWMK/8rASugSaQgDXAqcApwJqhUIroVaMGjKR1knZKeryl1rHZl6STy+yuv+yrkY4RsZ9YClxflq8Hzm2p3+DGPcAMSccAZwKbbe+yvRvYDCyZ7E5HjMW+nMF8ndf/Indy9nVNaTu035JRjhEx1Rj4R0kPSFpZakfbfgagvB5V6rOBp1v2HSi1vdUjetaoAWP7B8CuYeWOzL7KtsNs323bwA3D3qvdMSKmmtNsz6eZgK2S9P4R2qpNzSPUX7uztFJSn6S+wcHB8fU2okPGew+mU7Ov2WV5eH2kY7xOBlX0Mts7yutO4Ds0Z/HPlgkW5XVnaT4AHNuy+xxgxwj14cdaa3uB7QWzZs3q9EeJGJNO3+Qf6+xrn2Zlo8mgil4l6VBJbxlaBhYDjwMbgaF7kcuBW8ryRuCCcj9zIbCnTLBuAxZLmlkuLy8utYieNX2c+z0r6Rjbz4xh9vWBYfW7Sn1Om/YjHSNiKjka+E55dmU68E3b35d0P3CzpBXAU8B5pf0m4GygH3gB+DiA7V2SLgPuL+0utT380nVETxlvwAzNvq7g9bOvCyWtp7mhv6cExG3A37Tc2F8MXFIGzfNlpnYvcAHwv0Y5RsSUYXs7cFKb+j8Di9rUDazay3utA9Z1uo8RtYwaMJJuojn7OFLSAM3TYFfQudnXJ2ieVDsEuLX8MMIxIiJiChg1YGyfv5dNHZl92e4DTmxTbzvDi4iIqSHf5I+IiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiEkgaZqkhyR9t6wfJ+leSdskfUvSQaV+cFnvL9vntrzHJaX+hKQzu/NJIvZdAiZiclwEbG1ZvxK4yvY8YDewotRXALttvwO4qrRD0vHAMuAEYAnwFUnTJqnvEeMyoYCR9J8lbZH0uKSbJL2xkzMzSUtKrV/S6on0NaJbJM0BzgGuLesCTgc2lCbXA+eW5aVlnbJ9UWm/FFhv+0XbPwX6gVMm5xNEjM+4A0bSbODTwALbJwLTaGZYHZmZldnZl4GzgOOB80vbiKnm74DPAb8v628FnrP9clkfAGaX5dnA0wBl+57S/g/1NvtE9KSJXiKbDhwiaTrwJuAZOjczOwXot73d9kvA+tI2YsqQ9CFgp+0HWsttmnqUbSPt03q8lZL6JPUNDg6Oub8RnTTugLH9c+BvgadogmUP8ACdm5nt84wtgyp62GnAhyU9STNJOp3mjGZGmZgBzAF2lOUB4FiAsv1wYFdrvc0+f2B7re0FthfMmjWr858mYgwmcolsJs0ZxXHA24BDaS5nDTfemdk+zdgggyp6l+1LbM+xPZfmUvAdtj8G3Al8pDRbDtxSljeWdcr2O2y71JeVe5nHAfOA+ybpY0SMy/TRm+zVB4Gf2h4EkPRt4M8oM7NyltJuZjYwhpnZqDO2iCnqYmC9pC8ADwHXlfp1wDck9dOMj2UAtrdIuhn4EfAysMr2K5Pf7Yh9N5GAeQpYKOlNwG+BRUAfr87M1tN+ZnY3LTMzSRuBb0r6Is2Z0NDMTMC8Mlv7Oc1A++gE+hvRVbbvAu4qy9tp8xSY7d8B5+1l/8uBy+v1MKKzxh0wtu+VtAF4kGZG9RCwFvgeHZqZSboQuI3mCbV1treMt78RETG5JnIGg+01wJph5Y7NzGxvAjZNpI8REdEd+SZ/RERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERUJOmNku6T9IikLZL+e6kfJ+leSdskfUvSQaV+cFnvL9vntrzXJaX+hKQzu/OJIvbdhAJG0gxJGyT9WNJWSX8q6QhJm8vA2SxpZmkrSVeXAfKopPkt77O8tN8maXlL/WRJj5V9rpakifQ3ogteBE63fRLwXmCJpIXAlcBVtucBu4EVpf0KYLftdwBXlXZIOh5YBpwALAG+ImnapH6SiDGa6BnMl4Dv234XcBKwFVgN3F4Gzu1lHeAsYF75WQlcAyDpCGANcCpwCrBmKJRKm5Ut+y2ZYH8jJpUbvy6rbyg/Bk4HNpT69cC5ZXlpWadsX1QmVkuB9bZftP1ToJ9mvET0rHEHjKTDgPcD1wHYfsn2c7x2gAwfODeUAXcPMEPSMcCZwGbbu2zvBjbTzPKOAQ6zfbdtAze0vFfElCFpmqSHgZ00v9//BDxn++XSZACYXZZnA08DlO17gLe21tvsE9GTJnIG83ZgEPiapIckXSvpUOBo288AlNejSvu9DZCR6gNt6hFTiu1XbL8XmENz1vHuds3Ka7vLwB6h/hqSVkrqk9Q3ODg43i5HdMREAmY6MB+4xvb7gN/w6uWwdsY6cPZpQEEGVUwN5Qz/LmAhzRn89LJpDrCjLA8AxwKU7YcDu1rrbfZpPcZa2wtsL5g1a1aNjxGxzyYSMAPAgO17y/oGmsB5tlzeorzubGnfboCMVJ/Tpv46GVTRqyTNkjSjLB8CfJDmXuWdwEdKs+XALWV5Y1mnbL+jXCLeCCwrT5kdR3NP8r7J+RQR4zPugLH9C+BpSe8spUXAj3jtABk+cC4oT5MtBPaUS2i3AYslzSw39xcDt5Vtz0taWG5yXtDyXhFTxTHAnZIeBe6nud/4XeBi4LOS+mnusVxX2l8HvLXUP0u5KmB7C3AzzRj7PrDK9iuT+kkixmj66E1G9CngxvIM/3bg4zShdbOkFcBTwHml7SbgbJqnX14obbG9S9JlNIMP4FLbu8ryJ4CvA4cAt5afiCnD9qPA+9rUt9PmKTDbv+PVMTN82+XA5Z3uY0QtEwoY2w8DC9psWtSmrYFVe3mfdcC6NvU+4MSJ9DEiIroj3+SPiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBExERVSRgIiKiigRMRERUkYCJiIgqEjAREVFFAiYiIqpIwERERBUJmIiIqCIBE1GRpGMl3Slpq6Qtki4q9SMkbZa0rbzOLHVJulpSv6RHJc1vea/lpf02Scu79Zki9tWEA0bSNEkPSfpuWT9O0r1lEHxL0kGlfnBZ7y/b57a8xyWl/oSkM1vqS0qtX9LqifY1ogteBv7S9ruBhcAqSccDq4Hbbc8Dbi/rAGcB88rPSuAaaAIJWAOcCpwCrBkKpYhe1YkzmIuArS3rVwJXlYGzG1hR6iuA3bbfAVxV2lEG2zLgBGAJ8JUSWtOAL9MMuOOB80vbiCnD9jO2HyzLz9OMldnAUuD60ux64NyyvBS4wY17gBmSjgHOBDbb3mV7N7CZZrxE9KwJBYykOcA5wLVlXcDpwIbSZPjAGRpQG4BFpf1SYL3tF23/FOinmaGdAvTb3m77JWB9aRsxJZWz9vcB9wJH234GmhACjirNZgNPt+w2UGp7q0f0rImewfwd8Dng92X9rcBztl8u662D4A8DpGzfU9pPeEBJWimpT1Lf4ODgBD9SROdJejPwD8BnbP9qpKZtah6hPvw4GQvRM8YdMJI+BOy0/UBruU1Tj7JtQgMKwPZa2wtsL5g1a9YIvY6YfJLeQBMuN9r+dik/Wy59UV53lvoAcGzL7nOAHSPUXyNjIXrJRM5gTgM+LOlJmstXp9Oc0cyQNL20aR0EfxggZfvhwC4mOKAielm5DHwdsNX2F1s2bQSGngRbDtzSUr+gPE22ENhTLqHdBiyWNLPc3F9cahE9a9wBY/sS23Nsz6W5SX+H7Y8BdwIfKc2GD5yhAfWR0t6lvqw8ZXYczdMz9wH3A/PKU2kHlWNsHG9/I7rkNOAvgNMlPVx+zgauAM6QtA04o6wDbAK209yL/CrwSQDbu4DLaMbF/cClpRbRs6aP3mTMLgbWS/oC8BDN7I3y+g1J/TRnLssAbG+RdDPwI5pHOlfZfgVA0oU0s7RpwDrbWyr0N6Ia2z+k/eVegEVt2htYtZf3Wges61zvIurqSMDYvgu4qyxvp3kCbHib3wHn7WX/y4HL29Q30czoIiJiisk3+SMioooal8giYj82d/X3xr3vk1ec08GeRK/LGUxERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJiIiqkjAREREFQmYiIioIgETERFVJGAiIqKKBExERFSRgImIiCoSMBERUUUCJqIiSesk7ZT0eEvtCEmbJW0rrzNLXZKultQv6VFJ81v2WV7ab5O0vBufJWKsxh0wko6VdKekrZK2SLqo1Ds2eCSdLOmxss/VkjSRDxvRBV8HlgyrrQZutz0PuL2sA5wFzCs/K4FroBlTwBrgVOAUYM3QuIroZRM5g3kZ+Evb7wYWAqskHU9nB881pe3QfsMHakRPs/0DYNew8lLg+rJ8PXBuS/0GN+4BZkg6BjgT2Gx7l+3dwGYyFmIKGHfA2H7G9oNl+XlgKzCbDg2esu0w23fbNnBDy3tFTGVH234GmnEEHFXqs4GnW9oNlNre6q8jaaWkPkl9g4ODHe94xFh05B6MpLnA+4B76dzgmV2Wh9fbHT+DKvYH7S4Be4T664v2WtsLbC+YNWtWRzsXMVYTDhhJbwb+AfiM7V+N1LRNbaTBk0EV+6tnyxk65XVnqQ8Ax7a0mwPsGKEe0dMmFDCS3kATLjfa/nYpd2rwDJTl4fWIqW4jMPQwy3Lglpb6BeWBmIXAnnIV4DZgsaSZ5f7k4lKL6GkTeYpMwHXAVttfbNnUkcFTtj0vaWE51gUt7xUxJUi6CbgbeKekAUkrgCuAMyRtA84o6wCbgO1AP/BV4JMAtncBlwH3l59LSy2ip02fwL6nAX8BPCbp4VL7K5rBcnMZSE8B55Vtm4CzaQbPC8DHoRk8koYGD7x28HyC5jHPQ4Bby0/ElGH7/L1sWtSmrYFVe3mfdcC6DnYtorpxB4ztH9L+Pgl0aPDY7gNOHG8fIyKieyZyBhMRMSZzV39vQvs/ecU5HepJTIb8qZiIiKgiARMREVUkYCIioooETEREVJGAiYiIKhIwERFRRQImIiKqSMBEREQVCZiIiKgiARMREVUkYCIioooETEREVJE/dhkRU8ZE/lhm/lDm5MsZTEREVJGAiYiIKhIwERFRRQImIiKqyE3+iDgg5AGByZeA2Uf55YyIGJtcIouIiCp6PmAkLZH0hKR+Sau73Z+Ibsp4iKmkpy+RSZoGfBk4AxgA7pe00faPutuziMmX8dA9uUQ+Pj0dMMApQL/t7QCS1gNLgSk1oPLLGR2yX4yHA81Exv9Edfvfj14PmNnA0y3rA8CpwxtJWgmsLKu/lvTEXt7vSOCXHe3hvhn3cXVld47bAVPuf+tR/HGF9xyrUcfD/jwWpvCxu/aZdWV3x0OvB4za1Py6gr0WWDvqm0l9thd0omNjcaAdt5vH7uZnngSjjoeMhd479oH4mYf0+k3+AeDYlvU5wI4u9SWi2zIeYkrp9YC5H5gn6ThJBwHLgI1d7lNEt2Q8xJTS05fIbL8s6ULgNmAasM72lgm85aiXDio50I7bzWN38zNX1eHxcCD+/5PPPMlkv+6WRkRExIT1+iWyiIiYohIwERFRRQImIiKq6Omb/BMh6V0033KeTfNdgR3ARttbu9qxSSTpBtsXdLsftbQ8SbXD9v+W9FHgz4CtwFrb/6+rHewhB/p4yFjozljYL2/yS7oYOB9YT/PdAWi+M7AMWG/7isrHfxfNQL7X9q9b6ktsf7/SMYc/rirgz4E7AGx/uMZxy7FPBbba/pWkQ4DVwHyaP2HyN7b3VDrujTSTpDcBzwFvBr4NLKL53V5e47hTTTfHQ8bCgT0W9teA+QlwwvDULim/xfa8isf+NLCKZubwXuAi27eUbQ/anl/puA/S/BJfSzNDFXATzT8i2P4/NY5bjr0FOKk8RrsWeAHYQPPLfZLtf1vpuI/afo+k6cDPgbfZfkWSgEdsv6fGcaeabo2HjIWMhf31EtnvgbcBPxtWP6Zsq+k/Aifb/rWkucAGSXNtf4n2f+qjUxYAFwGfB/6r7Ycl/bbmYGrxR7ZfHupHyz8cP5T0cM3jln8kD6WZuR0O7AIOBt5Q8bhTTbfGQ8bCAT4W9teA+Qxwu6RtvPrHAf8l8A7gwsrHnjZ0KcD2k5I+QDOw/piKg8r274GrJP19eX2Wyfv/93FJH7f9NeARSQts90n6E6Dmtd/rgB/TfOnw88DfS9oOLKS5HBSNbo2HjIUDfCzsl5fIACT9Ec2fN59N88s8ANxv+5XKx70D+Kzth1tq04F1wMdsT6t5/JZjngOcZvuvJuFYhwNfAv41zV9unU/zD9nTwKdtP1Lx2G8DsL1D0gzgg8BTtu+rdcypqBvjIWMhY2G/DZhukTQHeNn2L9psO832/+1CtyaFpLcAb6eZLQ7YfrbLXYouyljIWEjAREREFfmiZUREVJGAiYiIKhIwERFRRQImIiKq+P+VBWxsmFzCFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[7]].hist(by=df['SeriousDlqin2yrs'], bins=range(0,10))\n",
    "print(df.columns[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberRealEstateLoansOrLines\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[8]].hist(by=df['SeriousDlqin2yrs'], bins=range(0,15))\n",
    "print(df.columns[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime60-89DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[9]].hist(by=df['SeriousDlqin2yrs'], bins=range(0,7))\n",
    "print(df.columns[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfDependents\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.columns[10]].hist(by=df['SeriousDlqin2yrs'],bins=range(0,7))\n",
    "print(df.columns[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that generally deliquent people had more difficulties paying their debts, more dependents and more credit lines. That was expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.violinplot(x=df.columns[0], y=df.columns[1], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.violinplot(x=df.columns[0], y=df.columns[4], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.violinplot(x=df.columns[0], y=df.columns[5], data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "METHOD: pearson , TOP 10\n",
      "0.43307405909281743 NumberOfOpenCreditLinesAndLoans - NumberRealEstateLoansOrLines\n",
      "0.31513512241750974 MonthlyIncome - NumberRealEstateLoansOrLines\n",
      "0.31159465101130523 SeriousDlqin2yrs - NumberOfTimes90DaysLate\n",
      "0.3060145829804228 NumberOfTime30-59DaysPastDueNotWorse - NumberOfTime60-89DaysPastDueNotWorse\n",
      "0.29465778861787384 NumberOfTimes90DaysLate - NumberOfTime60-89DaysPastDueNotWorse\n",
      "0.2706425219727398 SeriousDlqin2yrs - NumberOfTime30-59DaysPastDueNotWorse\n",
      "0.26523223470981055 SeriousDlqin2yrs - NumberOfTime60-89DaysPastDueNotWorse\n",
      "0.23236597937491046 MonthlyIncome - NumberOfOpenCreditLinesAndLoans\n",
      "0.2183845970480379 NumberOfTime30-59DaysPastDueNotWorse - NumberOfTimes90DaysLate\n",
      "-0.2183270107954695 age - NumberOfDependents\n",
      "METHOD: spearman , TOP 10\n",
      "0.47205178322882424 NumberOfOpenCreditLinesAndLoans - NumberRealEstateLoansOrLines\n",
      "0.39886976652193795 DebtRatio - NumberRealEstateLoansOrLines\n",
      "0.3477266618057114 MonthlyIncome - NumberRealEstateLoansOrLines\n",
      "0.331805481089453 SeriousDlqin2yrs - NumberOfTimes90DaysLate\n",
      "0.2962480006962267 NumberOfTimes90DaysLate - NumberOfTime60-89DaysPastDueNotWorse\n",
      "-0.2792832919959261 RevolvingUtilizationOfUnsecuredLines - age\n",
      "0.2735069484214131 MonthlyIncome - NumberOfOpenCreditLinesAndLoans\n",
      "0.26532613942892563 SeriousDlqin2yrs - NumberOfTime60-89DaysPastDueNotWorse\n",
      "0.26441618237256675 NumberOfTime30-59DaysPastDueNotWorse - NumberOfTime60-89DaysPastDueNotWorse\n",
      "0.24762164866946057 SeriousDlqin2yrs - NumberOfTime30-59DaysPastDueNotWorse\n"
     ]
    }
   ],
   "source": [
    "#Lets see the correlation between columns\n",
    "def correlation(data, method='pearson',top=10):\n",
    "    \"\"\"Calculates the correlation coefficients between columns.\n",
    "    Displays them in descending order of their absolute values.\"\"\"\n",
    "    columns = list(data)\n",
    "    coefficients = data.astype(float).corr(method=method)\n",
    "    results = []\n",
    "    for i in range(len(columns)):\n",
    "        for j in range(i + 1, len(columns)):\n",
    "            coefficient = coefficients[columns[i]][columns[j]]\n",
    "            results.append((\n",
    "                abs(coefficient), coefficient,\n",
    "                columns[i] + ' - ' + columns[j]))\n",
    "    print('METHOD:', method,', TOP', top)\n",
    "    for result in list(reversed(sorted(results)))[:top]:\n",
    "        abs_coefficient, coefficient, columns_pair = result\n",
    "        print (coefficient, columns_pair)\n",
    "        \n",
    "correlation(df,'pearson',10)\n",
    "correlation(df,'spearman',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "      <td>149102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066713</td>\n",
       "      <td>0.337401</td>\n",
       "      <td>52.297414</td>\n",
       "      <td>0.245060</td>\n",
       "      <td>317.365003</td>\n",
       "      <td>6220.245631</td>\n",
       "      <td>8.443334</td>\n",
       "      <td>0.090435</td>\n",
       "      <td>1.010946</td>\n",
       "      <td>0.064748</td>\n",
       "      <td>0.727925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249525</td>\n",
       "      <td>1.507817</td>\n",
       "      <td>14.786549</td>\n",
       "      <td>0.696924</td>\n",
       "      <td>942.248543</td>\n",
       "      <td>4465.381742</td>\n",
       "      <td>5.137266</td>\n",
       "      <td>0.485776</td>\n",
       "      <td>1.112477</td>\n",
       "      <td>0.329958</td>\n",
       "      <td>1.080696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029735</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175208</td>\n",
       "      <td>3900.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.153343</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366227</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.556142</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.861201</td>\n",
       "      <td>7370.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>250.583333</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>8501.000000</td>\n",
       "      <td>70833.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     149102.000000                         149102.000000  149102.000000   \n",
       "mean           0.066713                              0.337401      52.297414   \n",
       "std            0.249525                              1.507817      14.786549   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.029735      41.000000   \n",
       "50%            0.000000                              0.153343      52.000000   \n",
       "75%            0.000000                              0.556142      63.000000   \n",
       "max            1.000000                            250.583333     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         149102.000000  149102.000000  149102.000000   \n",
       "mean                               0.245060     317.365003    6220.245631   \n",
       "std                                0.696924     942.248543    4465.381742   \n",
       "min                                0.000000       0.000000       0.000000   \n",
       "25%                                0.000000       0.175208    3900.000000   \n",
       "50%                                0.000000       0.366227    5400.000000   \n",
       "75%                                0.000000       0.861201    7370.000000   \n",
       "max                               13.000000    8501.000000   70833.000000   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    149102.000000            149102.000000   \n",
       "mean                          8.443334                 0.090435   \n",
       "std                           5.137266                 0.485776   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                17.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 149102.000000                         149102.000000   \n",
       "mean                       1.010946                              0.064748   \n",
       "std                        1.112477                              0.329958   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             11.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       149102.000000  \n",
       "mean             0.727925  \n",
       "std              1.080696  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max              5.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.linear_model.logistic.LogisticRegression'> :\n",
      "ROC 0.5722195258536272 Recall: 0.15180896552463213 Accuracy: 0.9363654274532915\n",
      "ROC AUC without skf cv: 0.5682724254660656 \n",
      "\n",
      "<class 'sklearn.tree.tree.DecisionTreeClassifier'> :\n",
      "ROC 0.6061304631912255 Recall: 0.2720828466201565 Accuracy: 0.8954715548085874\n",
      "ROC AUC without skf cv: 0.6008427829782534 \n",
      "\n",
      "<class 'sklearn.naive_bayes.GaussianNB'> :\n",
      "ROC 0.6659031553496746 Recall: 0.3697676100864831 Accuracy: 0.9224062731405575\n",
      "ROC AUC without skf cv: 0.667669665520622 \n",
      "\n",
      "<class 'sklearn.neighbors.classification.KNeighborsClassifier'> :\n",
      "ROC 0.5042228534859154 Recall: 0.011359171105133618 Accuracy: 0.9311251493482848\n",
      "ROC AUC without skf cv: 0.5057503215789152 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score,recall_score,accuracy_score\n",
    "from sklearn import linear_model, tree, svm, naive_bayes, neighbors\n",
    "from sklearn import model_selection\n",
    "\n",
    "def predict(x_train, y_train, x_test, model):\n",
    "    \"\"\"Predicts the probability of serious delinquency.\"\"\"\n",
    "    model.fit(x_train, y_train)\n",
    "    y_predict = model.predict(x_test)\n",
    "    y_predict[y_predict < 0.5] = 0\n",
    "    y_predict[y_predict >= 0.5] = 1\n",
    "    return y_predict\n",
    "\n",
    "def score_strat_k_fold(x, y, model):\n",
    "    \"\"\"\n",
    "    Estimates the area under ROC curve and recall of a model,\n",
    "    using stratified K-fold CV.\n",
    "    \"\"\"\n",
    "    # Use k-fold cross-validation and average the scores.\n",
    "    kfold = model_selection.StratifiedKFold(n_splits=5)\n",
    "    auc_scores = []\n",
    "    recall_scores = []\n",
    "    acc_scores = []\n",
    "    for train_index, test_index in kfold.split(x,y):\n",
    "        x_train = x.iloc[train_index]\n",
    "        y_train = y.iloc[train_index]\n",
    "        x_test = x.iloc[test_index]\n",
    "        y_test = y.iloc[test_index]\n",
    "        pred = predict(x_train, y_train, x_test, model)\n",
    "        auc_score = roc_auc_score(y_test, pred)\n",
    "        auc_scores.append(auc_score)\n",
    "        recall_scores.append(recall_score(y_test,pred))\n",
    "        acc_scores.append(accuracy_score(y_test,pred))\n",
    "        \n",
    "    auc = np.mean(auc_scores)\n",
    "    recall = np.mean(recall_scores)\n",
    "    acc = np.mean(acc_scores)\n",
    "    return auc,recall,acc\n",
    "\n",
    "\n",
    "    \n",
    "simpleModels = [linear_model.LogisticRegression(),tree.DecisionTreeClassifier(),naive_bayes.GaussianNB(),neighbors.KNeighborsClassifier()]\n",
    "\n",
    "X = df.drop('SeriousDlqin2yrs', axis=1)\n",
    "y = df['SeriousDlqin2yrs']\n",
    "\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25)\n",
    "\n",
    "def print_metrics(models):\n",
    "    for model in models:\n",
    "        roc, recall, acc = score_strat_k_fold(X_train,y_train,model)\n",
    "        print(model.__class__,':\\nROC', roc,'Recall:', recall,'Accuracy:',acc)\n",
    "        print('ROC AUC without skf cv:',roc_auc_score(y_test,predict(X_train,y_train,X_test,model)),'\\n')\n",
    "\n",
    "print_metrics(simpleModels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive bayes performed best with default parameters. ROC 0.6763124956815666 Recall: 0.3961307538358906"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.ensemble.weight_boosting.AdaBoostClassifier'> :\n",
      "ROC 0.5957212919223304 Recall: 0.20352773282750886 Accuracy: 0.9354264527182\n",
      "ROC AUC without skf cv: 0.593036659996329 \n",
      "\n",
      "<class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'> :\n",
      "ROC 0.587270385717025 Recall: 0.1836166093327475 Accuracy: 0.9369019745057562\n",
      "ROC AUC without skf cv: 0.5823550731125705 \n",
      "\n",
      "<class 'sklearn.ensemble.forest.RandomForestClassifier'> :\n",
      "ROC 0.5771453590277758 Recall: 0.16651002539839038 Accuracy: 0.9328242167470865\n",
      "ROC AUC without skf cv: 0.579383429322028 \n",
      "\n",
      "<class 'sklearn.ensemble.bagging.BaggingClassifier'> :\n",
      "ROC 0.5813583681737384 Recall: 0.1774661622710662 Accuracy: 0.9311966673103113\n",
      "ROC AUC without skf cv: 0.5829428546433459 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier,BaggingClassifier\n",
    "ensembleModels = [AdaBoostClassifier(),GradientBoostingClassifier(),RandomForestClassifier(),BaggingClassifier()]\n",
    "\n",
    "print_metrics(ensembleModels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble models with custom base classifier\n",
      "<class 'sklearn.ensemble.weight_boosting.AdaBoostClassifier'> :\n",
      "ROC 0.49764892353776047 Recall: 0.4353528804489549 Accuracy: 0.5515967997199418\n",
      "ROC AUC without skf cv: 0.5387857700583916 \n",
      "\n",
      "<class 'sklearn.ensemble.bagging.BaggingClassifier'> :\n",
      "ROC 0.6662224720978408 Recall: 0.3707033496583184 Accuracy: 0.9221916408847634\n",
      "ROC AUC without skf cv: 0.6702844527786358 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Ensemble models with custom base classifier')\n",
    "ensembleModels = [AdaBoostClassifier(base_estimator=naive_bayes.GaussianNB()),\n",
    "                 BaggingClassifier(base_estimator=naive_bayes.GaussianNB())]\n",
    "print_metrics(ensembleModels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nBagging:\\nBest params: {'n_estimators': 20} \\nBestScore: 0.831231784460619 \\n\\n\\nADA:\\nBest params: {'n_estimators': 100} \\nBestScore: 0.8576901057644939 \\n\\n\\nGB:\\nBest params: {'n_estimators': 100, 'max_depth': 4, 'loss': 'deviance'} \\nBestScore: 0.8631587083574075 \\n\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "bg = BaggingClassifier(base_estimator=naive_bayes.GaussianNB())\n",
    "ada = AdaBoostClassifier()\n",
    "gb = GradientBoostingClassifier()\n",
    "\n",
    "\n",
    "bgHyperParams = {'n_estimators': [10,20,50,100,200,400]}\n",
    "adaHyperParams = {'n_estimators': [10,20,50,100,200,400]}\n",
    "gbHyperParams = {'loss' : ['deviance', 'exponential'],\n",
    "                 'n_estimators': [10,20,40,100,200],\n",
    "                 'max_depth': [1,2,4,6,8,10]}\n",
    "\n",
    "#gridSearchAda = RandomizedSearchCV(estimator=ada, param_distributions=adaHyperParams, n_iter=5,\n",
    "#                                   scoring='roc_auc', fit_params=None, cv=None, verbose=2).fit(X_train, y_train)\n",
    "#gridSearchGB = RandomizedSearchCV(estimator=gb, param_distributions=gbHyperParams, n_iter=10,\n",
    "#                                   scoring='roc_auc', fit_params=None, cv=None, verbose=2).fit(X_train, y_train)\n",
    "#gridSearchBagging = RandomizedSearchCV(estimator=bg, param_distributions=bgHyperParams, n_iter=5,\n",
    "#                                   scoring='roc_auc', fit_params=None, cv=None, verbose=2).fit(X_train, y_train)\n",
    "\n",
    "\n",
    "#print('Bagging:\\nBest params:',gridSearchBagging.best_params_,'\\nBestScore:', gridSearchBagging.best_score_,'\\n\\n')\n",
    "#print('ADA:\\nBest params:',gridSearchAda.best_params_,'\\nBestScore:', gridSearchAda.best_score_,'\\n\\n')\n",
    "#print('GB:\\nBest params:',gridSearchGB.best_params_,'\\nBestScore:', gridSearchGB.best_score_,'\\n\\n')\n",
    "'''\n",
    "Bagging:\n",
    "Best params: {'n_estimators': 20} \n",
    "BestScore: 0.831231784460619 \n",
    "\n",
    "\n",
    "ADA:\n",
    "Best params: {'n_estimators': 100} \n",
    "BestScore: 0.8576901057644939 \n",
    "\n",
    "\n",
    "GB:\n",
    "Best params: {'n_estimators': 100, 'max_depth': 4, 'loss': 'deviance'} \n",
    "BestScore: 0.8631587083574075 \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.ensemble.weight_boosting.AdaBoostClassifier'> :\n",
      "ROC 0.5925862587398318 Recall: 0.19684556278332066 Accuracy: 0.9353638545091527\n",
      "ROC AUC without skf cv: 0.5921369525137622 \n",
      "\n",
      "<class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'> :\n",
      "ROC 0.5855896012307479 Recall: 0.18054254676911757 Accuracy: 0.9364280320598665\n",
      "ROC AUC without skf cv: 0.5827141446970816 \n",
      "\n",
      "<class 'sklearn.ensemble.bagging.BaggingClassifier'> :\n",
      "ROC 0.6673096136097303 Recall: 0.3733759854825515 Accuracy: 0.9219054874681788\n",
      "ROC AUC without skf cv: 0.6709112658569742 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "bestModels = [AdaBoostClassifier(n_estimators=100),GradientBoostingClassifier(n_estimators=100,max_depth=4),\n",
    "              BaggingClassifier(base_estimator=naive_bayes.GaussianNB(),n_estimators=20)]\n",
    "\n",
    "print_metrics(bestModels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7483\n",
      "104343\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "data_for_resampling = pd.concat((X_train, y_train), axis=1, join='inner')\n",
    "positives = data_for_resampling[y_train == 1]\n",
    "negatives = data_for_resampling[y_train == 0]\n",
    "print(len(positives))\n",
    "print(len(negatives))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "n_samples: 10000\n",
      "ROC 0.705294858165529 Recall: 0.4691897163310578 Accuracy: 0.7392884486146674\n",
      "ROC AUC without skf cv: 0.7086765264925814\n",
      "\n",
      "n_samples: 20000\n",
      "ROC 0.6919051670185291 Recall: 0.43391033403705803 Accuracy: 0.8094091417867467\n",
      "ROC AUC without skf cv: 0.6900154130311835\n",
      "\n",
      "n_samples: 30000\n",
      "ROC 0.6855922041944853 Recall: 0.4222844083889704 Accuracy: 0.8437690693495469\n",
      "ROC AUC without skf cv: 0.68486907783702\n",
      "\n",
      "n_samples: 40000\n",
      "ROC 0.6832166734538596 Recall: 0.4141333469077192 Accuracy: 0.8674894763468515\n",
      "ROC AUC without skf cv: 0.6834806599381315\n",
      "\n",
      "n_samples: 50000\n",
      "ROC 0.6790133969900586 Recall: 0.4029067939801171 Accuracy: 0.8832351094706319\n",
      "ROC AUC without skf cv: 0.6812573539725815\n",
      "\n",
      "n_samples: 60000\n",
      "ROC 0.6772680614348127 Recall: 0.4002361228696252 Accuracy: 0.8928619189958962\n",
      "ROC AUC without skf cv: 0.6751442768279635\n",
      "\n",
      "n_samples: 70000\n",
      "ROC 0.6744134234345953 Recall: 0.39208398972633324 Accuracy: 0.9022108514083147\n",
      "ROC AUC without skf cv: 0.6740648470224478\n",
      "\n",
      "n_samples: 80000\n",
      "ROC 0.6713714331068555 Recall: 0.3844678662137109 Accuracy: 0.9091938048351565\n",
      "ROC AUC without skf cv: 0.679224449212169\n",
      "\n",
      "n_samples: 90000\n",
      "ROC 0.6686436057498241 Recall: 0.3783205448329815 Accuracy: 0.9143953588263918\n",
      "ROC AUC without skf cv: 0.6700671678373332\n"
     ]
    }
   ],
   "source": [
    "def resampled_scores(n_samples):\n",
    "    print('\\nn_samples:',n_samples)\n",
    "    negative_sample=resample(negatives,n_samples=n_samples, random_state=42)\n",
    "\n",
    "    undersampled_df = pd.concat((negative_sample, positives), axis=0)\n",
    "    x_under = undersampled_df.drop('SeriousDlqin2yrs', axis=1)\n",
    "    y_under = undersampled_df['SeriousDlqin2yrs']\n",
    "    model = BaggingClassifier(base_estimator=naive_bayes.GaussianNB(),n_estimators=20)\n",
    "    roc, recall, acc = score_strat_k_fold(x_under,y_under,model)\n",
    "    print('ROC', roc,'Recall:', recall,'Accuracy:',acc)\n",
    "    print('ROC AUC without skf cv:',roc_auc_score(y_test,predict(x_under,y_under,X_test,model)))\n",
    "    \n",
    "    \n",
    "for n in [x*10000 for x in range(1,10)]:\n",
    "    resampled_scores(n)"
   ]
  }
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