P7_FE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "551ce207-0976-48c3-9242-fac3e6bdf527",
    "_uuid": "66406036d8dd7a0071295d1aee64f13bffc44e3a"
   },
   "source": [
    "# Introduction\n",
    "\n",
    "Our notebooks have been initialized thanks to several sources provided around the Home Credit default risk [Kaggle](https://www.kaggle.com/c/home-credit-default-risk) competition: data from Home Credit, Kernels-Notebooks from competitors, Discussions threads involving host and competitors.\n",
    "\n",
    "While the objective of competitors is to get the best AUC score _Area under the ROC Curve_ by *__any means__*, we'll rather focuse on the ability to provide *__interpretable__* decisions, including the possibility given to customers to explore their case.\n",
    "\n",
    "# Notebooks & dashboard\n",
    "\n",
    "There are 4 different notebooks:\n",
    "* __P7_EDA__: focusing on Exploratory Data Analysis,\n",
    "* __Current : P7_FE__: focusing on Feature Engineering\n",
    "* __P7_FS__: model-agnostic Feature Selection,\n",
    "* __P7_Model__: focusing on scoring & model evaluation,\n",
    "* __P7_Interpretation__ : focusing on model interpretation,\n",
    "And a dashboard python file.\n",
    "\n",
    "\n",
    "# Data\n",
    "\n",
    "This diagram shows data relationship :\n",
    "\n",
    "<img src=\"https://storage.googleapis.com/kaggle-media/competitions/home-credit/home_credit.png\" class=\"svg\" alt=\"\" width=\"700\" height=\"700\">\n",
    "\n",
    "There are 7 different sources of data:\n",
    "Current application:\n",
    "* __application__: details about each `SK_ID_CURR` loan application and applicant + `TARGET` indicating the risk of failure to repay loan on time.\n",
    "* __test__: where the `TARGET` has not been calculated yet.\n",
    "\n",
    "External previous credits information:\n",
    "* __bureau__: details about each `SK_ID_BUREAU` client's previous credits oustide our company, with cardinality 1:n from current application.\n",
    "* __bureau_balance__: monthly details of each `SK_ID_BUREAU`.\n",
    "Internal previous credit information:\n",
    "* __previous_application__: details about each `SK_ID_PREV` client's previous credits applications within our company, with cardinality 1:n from current application.\n",
    "* __POS_CASH_balance__: monthly details of each `SK_ID_PREV` (credits or cash types).\n",
    "* __credit_card_balance__: monthly details of each `SK_ID_PREV` (credit cards revolving type).\n",
    "* __installments_payment__: payment history of each `SK_ID_PREV`.\n",
    "\n",
    "Moreover, we are provided with the features definitions (in `HomeCredit_columns_description.csv`). \n",
    "\n",
    "# Forewords\n",
    "\n",
    "In this FE notebook, we process datasets aggregation in order to get to an intermediate __fully understandable__ dataset, i.e. with any feature dealing with relevant details that could be discussed by counsellor in front of an applicant.\n",
    "This will surely affect the ability to reach the amazing scores of Kaggle competitors, and it implies to spend additionnal time for FE.<br/>\n",
    "Furthermore, we have to consider this task according to the expectations of the study, which consists in performing __model optimization__ and __model interpretation__. <br/>\n",
    "This will lead to consider as further optional tasks many EDA & FE ideas, in order to use __model__ and its __interpretation__ bring value in that perspective. <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d632b08c-d252-4238-b496-e2c6edebec4b",
    "_uuid": "eb13bf76d4e1e60d0703856ec391cdc2c5bdf1fb"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pycodestyle_magic\n",
    "%flake8_on\n",
    "# PEP8 tested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy and pandas for data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# File system manangement\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom functions\n",
    "# visuals\n",
    "from ipynb.fs.full.functions import plot_bar_gen, heatmap, kde_plot\n",
    "# processing\n",
    "from ipynb.fs.full.functions import one_hot_encoder, label_encoder\n",
    "# analysis\n",
    "from ipynb.fs.full.functions import target_corrs, missing_values_table\n",
    "# df optimization - disabled\n",
    "from ipynb.fs.full.functions import convert_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paramètres\n",
    "# to display any columns\n",
    "pd.options.display.max_columns = 1000\n",
    "pd.options.display.max_rows = 1000\n",
    "sns.set(style=\"ticks\")\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0b1a02afd367d1c4ee3a3a936382ca42fb921b9d"
   },
   "source": [
    "## Feature Engineering as done during EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fist set of transformed datasets have been dumped with pickle files, we'll load them right before using those intermediate sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load target (for correlation study)\n",
    "with open(\"Data/target.pkl\", mode=\"rb\") as df:\n",
    "    target = pickle.load(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering along a Bottom Up aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate over Installments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# third level\n",
    "installments = pd.read_csv('Data/installments_payments.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While every payments are detailed over any previous application SK_ID_PREV, what matters for us, somehow already accessible through both credit balance or card balance with SK_DPD, is the rough info where (i.e. when and how much) a client failed to pay the installement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternate option would be to put that missing info as X_STATUS\n",
    "installments.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2922.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installments['DAYS_INSTALMENT'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the older installement is 8 years before current application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's catch overdue in time and amount :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3:80: E501 line too long (96 > 79 characters)\n",
      "4:80: E501 line too long (104 > 79 characters)\n",
      "6:80: E501 line too long (97 > 79 characters)\n",
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      "15:80: E501 line too long (88 > 79 characters)\n",
      "19:80: E501 line too long (81 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# manual FE installements\n",
    "# overdue amounts\n",
    "installments['N_INS_AMT_OVERDUE'] = installments['AMT_INSTALMENT'] - installments['AMT_PAYMENT']\n",
    "installments['N_INS_AMT_OVERDUE'] = installments['N_INS_AMT_OVERDUE'].apply(lambda x: x if x > 0 else 0)\n",
    "# prepayed amounts\n",
    "installments['N_INS_AMT_PREPAYED'] = installments['AMT_PAYMENT'] - installments['AMT_INSTALMENT']\n",
    "installments['N_INS_AMT_PREPAYED'] = installments['N_INS_AMT_PREPAYED'].apply(lambda x: x if x > 0 else 0)\n",
    "# overdue ratio\n",
    "installments['N_AMT_OVERDUE_RATIO'] = installments['N_INS_AMT_OVERDUE'] / (1 + installments['AMT_INSTALMENT'])\n",
    "# with positive values to get a match with SK_DPD\n",
    "installments['N_INS_DPD'] = installments['DAYS_ENTRY_PAYMENT'] - installments['DAYS_INSTALMENT']\n",
    "installments['N_INS_DPD'] = installments['N_INS_DPD'].apply(lambda x: x if x > 0 else 0)\n",
    "# same with DBD Before Due may be somehow valuable\n",
    "installments['N_INS_DBD'] = installments['DAYS_INSTALMENT'] - installments['DAYS_ENTRY_PAYMENT']\n",
    "installments['N_INS_DBD'] = installments['N_INS_DBD'].apply(lambda x: x if x > 0 else 0)\n",
    "# DPD categories to get a match with Bureau data\n",
    "bins = [-np.inf, 1, 30, 60, 90, 120, np.inf]\n",
    "values = ['0', '1', '2', '3', '4', '5']\n",
    "installments['N_STATUS'] = pd.cut(installments['N_INS_DPD'], bins, labels=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (95 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of overdue we need to catch min of NUM_INSTALMENT_NUMBER out of !=0 N_STATUS\n",
    "focus_installments = installments[installments['N_STATUS'] != '0']\n",
    "focus_installments_agg = focus_installments.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'NUM_INSTALMENT_NUMBER': ['min']})\n",
    "focus_installments_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'INS_N_PREV_EARLIER_OVERDUE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The less this \"earlier overdue\" is, the more is the risk of failure. But from Null value point of view, we won't be able to set a right replacement value for clients with no overdue at all : let's first consider other aggregative data to compute a valid overdue earliness index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute valuable features by SK_ID_PREV\n",
    "installments_agg = installments.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'NUM_INSTALMENT_VERSION': ['max'],\n",
    "     'N_INS_AMT_OVERDUE': ['max'],\n",
    "     'NUM_INSTALMENT_NUMBER': ['max'],\n",
    "     'DAYS_INSTALMENT': ['max']})\n",
    "installments_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'INS_N_PREV_CNT_PROLONG',\n",
    "    'INS_N_PREV_INS_AMT_MAX_OVERDUE',\n",
    "    'INS_N_PREV_CNT_TOT_INSTALMENT',\n",
    "    'INS_N_PREV_LAST_DAYS_INSTALMENT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (94 > 79 characters)\n",
      "4:80: E501 line too long (159 > 79 characters)\n",
      "7:80: E501 line too long (101 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# merge with raw earlier overdue\n",
    "installments_agg = installments_agg.merge(focus_installments_agg, how='left', on='SK_ID_PREV')\n",
    "# we compute a ratio with ability to fill Null with Zero after aggregation\n",
    "installments_agg['INS_N_PREV_EARLINESS_OVERDUE_RATIO'] = 1 - installments_agg['INS_N_PREV_EARLIER_OVERDUE'] / installments_agg['INS_N_PREV_CNT_TOT_INSTALMENT']\n",
    "installments_agg.drop(columns=['INS_N_PREV_EARLIER_OVERDUE'], inplace=True)\n",
    "installments_agg['INS_N_PREV_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "installments_agg['INS_N_PREV_LOAN_AGE'] = - installments_agg['INS_N_PREV_LAST_DAYS_INSTALMENT'] / 365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'INS_N_PREV_CNT_PROLONG',\n",
       "       'INS_N_PREV_INS_AMT_MAX_OVERDUE', 'INS_N_PREV_CNT_TOT_INSTALMENT',\n",
       "       'INS_N_PREV_LAST_DAYS_INSTALMENT', 'INS_N_PREV_EARLINESS_OVERDUE_RATIO',\n",
       "       'INS_N_PREV_LOAN_AGE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installments_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge with installments\n",
    "installments_agg = installments_agg.merge(\n",
    "    installments.filter(['SK_ID_CURR', 'SK_ID_PREV']),\n",
    "    how='left',\n",
    "    on='SK_ID_PREV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess installments_agg\n",
    "def installments_agg_id(ins, num_rows=None, nan_as_category=True):\n",
    "    # General aggregations\n",
    "    ins.drop(['SK_ID_PREV'], axis=1, inplace=True)\n",
    "    ins_agg = ins.groupby('SK_ID_CURR').agg(\n",
    "        ['min', 'max', 'mean', 'sum', 'var'])\n",
    "    ins_agg.columns = pd.Index(\n",
    "        ['INSAGG_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist(\n",
    "        )])\n",
    "    return ins_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# installements FE by SK_ID\n",
    "sk_id_installments_agg = installments_agg_id(installments_agg, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (81 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# merge to target for corr study\n",
    "ins_agg_target = target.join(sk_id_installments_agg, how='left', on='SK_ID_CURR')\n",
    "ins_agg_target_corrs = target_corrs(ins_agg_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>abs_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MIN</td>\n",
       "      <td>0.067380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MEAN</td>\n",
       "      <td>0.061135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>INSAGG_INS_N_PREV_LOAN_AGE_MAX</td>\n",
       "      <td>0.055247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MIN</td>\n",
       "      <td>0.055247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>INSAGG_INS_N_PREV_LOAN_AGE_VAR</td>\n",
       "      <td>0.048508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_VAR</td>\n",
       "      <td>0.048508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MAX</td>\n",
       "      <td>0.046228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MIN</td>\n",
       "      <td>0.038081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_SUM</td>\n",
       "      <td>0.037989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>INSAGG_INS_N_PREV_LOAN_AGE_SUM</td>\n",
       "      <td>0.037989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MEAN</td>\n",
       "      <td>0.033523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>INSAGG_INS_N_PREV_LOAN_AGE_MEAN</td>\n",
       "      <td>0.033523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MEAN</td>\n",
       "      <td>0.032095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MIN</td>\n",
       "      <td>0.031680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_PROLONG_MAX</td>\n",
       "      <td>0.019338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MAX</td>\n",
       "      <td>0.019139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_PROLONG_SUM</td>\n",
       "      <td>0.019059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_PROLONG_MEAN</td>\n",
       "      <td>0.015745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_SUM</td>\n",
       "      <td>0.014968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_VAR</td>\n",
       "      <td>0.014713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_SUM</td>\n",
       "      <td>0.014118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_SUM</td>\n",
       "      <td>0.012366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_PROLONG_MIN</td>\n",
       "      <td>0.011789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_PROLONG_VAR</td>\n",
       "      <td>0.009843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MEAN</td>\n",
       "      <td>0.006277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MAX</td>\n",
       "      <td>0.005897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>INSAGG_INS_N_PREV_LOAN_AGE_MIN</td>\n",
       "      <td>0.003417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MAX</td>\n",
       "      <td>0.003417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>0.002108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_VAR</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_VAR</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           feature  abs_corr\n",
       "21   INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MIN  0.067380\n",
       "23  INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MEAN  0.061135\n",
       "27                  INSAGG_INS_N_PREV_LOAN_AGE_MAX  0.055247\n",
       "16      INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MIN  0.055247\n",
       "30                  INSAGG_INS_N_PREV_LOAN_AGE_VAR  0.048508\n",
       "20      INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_VAR  0.048508\n",
       "22   INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MAX  0.046228\n",
       "6        INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MIN  0.038081\n",
       "19      INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_SUM  0.037989\n",
       "29                  INSAGG_INS_N_PREV_LOAN_AGE_SUM  0.037989\n",
       "18     INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MEAN  0.033523\n",
       "28                 INSAGG_INS_N_PREV_LOAN_AGE_MEAN  0.033523\n",
       "8       INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MEAN  0.032095\n",
       "11        INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MIN  0.031680\n",
       "2                INSAGG_INS_N_PREV_CNT_PROLONG_MAX  0.019338\n",
       "7        INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MAX  0.019139\n",
       "4                INSAGG_INS_N_PREV_CNT_PROLONG_SUM  0.019059\n",
       "3               INSAGG_INS_N_PREV_CNT_PROLONG_MEAN  0.015745\n",
       "9        INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_SUM  0.014968\n",
       "25   INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_VAR  0.014713\n",
       "14        INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_SUM  0.014118\n",
       "24   INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_SUM  0.012366\n",
       "1                INSAGG_INS_N_PREV_CNT_PROLONG_MIN  0.011789\n",
       "5                INSAGG_INS_N_PREV_CNT_PROLONG_VAR  0.009843\n",
       "13       INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MEAN  0.006277\n",
       "12        INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MAX  0.005897\n",
       "26                  INSAGG_INS_N_PREV_LOAN_AGE_MIN  0.003417\n",
       "17      INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MAX  0.003417\n",
       "0                                       SK_ID_CURR  0.002108\n",
       "15        INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_VAR  0.000253\n",
       "10       INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_VAR  0.000044"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_agg_target_corrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "del installments_agg\n",
    "del focus_installments_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_PREV                   int64\n",
       "SK_ID_CURR                   int64\n",
       "NUM_INSTALMENT_VERSION     float64\n",
       "NUM_INSTALMENT_NUMBER        int64\n",
       "DAYS_INSTALMENT            float64\n",
       "DAYS_ENTRY_PAYMENT         float64\n",
       "AMT_INSTALMENT             float64\n",
       "AMT_PAYMENT                float64\n",
       "N_INS_AMT_OVERDUE          float64\n",
       "N_INS_AMT_PREPAYED         float64\n",
       "N_AMT_OVERDUE_RATIO        float64\n",
       "N_INS_DPD                  float64\n",
       "N_INS_DBD                  float64\n",
       "N_STATUS                  category\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installments.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object type represents category\n",
    "installments['N_STATUS'] = installments['N_STATUS'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17:80: E501 line too long (102 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess installments_payments.csv\n",
    "def installments_payments(ins, num_rows=None, nan_as_category=False):\n",
    "    ins, cat_cols = one_hot_encoder(ins, nan_as_category=False)\n",
    "    # Features: Perform aggregations\n",
    "    aggregations = {\n",
    "        'DAYS_INSTALMENT': ['mean'],\n",
    "        'DAYS_ENTRY_PAYMENT': ['max'],\n",
    "        'N_INS_DPD': ['max', 'mean', 'sum'],\n",
    "        'N_INS_DBD': ['mean'],\n",
    "        'N_INS_AMT_OVERDUE': ['mean'],\n",
    "        'N_AMT_OVERDUE_RATIO': ['max', 'mean'],\n",
    "        'AMT_INSTALMENT': ['max', 'mean', 'sum']\n",
    "    }\n",
    "    for cat in cat_cols:\n",
    "        aggregations[cat] = ['sum']\n",
    "    ins_agg = ins.groupby('SK_ID_CURR').agg(aggregations)\n",
    "    ins_agg.columns = pd.Index(['INS_' + e[0] + \"_\" + e[1].upper() for e in ins_agg.columns.tolist()])\n",
    "    # Count installments accounts\n",
    "    ins_agg['INS_N_INSTAL_COUNT'] = ins.groupby('SK_ID_CURR').size()\n",
    "    return ins_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# installements FE by SK_ID\n",
    "sk_id_installments = installments_payments(installments, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del installments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(339578, 19)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_id_installments.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>INS_DAYS_INSTALMENT_MEAN</th>\n",
       "      <th>INS_DAYS_ENTRY_PAYMENT_MAX</th>\n",
       "      <th>INS_N_INS_DPD_MAX</th>\n",
       "      <th>INS_N_INS_DPD_MEAN</th>\n",
       "      <th>INS_N_INS_DPD_SUM</th>\n",
       "      <th>INS_N_INS_DBD_MEAN</th>\n",
       "      <th>INS_N_INS_AMT_OVERDUE_MEAN</th>\n",
       "      <th>INS_N_AMT_OVERDUE_RATIO_MAX</th>\n",
       "      <th>INS_N_AMT_OVERDUE_RATIO_MEAN</th>\n",
       "      <th>INS_AMT_INSTALMENT_MAX</th>\n",
       "      <th>INS_AMT_INSTALMENT_MEAN</th>\n",
       "      <th>INS_AMT_INSTALMENT_SUM</th>\n",
       "      <th>INS_N_STATUS_0_SUM</th>\n",
       "      <th>INS_N_STATUS_1_SUM</th>\n",
       "      <th>INS_N_STATUS_2_SUM</th>\n",
       "      <th>INS_N_STATUS_3_SUM</th>\n",
       "      <th>INS_N_STATUS_4_SUM</th>\n",
       "      <th>INS_N_STATUS_5_SUM</th>\n",
       "      <th>INS_N_INSTAL_COUNT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100001</th>\n",
       "      <td>-2187.714286</td>\n",
       "      <td>-1628.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.857143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17397.900</td>\n",
       "      <td>5885.132143</td>\n",
       "      <td>41195.925</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100002</th>\n",
       "      <td>-295.000000</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.421053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53093.745</td>\n",
       "      <td>11559.247105</td>\n",
       "      <td>219625.695</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100003</th>\n",
       "      <td>-1378.160000</td>\n",
       "      <td>-544.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>560835.360</td>\n",
       "      <td>64754.586000</td>\n",
       "      <td>1618864.650</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            INS_DAYS_INSTALMENT_MEAN  INS_DAYS_ENTRY_PAYMENT_MAX  \\\n",
       "SK_ID_CURR                                                         \n",
       "100001                  -2187.714286                     -1628.0   \n",
       "100002                   -295.000000                       -49.0   \n",
       "100003                  -1378.160000                      -544.0   \n",
       "\n",
       "            INS_N_INS_DPD_MAX  INS_N_INS_DPD_MEAN  INS_N_INS_DPD_SUM  \\\n",
       "SK_ID_CURR                                                             \n",
       "100001                   11.0            1.571429               11.0   \n",
       "100002                    0.0            0.000000                0.0   \n",
       "100003                    0.0            0.000000                0.0   \n",
       "\n",
       "            INS_N_INS_DBD_MEAN  INS_N_INS_AMT_OVERDUE_MEAN  \\\n",
       "SK_ID_CURR                                                   \n",
       "100001                8.857143                         0.0   \n",
       "100002               20.421053                         0.0   \n",
       "100003                7.160000                         0.0   \n",
       "\n",
       "            INS_N_AMT_OVERDUE_RATIO_MAX  INS_N_AMT_OVERDUE_RATIO_MEAN  \\\n",
       "SK_ID_CURR                                                              \n",
       "100001                              0.0                           0.0   \n",
       "100002                              0.0                           0.0   \n",
       "100003                              0.0                           0.0   \n",
       "\n",
       "            INS_AMT_INSTALMENT_MAX  INS_AMT_INSTALMENT_MEAN  \\\n",
       "SK_ID_CURR                                                    \n",
       "100001                   17397.900              5885.132143   \n",
       "100002                   53093.745             11559.247105   \n",
       "100003                  560835.360             64754.586000   \n",
       "\n",
       "            INS_AMT_INSTALMENT_SUM  INS_N_STATUS_0_SUM  INS_N_STATUS_1_SUM  \\\n",
       "SK_ID_CURR                                                                   \n",
       "100001                   41195.925                 6.0                   1   \n",
       "100002                  219625.695                19.0                   0   \n",
       "100003                 1618864.650                25.0                   0   \n",
       "\n",
       "            INS_N_STATUS_2_SUM  INS_N_STATUS_3_SUM  INS_N_STATUS_4_SUM  \\\n",
       "SK_ID_CURR                                                               \n",
       "100001                       0                   0                   0   \n",
       "100002                       0                   0                   0   \n",
       "100003                       0                   0                   0   \n",
       "\n",
       "            INS_N_STATUS_5_SUM  INS_N_INSTAL_COUNT  \n",
       "SK_ID_CURR                                          \n",
       "100001                       0                   7  \n",
       "100002                       0                  19  \n",
       "100003                       0                  25  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_id_installments.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (128 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "sk_id_installments['INS_N_FAULT_RATE'] = 1 - sk_id_installments['INS_N_STATUS_0_SUM'] / sk_id_installments['INS_N_INSTAL_COUNT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6:80: E501 line too long (92 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "sk_id_installments['INS_N_FAULT_GRAVITY'] = (\n",
    "    sk_id_installments['INS_N_STATUS_1_SUM'] +\n",
    "    2 * sk_id_installments['INS_N_STATUS_2_SUM'] +\n",
    "    3 * sk_id_installments['INS_N_STATUS_3_SUM'] +\n",
    "    4 * sk_id_installments['INS_N_STATUS_4_SUM'] +\n",
    "    5 * sk_id_installments['INS_N_STATUS_5_SUM']) / sk_id_installments['INS_N_INSTAL_COUNT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (98 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "sk_id_installments['INS_N_FAULT_ADJ_RATE'] = (\n",
    "    1 - (sk_id_installments['INS_N_FAULT_RATE'] / -2922)) * sk_id_installments['INS_N_FAULT_RATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_id_installments.drop(columns=[\n",
    "    'INS_N_STATUS_1_SUM',\n",
    "    'INS_N_STATUS_2_SUM',\n",
    "    'INS_N_STATUS_3_SUM',\n",
    "    'INS_N_STATUS_4_SUM',\n",
    "    'INS_N_STATUS_5_SUM'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge to target for corr study\n",
    "ins_target = target.join(sk_id_installments, how='left', on='SK_ID_CURR')\n",
    "ins_target_corrs = target_corrs(ins_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>abs_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>0.070791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>INS_N_FAULT_ADJ_RATE</td>\n",
       "      <td>0.070790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>INS_N_FAULT_GRAVITY</td>\n",
       "      <td>0.067062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>INS_N_AMT_OVERDUE_RATIO_MEAN</td>\n",
       "      <td>0.063052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>INS_N_AMT_OVERDUE_RATIO_MAX</td>\n",
       "      <td>0.045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INS_DAYS_INSTALMENT_MEAN</td>\n",
       "      <td>0.043423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>INS_N_INS_AMT_OVERDUE_MEAN</td>\n",
       "      <td>0.037576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INS_N_STATUS_0_SUM</td>\n",
       "      <td>0.026999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>INS_N_INSTAL_COUNT</td>\n",
       "      <td>0.021214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>INS_N_INS_DBD_MEAN</td>\n",
       "      <td>0.021116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>INS_AMT_INSTALMENT_SUM</td>\n",
       "      <td>0.019944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>INS_AMT_INSTALMENT_MEAN</td>\n",
       "      <td>0.018380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INS_N_INS_DPD_MEAN</td>\n",
       "      <td>0.008937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>INS_N_INS_DPD_SUM</td>\n",
       "      <td>0.006548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INS_N_INS_DPD_MAX</td>\n",
       "      <td>0.003841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>INS_AMT_INSTALMENT_MAX</td>\n",
       "      <td>0.002309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INS_DAYS_ENTRY_PAYMENT_MAX</td>\n",
       "      <td>0.002298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>0.002108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         feature  abs_corr\n",
       "15              INS_N_FAULT_RATE  0.070791\n",
       "17          INS_N_FAULT_ADJ_RATE  0.070790\n",
       "16           INS_N_FAULT_GRAVITY  0.067062\n",
       "9   INS_N_AMT_OVERDUE_RATIO_MEAN  0.063052\n",
       "8    INS_N_AMT_OVERDUE_RATIO_MAX  0.045680\n",
       "1       INS_DAYS_INSTALMENT_MEAN  0.043423\n",
       "7     INS_N_INS_AMT_OVERDUE_MEAN  0.037576\n",
       "13            INS_N_STATUS_0_SUM  0.026999\n",
       "14            INS_N_INSTAL_COUNT  0.021214\n",
       "6             INS_N_INS_DBD_MEAN  0.021116\n",
       "12        INS_AMT_INSTALMENT_SUM  0.019944\n",
       "11       INS_AMT_INSTALMENT_MEAN  0.018380\n",
       "4             INS_N_INS_DPD_MEAN  0.008937\n",
       "5              INS_N_INS_DPD_SUM  0.006548\n",
       "3              INS_N_INS_DPD_MAX  0.003841\n",
       "10        INS_AMT_INSTALMENT_MAX  0.002309\n",
       "2     INS_DAYS_ENTRY_PAYMENT_MAX  0.002298\n",
       "0                     SK_ID_CURR  0.002108"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_target_corrs.head(43)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most interesting feature study through plot\n",
    "kde_plot(ins_target, 'INS_N_FAULT_RATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                      0\n",
       "TARGET                          0\n",
       "INS_DAYS_INSTALMENT_MEAN    15876\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ins_target.isna().sum().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those installments aggregative data produce missing values once merged with SK_ID, meaning we have no previous internal knowledge about those clients before application.\n",
    "N_INS_INSTAL_COUNT is therefore zero for those 15876 SK_ID, and mostly nan items could here be replaced by zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INS_DAYS_INSTALMENT_MEAN        0\n",
       "INS_DAYS_ENTRY_PAYMENT_MAX      0\n",
       "INS_N_INS_DPD_MAX               0\n",
       "INS_N_INS_DPD_MEAN              0\n",
       "INS_N_INS_DPD_SUM               0\n",
       "INS_N_INS_DBD_MEAN              0\n",
       "INS_N_INS_AMT_OVERDUE_MEAN      0\n",
       "INS_N_AMT_OVERDUE_RATIO_MAX     0\n",
       "INS_N_AMT_OVERDUE_RATIO_MEAN    0\n",
       "INS_AMT_INSTALMENT_MAX          0\n",
       "INS_AMT_INSTALMENT_MEAN         0\n",
       "INS_AMT_INSTALMENT_SUM          0\n",
       "INS_N_STATUS_0_SUM              0\n",
       "INS_N_INSTAL_COUNT              0\n",
       "INS_N_FAULT_RATE                0\n",
       "INS_N_FAULT_GRAVITY             0\n",
       "INS_N_FAULT_ADJ_RATE            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_id_installments.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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111xzjT799NM6j7/++uv1wQcfqLi4WFJ5UWPYsGEO+9T2fRIcHKwnnnhCGRkZio+PV58+5s5rDgAAAACondcLHaGhoZo/f77mzJmjM2fOOGybN2+e8vLyNGHCBB06dEjjxo3Tpk2bnGo3NTXV4bkZdTlx4oS2b9+uuLg4NWvWTMOGDavyaX10dLS2bNmisrIyrV+/vtaCgTMMw1BpaWmV9SUlJfbtzZs317Bhw+zXnZOT4zDso2LoSuUlMvLn+cyXL1+utWvXau7cuTp16pRiY2Ptzy6pKVOzZs3s/38hm80mi+Xnb4MLz+1MT5Pf//73ysnJ0SuvvKLi4mINGTLE3qOloKBAhw8f1sCBA9WhQwf16tVLa9asqbPNyvmru6fFxcW1XneF4OBg9e3bV++++6727dunTp062e+H5Nz3ya5duxQeHq733nvP/loCAAAAgF+zlXlnMYHXCx2SNHjw4CpDWDZv3qzc3Fy1b99eycnJeuqppzR79mynn3dgsVi0cOFCp4ew5OTkyGazacyYMYqOjtZ7772n7Oxs+9AZqXz4Ss+ePbVz5069//77Hg1bkaTevXvr66+/1g8//OCwfvfu3erUqZN9OEdycrLWrVunzz77TK1atdJll13m8rlGjhypIUOGaNasWbXut3fvXvuDPsPCwuw9SSqcOHHCa0MxfvnLX+q+++7TAw88YD/P6tWrVVRUpJEjRyo6OlpfffWVS8NXevfurY8//rjK+t27d9sfslqX2NhYbdy4UevXr1d8fLzDtrq+T/bv368lS5YoKytLwcHBWrZsmdPZAQCAv7nwQ5a6P3QBADS8eil0SD8PYTl69KgkqVmzZlq8eLEKCwsllfckKCgoUK9evZxu87LLLlNaWppeeumlOve1Wq3KyMhQXl6e8vLytG3bNoWFhSk3N9dhv7i4OC1evFhRUVEKDPTs2ay/+MUvlJCQoIceekhnz56VJH3zzTdatGiRpk6dat+vX79+OnLkiF577TX7A0/dMX36dO3cuVObN2+udnteXp4KCgrsPVUGDBig1atX24f/fPDBB/rpp5+qzPLiiVGjRqljx4567rnnVFRUpLVr1+qVV16xvw5vvfWWjh075vDA0drcdttt2rlzp8PDStesWaNdu3ZpwoQJTrVx/fXXa8eOHdqyZYuuv/56h221fZ+cP39e9957r+6//3516tRJGRkZevXVV6stvMA5dfXCcaaXDgAAZmnZpq1atmlXaWHGFQBojLw660plFUNYJk2aJKn8TfbUqVOVlpZmf17CkCFDdM8997jUbmpqap3DXT799FOdOnVKN954o32dxWLRxIkTlZWVpWuvvda+ftiwYXrooYc0ffp0l3LUZO7cuXr++ec1ZswYBQQEKDg4WNOnT6/SkyAxMVEvvPCCZs+e7bC+4hkdlV1zzTVV9pOkSy65RJMnT9Zjjz2mwYMHSyp/hkfFc0bCw8P10ksv2YeR3H333VqwYIFGjRolwzAUFham5557zqHAc+G5u3Tpomeeecale/DAAw/ojjvuUOvWrdWxY0ddeeWV9m2hoaEaO3assrKy1L9//zrbCg8P1//93//pscce07Jly2Sz2XT55Zfrr3/9q9MzyQQHB+vqq6+WJIdpfev6PtmzZ48uv/xy+z3p2LGjZs6cqfvvv19r1qxRixYtnDq/V1ksUlkt3b8s9Va79Iou7duYHcFtFsNQmRPPCLI04mKNYRi1PueosRea6noNGvO9B5o6X//9AwBNk03yeNYUc2ZdMWy+MnE54GPOnz+v/Px8RUVFORRYfMnOnTvVt29fs2O4jfzmIn+5I7t3yFZbgbISw2JR+z51F4Gd4Wn+P+e953GG30Rf5/ax7uQf+sizbp+vLpsfmVr3TpV4cv8f+qv3ZktbMGGUW8fx82sub+X/58cFLh8z/Crne1vXxBv5cz74xO1jR1/b26NzeyP/37bvdPvYcYM8O7en+Rfn5Hl0fkmaMTra7WM9zb8k9x23j60wLf4Gt4/15ftf8f6lR48e2rdvnzpefJGCPPwgtbisTN+e/k+Dvyeqtx4drpgxY0aVGVGk8oeF1tbTIjMzU++++26V9VFRUVqwYIFXM1b45ptvNG3atGq3Pfroo9XOEOLr3H19GkJKSop+/PHHKuvHjx/v9NAWAKhP3ipcAICrAiwWlTpZaK3Yv7EItFhU4kL2yscBQKModCxevNit4x588EEvJ6lb586dHZ4X0RS4+/o0hJUrV5odAQD8UlCARcWl7j8pPSiANxuA2Yb1jqx7p0Yqvp9zD51vrAIDLCpx43doIL870ZjYyrwwcsWcWVcaRaEDAAA0Lr++gZ4oAOCupAF9zI7gtuDAABWVlHp0vJmCAgJUXOp+/qAAc/P7+v1vLCh0AAAANCKe/iO3cjsA4CpPnk/RGKSNHGx2BI/4+v1vLCh0AAAANCJzx8aZHQEAAKnMJo9nTSkzZ+4TBoEBAAC/0Dw4yKfaBQAA9YMeHQAAwC/kzppidgQAAPyHzeb5w0Q9fpqpe+jRAQAAAAAA/AaFDgAAAAAA4DcYugIAAAAAABzYbDaPR57YGLoCAAAAAADgGQodAAAAAADAbzB0BQAAAAAAOLLZJE9HnjB0BQAAAAAAwDMUOgAAAAAAgN9g6AoAAAAAAHBgs5XJZjM8bIOhKwAAAAAAAB6h0AEAAAAAAPyGYTOrLwng586fP6/8/HyzYwAAAACA03r06KF9+/apQ5AUaHg2dKXEZtN3xVJUVJRCQkK8lLBuPKMDqGcN/UPtTTt37lTfvn3NjuE28puL/OYiv7nIby7ym4v85iK/uXw5vz99UMvQFQAAAAAA4Dfo0QEAAAAAABzZbJI8fNKFTZI8G/7iDnp0AAAAAAAAv0GhAwAAAAAA+A2GrgAAAAAAAAc2m83TgSvlo18YugIAAAAAAOA+Ch0AAAAAAMBvMHQFAAAAAAA4spV5oQ3JjP4V9OgAAAAAAAB+g0IHAAAAAADwGwxdAQAAkqT4hc/rp6Jir7fbPDhIubOmeL1dAABQj8psnk+Y4um0LW6iRwcAAJCkeily1Ge7AAAA1aFHBwAAQCPycFauzpeUeNxOSGCg/jA+3guJAADwLRQ6AAAAGhFvFDm82Q6ApuWZ3M0qKil1+/jgwAD9Pn6o9wK56PlN21Vc6n7+oIAATRkxyIuJXNOY7r9NNtk8HHpi0sgVCh0AAMD/vPrODhWXuj8tXlCARb++ob8XEwGAb/DkTbY3jveUJ0UObxzvKV+//40FhQ4AAOB3PClyeON4AAB8ns0mz/tkGJ4/0NQNFDoAAAAAP/XBF1+prKzuNyoWi6FrL+/aAIkAoP5R6AAAAABq8cW3R2SrZaC6YRi6vGP7BkzkPGeKHK7sBwC+gEIHALjg22Mnqn0ok2FIHdte0vCBXHTw2PEa83dq26bhA7nIl/Of/PrfqvWJXoah1pd1a7hAQAPz5d+ftRU5nNkOfHP0eLXfJ4ZhqHO7xv33C02XzWaTzcOhKzbJlKErlvpsPDIyUpJUWFioyMhIbd++3WF7dHS0CgsLJUkbNmxQUlKSRo8erYSEBL344ot1tp+enq7bb7/d4ZeG1WpVenp6rcdV3mfJkiUaOXKkzp07Z9++Y8cOpaSkSJKKioo0b948jRo1SgkJCbr99tv1ySefOHH10qlTp3TFFVfo5ZdfrpK7Z8+eOnLkiMP6u+++W9HR0ZKksWPHKjExUUOHDtW1116rxMREJSYmau/evbWe85NPPlFqaqpGjhypm266STNnztTJkyclSffdd5+WL1/usL/NZlNMTIz+9a9/KT09XUOHDrWfq2IpLS2V1Wp1yDFq1CiNGDFC//znP+33tGL76NGjFRsbq2effVal/32YT3WvS+X7vGTJEg0aNKjKub/77rsar7Xi++rhhx92WF9QUKDIyEhZrVZJ5d9n8fHxDu3OnDnT4Zhp06YpISGhSr6oqCh98cUXDusrvq/RNNX0b1lf+Tcu+U1UV0ifuAjAfT798wt4qKZiGEUyoH40WI+OoKAgzZkzRzk5OQoNDXXYduTIEWVmZspqtSo8PFxnz55VSkqKunbtqpiYmFrb3bNnj1asWKGJEye6ne3bb7/Vk08+qVmzZlXZ9sorr6isrExr166VYRjauXOn7r77br399tsKCgqqtd21a9cqOjpaq1at0m9+8xsZxs+lrPbt22vTpk32N/pnzpzR559/LoulvPb097//XVJ5geCDDz5QRkZGndexf/9+3X333Xrsscc0cOBAlZWV6cUXX1RqaqpWr16t5ORkLVy4UHfeeaf9mJ07d6pVq1bq2bOnJOn3v/+9kpKSqm0/OjraIcc///lPPfzwwxo+fHiV7T/99JPuvvtuLVmyRP/7v/9bZ3ZJGj9+vKZNm+bUvhVatWqlrVu3qrS0VAEBAZKk3NxctW7d2mG/5cuXKyIioto2Tp48qc8//1xt27bVrl27dPXVVztsT09P19/+9jd7+43BjydPVFnXsnXj/jTsQj+cPm3//7CLLzYxCQAA8AXHv/+hyro2rcJMSAKgsavXHh2VtWvXTgMHDlRmZmaVbadOnVJxcbG9V0WLFi2UkZGh7t2719nupEmTtGzZMh04cMDtbLfeeqtyc3P10UcfVdl2/PhxFRcXq7i4WJLUt29fLVy4UGVldT+N3Wq16rbbblNwcLDef/99h20jRozQxo0b7V//85//1NChQ92+Bkl68cUXdeutt2rgwIGSJIvFojvvvFPNmjXT+vXrNWDAAJ09e9ahV0h2draSk5PdOt+3336rsLDq/7g0b95c/+///T/99a9/rddKdYsWLdSrVy99+OGH9nXbt2+33wNnrF27Vtdcc41GjBihrKwsh219+vRRWFiYXnjhBa9lBgAAAIBGz1bmncUEDVbokMo/Gd+2bVuVISw9e/ZUTEyMhg8frjFjxujxxx9XWVmZunTpUmebXbp0UVpammbNmuX2G+pWrVrpkUce0UMPPeQwhEWSUlNTtWfPHl133XW66667tGLFCvXp00chISG1tvmvf/1Lx48fV79+/RQXF6dVq1Y5bO/Vq5dOnDih48ePS5LWr1+vuLg4t/JX+PTTT9W7d+8q66+55hrl5+fLMAwlJSVp3bp1ksqH5bz99tsaNWqUfd9nnnnGYYjHvHnz7Nvy8vKUmJiomJgYDRo0SJ999pmee+65GvNcfvnl+v777+1DZ+qSlZXlcO577rnHqePi4uLsRaNPPvlEkZGRVXrb3HnnnQ5tr1692r7NarUqLi7O3s7333/vcOyjjz6qV155pcoQFgAAAABA49OghY7Q0FDNnz9fc+bM0ZkzZxy2zZs3T3l5eZowYYIOHTqkcePGadOmTU61m5qaKpvNphUrVridbfjw4YqKitKT/5+9O4+LqnzbAH4NCLigghumIpoGaOCeKy6BKZCI4ooKapaSuVtJKIi7aGquua+lokiKimKJK5YlmUapuORCJoLgAoiDzPP+wcv5OQ7LsJ4ZvL6fzynnLM+55+FwmHPPsyxdqra+Xr16OHToELZs2YLmzZtj//79cHd3x9OnT/MsLyQkBM7OzjA0NISrqyt++uknKamRrUePHjh27BiePHmClJQU1K1bt9DxA1mDGb18+VJjfXZrFADo27cvwsPDIYTA8ePH0b59e1SpUkXaPmHCBBw4cEBaZs6cKW1zdHSU1ltZWaFBgwZo2DD3aciyu+qYmJhIXXJeJYRQ684zePBgtXOvXr1aq/ft6OiI06dPQ6VS5ZowWr9+vVrZ2a1Yrly5ggcPHqBjx45466230KRJE+zfv1/t2Dp16mDy5Mnw9fWVxhwhIiIiIiIi3VSqiQ4AcHBw0OjCcvLkSYSHh8PCwgL9+vXDsmXLMGPGDISEhGhVpoGBAebPn1/kLiwzZsxAeHg4oqOjpXVLly7Fw4cP0axZM/j4+CA0NBS1atXSaJXyKqVSiUOHDuHo0aNwdHTERx99BADS4JjZslsQ/Pjjj/jggw8KHXe2Zs2a4Y8//tBYf/HiRdjZ2QEA6tatC0tLS/z+++84cOAA+vfvX+DzmJqaIigoCOvXr8fFixdz3e/atWuoXbs2TE1NUaVKFY3kUFJSUq5dXwqiUqVKsLW1RXR0NH755ZcCdVvZt28flEolevbsCUdHR/zzzz8a3VeArO5NutSFpUq16hqLvqlaubK0EBERERGRjlGJ4llkUOqJDuB/XVgePnwIAChfvjyWLFkizcAihMCVK1fQpEkTrcts0KABfHx8sGnTpkLHZW5ujsDAQLXuGPHx8Vi9ejWUSiUAICEhAUlJSbC2ts61nBMnTsDc3Bxnz55FZGQkIiMjMXv2bAQHB6t1r7G1tUViYiL27NkDZ2fnQsedbcyYMdi3b5+UhBFCYM2aNUhPT1dr5eDh4YGQkBDcuXMH7dq1K9S5LC0tMWzYMMybNy/HLkPPnj3D8uXLMXToUABAixYtcPnyZdy9exdAVjLohx9+QIcOHQp1/te5uLhgyZIlsLOzQ7ly2o2xq1QqcfDgQWzdulX6OR0/fhwJCQk4f/68xv7ZXVjozabIZXqs3NbrGsYvo/yC1Is3QVR4ev37SzqhhllVjUVfKHK50HNbT0RFU2qzrrwquwvLqFGjAADt27fHuHHj4OPjI3Wz6Ny5s9ZjNGTz9vbWurtLbrp3746ePXtKSRh/f38EBQXB2dkZFSpUgJGRET7//HM0atQo1zKyByF9Va9evbB06VKcOXNGbf0HH3yAX3/9FbVr15YSPYVlZWWFTZs24euvv8bcuXORmZmJ1q1bY8eOHWpjivTs2RNz587F8OHDNW6uK1aswLZt29TWLVmyJMfzjRkzBiEhITh48CCA/43hoVAokJmZiR49euCTTz4BAFSrVg1z5szBpEmTkJmZCaVSiR49emDQoEFSebt375amq802bdo0rVpovP/++5g+fTomTpyY4/bRo0erjdtRoUIFjBgxAnXr1kXz5s2l9aamphgwYAB2796NwYMHq5VRp04dTJkyBf7+/vnGQ2VX3Zr613rmVZY1a8gdQpHoc/zVGuT+d4PoTaDP90+FQpHnWHB8WKX81K+lv3+/iPSRQnDyZqIS8eLFC8TExMDOzi7fwWt1VXR0NFq3bi13GIXG+OXF+OVVmPi7Ba4qoWiAk4HjCrR/Uet/S+TPhT4220jHwrc6LEr8074LK/R5Xxc0rHehjnsTr39dUpzx/3r9H6i0aDpuYKBA23dyH3utIFj/8ipq/F+HHS9yDJ/3dir0sUWNf9WR04U+Nts4ly6FPlaf6z/7+cXa2hqxsbGoqXyGcihauuAlFEgwrlzqz0SytOgoiKlTp+LGjRsa6x0dHXP99h4AgoKCcO7cOY31dnZ2mDdvXpHjunv3LsaPH5/jtrlz58Le3r7I58hJSb8vXXPhwgXMmTMnx23r16+HhYVFKUdEREREpD+KK3lBRKRPdD7RkVu3ifxMmzatmCNRV79+fRw4cKBEz5GTkn5fuqZNmzay1DMREek3I0MDZGSqinQ8ERER6SedT3QQERERFdSwroUbbJuI6E1nXM4QypeZRTpeTkaGhsjILHz8Robyxq9T9S8EUMSuK3JhooOIiIhIh5iUK4cXL18WSzlERAU1wbWb3CEUyZgeneQOoUj0vf51Bf8CEhEREemQ2YNd5Q6BiIhIrzHRQURERACAisZGSFNmlEi5REREpG8EIAo/3hUAQCHPmFdMdBAREREAINxvjNwhEBERERUZhxQnIiIiIiIiojKDLTqIiIiIiIiISI1QCQhRtFlXhEKeWVvYooOIiIiIiIiIygwmOoiIiIiIiIiozGDXFSIiIiIiIiJSJ0TWUrRCiiWUgmKLDiIiIiIiIiIqM9iig4iIiIiIiIjUCRWEUMkdRaGwRQcRERERERERlRlMdBARERERERFRmcGuK0RERERERESkjoOREhERERERERHJTyFEkVM0RJSDFy9eICYmRu4wiIiIiIiItGZtbY3Y2FjUSEmEYREHI81UGCDRtAbs7OxgYmJSTBHmj11XiEpYaf9SF6fo6Gi0bt1a7jAKjfHLi/HLi/HLi/HLi/HLi/HLi/HLS5/j1/iiVgigyLOuKIp4fOGw6woRERERERERlRlMdBARERERERFRmcGuK0RERERERESkRggBoSrakJ7CgLOuEBEREREREREVCRMdRERERERERFRmsOsKEREREREREakTqqLPuiI46woRERERERERUZEw0UFEREREREREZQa7rhARERERERGROiGylqKWIQO26CAiIiIiIiKiMoMtOoiIiIqJ6/x1SFNmFHu5FY2NEO43ptjLJSIiIiqLmOggIiIqJiWR5CjJcomIiIhyI4SAKGLXk6IeX1jsukJEREREREREZQYTHURERERERERUZrDrChERERERERGpU6myliJRFEsoBcUWHURERERERERUZrBFBxEREZVp8RfPQ2jxjZTCwAAWLduVQkREpI0H0T9DqDK13l9hYIjarTuUYEQF8+BCVMHjb9OpBCMienMw0UFERERlmjZJjoLsJ4fEG1cBkUd8CgPUaGxbegGR3ki8+qfWib4atvalEJH2CpIkKMz+JU3f4weAhL/+0Pr6qflui5IPqID0+foH5K9/IYo+a4pMk64w0UGkb/5NeJTjDUOhAOrWrF76ARHRGyHp9s28P60oFKjWoFHpBfSmySvJoc12HZAcdyfna0ihgHk9q9IP6A1RFhJ9JB99v34Y/5uLY3QUIxsbGwBAXFwcbGxsEBUVpbbd0dERcXFxAICjR4/Cw8MDvXv3hpubGzZu3Jhv+b6+vhg6dKhaVi00NBS+vr55HvfqPitXrkTPnj2Rnp4ubT9//jy8vLwAAEqlErNmzUKvXr3g5uaGoUOH4vLly1q9d3d3d7i7u8PFxQXjxo3DnTt3pPqws7ODu7s7evfuDRcXFyxZsgSq//+F9PX1Rbdu3eDu7g43Nzf07dsX4eHh+Z5z5cqVsLGxwcWLF9XWz5s3T/pZnD9/Hi1btpRiy15+/PFHaf/k5GTY29tj8+bNauUUtr5LWm7PGXJlSwvqcfx/ePzgvuYS/5/coWnlycN4PIl/oLk8jJc7NK09TUzA08SHrywJcoektbJQ/3orv5uMvtyESD76/geMiIj0Blt0lBAjIyP4+/sjLCwMpqamatvi4+MRFBSE0NBQmJubIzU1FV5eXmjYsCGcnJzyLPfSpUvYvn07hg8fXujY/v33XyxduhR+fn4a27Zu3QqVSoWDBw9CoVAgOjoaY8eOxYkTJ2BkZJRnuQcOHJD+vWvXLowaNUpKWNSqVUvanpGRAU9PT7Rp0wZdu3YFAEyYMAEeHh4AgHv37mHIkCEwMzNDx44d8zxn7dq1ERERgZYtWwLIalr122+/qe1jZ2eHHTt25FrGwYMH4ejoiODgYIwcORIKxf9GBi6O+qbX6PsHXX2PHwDweqx6FHuZqH8i0mfPkpOlf1c2N5cxEiKiEiZURW8xKFOLQ7boKCG1atVCx44dERQUpLEtOTkZGRkZUquKSpUqYeHChWjcuHG+5Y4aNQrffvut1FqiMAYNGoTw8HBcuHBBY1tiYiIyMjKQkZEBAGjdujXmz58vtb7QlqenJ0xMTHDmzBmNbS9evEBGRgbMc/lwYGlpCW9vb+zcuTPf8zg5OeH48ePS6wsXLqBFixYFijU0NBRDhgyBsbExfvnlF7VtxVHfREREREREVHqY6ChBvr6+OHv2rEYXFltbWzg5OaF79+7o378/Fi9eDJVKBSur/PunWllZwcfHB35+foUeGMbMzAyBgYGYPn26WhcWAPD29salS5fQoUMHfPrpp9i+fTtatmwJExOTAp+ncePGuHXrFgDg4cOHUteVrl27onr16rC1zX3QNGtra+nYvJibm8PS0lLqXhMeHg5XV1e1fWJiYjS6riT//7cxV69eRWJiItq0aQMXFxcEBwerHVsc9U1ERERERKR/RPaIpIVfZGo5zERHCTI1NcWcOXPg7++PlJQUtW2zZs1CZGQkPD09cf/+fQwcOBDHjh3Tqlxvb28IIbB9+/ZCx9a9e3fY2dlh6dKlauvr1auHQ4cOYcuWLWjevDn2798Pd3d3PH36tMDnUCgUKF++PID/dV0JCwvDzz//jIoVK2Lu3Ll5Hp99bH5cXFwQERGBzMxMXLx4EW3atFHbbmdnhwMHDqgt2a1JQkJC4OzsDENDQ7i6uuKnn35CYmKi2vHFUd9ERERERERUOpjoKGEODg4aXVhOnjyJ8PBwWFhYoF+/fli2bBlmzJiBkJAQrco0MDDA/Pnzi9ylYsaMGQgPD0d0dLS0bunSpXj48CGaNWsGHx8fhIaGolatWhqtUrRx7dq1HLvjGBsbo0+fPvj999/zPLZRI+1G7+/evTuOHz+OX3/9FW3atIGBgXaXtVKpxKFDh3D06FE4Ojrio48+ApDVleVVxVXf9P9eGQNFq/W6Rt/jBwC8HqsexV4m6p+I9Fllc3NpISIi3cRERynI7sLy8OFDAFktFZYsWSLNwCKEwJUrV9CkSROty2zQoAF8fHywadOmQsdlbm6OwMBArFmzRloXHx+P1atXQ6lUAgASEhKQlJQEa2vrApW9c+dOKBQKtGvXLsftv/zyC5o2bZrjttu3b2Pnzp3w9PTU+n3UrVsXy5cv1+i2kpcTJ07A3NwcZ8+eRWRkJCIjIzF79mwEBwdrdFMpjvouLvr+nGdm8RbMatfRXCzekjs0rVStZYGqFrU1l1oWcoemtSo1aqJKjVqvLDXlDklrZaH+9VZ+Nxl9uQmRfPT9DxgR0RtGCFWxLHLgrCulILsLy6hRowAA7du3x7hx4+Dj4yMN+tm5c2d89tlnBSrX29tb6+4uuenevTt69uwpJWH8/f0RFBQEZ2dnVKhQAUZGRvj888+1al3h7u4OAFCpVLC0tMSGDRuk1hXZY3QoFApkZmaibt26mDNnjnTsihUrsG3bNigUChgaGmLatGlo1aqV1u/D2dkZq1evlmZfeVX2GB2v+vDDDxEdHY0hQ4aore/VqxeWLl2a4yCqxVHfxaFuzepyh0BEb6BqDbRrZUclRGGQ98j1Ct3/7sq8Xv5jkVHxUxgYQGgxqLxCyxax9GbR9+uH8b+5mOgoRteuXQOQNc5FZGSk2jYHBwdpOwD07dsXffv2LVD5CxcuVHttYGCg1cwkHh4e0tSt48eP19i+ZMkS6d/ZSZmCevW9va5evXqIiYnJdfvr70tbr76XAQMGYMCAARrxtGvXDhcvXtS6TGNjY5w9exYA0KVLF7Vt2tY3ERHplrLwQbFG49wH8CbKSw1be7lDKDSFgSGEKrNA++sSfY8fAGq+20LuEIpEn69/QP/rX05MdOiYqVOn4saNGxrrHR0dMXHixFyPCwoKwrlz5zTW29nZYd68eUWO6+7duzkmSQBg7ty5sLcvmZtISb8vIiIq+yxa5tyNkoh0W+3WHeQOoUhqt+kkdwhERaMSWUtRKOSZdYWJDh3zauuKgpg2bVoxR6Kufv36OHDgQImeIycl/b6IiIiIiIiobNHdNppERERERERERAXEFh1EREREREREpE6IrKWoZciALTqIiIiIiIiIqMxgooOIiKiYVDQ20qtyiYiIiMoidl0hIiIqJuF+Y+QOgYiIiKhYCCEgRP7Ts+dXhhzYooOIiIiIiIiIygwmOoiIiIiIiIiozGDXFSIiIiIiIiJSx1lXiIiIiIiIiIjkx0QHEREREREREZUZ7LpCRERERERERGqEUBXDrCtFO76w2KKDiIiIiIiIiMoMJjqIiIiIiIiIqMxg1xUiIiIiIiIiUicEoCrirCkGnHWFiIiIiIiIiKhImOggIiIiIiIiojJDIYSQpy0JURn34sULxMTEyB0GERERERGR1qytrREbG4sqd/+G4cuMIpWVWc4IT+s3hZ2dHUxMTIopwvxxjA6iElbav9TFKTo6Gq1bt5Y7jEJj/PJi/PJi/PJi/PJi/PJi/PJi/PLS5/jL0he1THQQERERERERkRohgKJ2AJGr/wjH6CAiIiIiIiKiMoOJDiIiIiIiIiIqM9h1hYiIiIiIiIjUCVXWUtQyZMAWHURERERERERUZjDRQURERERERERlBruuEBEREREREZE6lchailqGDNiig4iIiIiIiIjKDCY6iIiIiIiIiKjMYNcVIiIiIiIiIlInBIQoYteToh5fSEx0EBGRznCdvw5pyoxiL7eisRHC/cYUe7lEREREpHvYdYWIiHRGSSQ5SrJcIiIiItI9bNFBRERERERERK9RAUJV9DJkwEQHEVEuvj16BhmZmYU+3sjQEJ86dy7GiLT3ddhxKF8WPvZsxuUM8Xlvp2KIiIiIiIiodLDrChFRLoqS5CiO44uiOJIcxVkOEREREVFpYYsOIiIiIiIiKlN+/OMKMlXad5swNDDABy2alGBE+keoBEQB6jC3MuTAFh1ERERERERUphQkyVGY/Um3sUUHERERlXnX4x5AJXL/VslAocA79WqXYkQFc+3ef/nGb2P5VilGRERvgku37uV578lmoFCg+duWpRDRm4X1X3hMdBDpmeR/7wE53fAUCpjX5Q2OiErOvYTE3G4/sKxZo/QDKoD8Pihq80FSTvoeP1FR/ZvwKNf7T92a1Us/oAL450ECRB6/owqFAg1r1yzFiLSn7b2F96CSIXv9C5Hzc0dBy5ABu66UEhsbGwBAXFwcbGxsEBUVpbbd0dERcXFxAICjR4/Cw8MDvXv3hpubGzZu3Jhv+b6+vhg6dKjaTTQ0NBS+vr55HvfqPitXrkTPnj2Rnp4ubT9//jy8vLwAAEqlErNmzUKvXr3g5uaGoUOH4vLly1q9d3d3d7i7u8PFxQXjxo3DnTt3NLb36dMHbm5uGDx4MK5duya9r27dusHd3R1ubm7o27cvwsPD8z1nXrHGxcXB0dExxziz37ONjQ3WrVuntv2nn36CjY0Nzp8/n+/5S1RuNwv+gSHSeU8exuNJ/APN5WG83KFphbcfIv2WmPwECa8siclP5A5Ja/p8/8kryaHNdiIqOLbokIGRkRH8/f0RFhYGU1NTtW3x8fEICgpCaGgozM3NkZqaCi8vLzRs2BBOTnlP8Xjp0iVs374dw4cPL3Rs//77L5YuXQo/Pz+NbVu3boVKpcLBgwehUCgQHR2NsWPH4sSJEzAyMsqz3AMHDkj/3rVrF0aNGoXw8HAYGxtrbN+xYwcCAgIQHBwMAJgwYQI8PDwAAPfu3cOQIUNgZmaGjh075nq+vGLVhoWFBSIiIjBmzBhpXXh4OKpVq6bV8ZS/5KfPpH+bV6ksYyREpUifP6kTEZ4mPdJYV6WabrcmeNXrdxreeYiorGKLDhnUqlULHTt2RFBQkMa25ORkZGRkSK0qKlWqhIULF6Jx48b5ljtq1Ch8++23aq0lCmrQoEEIDw/HhQsXNLYlJiYiIyMDGRkZAIDWrVtj/vz5UBVw4B5PT0+YmJjgzJkzOW5v164dYmNjc9xmaWkJb29v7Ny5M89zFDVWKysrqFQq3Lt3DwCQnp6OO3fuaPVzICIiotw9e/pUWoiISIcJVfEsMmCiQya+vr44e/asRhcWW1tbODk5oXv37ujfvz8WL14MlUoFKyurfMu0srKCj48P/Pz8Ct0EzszMDIGBgZg+fbpaFxYA8Pb2xqVLl9ChQwd8+umn2L59O1q2bAkTE5MCn6dx48a4deuWxnohBA4fPoyWLVvmeqy1tXWOxxZ3rM7OzoiIiAAAnDhxAu+//77WxxIREREREZE8mOiQiampKebMmQN/f3+kpKSobZs1axYiIyPh6emJ+/fvY+DAgTh27JhW5Xp7e0MIge3btxc6tu7du8POzg5Lly5VW1+vXj0cOnQIW7ZsQfPmzbF//364u7vjaSG+kVEoFChfvrz0OnsMD1dXV9y8eROzZ8/O8/hXj81JXrEaGGhe9kIIKBQKtXUuLi5SvR85cgQuLi7avj0iIiLKReUqVaSFiIioJHCMDhk5ODhodGE5efIk0tLS4Orqin79+qFfv37Ys2cPQkJC0KNHj3zLNDAwwPz58zF48GAMGjSo0LHNmDEDbm5uMDMzk9YtXboUQ4cORbNmzdCsWTP4+Phg8ODBiIqKKnAS4Nq1a2rxvTpGhzbHNmrUKM998oq1c+fOePbsmdr+jx49QtWqVdXWWVlZISMjAzdu3MCDBw/yPScVDMfloDeSQpHrrElEpPv0aTyOnCigPi4H7zxElBchRJEHy5VrsF226JBZdheWhw8fAshqqbBkyRJpBhYhBK5cuYImTZpoXWaDBg3g4+ODTZs2FTouc3NzBAYGYs2aNdK6+Ph4rF69GkqlEgCQkJCApKQkWFtbF6jsnTt3QqFQoF27dgWO6/bt29i5cyc8PT3z3C+vWE1NTWFlZSV1SwGA4OBgdOjQQaMcZ2dnzJgxI8dZWmST2wMRH5SIdF7VWhaoalFbc6llIXdoWuHth0i/1TCvipqvLDXMq+Z/kI7Q5/vP662GC7qdiAqOLTpklt2FZdSoUQCA9u3bY9y4cfDx8ZEG0uzcuTM+++yzApXr7e2tdXeX3HTv3h09e/aUkjD+/v4ICgqCs7MzKlSoACMjI3z++edatXRwd3cHAKhUKlhaWmLDhg05diHJyYoVK7Bt2zYoFAoYGhpi2rRpaNWqVZ7H5Bfr4sWLERgYiNWrVyMjIwM2NjYICAjQKMfFxQVLly7FokWLtIq1NJjXtZQ7BCJ6Q1nWrCF3CIVmoFBAlce3SgY6/qCh7/ETFVXdmvrbmqZh7Zpyh1Bo+d17Xt2Pih/rv/CY6Cgl165dA5A1dkRkZKTaNgcHB2k7APTt2xd9+/YtUPkLFy5Ue21gYJDvzCQA4OHhIU3dOn78eI3tS5Yskf6dnZQpqFffW0G3v/6+tJVfrA0bNsS2bdty3NauXTuptUn9+vXV4tuxY0eh4iEiInm9U6+23CEUiY3lW3KHQERvoOZv8ws2Ocle/ypV1lLUMmTARIcemTp1Km7cuKGx3tHRERMnTsz1uKCgIJw7d05jvZ2dHebNm1fkuO7evZtjkgQA5s6dC3t7+yKfIycl/b6IiIiIiEg/GRoYILMAD9mGWrY2J/3ARIceebV1RUFMmzatmCNRV79+/QINJlpcSvp9ERERERGRfvqghfZjHFLOBIphMFJwMFIiIp1iZGgo6/FFYVyueM5dXOUQEREREZUWtuggIsrFp86d5Q6h0D7v7SR3CEREREREsmCig4iIiIiIiIjUCQGIIg4mWsSuL4XFritERKQzKhob6VW5RERERKR72KKDiIh0RrjfGLlDICIiIiI9x0QHEREREREREakTouhdT9h1hYiIiIiIiIioaJjoICIiIiIiIqIyg11XiIiIiIiIiEiNUAkIVdG6nhT1+MJiiw4iIiIiIiIiKjOY6CAiIiIiIiKiMoNdV4iIiIiIiIhInVBlLUUtQwZs0UFEREREREREZQYTHURERERERERUZrDrChERERERERGpEyJrKWoZMlAIIdOZicq4Fy9eICYmRu4wiIiIiIiItGZtbY3Y2FhU+OM0DJTpRSpLZVwez1t0gZ2dHUxMTIopwvyxRQdRCSvtX+riFB0djdatW8sdRqExfnkxfnkxfnkxfnkxfnkxfnkxfnnpc/xl6YtaJjqIiIiIiIiISI0QAqKIs6bI1YGEg5ESERERERERUZnBRAcRERERERERlRnsukJERERERERErymGWVfAritEREREREREREXCRAcRERERERERlRnsukJEREREREREaoRKBaEq4qwrRTy+sNiig4iIiIiIiIjKDLboICIiIiIiIiJ1ohgGIy3yYKaFwxYdRERERERERFRmMNFBRERERERERGUGu64QEZUhrvPXIU2ZUezlVjQ2QrjfmGIvl4iIiIh0lFBlLUUtQwZs0UFEVIaURJKjJMslIiIiIipuTHQQERERERERUZnBritERDlYEHoMypeZRS7HuJwhvvLoUQwRvVm+DjtebPX/eW+nYoiIiIiI6M0ihIAo4qwpRT2+sJjoICLKQXE8ZBdnOW8a1j8REREV1g/n/8DLzIKPDVHO0AB927Uo/oCo1LHrChEREREREZUZhUlyFOU40j1s0UFEREREREQaoq/fhkqLrgcGCgVav9Og5AN6w8he/yoAqiJ2PZEpd8REBxEREZEOi7kdp/UHXbsG9UohIiJ6U2hz7ynIflQwrP/CY6KDSI8k37sN5HYjUyhgbtmgNMMhojfMvYTEHG9BCgVgWbNG6QdUADfux+c5IJpCoUDjOhalGJH2+EGXiIioYDhGRzGysbEBAMTFxcHGxgZRUVFq2x0dHREXFwcAOHr0KDw8PNC7d2+4ublh48aN+Zbv6+uLoUOHqn1QCw0Nha+vb57HvbrPypUr0bNnT6Snp0vbz58/Dy8vLwCAUqnErFmz0KtXL7i5uWHo0KG4fPmyVu/d3d0d7u7ucHFxwbhx43Dnzh2N7X369IGbmxsGDx6Ma9euSe+rW7ducHd3h5ubG/r27Yvw8PB8z7ly5UrY2Njg4sWLauvnzZsn/SzOnz+Pli1bSrFlLz/++KO0f3JyMuzt7bF582a1cgpb3yUqrw+xevQB9+mjRI2F6E3xNDEBTxMfvrIkyB2S1nK7zejD7Se/Ud/lGhWeiIhId6kAUcRFpr4rbNFRQoyMjODv74+wsDCYmpqqbYuPj0dQUBBCQ0Nhbm6O1NRUeHl5oWHDhnByynsaxEuXLmH79u0YPnx4oWP7999/sXTpUvj5+Wls27p1K1QqFQ4ePAiFQoHo6GiMHTsWJ06cgJGRUZ7lHjhwQPr3rl27MGrUKISHh8PY2Fhj+44dOxAQEIDg4GAAwIQJE+Dh4QEAuHfvHoYMGQIzMzN07Ngxz3PWrl0bERERaNmyJYCsD6q//fab2j52dnbYsWNHrmUcPHgQjo6OCA4OxsiRI6FQKKRtxVHfRETqXn+g5gM2vTmeJServa5sbi5TJIXzNOmRxroq1arLEEnh6Hv9A+rvQR/jJ6LSwRYdJaRWrVro2LEjgoKCNLYlJycjIyNDalVRqVIlLFy4EI0bN8633FGjRuHbb79Vay1RUIMGDUJ4eDguXLigsS0xMREZGRnIyMgAALRu3Rrz58+HSlWwTJynpydMTExw5syZHLe3a9cOsbGxOW6ztLSEt7c3du7cme95nJyccPz4cen1hQsX0KJFiwLFGhoaiiFDhsDY2Bi//PKL2rbiqG8iIiIiomwvXmlZTUQlg4mOEuTr64uzZ89qdGGxtbWFk5MTunfvjv79+2Px4sVQqVSwsrLKt0wrKyv4+PjAz8+v0M1szczMEBgYiOnTp6t1YQEAb29vXLp0CR06dMCnn36K7du3o2XLljAxMSnweRo3boxbt25prBdC4PDhw1IrjJxYW1vneOzrzM3NYWlpKXWvCQ8Ph6urq9o+MTExGl1Xkv//24CrV68iMTERbdq0gYuLi9TCJFtx1DcREREREZHeEQKiiItc/VuZ6ChBpqammDNnDvz9/ZGSkqK2bdasWYiMjISnpyfu37+PgQMH4tixY1qV6+3tDSEEtm/fXujYunfvDjs7OyxdulRtfb169XDo0CFs2bIFzZs3x/79++Hu7o6nT58W+BwKhQLly5eXXmcnGVxdXXHz5k3Mnj07z+NfPTYvLi4uiIiIQGZmJi5evIg2bdqobbezs8OBAwfUFvP/b+oYEhICZ2dnGBoawtXVFT/99BMSE9XHiyiO+iYiIiIiAgATLT/jElHhcYyOEubg4KDRheXkyZNIS0uDq6sr+vXrh379+mHPnj0ICQlBjx498i3TwMAA8+fPx+DBgzFo0KBCxzZjxgy4ubnBzMxMWrd06VIMHToUzZo1Q7NmzeDj44PBgwcjKioKLi4uBSr/2rVravG9OkaHNsc2atRIq327d+8OT09PODg4oE2bNjAw0C5/p1QqcejQIZQrVw6RkZHS+tDQUIwePVp6XVz1Tf9Tpbpuz85AVLIUUB+XQ5HbjkRljr6PqaBP43HkRN/rHygb74GISh5bdJSC7C4sDx8+BJDVUmHJkiXSDCxCCFy5cgVNmjTRuswGDRrAx8cHmzZtKnRc5ubmCAwMxJo1a6R18fHxWL16NZRKJQAgISEBSUlJsLa2LlDZO3fuhEKhQLt27Qoc1+3bt7Fz5054enpqtb+5uTnq1q2L5cuXa3RbycuJEydgbm6Os2fPIjIyEpGRkZg9ezaCg4M1uqkUR30XC0UeD0R5bSMinVGlRk1UqVHrlaWm3CFpLbfbjD7cfhT5BJnfdiIiojdOUWdckWZeKX1s0VEKsruwjBo1CgDQvn17jBs3Dj4+PtKgn507d8Znn31WoHK9vb217u6Sm+7du6Nnz55SEsbf3x9BQUFwdnZGhQoVYGRkhM8//1yr1hXu7u4AAJVKBUtLS2zYsEHr1hUrVqzAtm3boFAoYGhoiGnTpqFVq1Zavw9nZ2esXr06x3E/ssfoeNWHH36I6OhoDBkyRG19r169sHTp0hwHUS2O+i4qc8sGsp6fiN5sljX1tzVW4zoWcodQaAYKBVRa9HE2YLKGiIgIABMdxeratWsAssa5eLUrBJDVhSV7OwD07dsXffv2LVD5CxcuVHttYGCg1cwkHh4e0tSt48eP19i+ZMkS6d/ZSZmCevW9FXT76+9LW6++lwEDBmDAgAEa52vXrh0uXryodZnGxsY4e/YsAKBLly5q27StbyIiouJk16Ce3CEQ0RuKiVZ5sf4Lj4kOHTN16lTcuHFDY72joyMmTpyY63FBQUE4d+6cxno7OzvMmzevyHHdvXs3xyQJAMydOxf29vZFPkdOSvp9ERERERFRzlq/00DuEN5octd/ccyaItfMlUx06JhXW1cUxLRp04o5EnX169cv0GCixaWk3xcREREREZUt5QwN8DKz4GNDlDPkEJZlBRMdREQ5MC5nCOXLzGIphwqO9U9ERESF1bddC7lDIJkx0UFElIOvPPKf6plKzue9neQOgYiIiOjNphKAqoizpqjk6brCtjlEREREREREVGYw0UFEVIZUNDbSq3KJiIiIiIobu64QEZUh4X5j5A6BiIiIiMqCYph1pcjHFxJbdBARERERERFRmcEWHURERERERESkRhRDiw7BFh1EREREREREREXDRAcRERERERERlRnsukJEREREREREr1EBQlX0MmTAFh1EREREREREVGYw0UFEREREREREZQa7rhARERERERGROpXIWopahgzYooOIiIiIiIiIygwmOoiIiIiIiIiozFAIIeRpS0JUxr148QIxMTFyh0FERERERKQ1a2trxMbGwjAyBIrnqUUqS1SohEzH/rCzs4OJiUkxRZg/jtFBVMJK+5e6OEVHR6N169Zyh1FojF9ejF9ejF9ejF9ejF9ejF9ejF9e+hx/Wfqill1XiIiIiIiIiKjMYIsOIiIiIiIiIlInABR1pAuZBspgiw4iIiIiIiIiKjOY6CAiIiIiIiKiMoNdV4iIiIiIiIhIjRAqQKiKXoYM2KKDiIiIiIiIiMoMJjqIiIiIiIiIqMxg1xUiIiIiIiIiUidEMcy6Is+0K2zRQURERERERERlBhMdRERERERERFRmsOsKEREREREREakTAlDpZ9cVJjqIiF7hOn8d0pQZxV5uRWMjhPuNKfZyiYiIiIhIHbuuEBG9oiSSHCVZLhERERERqWOLDiKiMmhB6DEoX2YWuRzjcob4yqNHMURERERERPpECBUgVEUvQwZMdBARlUHFkeQoznIK49ujZ5CRWfjzGxka4lPnzsUYERERERHpA3ZdISIinVSUJEdxHE9ERERE+oktOoiIiIiIiKhM+fGPK8hUad9twtDAAB+0aFKCEekhIYo+a4pMs66wRQcRERERERGVKQVJchRmf9JtbNFBREREZVrM7TiotPhGyUChgF2DeqUQUcFcuXtf6/ib1K9TChER0ZtC3++f+k72+heiyIORytWig4kOIj2SdPefvG8WCgWq1W9YegER0Rvl34RHOd6CFAqgbs3qpR+QlrT5kFiQ/UqbvsdPRLx/UuGw/guPXVdKkY2NDQAgLi4ONjY2iIqKUtvu6OiIuLg4AMDRo0fh4eGB3r17w83NDRs3bsy3fF9fXwwdOhTilQs9NDQUvr6+eR736j4rV65Ez549kZ6eLm0/f/48vLy8AABKpRKzZs1Cr1694ObmhqFDh+Ly5ctavXd3d3e4u7vDxcUF48aNw507dzS29+nTB25ubhg8eDCuXbsmva9u3brB3d0dbm5u6Nu3L8LDw/M958qVK2FjY4OLFy+qrZ83b570s8gWGRkJGxsbxMTESOuSkpLg4OCAn3/+WVp38eJF9OzZE8+ePcv3/CUiv5sYb3JEOu3Jw3g8iX+guTyMlzs0reR2i+Gth4hK2oPEJPyXw/IgMUnu0LTC+ydR6WKLDpkYGRnB398fYWFhMDU1VdsWHx+PoKAghIaGwtzcHKmpqfDy8kLDhg3h5OSUZ7mXLl3C9u3bMXz48ELH9u+//2Lp0qXw8/PT2LZ161aoVCocPHgQCoUC0dHRGDt2LE6cOAEjI6M8yz1w4ID07127dmHUqFEIDw+HsbGxxvYdO3YgICAAwcHBAIAJEybAw8MDAHDv3j0MGTIEZmZm6NixY57nrF27NiIiItCyZUsAgBACv/32m8Z+oaGhcHZ2RnBwMOzs7AAA1apVw9y5c+Hn54ewsDBkZmbiyy+/xNdff43KlSvneV7KX/LT/yWLzKuwPukNwU+6RHrt6aNEjXVVqteQIZLCefTkqca66lWryBBJweV2l+Tdk6jkiGIYjFRwMNI3S61atdCxY0cEBQVpbEtOTkZGRobUqqJSpUpYuHAhGjdunG+5o0aNwrfffqvWWqKgBg0ahPDwcFy4cEFjW2JiIjIyMpCRkQEAaN26NebPnw9VAQfv8fT0hImJCc6cOZPj9nbt2iE2NjbHbZaWlvD29sbOnTvzPY+TkxOOHz8uvb5w4QJatGihtk9SUhJ++eUXfPHFFzhy5AhSUlKkbd26dUOXLl0wd+5cBAQEYNCgQWjevLkW75CIiIhy8uzpU2khIiIqCUx0yMjX1xdnz57V6MJia2sLJycndO/eHf3798fixYuhUqlgZWWVb5lWVlbw8fGBn59fobNnZmZmCAwMxPTp09W6sACAt7c3Ll26hA4dOuDTTz/F9u3b0bJlS5iYmBT4PI0bN8atW7c01gshcPjwYakVRk6sra1zPPZ15ubmsLS0lLrXhIeHw9XVVW2fsLAwdOrUCfXq1YOdnR3CwsLUtvv6+uLChQt49OgRRo0apc1bIyIiIiIiIpkw0SEjU1NTzJkzB/7+/mqtCABg1qxZiIyMhKenJ+7fv4+BAwfi2LFjWpXr7e0NIQS2b99e6Ni6d+8OOzs7LF26VG19vXr1cOjQIWzZsgXNmzfH/v374e7ujqeF+FZGoVCgfPny0uvsMTxcXV1x8+ZNzJ49O8/jXz02Ly4uLoiIiEBmZiYuXryINm3aqG3/4Ycf0KtXLwCAq6srdu/erbb9zp07yMjIwK1btxAfrx/96ImIiHRV5SpVpIWIiHSYSlU8iww4RofMHBwcNLqwnDx5EmlpaXB1dUW/fv3Qr18/7NmzByEhIejRo0e+ZRoYGGD+/PkYPHgwBg0aVOjYZsyYATc3N5iZmUnrli5diqFDh6JZs2Zo1qwZfHx8MHjwYERFRcHFxaVA5V+7dk0tvlfH6NDm2EaNGmm1b/fu3eHp6QkHBwe0adMGBgb/y+/99ddfiI2Nxbx587BgwQJkZmbi4cOH+OOPP9CiRQs8f/4cU6ZMwYwZM3DlyhV8+eWX2Lp1q1oZVDgcl4PeSApFzn1dFYrSj4WICkyfxuPIib6Mx5ETBXIej4N3TyLKCZ/WdEB2F5aHDx8CyGqpsGTJEmkGFiEErly5giZNmmhdZoMGDeDj44NNmzYVOi5zc3MEBgZizZo10rr4+HisXr0aSqUSAJCQkICkpCRYW1sXqOydO3dCoVCgXbt2BY7r9u3b2LlzJzw9PbXa39zcHHXr1sXy5cs1uq2EhoZi4MCBOHnyJCIjI3Hq1Cm4u7tLrTrmzp2Lli1bokePHvjss8+QmpqKzZs3FzjmYpPfwxAfloh0WtVaFqhqUVtzqWUhd2haye0Ww1sPEZW02jWq4a0clto1qskdmlZ4/yQqXWzRoQOyu7Bkj//Qvn17jBs3Dj4+PtKgn507d8Znn31WoHK9vb217u6Sm+7du6Nnz55SEsbf3x9BQUFwdnZGhQoVYGRkhM8//1yr1hXu7u4AAJVKBUtLS2zYsEHrlhErVqzAtm3boFAoYGhoiGnTpqFVq1Zavw9nZ2esXr1abdwPpVKJQ4cOaXTxGTFiBAYNGoQuXbrgwoUL+OGHHwAA5cqVw+LFizFo0CB06tSpQImn4lKtfsNSPycRUba6NavLHUKhGCgUUGkxbpWBjj5x6Hv8RMT7JxWO7PUvRNFnTZFp1hUmOkrRtWvXAGSNcxEZGam2zcHBQdoOAH379kXfvn0LVP7ChQvVXhsYGGg1M4mHh4c0dev48eM1ti9ZskT6d3ZSpqBefW8F3f76+9LWq+9lwIABGDBggMb5zp8/r3Gcra0tLl26BAAaLUDefvvtHKenJSIi3WXXoJ7cIRRJk/p15A6BiN5Q+n7/1Hes/8JjokPPTJ06FTdu3NBY7+joiIkTJ+Z6XFBQEM6dO6ex3s7ODvPmzStyXHfv3s0xSQJkdf+wt7cv8jlyUtLvi4iIiIiI9I+hgQEyCzAQpiHH4CtTmOjQM6+2riiIadOmFXMk6urXr1+gwUSLS0m/LyIiIiIi0j8ftCj9buZljwoQRZ01RZ5ZV5i2IiIqg4zLGepUOYVhZFi0cxf1eCIiIiLST2zRQURUBn3lkf9U1LruU+fOcodARERERHqIiQ4iIiIiIiIiUidQ9FlT5Jl0hV1XiIheVdHYSK/KJSIiIiIidWzRQUT0inC/MXKHQERERERERcBEBxERERERERGpESoBqIrY90QloCiecAqEXVeIiIiIiIiIqMxgooOIiIiIiIiIygx2XSEiIiIiIiIidUKVtRS1DBmwRQcRERERERERlRlMdBARERERERFRmcGuK0RERERERESkToispahlyIAtOoiIiIiIiIiozGCLDiIiIiIiIiJSI4ppMFJF8YRTIGzRQURERERERERlhkIImTrNEJVxL168QExMjNxhEBERERERac3a2hqxsbFQ7VsLpD4tWmGVqsCgnw/s7OxgYmJSPAFqgV1XiEpYaf9SF6fo6Gi0bt1a7jAKjfHLi/HLi/HLi/HLi/HLi/HLi/HLS5/j1/iiloOREhERERERERHJj4kOIiIiIiIiIioz2HWFiIiIiIiIiNQIIQBVUWddYdcVIiIiIiIiIqIiYaKDiIiIiIiIiMoMdl0hIiIiIiIiInWcdYWIiIiIiIiISH5MdBARERERERFRmcGuK0RERERERESkjl1XiIiIiIiIiIjkx0QHEREREREREZUZ7LpCRMXKdf46pCkzir3cisZGCPcbU+zlEhERERGRJiFUgFAVsZAiHl9IbNFBRMWqJJIcJVkuERERERGVLWzRQaSjZu09AuXLzCKXY1zOEDMHuBRDRERERERERLqPiQ4iHVUcSY7iLIeIiIiIiN4gKpG1FLUMGbDrChERERERERGVGWzRQUREREREOufkn7HIVGk/kKGhgQG62VuXYEQFcyrmeoHj72r3TglGRPTmYKKDiIiIiKgM+vnqTWQWoNm4oYECHWwblWBEBVOQJEFh9i9p+h4/EYQohllX5Om6wkQHEREREZWYK3fvQ5XPB10DhQJN6tcppYjeHAVJchRmf3ozPLoZm/fDrsIA1RvpTkua1zH+NxMTHURERFTm3YlPhMjjYVuhUMDKokYpRlRwjx/cz/mbMYUCZrV1N0mQX5JD233kdi8hMbfqh2VN3b52iIokv2/0i/qNf0lj/G+kEh2M1MbGBgAQFxcHGxsbREVFqW13dHREXFwcAODo0aPw8PBA79694ebmho0bN+Zbvq+vL7p16wZ3d3e4ubmhb9++CA8P19hv/PjxcHNzk17Pnj0bEyZMUNvn7NmzcHJyQkpKCu7fvw8fHx+4ubmhV69emDhxIh49epRnLOfPn0fLli3h7u6utvz4449SXezdu1ftGC8vL5w/fx7ffvuttL+NjY3072+//VajXDc3Nzg6OmLXrl149uwZunTpghMnTqiVO2XKFAQFBeUZb/Z5+vTpAzc3NwwePBjXrl2Tti9cuBDt27eHUqkEAGzatAkDBgyA6pUmdbGxsejQoQMePHgAX19f2NraIj4+Xu08Y8eOhaOjIwAgNDQUbdu21aijS5cu5XuNzJo1C+7u7nB1dYWdnZ107L59+/L8mdjY2GDdunVq63/66SfY2Njg/Pnz0rrk5GTY29tj8+bN0johBIYPH46VK1dK6x4/fowePXogJiYmz/ol0mVPHyVqLFQ6Hsf/h8cP7msu8f/JHVqZl1eSQ5vtOiG3GPUh9jKA1U9EbxoBASGKuKCMd10xMjKCv78/wsLCYGpqqrYtPj4eQUFBCA0Nhbm5OVJTU+Hl5YWGDRvCyckpz3InTJgADw8PAMC9e/cwZMgQmJmZoWPHjgCApKQk/P3336hZsyZ+//13tGrVClOnToWbmxuOHz8OJycnpKWlITAwEPPnz4epqSkmTZqEPn36oFevXgCAdevWYebMmVi1alWesdjZ2WHHjh25bl+yZAkcHBzw1ltvqa3/9NNP8emnnwLISkAcOHBA2nb+/HmNcq9cuYL+/fvDzc0Ns2fPxqxZs9C2bVtUqlQJp06dwtWrV7FgwYI8YwWgdp4dO3YgICAAwcHBePnyJY4cOYKWLVsiIiICbm5uGDFiBI4ePYrvvvsO3t7eUKlUmDFjBqZNm4batWsDACwsLHDs2DF4eXkBAFJSUvD333/DwOB/+TRHR0csXLhQI5a4uLg8r5GZM2dK+3l7e6vFnhcLCwtERERgzJgx0rrw8HBUq1ZNbb+DBw/C0dERwcHBGDlyJBQKBRQKBYKCgtC3b1907doV9vb28PX1xZAhQ2BnZ6fV+ansSn76TPq3eZXKMkbyZtLb+i8jT0pPnv2v/qtW1qP6JyIiojdCqU0vW6tWLXTs2DHHlgbJycnIyMhAeno6AKBSpUpYuHAhGjduXKBzWFpawtvbGzt37pTWHTx4EO+99x569OiB3bt3S+XPnTsXc+bMQVpaGlasWAFHR0e0a9cOAJCYmIjnz59LZQwdOhRDhw4t8Ht+3fDhwzFjxowil/Pvv/+iQoUKMDY2Rrdu3dCmTRusWLECaWlpmDNnDoKCgmBiYlKgMtu1a4fY2FgAwMmTJ1G/fn306dNHqjNDQ0MsWLAAa9asQXx8PL7//nvUrFkTffr0kcro0aMHIiIipNc//fQTunXrpnUMeV0jhWVlZQWVSoV79+4BANLT03Hnzh2Nays0NBRDhgyBsbExfvnlF2l97dq14e/vjy+++AKbN2+WWnkQERERERGRbiq1RAeQ1dXk7NmzGt0TbG1t4eTkhO7du6N///5YvHgxVCoVrKysCnwOa2tr3Lp1S3odGhoKFxcXuLi4ICIiAo8fPwYAdOzYEQ4ODvjqq68QFRWFKVOmSMdMmTIFX3/9Nbp06YJp06bh1KlTaNu2bb7njomJ0eiWkZycLG3/5JNPkJycrNGFRdtye/TogXbt2iEsLAybN2+GsbExAGD69Ok4cuQIvvrqK/Tu3Rv29vYFKl8IgcOHD6Nly5YAsurM2dkZXbt2xZUrV3Djxg0AQOPGjTF8+HD4+/tjx44dmD17tlo5TZo0waNHj5CYmNUM/siRI3BxcVHbJzIyUq1+BgwYoLY9t2ukKJydnaUEzIkTJ/D++++rbb969SoSExPRpk0buLi4IDg4WG27q6srmjZtirVr12LBggVQKBTFFhsREREREZFOEqriWWRQqokOU1NTzJkzB/7+/khJSVHbNmvWLERGRsLT0xP379/HwIEDcezYsUKdp3z58gCyung8ePAAHTt2xFtvvYUmTZpg//790n6+vr6IiorCjBkzpGMAoEuXLjh9+jTmzp2LatWqYfHixRg/fny+57Wzs8OBAwfUFnNzc2l7uXLlsHDhQixZsgT//ad9f+zscg8dOoT27dujcuXKaNasmbTdzMwMkydPxpUrV6QuMNrITja4urri5s2bmD17Nh49eoSoqCi4uLigfPnyeP/996VWHUBWsubOnTvw8fFB9erVNcrs0aMHjh07hidPniAlJQV169ZV2+7o6KhWP68nffK6RgrLxcVFupZySr6EhITA2dkZhoaGcHV1xU8//SQlawAgNTUVV65cgbGxMS5dulQsMREREREREVHJKPVZVxwcHDS6J5w8eRJpaWlwdXVFv3790K9fP+zZswchISHo0aNHgcq/du0aGjXKmv973759UCqV6NmzJ4CsB9bdu3djxIgRALIeqqtUqaL2MP748WOsWbMGfn5+6NKlC7p06YKxY8fCwcEBSUlJGmM7FJS1tXWhu7AYGxtj7ty56NmzJ8LDw+Hq6iptq1OnDiwsLGBkZKR1eTmNc7FlyxYIIdC/f38AWV09MjIy8Pnnn6N8+fIoV64catWqpZHAyObi4oIFCxbA2NgYH3zwQQHfYZacrpGisLKyQkZGBm7cuIEHDx5I1wcAKJVKHDp0COXKlUNkZKS0PjQ0FKNHjwaQNXitg4MDHB0d8cUXXyAsLCzHJA+9WfRqXIjXVKmu/7MD6G39KxS5zpqhTzguh0zKyPVDRET6QQhR9HHEZBqHTJbpZX19feHm5oaEhAQAWS0w5syZg2bNmqFevXoQQuDKlSto0qRJgcq9ffs2du7ciaVLl0KpVOLgwYPYunUrmjdvDiBrcMyuXbvi/Pnz0ngcr6tcuTIiIyPRtGlTafyJGzduoHr16qhatWrh3/QrPvnkE/z444/SmBgFUblyZYwfPx6LFi2Co6OjWkuU4hAaGoqFCxdKSRSVSoUePXogPDxcGvQ1L7a2tkhMTMSePXuwYsUKvHz5slBxvH6NFJWzszNmzJghzQCT7cSJEzA3N8eRI0ekdaGhoVi9ejU++eQTHDx4EH///TdCQkJgYmICV1dX+Pn5aczkQkSkDTOLt/LfiUqEQqHId3pZXafLU8i+CZhnIiLSH6XadSVbdveEjIwMAED79u0xbtw4+Pj4oGfPnlI3gs8++yzfslasWCFNkzp16lRMmzYNrVq1QmRkJOrWrSslObLPO2DAALWuGK8zNDTE+vXrER4ejvfffx8uLi745ptvsHbtWhgaGuYZS05jdKxfv15jv+wuLIU1YMAAVKxYEVu2bCl0GTn5888/kZycrNYSw8DAAMOHD8+zzl73wQcfoFy5ctJsLK96fYwOd3d3te5E2V6/RorKxcUFFy9eVGsFA/xvENJX9erVCy9evMDOnTsxf/58LF68WBrc9fPPP0dcXJzagLdERKT7rCxqoEHtmrkuVhb639JJVxlokQnQZh+5Wdasgfq1NBfLmrx2qIxT5PPImN92uTH+N5JC6MXE8UT658WLF4iJiYGdnV2BZ8EBgOm7DhVbLPM8exXquOjoaLRu3bpAx3QLzHsa5qI4GTiuQPsXJn5dwvjlxfjlxfjlxfjlVVzx/3z1JjJV2n/UNzRQoINto/x3zEdxxX/yz1hkqrQfyNDQwADd7K2LfN7iiv9UzPUCx9/V7p0in5fXv7z0Of7s5xdra2vExsZCuW0x8Oxx0QqtbAbj4V8U+pmosGTpulIQU6dOlWb9eJWjoyMmTpxYqrGEh4fn2mUhp/Eu5HT37t1cB1CdO3dugWdm0VX69DMhIiIiKk3FkbSQU3EkLeRUHEkLIiocnU90LFmyRO4QJK6urhpdH3RV/fr134gHfX36mRAREREREVHJ0/lEB9GbyricIZQvM4ulHCIiIiIiogIRqqylqGXIgIkOIh01c4CL3CEQERERERHpHQ7RSkRERERERERlBlt0EFGxqmhshDRl8UwL/Hq5RERERERUOoTI/k9RCimWUAqMiQ4iKlbhfmPkDoGIiIiIiN5g7LpCRERERERERGUGW3QQERERERERkTo9nnWFLTqIiIiIiIiIqMxgooOIiIiIiIiIygx2XSEiIiIiIiIidUIAqqLOuiLPtCts0UFEREREREREZQYTHURERERERERUZrDrChERERERERGpEyqIIs6aouCsK0RERERERERERcNEBxERERERERGVGQohZBoGlaiMe/HiBWJiYuQOg4iIiIiISGvW1taIjY3Fiw1zIJ4mF6ksRRVzmHziDzs7O5iYmBRThPnjGB1EJay0f6mLU3R0NFq3bi13GIXG+OXF+OXF+OXF+OXF+OXF+OXF+OWlz/GXpS9q2XWFiIiIiIiIiMoMtuggIiIiIiIiIjVCiCLPugKZRspgiw4iIiIiIiIiKjPYooOIiIiIiIiI1AlR9BYZbNFBRERERERERKTpm2++wcqVK7Xal4kOIiIiIiIiItJJz549g5+fH7Zs2aL1Mey6QkRERERERETqVCJrKWoZRXT8+HE0aNAAI0eO1PoYJjqIiIiIiIiIqMQ8ePAAhoaGauuqVKmCKlWq5Htsnz59AEDrbisAEx1EREREREREVIKGDBmCxMREtXXjxo3D+PHjpddHjhzBggUL1PZ5++23sXXr1gKfj4kOIiIiIiIiIlLTZnUITExMilTGixcvEBMTg507d+bYouNVLi4ucHFxKdL5sjHRQUREREREREQlpnbt2kVOmhQEEx1EOsZ1/jqkKTOKvdyKxkYI9xtT7OUSERERERHpEiY6iHRMSSQ5SrJcIiIiIiKikvbqeB75YaKDiErErL1HoHyZWeRyjMsZYuaA4umrR0REREREZZ+B3AEQUdlUHEmO4iyHiIiIiIjeDEx0EBEREREREVGZwUQHEREREREREZUZHKODiIiIiIh0UkLM7xAqVb77KQwMUNOuVSlEVDAP/4zWOv5a9q1LIaKCeXj5N+3jb/ZeKURUMA//+FX7+Fu0LYWICkbf619OTHQQERFRmZd4/QqQ14dFAwPUeKdJ6QVUQI9uxgIij/gVBqjeyLr0AnrD3LgfDyFErtsVCgUa17EoxYjeHNo85BVkv9LG+OXF+N9cTHQQEWkp6e4/QB4fdKFQoFr9hqUXUAEl37udb/zmlg1KK5xCSf73Xs7vQaGAeV3L0g+ogP5NeJRb+Khbs3rpB/Qmye9DoK5/SMwryaHNdiqSvJIc2mwnIqLSpRdjdNjY2AAA4uLiYGNjg6ioKLXtjo6OiIuLAwAcPXoUHh4e6N27N9zc3LBx48Z8y/f19UW3bt3g7u4ONzc39O3bF+Hh4Rr7jR8/Hm5ubtLr2bNnY8KECWr7nD17Fk5OTkhJScH9+/fh4+MDNzc39OrVCxMnTsSjR4/yjCUuLg6Ojo4AgPPnz8POzg7Xr1/PsT4A4Pvvv4e7uzt69+4Nd3d37N+/HwCwb98+uLu7w93dHXZ2dnB1dYW7uztmzZoFAEhOToa9vT02b96sVraXlxfOnz+fYx3Z2toiPj5ebf3YsWOleENDQ9G2bVvpvNnLpUuX8v3ZzZo1C+7u7nB1dYWdnZ107L59+3Ktq/Pnz8PGxgbr1q1TW//TTz/BxsZGeh82NjYaMS1btkztGA8PD/j4+KitCw0NRbt27ZCYmCite/XnQ2+g/D7I6voHXX2PH8g9Rn2IHXofPhHpsScJD/HkYbz6kvBQ7rCIiEqE3rXoMDIygr+/P8LCwmBqaqq2LT4+HkFBQQgNDYW5uTlSU1Ph5eWFhg0bwsnJKc9yJ0yYAA8PDwDAvXv3MGTIEJiZmaFjx44AgKSkJPz999+oWbMmfv/9d7Rq1QpTp06Fm5sbjh8/DicnJ6SlpSEwMBDz58+HqakpJk2ahD59+qBXr14AgHXr1mHmzJlYtWpVgd6zr68v9uzZA0NDQ7X1ly5dwt69exEcHIzy5cvj0aNH6NevH2xtbdGvXz/069cPQFYyYf369ahXr5507MGDB+Ho6Ijg4GCMHDkSCoUi3zgsLCxw7NgxeHl5AQBSUlLw999/w8Dgf/kyR0dHLFy4UOPYuLi4PH92M2fOlPbz9vbGgQMHtKobCwsLREREYMyYMdK68PBwVKtWTW2/vMq7evUqjI2NcfXqVfz333946623pG2pqamYOXMmVq9erVU8RERl3dMkzYR9lWpsjULaeZacrPa6srm5TJG8gXLKqDLLSkRllF606HhVrVq10LFjRwQFBWlsS05ORkZGBtLT0wEAlSpVwsKFC9G4ceMCncPS0hLe3t7YuXOntO7gwYN477330KNHD+zevVsqf+7cuZgzZw7S0tKwYsUKODo6ol27dgCAxMREPH/+XCpj6NChGDp0aIFiadmyJapWrYoNGzZobEtISIAQQjpH9erVsWLFCphr8aEhNDQUQ4YMgbGxMX755RetYunRowciIiKk1z/99BO6deum3RtB3j+7wrKysoJKpcK9e/cAAOnp6bhz506BfuahoaHo1KkTnJycsGfPHrVtPXv2xJ07d3Dw4MFii5mIiIiIiIhKjt4lOoCsFg5nz57V6AZha2sLJycndO/eHf3798fixYuhUqlgZWVV4HNYW1vj1q1b0uvQ0FC4uLjAxcUFERERePz4MQCgY8eOcHBwwFdffYWoqChMmTJFOmbKlCn4+uuv0aVLF0ybNg2nTp1C27YFH8137ty52Lp1q0YXli5duqBu3bro3Lkzhg0bhpUrV8LMzAwWFnkPhnX16lUkJiaiTZs2cHFxQXBwsFZxNGnSBI8ePZK6chw5cgQuLi5q+0RGRqp1ERkwYIDa9tx+dkXh7OwsJWBOnDiB999/X2Of17uunDlzBgCQkZGBgwcPSj/bkJAQvHz5UjrOyMgICxYswMKFC9W6sBAREREREZFu0stEh6mpKebMmQN/f3+kpKSobZs1axYiIyPh6emJ+/fvY+DAgTh27FihzlO+fHkAwJUrV/DgwQN07NgRb731Fpo0aSKNhQFkPbxHRUVhxowZ0jFAViLi9OnTmDt3LqpVq4bFixdj/PjxBY6jTp06mDx5Mnx9fZGZmSmtNzY2xpo1a3D48GG4uLjgr7/+Qu/evfHHH3/kWV5ISAicnZ1haGgIV1dX/PTTT1o/xPfo0QPHjh3DkydPkJKSgrp166ptd3R0xIEDB6Rl7969atvz+tkVlouLi/Qzzin5AkAtpgMHDqBz584AgJMnT6JmzZpo3LgxWrduDQMDA5w4cULtWHt7e/Tr10/qXkNERERERES6S+/G6Mjm4OCg0Q3i5MmTSEtLg6urqzRGxZ49exASEoIePXoUqPxr166hUaNGALIG9lQqlejZsyeArHEbdu/ejREjRgDIenivUqWK2kP/48ePsWbNGvj5+aFLly7o0qULxo4dCwcHByQlJWmMIZGfQYMGISIiQq0Ly/79+2FhYYEOHTrAysoKQ4cOxbJly3DgwAG0aNEix3KUSiUOHTqEcuXKITIyUlofGhqK0aNH5xuHi4sLFixYAGNjY3zwwQcFeg/ZcvrZFYWVlRUyMjJw48YNPHjwQPq5aWPfvn3477//pAFGU1JSsHv3bo33Nm7cOHh4eODQoUPFEjMRvZkUilwnjdEbHI+DioJjcsgopxuQPt18iIgKQG8THUBWSwo3NzckJCQAyGqBMWfOHDRr1gz16tWDEAJXrlxBkyZNClTu7du3sXPnTixduhRKpRIHDx7E1q1b0bx5cwBZD8Ndu3bF+fPnpfE4Xle5cmVERkaiadOm6NOnDwDgxo0bqF69OqpWrVqo9zt37lxpwFQAyMzMxJIlS7B+/XpUq1YNSqUS169fz7HrRrYTJ07A3NwcR44ckdaFhoZi9erV+OSTT/KNwdbWFomJidizZw9WrFih1s2jIF7/2RWVs7MzZsyYUaAZURITE3Hu3Dn8+OOPUnefe/fuwdnZWRrzI5uxsTEWLFgALy+vAiepqAzJ7Sn11e26TN/jB/Q+U8ApZIn0k0KhyHMKWW0GdZdb1Zq15A6BiKjU6HWiI7sbxKhRowAA7du3x7hx4+Dj44OMjAwAQOfOnfHZZ5/lW9aKFSuwbds2KBQKGBoaYtq0aWjVqhWOHj2KunXrSkmO7PMOGDAAu3fvzjXRYWhoiPXr12PhwoVYvnw5ypcvj1q1amHt2rUas6doq06dOpgyZQr8/f0BAP369UNycjI8PT2lmU8+/PBD9O/fP9cysgchfVWvXr2wdOlSadyKTz75RC3Gw4cPq+3/wQcf4Ndff0Xt2rWlaX2zZY/R8aqRI0eiTZs2aute/9kVlYuLC5YuXYpFixbluP31mKysrNC8eXN07dpVbUwTS0tLaTaat99+W+0Ye3t7DB8+nAOTvsGq1W8odwhFYm7ZQO4Qisy8rqXcIZC+MjAAVKq8t+syhQEg8ohfoePx67nGdfIe/4xKjsLAACKv391X9iMiyqYQeaWniajQXrx4gZiYGNjZ2cHExETr47oFFmz64YI4GTiuQPtHR0ejdevWhTrX9F3F181nnmevQh1XlPh1AeOXF+OXF+OXF+OXF+OXV3HG//DPaK0TNbXsi+ecxRr/5d+0j7/Ze8VyzmKN/49ftY+/RcEnjciJPtd/9vOLtbU1YmNjC/wck1eZxVFWQeh1i46CmDp1Km7cuKGx3tHRERMnTizVWMLDw7Fu3boctx04cKBUY9F1rCsiIiIi0lfFlbyQS3ElL+RSXMkLueh7/cvpjUl0LFmyRO4QJK6urnB1dZU7DL3AuiIiIiIiIqKCYGc2IioRxuUKNxZNSZVDRERERERvhjemRQcRla6ZA1zkDoGIiIiIiN5AbNFBRERERERERGUGEx1EOqaisZFelUtERERERKRL2HWFSMeE+42ROwQiIiIiIiK9xRYdRERERERERFRmMNFBRERERERERGUGEx1EREREREREVGYw0UFEREREREREZQYTHURERERERERUZjDRQURERERERERlBhMdRERERERERFRmMNFBRERERERERGUGEx1EREREREREVGaUkzsAorJKCAEAUCqVMkdSNC9evJA7hCJh/PJi/PJi/PJi/PJi/PJi/PJi/PLS1/izn1tUKpXa6+IoM/vZqLQoRGmfkegN8ezZM8TGxsodBhERERERkdbq1auHuLi4Yi3T2toalStXLtYy88JEB1EJUalUSE1NhZGRERQKhdzhEBERERER5UoIgYyMDFSoUAHPnz8vlueY7DIrVaoEA4PSGzmDiQ4iIiIiIiIiKjM4GCkRERERERERlRlMdBARERERERFRmcFEBxERERERERGVGUx0EBEREREREVGZwUQHEREREREREZUZTHQQERERERERUZnBRAcRERERERERlRlMdBARERERERFRmcFEBxEREZVJ9+7dkzuEN9qzZ8/kDoH0mEqlkjuEIlEqlXKHUCT6Hr++e/z4sdwh6D0mOoh02IYNG5CRkSF3GIUWHh6u1/FfvXpV7hCKZPfu3YiIiJA7jEI7ffo0UlNT5Q6j0PS9/n/99Ve5QyiSQ4cOYdu2bQD084Hp5MmTuHHjhtxhFNqyZcvw008/yR1GoZ0+fVruEIrkn3/+kTuEIlm8eDGOHz+utw/bgYGB+Pnnn+UOo9CWLFmi13+/5s+fj/v378sdRqEtXLgQ4eHhcoeh95joINJRGRkZ+O233zBq1Ci8fPlS7nAKLD4+HsuXL0dSUpLcoRTK2bNnMW/ePACAEELmaAruyZMnePz4MY4dO4ZTp07JHU6BhYWFYfTo0QgLC0NycrLc4RSYvtf/7du34e3tjeDgYLlDKbRnz57h33//BQAYGOjXx527d+9i165dOH78OG7fvi13OIViZGQk1X9mZqbM0RTMf//9h9GjR2Pjxo1yh1IogYGBmDBhAn777Te5QymUpKQkfP/999i+fTtOnz6N58+fyx1SgQQGBuLx48fo2rWr3KEUSmBgIG7evAk3NzcA+vkZ6O+//0ZCQgIA/Ys/MDAQd+/exZAhQwBAL58BdIV+/eUnekNkZmbCyMgIy5cvh4WFBT766CO9utEJIWBqaoratWvr5TepAGBiYgKFQgEA0v/1SdWqVeHm5oYWLVogLCwMJ06ckDukAmncuDGsrKxw5MgRnDp1Su+acOpz/atUKjRo0ADfffcd5s6dq7fJjvbt26NSpUrSa336sFu/fn2MHj0at27dwtGjR/Xy2/lq1arh5s2bAABDQ0OZo9FeZmYm3nrrLezfvx+rVq3Sy2RHnTp18PDhQ+zYsQPHjx+XO5wCq1q1KoYNG4YXL14gJiYGZ8+eRXp6utxhacXf3x9hYWHSQ6q+JfnmzJmDR48eYc2aNQCAH3/8EVeuXJE5Ku1ltyKuUKECLl26BEC/PsPNnTtXrf7DwsIQHR0tc1T6i4kOIh2U/aGwQoUKmD9/PiwsLDB8+HC9SHbEx8dDoVCgUqVKaNiwodofSH2IP1vz5s1hZmaGpKQkCCEghNCLDyx37tyR/l23bl306dMH9vb2CA8P16sPvE2bNoWzszNSUlLwww8/4OjRo3j69KncYeWrLNR/duuHNm3aYP369Zg9ezZ27twpc1TamTt3LrZu3Yr9+/fj3r17uHfvntSqQB8+7L6a0GvdujWGDBmC69evIyIiQkoa6LK9e/di//79uHv3Lpo0aYLKlStr7KPrye/sv7+2trbYsWMHVq5cifXr18sclXay/8Y6OzujW7dusLe3R2RkpN51ITI0NMSHH34IAwMD3Lt3D1FRUTh79ixevHghd2h5+uqrr/DgwQOMGzcO+/fvR1RUlF4l+SZMmIDbt29j5cqVAIDt27dj5cqVsLCwkDky7RkZGQEAPvzwQ+kzg74kuadOnYro6Gip/nfs2IENGzagYcOGMkemv8rJHQAR/c8333yDkJAQtGzZEp07d0a5cuXwwQcfYNq0aViyZAkmTJiAlStX6uwfzmXLluHs2bOoUKECzMzM8PTpU9SoUQPNmzdH1apVUa6cbt9yZsyYAQMDA5ibm6NGjRpISEhAXFwcqlWrBkD3v5XcunUrli1bhrZt26JChQpwcHBAo0aN0LRpU5iYmCA8PBxVq1ZFmzZt5A41R5cvX0bDhg2lh6MhQ4bAwsICVatWxa5du6BSqdCrVy9UqVJF5khzpu/1HxAQgLS0NNjY2MDe3h4WFhbo0KEDDh48CDc3N1StWhUffvih3GHm6tmzZ6hSpQru37+PsLAwNGzYEJcuXcL48ePRvn17VK5cGd26dcPz58/RqlUrucPVsGDBApw9exadO3eGgYEBevbsidq1a2P48OEICQnB6dOnUb58edStW1fuUHMUExOD33//HVevXkVwcDBSUlJw/fp1VK5cGeXKlUPnzp1Ru3ZtZGZmwsrKSu5wNQQEBODRo0ewtbVFy5YtUaNGDdjb2+PYsWPo3r07zMzMMHDgQLnDzJVSqYSxsTGArBZBlSpVQkxMDDp37oyIiAgYGBjA0dFR5ihzt3nzZtSuXRuurq5QqVRo0qQJBg8ejPLlyyMuLg4//vgjAMDBwQHly5eXOVpNDx48QFpaGjZs2ICkpCSEhITghx9+gKGhIdq3by93ePlSKpWoX78+9u/fDwDYv38/wsPDsXbtWlSvXl3aLzU1Va2lnK7YtGkTTp48ie7du6NJkyZ4/Pgxzp8/j9GjR0u/F7osKSkJ77//PiIiIpCQkIBffvkFhw8fxrp161CrVi1pv/v376NOnToyRqpfdPupg+gNkpmZCRsbG9SpUwe3bt1CjRo1EBsbiwMHDuDRo0do2rQpIiMjMXz4cGzfvl0n+5z37dsX3t7e+OWXX/D06VMcPXoUq1atQmxsLK5cuYKGDRuiZs2aGDlyJBo3bix3uBqaN2+O8uXLIzIyEikpKbh06RKGDx8OZ2dnKJVKtG3bFpUrV4azs7NO1n/jxo3RqlUrVKtWDZmZmbh37x6+++47mJqa4tGjR7h//z7++ecfLF68GI0aNZI7XDVBQUHYsmULOnXqhM8//xy1a9dGtWrVcOXKFTg7OyMgIABz5szB8+fPMWTIEFSoUEHukDXoc/0rlUqYmpoiLCwMv/zyC5ycnHD8+HHUqVMHXbp0QatWrTB16lSkpaVhwIABcoebo8qVK2PChAnSa5VKhczMTLz99tvIzMzEH3/8gZ9//hn169fXyUTH22+/jT179uDu3buoUaMG1qxZg5iYGHTs2BHnzp2DUqnEP//8gylTpsDMzEzucDXY2dlhwYIFEEJApVLh8uXLmDRpEipVqoS///4b165dw9WrVzF8+HCMHDlS7nDVZGRkoFatWggPD8elS5fw5MkTHD16FLVr10b79u3x3nvvSYnAESNGyB2uhi+++AJXr17FjBkzUKtWLTRs2BBffPEFVq1aJX3ZcPjwYbx48QIuLi5yh6vhzJkzWLRoEYCs8WkaNmwIBwcH1K9fH6GhoVJLrdDQULz11lt49913ZY5YXVBQEFq3bo3ly5cDyOq25eLiAgMDA4SEhACATic7/P39YWFhgc8//xxPnjzBu+++CxsbG+zatQsmJibSflOmTEHLli3h5eUlY7SaNm3ahNq1a6Ndu3b477//EBoaigYNGuDp06e4desWrK2tdfIzW7aAgACUK1cOAQEBSE5ORufOnWFpaSklKLNNnjwZ77zzDsaOHStjtPqFiQ4iHWFoaAgnJycYGhri4sWLsLCwwMyZM6FUKnHu3DlkZmbC2NgYNWrU0Nkbdr169VCuXDnpW9+6desiMzMTM2fORExMDFJSUnDz5k2dTHIAkB7gsgfgMjU1xcOHD9G9e3dERUXh4sWLUKlUcHV1lTPMXDk4OCAzMxNHjx5F8+bN0adPH0yYMAFKpRLR0dG4ceMGXrx4oXMP2QDQtWtXxMbG4tq1a9i4cSPq1q0LW1tbjBkzBv7+/li9ejVGjRqFq1ev6mSSA9Dv+jc2NsaYMWPQoEEDREREwNPTE1OnTsXt27dx9uxZtGjRAk+fPsVvv/2mc4mOpKQkqdUVkJW0MTIygoGBAQwNDVG5cmXpwTo9PV3nvg0WQkChUGDQoEEwMTHBt99+i3HjxqFp06a4d+8eUlNT0bRpU9y4cQOGhoY6l+R4vf5fvnwJIyMjtGzZEm3btkXDhg2lD+a3b99GgwYNZIo0d0ZGRhgxYgTq1auHQ4cOoXfv3pg6dSri4uJw6tQptG7dGqmpqfj777/lDlWDEAJJSUm4fv06wsLCkJKSgrZt26Jv376oVKkSypcvj4EDB+LZs2c6ObBzfHw8OnfujHnz5mH58uWIjY1F+fLl8dFHH2HJkiVITk7Gvn37MGLECNSvX1/nkhwA0KRJEyxYsAAvXryQPv9YWlqiZ8+eUCgUCAkJgVKpRJcuXWSOVFNAQACePHmCOXPmAMgao6NatWrYvXu3WkuIKVOmwNTUVOeSHAEBAUhPT8eoUaOkdcnJyTAyMpISZCNGjNDZZEd2S7LVq1cDALy8vFC1alX4+vri1q1b0uflKVOmoHLlykxyFJQgIlmdP39e7fWzZ8/E0aNHxdSpU8WGDRvUtqlUqhz/LafQ0FARFRWltk6pVAohhPjzzz/F8OHDczxOV+K/ceOGePbsmfRapVKJFy9eCCGEWL16tZgzZ45coWklKipK/PPPP2rrIiIixOTJk8WWLVvEvXv3cjxOV+rf399fBAUFCSGEuHDhgvD19RWTJ08Wf/75p/D09BSLFy8WXl5e4uTJk2rH6Ur8+l7/Dx8+VHv94MEDsWnTJjFs2DDx22+/qW17/vx5aYamlZUrV4pOnTqJ7777Thw5ckRje2hoqHR96eL9859//hGpqalq67Zs2SJ69Oghzpw5k+txuhK/v7+/6N+/vwgLCxP//vuvtD4jI0MIIYSfn59YvXq1xnG6Ev+rMQshREJCgvjuu+/EkCFDNOo/+++CELoT/8GDB8X169fFy5cvxfjx48XHH38szp8/L4YNGybWrVsnxo0bJzw8PMSDBw80rjNdcOTIEdGpUydx7do1IYQQu3fvFo6OjuLKlSvi0KFDYsaMGWLEiBFiyJAhOvn7m5mZKf372LFj4v333xcHDx5U2+fu3bti+fLlYufOnaUdXr78/f3Fu+++K86dOyeEEOLly5dq27p06SKePXsm/P39hb+/v7Tt1fctp+nTp4shQ4ZIr7PvO9nXh0qlEv7+/uLjjz8W9+/flyXGvAQEBAhbW1vpmnm1Xnfs2CHs7e3FpUuXxMyZM3Wy/vWB7qW2iN4g69evh7e3N6ZNm4ZDhw7h7t27MDU1xfvvvw9nZ2dcv35dbRC07MH0xP9/Ayi3ixcv4quvvoKvry+mTZuG8PBwKZMOANbW1njy5AmuX7+uMZCnLsS/fft2fPjhh5g5cya2bNmC58+fQ6FQSN9itGvXDnFxcRBCSCN5A7ozsNX+/fvx0Ucf4aOPPsKGDRtw/vx5AECPHj3g7u6OP//8E5GRkbh3757GsbpQ/wDg7e2NXbt2Ye3atWjdujX69euH1NRUXLhwATt27ICtrS3S09M1Zi3Rhfj1vf5XrFiBgQMHIiwsDKdPnwYAWFhYYNCgQejatStWrlyJCxcuSPtnt4TQlesfyJodKXsAxm+++QZTp07F/v37pd/XmjVr4tdff0VqaqpanetC/W/ZsgUffvghAgIC8P333yMtLQ0AMGLECAwbNgxz5szBzz//DEC9znXl/g9AGk9nx44dWLRokTQYY/YMGW3btsWjR480jtOF+FetWoUBAwZg37590mCdNWrUQL9+/eDi4oL169fj7Nmz0v7Zfxd0pf4fP36M48ePIzg4GPfu3cOSJUvw4sULREZGYuHChXj33XfRsGFD3Lp1C1evXkXFihUB6Nbvr7OzM/r27QtfX1/ExsZi0KBBGDp0KCZOnIimTZti5syZGDFiBBo3bqxzv783btyQrnMhBD744AP4+flh+fLlOHz4sLSfpaUlhg0bBk9PT7lCzdH06dORmpqKyZMnY9u2bThz5oza57TZs2ejW7duaNOmDTIzMzF79mwAWV0CdaFlxMyZM3H69GlUq1YN169fh1KplMaBUygUUKlUUCgUmD17NpydnfHWW2/JHLG6wMBApKSkYNmyZQgNDcX+/fvVplEeNmwYpk2bhoEDByItLU3n6l9fsOsKkYw6deqEn3/+Gc+fP8dvv/2GRYsWYeTIkWjfvj26d+8OAAgPD0dkZKTaIGK68EceAFq0aIExY8bA2toaMTExiIiIwOLFizFhwgQ0btwY9vb2ePbsGZ49e6aTA3na2tqiQ4cO6NOnD+bPn4+LFy/CzMwM48ePh7m5OWrWrIm///4bT58+RdWqVaXjdKX+u3fvjhEjRqBu3br49ddf8csvv2DVqlWYMmUKOnbsiOrVq2PFihVo3LgxLC0t5Q5Xg1KpROPGjREaGgoPDw+oVCqpWea3334LpVKJ0aNHo1evXjJHmjN9r38DAwM8efIE165dw4ULF7B3714MGjQIbdu2xahRo2BiYoJZs2ZhxYoVaqO+68r1DwCjRo3Cb7/9BmNjY4SEhGD16tWYN28eNm/ejCFDhuDff/+Fu7u7Tg6eZ2tri3bt2sHNzQ3z5s3DhQsXULVqVUyYMAFeXl4wNzfH2LFjsXfvXrXufrpU/46OjjA0NISjoyMUCgVWrlyJfv36oWXLlhg2bBhu3bold4i5qlChAtLT03Hz5k1cvnwZu3btwrBhw/Dee+9h2LBhMDIyQlBQEGrWrAkbGxvpOF2pfzMzM3zxxRdYv349vv/+ewwbNgybNm3CRx99hLVr18Lf3x+dOnVC69at0bVrV+k4XYk/uxvZ1KlTAQBffvklFi9ejI8++ggqlQo+Pj6YN28eunbtKsWvK0mm0NBQ+Pn5oU6dOmjZsiUqVaqELl26oFq1apg4cSK+/vprmJmZoVOnTgAgde3SlfgfPHiAd955RxpvJjMzE1u3boWBgQHee+89Kak3a9YsdO7cWfo8KoTQiYfs3bt3IyEhAadPn8by5cvxzTffYPTo0bCzs5M+axoYGCAzMxOGhobo168fAN2p/6SkJDRv3hx9+/YFkNXdLzg4GAYGBnBycpL+Xg0dOhR2dnZo3rw5AN2pf32iELqU2iV6w6Snp2Pjxo1466230K9fPxw7dgwTJ05ErVq10KFDB7Ro0QLvvPMOWrduLXeoal6+fCllzr/55hvExMRg48aNAIBu3bqhYsWKSE9PR7t27dC6dWv0799fznBzlZmZiUGDBqF79+4YPXo0zp49i7Fjx+Ltt9+Gvb09bG1tUaVKFbi7u8sdqprsD4hKpRJ+fn6oW7cuJk+ejJSUFLRr1w5vv/02AEgP4bo2CNrvv/8uDQaZPVPAP//8Aw8PD3z66acYPXo0Lly4IH3o7dmzJwDd+ZCi7/WfLT09HXPmzEH79u3h4OCAoKAgnDp1CrVr18aYMWNgaGiIatWq6dz9Z8+ePejRowfMzMyQkZGBLVu24MmTJ/jiiy8wY8YMJCQkwNbWFteuXYONjQ0mT54MQHeun2xKpRIDBw5E79694e3tjTNnzmDcuHFo3Lgxmjdvjv79+0OlUqFFixZyh6rmwoULsLGxQeXKlZGWloaRI0eiQ4cOmDRpEhYuXIiLFy/C3t4eoaGhCAgIQJ8+feQOOUcZGRmYM2cOWrZsiZ49eyIoKAhHjx5F9erVMXr0aJiYmKBu3bpo1qyZ3KGqSUlJgampqfT6/v37WLt2LcqXL48RI0agRo0aGDlyJCpWrIgNGzZI++nKN8FLly7FpEmTYGBggBcvXkiDXS5ZsgTHjh3Drl27UK1aNaxatQqpqamYNm2aTv3upqSkAMiaZe7u3buoX78+rKysEB0djdjYWDRo0EBqgRgSEgI7Ozs5w9Xaxo0b8fPPP2PUqFFo06aNxkwlunL9XL58GQYGBmr1umjRIty9exeffPKJWrJDF736+RmAlIwJDw/Hrl27MHDgQDg6Omok53Wl/vUNa4yolK1fvx7R0dEAspqCm5qaYsuWLQCAmzdv4r333sOqVavw6NEjpKenSw8ZupKT9PPzw7Zt2/Ds2TMAwKRJk/DixQvs378fc+bMgZWVFTZs2IBFixahZs2aUpJDpVLJGbZkyZIlePLkCYCsAWD9/Pxw7do1vHz5EidOnED79u3x0UcfoXLlyvjzzz+lJIeuxP/NN9/g4sWLEELA2NgYEydOxLFjx/DHH39gzZo1aN26NRYuXIjevXvj6tWr0kO2rlw/V69exUcffSRNYWdsbAylUomGDRsiJCQEu3fvRlRUlPQ+spMcgG58E6nv9b9z504kJydLDw7GxsaIjY2Fubk5ypcvj3fffRcODg7YsmUL/vnnH527/+zfvx8BAQFYuHAhHj9+DCMjI7Rr1w4hISHw9PTEnTt3sG7dOkyePBlz587VuSTHqlWrcP/+fahUKhgbG8PPzw8xMTEAgNOnT6N9+/bw8vKCQqHAd999JyU5dOX+s23bNgwbNgwRERFITExExYoVERAQgMuXL2P48OH4888/8f3332PGjBnYt2+flOTQletn+/btSEhIgBACQgiUL18ef/31FypWrIhy5cqhWbNmcHNzk7qDZCc5dCX+DRs2SNOXZqtTpw7GjBmDlJQUfPfddzA2NsamTZs0ZhbSlYek6OhoaQpZExMTvHjxAgAwdepUtGjRAjNnzoQQAuPGjcO0adMA6Ma9H8hqYevq6or09HSMHTsWrVq1QmpqKpycnLB8+XL88MMPmD59OpYuXYr58+frXJLjyy+/xIQJE7Bt2zaNbR9//DE6dOiATZs2ISoqSqO7sS5cPz///DPWrl2L+/fvQ6lUSuu//PJL1K9fH5s2bUJMTIzUnVHXBAQEYMiQIVi2bBni4+MBQErKuLq6wtPTE3v27MHhw4fV3h+gG/Wvj9iig6gUBQQE4PHjx1ixYoXa+gkTJuD3339HgwYNsHnzZhgbG6tlfXXlQ3pKSgo++eQTxMfHY+rUqejQoQOqVauGTZs2YcuWLbCzs8PKlSulMTqy6Ur8Fy9ehKenJ9555x3s3LkTlStXxu3bt+Hn54eUlBTUrFkTGzZs0PiDoivxz5o1C3FxcWrf0imVSnzzzTf48ccf0bBhQ6xevVpn6z/bTz/9hEWLFmHs2LHSg1BaWhoqVqyIhQsX4r333oOTk5O0v67Er+/1HxAQgJSUFCxdulRad/XqVXz88cfSjEnbtm2DoaEh7t+/jzp16sgYbc7S0tIwZswYpKamok6dOpg5cyZq1qyJZcuW4fjx4/juu+9gZmam9u2XrtT/7Nmzcfv2bWzevFlad+vWLUyfPh3p6emoVq0aNm7cqBOx5ubq1auYPHkyWrZsiTZt2qBLly4wNjbG5MmTkZycjNDQUABZrSWyfw905ZvIgIAAPHv2DMuWLZPW3b59G8OHD0f16tVRsWJFbNmyBUZGRoiPj4eFhYWM0ebsjz/+wDfffINOnTqhX79+arPd3Lx5E15eXti8eTNsbW2l9bpy/b96HUyYMAGxsbEIDw+HgYGB1Erlt99+Q2RkpJTgAHQnfiArlkWLFuHXX3+Vxk/bunUrEhISMGzYsBwTG7py/atUKixYsAAJCQkwMDBAbGws3N3d0alTJzRt2lTab9myZahcuTI+/vhjGaPVtGrVKnh7e+PgwYP4888/4ejoiC5duqjNorVo0SL8+eefmD9/vk52F924cSP++OMPNGzYEPv370efPn3QvHlzqWsQAOzduxePHj2Cj4+PjJGWHfL/5hG9IWbNmqWW5Lhw4QL+++8/AEDHjh1RqVIl7NixQ/qGW9eSHEDWdKu9evXCixcvcPDgQZw+fRqZmZn48MMPoVAo8OGHH8LIyEgjm64r8dvZ2aF37964ffs2+vTpg+TkZDRo0AB9+/bFgwcP4O/vDwMDA7X4daX+58yZg0ePHkkP2dkDvxobG6N9+/Z48OABpk2bBiMjI41vAnQh/sOHD+P7779HTEwMunfvjq+++gqrVq2SWnZkD5QXGxuL1NRUtWN1IX59r//AwEA8fvxYSnJcvXoVKSkpsLW1Re/evZGRkYHNmzfD0NAQmZmZUpJDl74LycjIQMWKFdGrVy+4uLjAzs4O/v7+SEpKQvfu3VGuXDmp5cOrDxa6UP+zZ89GYmKilOQ4deoUlEol3n77bXh4eCAuLg6BgYFQKBQa9x9dUr9+fbzzzju4e/cu/vzzT5w6dQqVK1fG4MGDER8fL01f+mqyTxce8rKv/+wkx5UrV/D06VM0aNBA6r+/efNmGBkZITMzU0py6Er9X7t2DdevX0eLFi3w1Vdf4ezZswgJCUFSUpIUY6NGjdC6dWtpgNhsunD9L1q0CNOnT5fG41ixYgUaNWoktezI7oqzfft2jetFF+IHsroYKBQKTJs2De+99x4+/vhjKBQKjBgxArVr15b+vr1OF65/ICuONm3a4MqVK5g1axY+/fRT3L9/H56enli5ciUOHToEAJg8ebLOJTmArC6vu3btwtChQ/H222/jxIkTOH36tDQgLJDVsmPw4ME6meQAgDZt2uCPP/5A//79sXz5cpiYmGDcuHEICAjAtm3bkJaWhgEDBjDJUYx047ePqIybMGEC0tPTpSTH1q1b8fXXX0sPd66urkhNTcX3338PAGp9I3Xhj/yrH7yHDh0KDw8PvPXWW9i3bx/27t2L2rVr46OPPsK5c+fw9OlTtf6HusTIyAgfffQRZs+ejVatWsHZ2RlJSUkYMGAA2rRpgwcPHgCAWvy6UP9fffUVYmJipOvnu+++w4oVK6SfS5cuXdC7d2+EhYUhPT1do2+t3AICAnDgwAHcvXsXJ0+exMuXL/H+++/Dz88PS5YswZYtW3Dnzh1MmTIFtWrVQu/eveUOWY2+17+/vz+ePHkixb9lyxasX79e+gDeokULJCUlSX3PX+3frAvX/6lTpwD87/fS2toaR48eRePGjWFjY4PZs2fD1tYW9evXx969e+UMNUf+/v64fv26VP/bt2/Ht99+K9V3165d0aFDB529//z111/SvytWrIhJkyahefPmqF69OqKiohAaGgo7Ozu0aNFCbV9dMWPGDI3r/9tvv5Wu8+bNmyMhIQGJiYkAdO/6/+qrr7Bo0SJMmzYN586dg42NDb744gucO3cOoaGhuHLlCgBgypQpMDU1Rd26dWWOWN2MGTNw584dfPzxx2jfvr2UCF69ejXs7Ozg4eGBrVu3YsyYMTA1NcUXX3wBQHeSTLt27UJKSoqUBAaAadOmoX379vj0009hZGSE/v37w9TUFH///bfM0Wp6dca4nj17wt7eHsHBwfjwww/x/Plz1K9fH3fu3MGcOXPUWivqSv1nJ6+9vLxw//59AMDo0aOlsVDOnDmjluz48MMPAehO/K/OpNKiRQv069cPu3fvRqtWrfDgwQM0a9YMRkZG2L17N1auXCljpGWTbj6NEJUhmZmZuHjxotRXf9euXYiIiMCSJUukmTyqVKmCUaNG6WSCYNGiRahYsSI8PT1RvXp1AFnfHGW37vjmm29gYmKCChUqwMrKClWqVJE5YnX79++Hvb093n77bSgUCpiYmOC3337Dl19+CUtLS/Tu3RsRERGoV68eoqKidHLgSFNTU1SsWBEpKSn48ccfcejQIWzevBk1a9aU9qlQoQJq1aql1oxTFyxZsgSPHz+Wmvk+fvwYCQkJSEtLg6OjI2rWrIn58+fjxo0bMDMzQ0BAAADdae4L6Hf9x8TEYO/evfjuu+8AZE0DGhERoZZo7dGjB44cOYLDhw/Dy8tLznA1rFixAmvWrIGHhwdsbW3Rv39/tGzZEqNGjcKtW7fQu3dv7NixA6NGjUJgYKA0EKyuiI+Px3///Yd27doByGqWHB4ejiVLlkjdDmrVqoXU1FScP38e7733npzhaggICMCePXswYMAAjBw5ElWqVMHbb78NExMTuLq64t69ezhw4ADS09Px4YcfwsHBQe6Q1Vy7dg0hISHSA9yr13/2YH9du3aFg4MDjh07Js1CoSv8/PyQmpqKTZs24cGDB6hRowZSU1NhZ2eH2bNnY+3atfjtt9+QkZEBCwsLLFiwAIDutETcv38/kpKSsGbNGgBZSaTz58/jl19+QY8ePfD1119LyckOHTpI9a8r9/+EhAScP38ev/32G+bMmYNKlSpJg0dOmTIFM2fOxN69ezFq1CiMHDlS57r7LVq0CElJSbC3t8fQoUMBZM32d/r0aXzxxRd48OABDh48CABITk6Gubm5dKwuXD8A1BLyK1euxHfffYdhw4ZhzJgx2LhxIyIjI5Geng5nZ2e1lmS6EP+sWbPw6NEj1KxZE/7+/gCyEqvff/89xo8fjydPnmDPnj0A/jcwOxUzQUQlZt++fUIIIdLT08X7778vnJ2dhZeXl0hISFDbLyAgQOzatUuOEPN048YN0bZtW/Huu++KIUOGiGPHjomYmBjx6NEjMWjQIHHjxg1x7tw50atXLxETEyMdp1KpZIz6f/bt2ydsbGzEwIEDxRdffCEePXokhBDixx9/FJ999pkQQoiZM2eK5s2bixcvXsgZao6+/vpr8eTJE6FSqcTq1avFBx98IHr27CnS0tLU9ps7d644dOiQTFHmbdasWeLnn38WQggREREhZsyYIbp06SJ69Ogh1qxZI4QQGu8nMzOz1OPMib7X/8WLF4UQQgQHBwtXV1cxa9Ys4enpKf7991+1/QIDA8W5c+dkiDB/586dE3379hVjxowRvr6+wsPDQ5w5c0bs379frFu3TmRkZIgHDx6IAwcOSMfoyv1nz549Ii0tTfzxxx9izpw54uOPPxa9evUSjx8/VttvyZIlavdPXXL8+HHRo0cP0bZtW7F27Voxbdo0ERMTI8LDw8XHH38sXr58KXbv3i327NkjHaMr9f/rr78KIYQICwsTPXr0EDNmzMjx+vf39xdnz56VI8Q8/ffff2LSpEnS63Pnzom1a9eK7t27iylTpoiff/5ZvHjxQjx48ED8/fff0n66cv8UQohDhw6J6dOnCyGE2Lt3rxg7dqzo37+/8PT0FGPGjBGxsbEax+hS/EII8e+//wp/f38xceJE8ezZMyFE1mc6IYTYtGmTWLt2rdr+unL9z5w5U4wcOVLs3r1b2NjYiN9//10IIcSLFy9Er169hIODg7SvUqmU/q0r9R8WFqbxe3nu3Dnx1VdfSZ/lhBBi2bJlIjg4uLTDy1dgYKAYNWqUiIqKEjY2NiIiIkLaNmbMGNG2bVvp9av1ryvXT1khf7qUqIw6c+aM1DTfwMAAR48eRcWKFVGxYkXUqFFD2m/y5MkQQmDw4MEAdKe5HQA0aNAAy5cvR69evZCeno6HDx/im2++wYkTJzBgwAAcPnwYHTp0wPz58/Huu+8C0J1vkoCszPmwYcPQuHFj3L17F4sXL8aaNWtgamqKzp0749q1awgMDISfn5+USdeV+r948SI2bNgALy8vpKamYuzYsXB1dYWZmRkePXok7Td16lQ8ffpU55prCiGkvtehoaHSLBiVK1fGwoULMW/ePOzfvx///fcfKlSooHacLnyTp+/1v3HjRgwePBhhYWEYOHAgxowZg8OHD2Po0KFq3zpOnToV6enp6NChAwDdiT9bhw4dMHXqVKSmpuKDDz5Ar169cPnyZRw/fhyrVq3C2rVrYWFhIXV30pX7z/79++Hv74+5c+fCxsYG/fr1Q8WKFdGhQwe163vq1Kn4999/1e6fusTR0RFz5sxBzZo1kZaWBnd3d6krSHJyMqKjozFo0CAMGDBAOkYX6n/btm3w8vLC3r174ebmhi+++AI//fQT+vfvr3H9Z2RkoFOnTgB0q/7LlSuHy5cvIyQkBN9++y0WL16M27dvw8PDA02bNsWRI0dgYGAACwsLNGnSBIDu3D+zWVhYICEhAR4eHtJA0zNnzsTOnTtRsWJFXL9+XeMYXYj/9OnT+PHHH7F//37UrFkTH330EWrUqAF/f388f/5cmhI3OjpaYypTXbj+s8eU2rx5MwYNGgQvLy+YmZkBgDRbWPPmzZGeno6XL1/q1Jg6QgjEx8fjwIED+P777/HRRx/h5s2bePToEezt7fHo0SMkJCRI+0+aNAkDBw6UMWJN2fW/ceNGdOzYEaNHj0bt2rWl7WPGjEHLli3x6NEjjfrXheunLNG9dvJEZUSDBg1gb2+PqKgomJmZYdiwYdi5cydcXFzw1VdfYcGCBfjyyy9RuXJlzJ49G4DufEjPZmhoiJYtW0KhUOD7779HcnIyFixYgHnz5iE1NRXnzp2Dl5cX7O3tAehe/I0aNUK/fv1w9OhR1KlTB5UqVULdunUxe/ZsZGZmomHDhli7dq30R1JXmssC/xs4NTw8HH379kVISAgmTZqEVatWYebMmZg2bRp27NiBSpUq6eT1o1AooFAo0KVLF/z++++4du0ali1bLvZtmwAAmFZJREFUhsaNG0tdtmxsbDTi1ZX49b3+27VrBysrK/zyyy8QQsDd3R0ZGRlYsWIF6tatixYtWmDq1Kk6Gz/wv3g6deqE58+fY9u2bRgwYABat26NtLQ0pKamSt1vsulK/D169MC+ffvw119/wdfXF3PmzMHo0aMREhKC77//Hn369MHy5ct1uv6ztW3bFtOmTUNgYCDatGmDjRs34syZM3j48CF+/fVXtG3bVu4QNbRr1w4NGzZEdHQ0DAwM0K9fPyiVSixduhRvvfWWlEDT5fqvUaMGJkyYII0RMWnSJNjY2MDS0hIXL17EnTt3NLq76lL8QFZ3g0mTJuHevXto2rQp6tWrJ21LS0vTye66X331FZKTk6VETHBwMHx8fDBw4EDs3r0bw4cPx+TJk/H999+jatWqOjdw5/Tp03HlyhVpBqTvv/8ex48fR926daFSqWBubo5WrVrBz88PN2/elJKsuiI5ORkWFhZYs2YNVCoVZs2ahY0bN+Lx48eYNGkSWrVqhdWrV+Prr79W6+qhK7+/c+fOxalTp/DTTz8ByBrTa+/evRBCIC0tDRYWFnj33Xdx8eJF/PXXX+jSpYvMEZdtnF6WqAQFBwfjhx9+gJGREVq1aoUJEybg5cuX+PDDDxEXF4fBgwcjMDAQgG49ZL9OqVTi999/x4YNG9CzZ08MHDgQiYmJ2L9/v879kc/Jn3/+iWPHjuHly5cYMmQIjI2N8cMPPyAlJQWff/653OHl6urVq7hy5QqioqJw7tw5HDlyBFWrVsWqVauwatUquLu7IygoCIBuXz+vyu6HOmHCBFSoUEGKXxfpc/0/e/YMX3zxBdLS0vD222+jZcuWcHd3x969e7Fx40aYm5vD2tpaesjTtfizvfrh9fjx49i2bRt69eqFgQMHSlNSvr6f3LKnVg0ODsbTp0+RkZGBK1euYMGCBfjnn3+wb98+nD59Gu3atZPGVNDV+n/VyZMnMW/ePEycOBG9evWSxirQRWlpafD19UVSUhIaNWoEe3t79O/fH4cPH8ayZctQtWpVvPvuuzp//QNAeno6DAwM1B7qxo0bB0tLS7VpWHXN67+TcXFx+O+//2BtbY3p06ejUqVKOnf/DwgIQFJSElatWiWtmzVrFu7evQs/Pz80atQIS5YsgUKhgIGBASZNmgRAd66f58+fY+PGjbhw4QIWL16MM2fOYOPGjWjUqBEMDQ1x+fJlCCHQrVs3WFpaYtSoUXKHrGbZsmU4evQoJk6cCHNzc6mlYXx8PE6ePInQ0FC0atUKBw8eRHBwsM4NvJuRkYH9+/fj4MGDmDBhAu7cuYNt27ahWbNmMDExwYkTJ/Dy5Uu8++676NKlCzw9PeUOucxjooOoGJ07dw5WVlbSzTclJQUHDhxAw4YNsWXLFtja2mLSpEnIzMzE+vXrMW7cOAC680cyL0qlEhcvXsT69evx7rvvYsqUKdI2fYj/r7/+Qnh4OF6+fIk+ffpITX0B3XlIen3g1H/++QcbNmzA5MmTsWvXLuzZsweHDx9G1apVcfToUTg7OwPQnfjzcvfuXaxatQqZmZl4+PAhatasKU11qivx63v9X716Fba2tmqvDxw4gPLly+POnTvo1q0bevfujZCQEFy+fFkvHvIA9fo9ceIEvv76awQEBEgDfOpK/Z86dQpdu3aV4rl48SLmzp2LTz/9FH/++Sfu3LmDuXPn4s6dO4iOjoa3tzcA3Yn/xYsXUpP83Jw6dQqTJk3C2rVr0bZtWygUCp25fv766y+1b6dv3bqFffv2oUKFCrh58yYcHBzQr18/hIeH49dff9WLLxmyr43r16/jhx9+QK1atfDzzz/D3NwcCxcuVNtHl2VmZmLHjh2IioqCSqVCrVq1dC7J9/TpU8yYMQPz5s1D5cqVkZ6eLg0uPWbMGBgYGODbb7/VOE5X4s+WnJyMsLAw7Ny5E0IIHDt2TNp28+ZN3L59G1euXNHJz5+LFy/Grl274OnpiZ9//hmNGzeWBqA2MjLCvXv3kJiYiCNHjsDPz0/ucHOUlpaGEydOYO3atUhOTsapU6ekhHB8fDweP36Mc+fOYeTIkQB0q/7LpJIZ+oPozbNu3TphY2Mj3N3dxYkTJ8SDBw+EEEIsXrxY7Ny5UyQnJ4tRo0aJwMBAkZGRIR2nKwM/nThxQmOQvNcplUpx6tQpsWHDhlKKqnj99ddfwt/fX4SFhckdigZtBk4NDAwUNjY2aoNh6sr1o42ff/5ZnDp1SkRGRkrrdCV+fa9/Pz8/YWNjI+bOnSvu3LkjkpKSxMuXL8Xy5cvF3bt3RUREhJg4caLYvXu32nG6Ev/rXo/r1QHachrAUG7Lly8XNjY24quvvhLbtm0TqampQgghDh8+LNatWydu3LghAgMDhbe3t3j+/Ll0nK7U/9KlS0V4eLhW+169erWEoyk4f39/YWNjI2bMmCFu3rwpDfi9fPlycfPmTXHy5EkxYcIEsX37drXjdKX+swe3zE1cXJzYtWuXCAoKEps2bZLW60r82fIaSFGpVIrExEQRHx8vrdOl+JOSkkTfvn3VBgZ++fKlEEKIP//8UwwbNkw8ffpULwaLTE5OFhs2bBADBw4UN27ckNZnv59suvJesq+Dp0+fii+//FIcPnxY3L9/X0ycOFF06tRJDBw4UBw/flxj0GZdun5elZaWJsLCwsSgQYPEyZMnpfWvfvYXQnfqvyzTvc5xRHqqa9eu+Oeff/Drr78iODgYNjY2qFChAnx8fDBlyhR07doVvr6+OHHihFq/VF3I5E6fPh0A0K1btzz3MzIyQpcuXaQ+hbqaic6tSXXTpk0xceJEaZpcoUPfhGUPnPr8+XPcvHkTixcvhqWlJVq1aiUNnDpz5kw0bdpUbfBOfaj/7Hp+fepeXbp+9L3+O3TogHPnzuHw4cOoWbMmbt++jY8//hiWlpZYtGgRVq5ciYcPH+Lly5dqx+lC/AcOHEBqaiqUSiVatWqFZs2awcDAQO36UCgUUpeQOnXqaLRekdt7772Hpk2bIikpCVeuXIGXlxcmT56MjIwMAICVlRU+/fRTREVFqU1BrAv1HxgYiGfPnknN8HOT/XttZWUlDdqpK/fPbt264fz58zh27Bjq1auHf/75B15eXnjnnXewYMECrF27Fg8fPoRSqVQ7Thfqf9myZbC1tYWLi0uu+9StW1casDybrtw/Dx06hNq1a6Ny5cqwsbHJ8e+qEAKGhoaoXr06VCoVAN2JH8iKr2LFiihfvjxiY2Px7rvvSnECgImJiTR9uK5c83kxMzNDv379UK5cOcyaNUsa2+L1+taF9yL+f+Dy7NiMjY3xzz//wNXVFZUrV4atrS2srKywatUqODs7q7Xa0pXr53UVKlSAk5MTFAoFNm3ahGfPnqFXr146P6ZOWcSuK0TFRAiBa9eu4cCBA7h16xamTJmCdevWwcTEBHFxcejRowe8vLzU9teFm5yfnx/i4+OxadMmALnHlf0h9/Hjx9i4caPOjG2xZs0aPHr0COXKlUOTJk3Qp08fAJoP29kPSSkpKbhw4UK+SR05XLlyBUePHoWxsbE0cOqyZcvUBk7NpisfErWt/5cvX6JcuXJISUnB+fPn4eTkJFPEudPH+n/V6dOnMW/ePLi7u8PGxgarV6+Gp6cn9uzZA39/fzRr1kzuEDX4+fkhOTkZdnZ2+OWXX1ClShW8/fbbmDp1KoD/3Y+yr6enT5/is88+w8yZM9G4cWOZo1cXFRWFtWvXYuTIkbhz5w6eP3+Oq1ev4uTJkxg9erTUVBzQnfu/v78/9u7di6tXrwLIvfvKq/U/YsQILFu2DFZWVqUdbp5+/fVXzJ49G05OTmjfvj0WLVqEQYMGISQkBF9++aVODpqanWT6+uuv87wesus/PT0dhob/196bx+Wc/f//j0ohQnZZSqgsg+wxg+ySFNNksqtIm1RSqatd2XdaKLtUlCkRsiuUbKOyZCiaolRXi/bz+6Pf9ZrrUszM+/sZ17ly7v/MXNf1et1uz47zOq9zHuc8H08ZkSoN4sTV1RWvX7/GsGHDcPr0abi6ujYq2Aj3n02bNsHNze1v06S+BYcPH8b79++hqKiI5cuXIz4+HmvWrMH27dtF/g4bGxt069YNzs7OYoy2IX83jhQWFuLs2bM4ffo09uzZQ90z6+npiU+fPkFOTg62trZo3749Hjx4AGtra/Tu3RuEEBw5cgTS0tLIzs5Gz549xR1yA77mVVReXo4rV64gMDAQ3t7eGDp06LcNjgG6ZmkMhgQjJSUFNTU16OvrQ1FREZGRkdi2bRv09fXRuXNn/P777w2uFzc8Hg9JSUnIzc3lynUJ72IIEJ6krFmzBmPHjv3WoTaKu7s7Hj9+DC0tLVRXVyMqKoo7nSIjI8PtOtbW1kJWVpZbJLVv316cYX+R/v37Y8qUKaioqEBeXh40NDQQGhoKAwODBos6GhbZ/6b9mzVrxrV/p06dxBn2F5G09v+c8ePHw8HBAZGRkVBUVMTevXsB1Puj3LlzR8zRNWTDhg34+PEj9u/fD0tLSwQHB0NHRwcvXrzA4cOHAaCByLF69WpYW1tTJXII+vm4ceOwZMkSHD58GJ06dYKBgQFWr16NkSNHUlkdxs3NDRUVFZg4cSL09fVRV1eH5s2bNzj183n7Ozs7U7dgAv6qDhMbG4uamhocOHAALVq04KrD0IabmxvCwsI4c8vKyspGrxNuf2NjY+Tk5HzjSBsnPT0df/zxB44dOwY7Ozv4+vpi8+bNiImJAQCR8V8Qv42NDfT09KgQOdzd3ZGcnIwuXbpAUVER79+/x8yZM+Hu7g5nZ2c4OzvD3d0dFhYWaN26NSdy0LI/fPDgQTx69Oir1ygqKkJXVxcODg7UPbM8Hg/v3r3D8OHDkZGRgSdPngAANDU1MX36dHz69AkhISHc6T6ByEFL+0dERACon+vU1tY2eo28vDwmTJgAT09PJnKIi2+ZJ8NgfA/U1dWRZ8+eEXt7e+Lu7t4gJ5IWduzYQVavXk0IqfcXmTlzJvnjjz8IIaJ5nIL/Ly4uJosXLybJycnfOtRGWb9+PbGzs+M+V1RUkOTkZLJ48WKydetW7nvh+JcsWULu3bv3zWP9t/z+++9k06ZNZMOGDSQtLU3kN1pyOln708u5c+fI5MmTSUJCAiGEiHiK0ISzszN59eoVIYRwvhUlJSVk//79ZN26dYSQv/oPn8+navz5HOF+cfnyZbJo0SJy6tQpQkj939TYdeIkMTGR+Pv7c5/NzMzIrFmzuM+CXHLh3Hma21+Yq1evkilTppCYmBhCSENfAhpwdXUlDg4OZOXKlWTOnDlcO3+ewy88fi5dupSq8TM3N5fzMxL062vXrpGRI0eS69evE0L++ntomz+EhIQQa2vrRn/79OkT+eOPP0hUVBQ5duyYiHcNLZ4QPB6PWFtb/+14Ioi3qqqKEELP+MPj8ciaNWu4z+vXrxfpG7/99huZNGkSte8uPp9PdHV1ydq1a7nvGhtnBN9VVlZ+s9gYojChg8H4HxAYjX6Juro68vz5c7Jy5UoSHh4u8j0N/Pnnn+T+/fvcZ4Fp4edihyDeoqIiqiYpERERZNq0aZwpnvAk8cqVK8TW1pYUFhZy3wsW2bTE/0+g2TiVtT/9nD9/ngwePJg8ePCAe45pmaQTUm/8N3HiRHLp0iXuO0GcaWlpREtLi+Tk5BBC6oWC+fPnU99/hMf3K1euEB0dHXLnzp1GfxcnYWFh5M6dOw36w4oVK0TEDsHiqKSkhPz888/UtP/fGXcSUr/gHjp0KLlz5w51/b+piExVVVXEzMyMuLq6inx/5swZYmJiwhnyFhcXk4ULF1IV/6ZNmxoVwrKyssj69es5M2phaOk/Li4uZOTIkdznLy2ihUWyefPmiZjAipOtW7eSOXPmcM/lsWPHyKhRo8j+/fvJuXPnOHHDxMSEE4tpJC8vjyxZsuSLYodw++vp6ZGsrKxvHiODCR0Mxr/Gy8uLnDx58m+vq6qqoubFIszVq1eJlpYWefDgASFEdGDeuXMn0dXV5cQOQur/DicnJ5EJu7jJysoizs7OZPfu3eTJkyciv+Xm5pJJkyZx7txlZWXExMSEqp0wYb6225ifn8/9Py2LJEJY+4uTqKiof7TQI4SQR48e/cfR/HtCQ0PJmzdvCCGEbNy4kWzbto28f/+eEPLXwvrDhw/Ezs6Om8Cnp6eT1NRU8QT8N0hadRhC6ndPLS0tyaNHj7g2F7By5Uqiq6sr8t2NGze494W4kfTqMJIuMgkQxF9aWkomTZpEnJycuN9yc3OJnZ0dqampIVVVVcTDw4PcvXtXXKE2yoYNG0hgYCAh5K+/pa6ujrx69Yro6+uT169fizO8L+Lm5kbMzMzIwoULiZubG/f95+8x4UX2smXLqGn/mpoasnnzZuLq6kqeP39OQkNDyZw5c8i2bduIq6srmTx5MtHV1SWjRo0iZ86cEXe4f0teXh5ZtGiRiNhRW1tL9Ums7w1mRspg/Avc3d1RXl6OzZs3f/U6YePF6upqKCoqfqMI/xnBwcGIjo6Gr69vg7zB3bt349ixYzh37hw6duyIiooKlJaWomPHjuIJVohDhw5BVlYWKioqGDJkCHx9fdGpUyfo6OiIVGCwsLCAvb09+vTpgzdv3qCqqgr9+vUTY+T1SLpxKmt/8eLu7o63b99yxsFA42Z0gr9HYCzZ2DXi4N27dzh06BCysrLg6+uLzMxM7NmzB5MnT8bUqVPRvXt3APXGf+3bt4eHh4d4A/6MxqrDAA2NaQX9p6ysDNnZ2VRVhxGwceNGZGVlYcWKFRg0aJBI/zcyMsLo0aNhZ2cH4Otme9+S/8W4U+AFQUP/B+rNO4uKirBixQr0799fxFTU3Nwc79694zwuAODmzZtQUFCgMr9fMM8pKyuDkZERfvjhB2hpaeHy5cto3bo1NmzYAAAoKCjgKp3RwpEjR3Dw4EGEh4ejS5cu3N8C1P870OhDc/bsWSQlJcHf3x+lpaUwNzdHjx494O/vD+Cvfi/4b0lJCaysrGBtbY0RI0aIOfq/xsmamhrs378fjx49QnZ2NoKDg9GrVy8A9e+zR48eISsrC3p6etx3tDy/wgj+nvfv38PR0RHdunWDn58f9ztt7f+9Qp+bGoNBKTweD6dOncL06dMB1E9mG9MJBcaLJSUlWLhwIbKzs791qH+LmZkZ9PT0sG7dOjx48EDkN2tra3h7e3PCRosWLagQOdzd3ZGQkID379/DxsYG165dw5o1a/DhwwfExcUhKysLALB27Vq0bdsWffr0AVBf1pGGRbakG6ey9hcvnp6eKCws5ESO9PR0AA0XcMLGfwsWLEB2djYVk8Tt27cjIyMDJiYm6Nu3L9zc3DBw4EAsWrQId+/ehb29PVxcXGBubg55eXlO5KBlL8bFxQUXLlxAYWEhEhISEBgYiK1btwKoN6ZtrP+Ym5s3KCcoLrKzs1FaWsp9XrduHdTU1LB//348efJE5H126tQpTuQAQIXI8b8ad2ZlZVHR/wX4+PigZ8+eCAwMRFpamoiJYUBAAOTl5bFt2zbuu7Fjx1IhcjT2HDZr1gw1NTVo1aoVIiMjoaysjJycHKiqqnIiBwDqRA4AWLx4MYYMGYJffvlF5Nmws7NDu3btqBM5AGDq1KmcqNG6dWts3boV7969g5OTE4D657SmpoYTOczMzKhZZG/fvh08Hg8bNmxATk4OVq1ahZEjR2LQoEH4+PEjKioqANS/z4YOHcqJHHV1dVQ8v7/99huOHj2K8+fP4927dwD+Gvc7d+6MTZs2caXFAaC0tBSmpqbUtP/3DDvRwWD8A7y8vPDx40fMmDED27dvh62tLVd6TFhtFp5kWVtbUzPIeXt7o3nz5hg6dChat26NUaNGoVmzZoiKisKhQ4fg6+uLQYMGNbiPlhKaPj4+KCgowPbt2wEAJ06cwI0bNxAQEIAXL17g0KFD6NKlC+7duwdVVVV4eXkBoCd+V1dXfPr0iVsYVVZW4smTJ9i9ezeGDBnSYOdU4E5vaWmJkSNHijN0AKz9xY2zszOKi4uxb98+AMDRo0dx+fJl7Nu3D/Ly8tz4I9iV5PP5sLW15SaT4sbDwwPFxcVc//nzzz9x5MgRvHnzBn5+fpCSksKDBw9QUlKCNm3aYPz48QDo6T8bNmxAVlYWV164oqICCQkJiImJgZaWFpYsWQKgYXWSVatWUVHSNDAwENu3b0fv3r0xYsQING/eHGPGjMGIESPg6+sLWVlZzJ8/Hz/88IPIooKWnVRBdZiSkhLk5ubizJkz3M6wsJD0eftbWFhQ0f+zs7OhqKiI1q1bc9/t3LkT6enpMDc3x8CBA9GsWTMq2vp/QdDunz+vtDy/nyMcl6urK54/f47a2lp06NAB7du358QEWvq/AEHcwvHn5eVh7dq16N69O3eagBCCkydPQk1NjYr55/r161FQUIAlS5bgzZs3mDlzJtq2bQtCCAIDA5GVlYUZM2ZAS0uLmrLJwri6uiIvLw/9+vXDrVu3oKGhgYkTJ0JHRwfl5eUoKSlBly5d8Ouvv2Lt2rXQ1NREXFwcunfvToVI+d3zTRNlGAwJ5M2bN2Tbtm3c55iYGKKtrU3i4+O57+rq6qg1XqypqSEWFhZEXV2d/PLLL8TS0pLo6uqSuXPnkrCwMPLjjz+S6dOnU5PD+TkeHh5k3LhxIt8lJCSQNWvWcF4FL168IJaWlsTT05O7hhbjMEk37mTtL14ePXpE9PT0SEREBCGkPsffyMiIvHv3TuQ6QU4/bcaFrq6uRF1dnfM7ERgt5uTkEH9/f2Jubk7evn3b4D5aPFEIkezqMB8+fCCE1HtADBkyhCQnJxNPT0+ybNkysmjRImJgYEDU1dWJlpYWlWZ5km7cGRAQQNTV1cmMGTOIq6sr8fb2JpcuXSKFhYXEwcGBODs7k0ePHjXo77T0/xMnTpBdu3aRy5cvf7UChuDf4dOnT+Tjx4/fKrz/mc+9dJ4+fUqePn3KfUfL+yssLIzs3LmT+9xYXLm5uURHR0fEuFMwTombgIAAsmLFCpHv8vLyiKmpKdm3bx93jbW1NUlPTxdHiF/F09OTq05ISP16YMuWLcTBwYHcvHmT3L17l1haWpKFCxcSBwcH7jpWZYUemNDBYHyFjRs3ktOnT3OfBZPZc+fOEW1tbXLx4kVCyF8vzbKyMmJkZESN8VBoaCh5+/YtqaysJFu2bCEGBgaksrKSfPjwgURFRZHg4GCyatUqMm7cOBFjK5p48OABGThwIFe9Jjc3l8ybN6+BIaywSzotkxRCJN+4U9Lb/+XLl8TZ2Zns2bNH4tr/ypUrhJD6Kgbe3t7E2tqaGBoaNqgIEBAQQHJzc0lJSQkxNDSkZpEnKIHo7+9P5s2bR/78809CyF/j5Z9//kk2b95MDA0NSWFhoRgj/TKSXB3GxcWFW0wQQoi+vr5ISeisrCzy8uVLcvHiRc6YkSYk3bhT0kUmNzc3smrVKrJ582Zy5syZL5ogCxsvWlpaUmvk+TmCfvV5/6Lp/RUXF0ccHBzIgQMHuO9qa2sblCEuLi7+1qH9I/z9/cm5c+e4z/n5+cTAwIBs2rSJmJiYkODgYFJVVUXOnz8vxigbx8fHh8yePZv7LBhnsrKyiKurKwkICCBlZWXk/v37ItXZaOo/DCZ0MBhf5fLly2TSpEkkMjKywW9xcXFk0qRJ5OzZs9x3jx49osbp3d3dnSxfvpz7XFNTQ9auXUuMjY0Jn88XufbzlyYtlJaWEkLq23XgwIFk165dZPny5eTIkSPcNV+reiBOQkNDybFjx8itW7dISUkJcXJyIlu3bm2wa7Fq1Sry8uVLQgghr1+/pqpSgyS3f0pKCsnMzCSEEPLq1Svi5OREtm3bJjHt//r1a/LLL79wC9CzZ88SIyMjEhAQIHLdmjVrOMf3a9euiZSNFie3b98mHh4e3GcfHx8yZ84crsKKoJ9kZ2eLTIRpQdKrw3h4eBALC4sG3+vq6pKlS5d+8T6aJumSXB1G0kWmvXv3ivSfgoIC8vTpU3L37l3uuSBEVORYvHgxNSdD/2k/FsT/6dMnkb9L3Pj4+HDj+pUrV4i9vT0JDg5ucN2BAwdETvfR8v4ViDHLly8nR48eJYTUxxYfH8/Nmfft20dCQkJE7qMlfkL+muPfuHGjwW/Xrl0jY8eObbDpQNP4yaiHCR0MRiMID7bXr18n2tra3NFxYc6cOUP8/Py+ZWj/CE9PT2Jtbc19TkxMJITUCxqurq7EyMiIlJSUEEJEB2ZaXjKbNm0irq6u3HHMqqoqkpGRQYYPHy5yDJJWgYbH45GFCxeSbdu2kWHDhpGYmBiSl5fHiR2CCZWDg4NIWT5akPT2X7duHVm2bBmZO3cuV6Lu+fPnxMnJiWzevJn69iekfjGdkpJCVq5cSfbv308IqS8t6+bmRqKiokhdXR1xdHQUOYn1+WKQFmpra0ltbS3ZsGFDo2KHAFrGn7dv3xIfHx+yYsUK8uHDB3Lnzh2ycOFC7oScAGtra+Lu7i6+QL+Au7u7yKL6wIED3IksQgiZM2cOMTExEUdo/xp/f39iYWFBHj582KCE5i+//EK2bt3Kff5aqehviaSLTHV1dWTr1q3k+vXrhJD6dF1HR0cyduxYsnTpUrJ8+XKSlZXFPa8CkYOWkzSCFJu/ez8JizQmJibkjz/++K9D+8cUFhaScePGEW9vb0JIfbqovb29iChmYGAgMs+jkVOnThEDAwNug0E4pcbExKTRTURxI/weOnfuHJk0aRKXqi54xxYUFBBbW1uWoiIBMDNSBuMzUlJSoKioiD59+nBlAlNSUuDo6AgbGxuuJOXnEEqMq9atW4eCggIcOHAAQL1x4cmTJ3HixAm0a9cO1dXV8PLywp07dxAbG8uV36MFHo+Hjx8/YuXKlUhNTcWFCxcwYMAAWFlZ4f379/j555/h5OSEBQsWiDvURpF0486m0P55eXnYvXs3YmNjERsbCxcXFzRv3hxycnLYsmULOnbsiPv371PZ/sLU1NTg0aNHCAoKwvDhw7FixQqcOXMGDx8+xN27dzFq1Ch4e3sDoGf82bhxIxQVFTF9+nR069YNcnJyXGy1tbXYsmUL7ty5g4CAAHTp0kXc4TZg+/btGDx4MAYOHIijR4/i1atX2Lx5MxITExEVFYXCwkKoqqri48ePaNeuHXXGhUFBQdizZw8eP34MoL6M5tmzZ7Fz50706NGDu05bWxvGxsYwMzMTV6iNIunGnYISuALj44MHD6JNmzYwNDQEAOjr66Njx47c+5k2cnNz0bVrVxw/fhy7du3C6NGjcffuXSxYsAAjR46EqqoqNm3aBEtLS6iqqqKkpASWlpawsbGhwvgyKioKzs7OuHfvHtq0aSNiWCv8jFZVVUFOTo4642AAXFnwkpISzJw5E9OmTQOPx8PVq1dx8eJFqKmp4dKlS2jbti32798PgJ7x53Oys7Nx8OBBlJaWYvny5RgwYAAAwNbWFnJycti0aZOYIxQlPT0dPXr0gIKCAvddbGwstm/fDgcHB64Igb29PRQUFKgrgc5oBPFpLAwGfVy7do2oq6sTdXV14uzsTObPn08OHjxI7ty5Q0JDQ8nEiRNJbGysuMP8Irm5uWT58uXcKZPw8PBGjQv5fD6JiooSQ4Rfx8XFpYFXyN27d4mNjQ3ZvXs3IaQ+JUFdXZ3cvn1bHCF+FUk37pT09nd3dyfa2trc54CAAPLTTz+RRYsWEV1dXXLlyhWSl5dHbfuHhYWREydOiHxXXV1NkpOTyapVq0h0dDSpra0lR44cIbt27eKuoSX+t2/fEm1tbTJt2jTi7+9Pli9fTl6+fNngeO+6devI9u3bxRPkV3B3dye2trbcZ4Fh6qpVq0hRUREpLi4m165dIzExMdxuNyH0tD8hhDx58oRYWVmRffv2kWPHjhFjY+MG/g+0pFd+jqQbdwYGBpIffviB+3z48GEyd+5ckp2dLXLdxIkTSVBQ0LcO72+5ePEiGTt2LLf7Hh0dzaX7CfdxExMTkpKSQggh5NixY9z/04KbmxsZP348KSoqIoTUj6GNne6gzbj22rVr3P8LTgrw+Xwybtw44uPjQwipT6fW1dUlZmZm3LU0jT+NkZycTPz9/cm4ceOIqakpsbCwIM7OztzvtMR/8uRJoq6uTtasWUN2795N+Hw+d+onJiaGTJkyhVy6dIk4ODiIzJNoGX8YjcOEDgbj/0fwcg8LCyPjxo0jx44dIxcuXCA7duwgBgYGZO3atZwIIkgFoQmBaervv/9OvL29ya+//koMDAwamFT5+/uL5PHTMkgnJiYSdXV1blJeVlbG/Xb79m0yfvx4boJOUy6tMJJs3NkU2j8uLo5MnjyZ3Llzh5w9e5YYGhqSvLw8kpubS0JDQ8nKlSsJIX+ZBBJCT/vz+Xxy9OhRYmdn10CErKioIEePHuUmV8KTdlriF3D69GliampKEhMTibu7O5k3bx5Zvnw5iY2NpdYwjxDJrw6zdu1asmHDBkJI/bvMxMSEDBkyhOTm5opcZ2NjIyKS0RK/pBt3EiLZIpOA7du3E319fREvo6KiIs5w19LSkvOOoJn169eTcePGcWKHgIULF5KrV6+S4uJismDBAmpEjqysLDJq1CgRAUwgdhQWFpJp06aRCxcukMrKSs48mxB6xv/GxpHPv0tLSyMPHz4UeQZoiZ8QQlJTU8mSJUtIYmIi0dfXJ5aWlsTBwYF7hm/evEk0NDRE0l1pip/ROEzoYDAIIY6OjiK51keOHBEpSVlaWkpqamrIkydPRFR3Wjh79ixRV1fn8jnT09OJtbV1A/8BOzs74ujoKI4Q/xE7duwgI0eO5Mwhq6qquAWHtbU1V4VCAE0vGUk27hQgye0vyP29cuUK+fHHH8mYMWNE8mcfPXpEVqxYwf07EUJf+1dWVpKIiAiyZs0azltEQHp6OlmzZo2IAEUjaWlpxNbWlpscGhkZET09PTJq1ChiaGjIVaoihJ72l/TqMAUFBcTIyIioq6tzJ68yMjKIubk52bx5M3fdmjVryPr168UV5heRdONOSReZCCEiFVW2bdtGdHV1SXp6OuHz+SQgIIBMmzaNmJqaEnt7e+46WsZ/X19fcuHCBfLs2TOR752cnMi4ceO4djYyMuJOQty8eZMakUMQ38OHD8msWbNE+rjgfbVjxw6Ryh7C94mbAwcO/K0J8D8RQmhgwYIFZMuWLYQQQpKSksjgwYPJtGnTiIODA7ly5YqIcElL/2d8nWbiTp1hMMSNh4cHKioqsHHjRgBAdXU1Fi1ahOrqalhZWWHz5s0YOnQoAGDgwIFcHiRNOf0jR46EhoYGYmJiUFhYiK1bt8LCwgKnTp3C5s2bsWrVKvj6+qJVq1acJwGhKKdT0JarV68GABgaGiIqKgrKysrcNWVlZZCTkxO5j4b237x5M/h8PlJTU7FkyRIYGBjg9OnTWLBgAYYPH45FixYBgEiusADW/v/vCNr/wYMHWLZsGcaOHYvNmzfDxcUFN27cwJQpUwDUexV0794drVq14u6lof0DAwORn5+Puro6jB07Fj///DMA4NatWyCEYO7cudx1nTp1gry8vDjDbUBoaCgUFBTQuXNnjB8/Hv379wchBDt27EDXrl0hIyODs2fPIj8/H+np6fjpp5+4e2lo/8TEREhLS2PXrl0AAF9fX5ibmyM4OBidOnUCIQRdu3bF/PnzMWDAALRr1068ATdC+/btsXr1agQFBWHfvn0oKiqCq6srVq9ejX379sHPzw8FBQVQUFCAp6cnAHreX56enigqKoKvry/3XVRUFGbPno1ly5YhNDQUPXv2BAD06dOHu4aW+D9+/IisrCz89ttvUFBQgJWVFdauXYsdO3bg6NGjcHBwAADY2dlBQUEB1tbW3L009P+NGzfCzs4OzZs353wr1qxZAykpKaxevRonT56EiYkJRo4cCXl5eWhoaACgq/0vXbqE/Px8pKamYv78+Rg0aBB+/PFH+Pn5wdPTEz/99BM6d+6Mzp07IyAgAADw448/ijnyvxD0gyFDhsDX1xfOzs4AgBUrVnDvqydPnqBbt26N3idO3N3dUVhYiOXLl3/1urq6OsjIyHC+dwAd8QsQ9GdnZ2cEBQWhrKwM8fHxGDVqFAwMDJCRkYHz589zniK09H/G38PMSBnfNS4uLqipqeEGr6CgIGRnZ3MGf4cOHcKJEyewYcMGKoy2GkMw4J4/fx58Ph+3b99GTU0N9u3bh4yMDISFhSEuLg5Tp07lJpM0DtLCMe3cuROHDh3CpUuX0LFjR9jZ2UFeXh4+Pj5ijlIUSTfuFEbS2//+/fuIj4/HDz/8gPnz5+Pt27fw9fWFjY0N7t27B0IIdSIfj8fDhw8fMGXKFLx79w4HDhyApaUlVq5cibNnz+L06dNo3749Kisr0bFjR+qMR11cXPDy5UuoqqoiJycHGzZsQI8ePfDu3TvMnz8f3bt3x7FjxxoIfLTE/zl1dXUA6hd/d+/eFRE7hOOlMf7i4mJcvnwZysrKcHR0xE8//QRPT09kZGTA2dkZysrK2LFjBwB6xn9JN+4UkJSUhKCgINy9exfGxsZwdXVFRkYG9u3bh27duqGgoACtWrWiTmQCAEtLS7x48QJxcXFo1qwZJ3YAgLOzM0pKSrB7924q+39OTg66desGNzc3dOjQATNnzsSBAweQk5ODdu3awczMDJqamti5cyfS0tIQGBgIAKitrYWMjIyYowfc3NzQq1cvaGpqiswvHz58CDs7O0yfPh36+voICAhA8+bNOeNjWli/fj0uXbqEe/fuAYBI3xFG0N58Ph8mJibYu3cvOnfu/K3D/Ufk5eXB3t4eBQUF6N69OwIDA6noK4z/B8RyjoTBoICzZ8+S4cOHk+fPnxNC6o3D5s+f3yCnNigoiPj7+4sjxK+SnJwsUk7v2rVrxMrKirx+/ZrY29tzZcfS0tJIaGgodx2NxwUFCB8F3LFjBxk2bBhZsmSJiHEkLfFLunFnYzSV9heUY7148SJnLiaAluOmTk5ODVLLUlNTycCBAzlD0ry8PHL69GmutB0h9MQvXMI6IyODrFmzhitZXVxcTGxsbMi2bdsIIXSWvfX39yeBgYHk9evXXIqToG/X1NQQf39/oq+v3yD9gBbc3d1JaGgoefz4Mffd3r17ybZt20hlZSWZOHEi99wKUnEIoaf/SLpxpzBFRUUkMjKSJCcnE21tbcLj8Qgh9Wks+vr6ZPXq1dy1tLS/q6srOXjwICGEkNWrV5OpU6dyaYqC51hgIkkjHh4e5NChQ4QQQjIzM4m2tjbJzMwkhBCyfPlyMnLkSLJgwQIyatQozl+EEHra/88//+TKDXt6ehJjY2Py+PFj7ll98+YNMTU1JTwejzMiJYSe+N3c3IiZmRlZuHChyHv48xLPwiV8ly1bRu7evftN4/xfiI+PJ6NGjeLWBsKeWLTMfxj/HCZ0ML5LysrKSH5+Ptm2bRvx8fEh/v7+ZOHChQ3M5uLj47mXPk3s3LmTqKurEycnJxISEsJ9HxISQrZs2ULy8/PJypUrycKFC0Xuo+Ul+TWEY9y5c6dInXha4m8Kxp1fQtLb/9atW2TChAnkxYsXhBAiYqpHyyQlLi6OjBw5kjOVE64KcPv2baKrq8tN2oWhpf15PB5ZunQp9zkqKooYGhqSly9fEj6fTwip90QZMWIEef36tbjC/CKSXh0mLS2NjB8/nmhra5Pp06eTAwcOcIKMt7c3uX//PikuLibq6uoiXi+09H9CJNu4U9JFJjc3N5FxnZB6/xBhsYMQQqysrMjGjRu/dXh/i5ubm4ghak1NDdmwYQMJCwsjnp6eZPHixaS2tpbk5eWRc+fOcdfR1P8JqfeCmzt3Lnn27Bnx8PAgJiYmxMDAgMTExHDjqDC09J/o6Giybt06Qki9KLZgwQLuMyF/iRuC/9JW3ebv+kFBQQGxtbWl0o+P8e9hqSuM7w4/Pz/cvn0bJ0+eREVFBUJCQnD27Fn4+flhwoQJ3HWWlpbo1asX1q1bB4Ce45qvX7/Gu3fvsG/fPigpKSErKws9evTAzJkz0aZNG2RmZuLnn39GZWUlTp06BRMTE3GHLEJpaSmKiorQo0ePL14jOEIufLyXlvYXsHPnThw/fhwnT55Enz59UF1dDSkpKTRr1gw2NjYwMDCAtrY2dz0tx5W/l/afO3cuJk6cyF1PS/sD9UeuIyMjufQmgQdQdXU1iouLYWtrCy8vL6iqqoo30EZ4+fIlXF1doaenB2NjYxw7dgwhISGQlZVFz5498eTJE0yaNAmysrIYMWIE9PT0xB1yo5w5cwbnz5/H8uXLER8fj99//x1t27bF3Llz8dNPP6FNmzbiDvGrxMfHIzk5GdLS0sjJyUGfPn3w4cMHDB06FJWVlVi0aBHKyspEPGlowNHREYqKinB2dkZGRga2bNmClJQUxMfHo0uXLtx1q1evRt++fTlPC1rGn/T0dJibm0NGRgZycnIwNDSErq4uunTpAh8fH+jo6KBv374YNWoU/Pz8YGBgAICe+Hk8HqKjo3H48GFoamqKeEfZ29sjMzMTc+bMwZ07d9C+fXv4+fkBoCv+8PBwrm0FaRGxsbFwcHDA0KFDERYWBkDUF4uW8X/jxo344YcfoKOjgw8fPsDf3x/Lly/HwIEDsWTJEuTl5aG6uhpycnKwtLSErq4uAHraHwDKy8tFvKLy8vLg4OCA7t27c+k1grYvKSmBmZkZHBwcqEj/PnjwIIYPH869c7+Ek5MTFBUVsW7dOqranvHvYWakjO8KQgjKysrw8uVLWFhYYOfOnVi1ahWaNWuGhIQEKCsrQ0VFBXZ2dujQoQN1Ikd8fDz27duHs2fPori4GLdu3YKxsTGys7ORnZ3NfV9aWgozMzNO5KAlfh6Ph8LCQjx48AB79uzB4MGDG518EEK4nM5bt25h6tSpnIGVuJFk487vqf0/j5eG9j9+/Dhqa2sxbdo0LFu2jPMAkpGRwQ8//ABZWVl07NgRzZs3R3l5ubjDbZS+ffvCxMQECQkJuHfvHl6/fo3IyEi0b98eRUVFuHnzJl69eoX3799zIgctiwxh+vfvj5s3b6JHjx7w8PDA/PnzkZ+fDy8vLygrK8PMzAxTp04FQM/4CfyV7z59+nSUlZXh9evXGDRoECZNmoSLFy/i0KFDePPmDYYPH47+/fsDoKf9Jd24E6jvNy4uLpzI9ODBA/D5fE5kevr0KYYNG4b79+9TZ3zs4uKCyspKmJiYIDQ0FDU1NdDU1OR+37p1K06dOgUZGRloaWlh6dKlAOjpP97e3igpKUFwcDB8fX1RU1PD+bno6uoiOTkZCgoKACBiegnQMf67u7vj48eP3LyyQ4cOaNGiBY4cOYJOnTqhpqYGFy5c4J4T4cU4Df1HQIsWLQD81S+6dOmCLVu2YO3atXB2doafnx+aNWsGQghiYmKoETn+iXGqYKwX9kOhqe0Z/x52ooPx3SAYlIuKirBnzx7k5ubi7du3OHDgAKSkpHDkyBG8f/8eWVlZ6NevHzw8PETuo4H09HRs2rQJoaGhAOrd6ZOTk6GpqYlJkyYhMzMTwcHBGDJkCKysrMQcrSiurq7g8/nYsmUL8vPz0aFDBzRr1qyB0ZNgIk/bToAwkmjcydpfvPB4PDx//hydO3fGkydPEBcXh3fv3iE+Ph6vX7+Gubk5+vTpA3t7e8jJyXE7qTQhvOC/du0ajh07Bk1NTZiZmTVqQvf5PeLk8+owAGBrawsZGRl07doVDx8+xPHjxxutDkMDHz58QKdOnQCImilGR0fj0aNH6NevH4yNjZGZmYmMjAzMmjVLnOF+EUk27hRu9zNnzuD169eQl5fnRKa4uDi8efMGERER6N+/P6SkpKiJ/+3bt4iLi8OKFSsAANu3b8ezZ89gZmaGIUOGNDAMFkBL/NnZ2YiKioKNjQ0A4OrVq/D19cWqVaswb948APVC8tmzZ3H8+HFqhHkBPB4P5eXl2LJlCwDgwYMH0NTURH5+PmbNmoWuXbsiOjq6wVhJS/ufOnUKeXl5XPs3FldeXh6WL1+OJUuW4JdffgEAVFRUcMKIOPm3xqnFxcWccarwSTOG5MGEDsZ3B5/PR3BwMEaMGIEXL14gJiYGISEhkJKSwpYtW0AI4RYZtLxkBNTV1WHRokVwcnLCDz/8AAA4e/Ysbt68iVGjRkFfXx9VVVVo3bo1AHoWGSkpKQgMDERwcDCA+lJpqampOHv2LCZNmoTZs2dDWVmZO+7I5/NhbW0Na2tr6hbZAj5fbB85cgQ//PADVFVVwePxALD2/y+RpPb38vJCfn4+du3ahezsbOzbtw/u7u5o0aIFnj9/josXL+Lt27d4/fo1+vfvD3d3dwD0xC+McEyXL1/GhQsXMHToUEydOpXaCaGkV4cJDw9HVlYW5s6dy6UzCSbkgl3T5ORk9OjRA8uWLeMm8LS9vwDJrA7TVESmz9mxYwcyMjL+VuwQN6WlpaiqqkL79u0B/PVvcOPGDXh7e2PFihUwNDREZWUl9uzZA3t7ezFHLMrBgwdx7NgxxMTEoHXr1ggNDUVMTAxCQ0PRunVr+Pn5oVWrVlizZs0XF+Di5vz587hy5Qo0NDS4k8J1dXWoq6sT6Td8Pp+6tD8ej4fc3Fx8+vQJvXv35qqvfV59R7g6jK2tLczNzTFq1Chxhc34P4LOUY3B+D/E3t4e1dXV0NPTw48//og2bdpg7Nix2LVrF3x8fLg0j4CAAPB4vAbH8sTNyZMnMXz4cDRv3hzKyspo27YtKisrud/nzJkDaWlpREdHQ1lZGaNHjwZAzyQdADp37gwFBQUkJSUhLS0NCQkJUFBQwE8//YTExES0bNkSJiYm3CLb1taW6kU2UH8UVjiNQkpKCi9fvuQW2bT0H4C1vzhZt24dioqKuNKGSUlJyMrKQkZGBoYOHQo1NTV07NgRAQEB6Nu3Lydy0BI/IDqWSElJcZ+nTJkCoH4S/OnTJxgZGVE3yfXy8kJpaSnCw8Px7NkzBAYGol27dgAABQUFDBs2DCoqKmjWrFmD4+60jJ+qqqpISUnBxYsXMW3aNKiqqkJGRgaVlZVo3rw59PT0UFpaipYtW4oskmjoPx4eHlBRUcHw4cPxww8/oG3btsjLy0NWVhYuXLiA6dOnw8vLCzweD/v370fXrl0B0NP/PxeZZGRkuAWR4N2bnJyMwMBALFu2DH369AFAT/wODg6Qk5PD0KFDuV12Aba2tti5cyeCg4OxZMkSjB49moqYheHxeCgoKMCjR4+we/duDB48mFucjh8/HjweD15eXigrK8PSpUs5kYO2+c+ECROQkJCAP//8E4mJidi5cyfatm0LAJgwYQLMzc1haGj4Ve8sceDr64vi4mJs2rQJLVq0wLlz53DgwAGYmppCWlqa6y8HDx7EzJkzoaSkBICe9j979iyqqqoQFBSE0tJSmJubw8nJCf7+/iLPsvBJVto3eRj/Dnaig9GkEdTEzs7OhpqaGqqrq+Hk5AQVFRXcuHEDADBt2jQ4OTmha9eusLW1BUDPIJ2SkoK9e/eiuLgYxcXFmDx5Mq5du4Zff/0VCxcuFJmUC45C0kRQUBA6d+4MXV1deHl54c2bN8jMzISDgwMGDRqEvn374tatW0hKSsLatWtRVVWFpUuXwtbWlgolXdKNO1n7i5ecnBy4uLhg4MCBWLt2LU6cOIGAgADIy8ujffv24PP56NmzJ8aPH4+RI0eib9++AOhZJP3+++8YNGhQo78Jt3FMTAz4fD4WLFjwLcP7W9zd3ZGVlcWl+kVHR+PEiRPw8/PjxL/Hjx/DxMQEkZGRIj4vtPHw4UOcOHECKioqmDFjhohRraenJ4yNjdGvXz8A9PR/STfuBOrfweHh4VBVVeVEJgCcyAQAJ06cQMuWLbn4acLZ2RmVlZUoLS1FYWEhZs+ejQkTJoj0dW9vb/Ts2ZPz5KCFf5puefnyZdy/f5/zvqCRc+fOISEhASkpKQgKCoKGhgaqqqogKysLKSkpxMXFQUdHR9xhNqCoqAi6urqYMWMGXF1dceXKFcTFxUFNTY1Lg5o7dy569OiBXbt2iTnahkiycSrj/wYmdDCaPGlpaQgJCUH//v2RmZmJli1borKyEnw+H926dYOzs7O4Q/wiwr4iBQUFeP78OVJSUvD8+XOsXr0aQ4YMaZCLSssksaqqClFRUYiJiYGFhQXGjh0LPp8PaWlpLrUGACwsLDBgwABYWVmhpqYGHz58QLdu3cQYeT3/1LhT+LgjTcadrP3FS0REBAwNDZGUlIRr167h999/R0VFBU6ePAlCCOrq6hAfH4+HDx+CEMJ5EtDy/Hp4eKB169acQWRj0BJrY0h6dZhTp06hc+fO0NbW5t4D9+/fR1hYGJSVlfHLL7+gc+fOWLhwIerq6nDixAlxh9woklodRhhJFJlSU1MxbNgwREVF4fDhwzh58iR+++03PHz4EAkJCVi5ciWUlZW5U1m08Xfplrq6ulBRUWlwHy3tL+DzVL9r165h0KBBmDZtGtq3b98gXlpEbuAvMa+kpAQzZ87EtGnTwOPxcPXqVVy8eBFqamq4dOkS2rZti/379wOgr/0F7Sncrnl5eVi7di26d+8uUlXo5MmTUFNTYyJHE4MJHYwmi/DAlpKSgtOnT2PQoEFQUVGBvLw8Nm7ciBcvXsDb25tT0mkZpHk8HjQ0NDBgwIBGy2Dt3bsXT548waJFi6ClpUXNi1GAoB1LSkpw5coVhIeHY8GCBdDR0UFSUhL++OMPtG/fHlFRUSIl7GhB0o07WfuLF09PT7x8+RJHjx5FXV0dUlNTERoaCkVFxa+apNIy/nh6euLDhw/Ys2cPgPqTNcLimABhkWnv3r1YtWoVlxZCA5cuXUJCQgIqKirw+vVrhISENFodxtfXFwAdiwxCCO7fv4+FCxeiZcuWmDdvHpSUlGBkZIRWrVohPT0doaGhUFVVRXR0NFRUVBAQEMDdS0P/ASTbuFPSRSYXFxe0atUK69evBwCsWrUKP/30E4yNjbF27Vr8/vvvUFNTw40bN+Dg4MCdxKKp/2RlZWHHjh0wNDQUSbfU0NBAcnIyJk+ezHlF0I5wu8bGxiIlJQW9evWCvr4+5ztCE9evX8eECRMA/GXaKRA7Zs6cifXr1yMhIQE7duxAt27dEBQUBICO8ROQfONUxv8tTOhgNDny8vLQuXNnkVxyAEhOTkZERAT69u2L+fPnQ1paGk+fPuU8LWghJycHS5YsQe/evdG1a1fk5uZi7dq16NChg8hL0dfXF0pKSli2bJkYo21IZmYmVFVVRSZM0dHRiIqKwuLFizFo0CBs3boV8vLyaNOmDezs7ADQ85KUdONO1v7ixdPTEwUFBdwx3ufPn0NNTQ13797FpUuXICsrC0tLS7Ru3VpkMUjLIsPb2xsfP37E9u3bAQBHjx6FtLQ05s+f/0XjNgsLC9ja2lLR/oBkV4cREBISgqqqKgDA48ePkZGRgcWLF2PSpEmQkpKCl5cXOnTowB2/puX5lWTjzqYgMrm6uqK0tJQzdCWE4NSpU3j06BEIIXj79i2OHTsGoH6uRJuB8L9Nt5QUhPtHXFwcEhISoKenxwkKtJCdnY2ff/4ZpqamMDMzA/CX2FFUVAQjIyPY2dlBW1sbL168wMCBAwHQM/4Akm2cyvi/hwkdjCbFmTNnEB8fDzs7O6ipqTUQOwS7Mp06dcKKFSu43UeaBmkA2L17N27fvg0vLy8EBQWhuLgYpaWlsLCwgIqKCnr27CnuEBslLCwMHh4eGDRoEJSUlKCmpoahQ4eiWbNmeP/+PU6cOAE3Nzfu5SiApvb/NztJAuNOCwsLKhZ5rP3Fi6urK/Lz87nFz5EjR5CUlISdO3eiWbNmuHfvHuLj41FdXQ03Nzcux58W9u7di927dyMjIwNAffzR0dHYs2cPZzInDG0ikzCSWB3G09OTEwIOHTqE1NRUTjCbOHEiFBQU8O7dOxgYGEBbWxs//vgjAHqe36ZSHUZSRSZ3d3f89ttvGDlyJLfLDtRXupk3bx7q6upw5coVABAx3qUl/v8l3ZImwsPD0bNnT2hpaTX6u/CY9DX/I3EhiO/Ro0dYv3499PT0OB8OQXrZzp07oaqqitmzZze4T9wIG6devXoV586dg4aGBkxNTUWuo9U4lfHfwKquMJoUc+fOxYMHD3Dw4EGYmJhwYodAkR4+fDjq6urw8uVLkSPWNLzknz9/DkII1NXVsWjRIrx48QIyMjLYsmULFi5ciPfv34PH46FVq1ZYvXo1pk6dCoCeQbqiogJjxozBrFmzQAiBrKws5OTkOKGmRYsWePToEczNzbF//37uJU8IoaL9hXeSWrdujYCAgC/uJAH1kzJzc3NqdrJZ+4uXO3fu4OHDh5yhcVhYGGJjY7Fjxw5uQTdmzBhUVlaisLCQOpGjtrYWJiYmOH36NDZs2IABAwbgwoUL2Llzp4jIcfnyZUyZMgV8Ph9WVlawsbGhov0Bya4Ow+PxUFxcDGNjYwDA0qVLER0djaNHjyInJwc9evTAvn37kJGRgYcPH3IiBy3PLyDZ1WGERSZpaWmkpaWJiEynT5/Grl27YGBggCVLllAnMm3atAnFxcVITEyEq6srTExMcPDgQQBA27ZtYWtri4SEBNTW1qKurk7Ex4iG+AkhkJOTg46ODlq0aIG9e/eiqKjoi+mWtIkcBQUFePfuHZKTk7m55ucIz0V79+6Nt2/fUlVlRTB2DhkyBL6+vpx/3YoVKzgPnSdPnjTw8KJh/gkAlpaW0NXVhY+PD1xdXUEIQVxcHIKCghoYpwqnPdESP+O/gZ3oYDQJPp9srF+/HlVVVTA1NUXv3r25SdUvv/wCR0dHbmJOi0jA4/Hw/Plz1NbWokePHti+fTucnZ0hLy+P1q1bIyUlBcePH0dOTg7evXuHkSNHijtkEWJjY3Hjxg2sX78eeXl5iI6OhqysLBYvXowOHTqgsrISL168QFpaGqqqqrBw4UJxhyyCpBt3svYXLwEBAZg+fTru3r2LtLQ0VFdX48WLFwgMDESHDh2464KCgjBnzhzuRAEt409ISAhXIaayshI///wzMjMz8ejRIxFBxs7ODu3atQOPx4ODgwPmz59Phcgh6dVh3NzccOHCBZw6dQqqqqpcWlZ0dDS2bdsGNTU17Nu3r0HaDS39RxhJNO4UiEw7d+7kvtPX18e8efOQk5ODJ0+eiIhMgkUTLfHX1dXhwoULmDlzJqSkpFBcXAw3NzeUl5fjwIEDAOr/XUxNTXHixAmoqamJOWJRJD3dUsCrV69w+fJlPH/+vNGxUTjdz9bWFo6OjtDQ0BBTtH/h5uaGXr16QVNTUyTmhw8fws7ODtOnT4e+vj4CAgLQvHlz7iQTTTQF41TGfwMTOhgSz/bt29G6dWv8/PPPaNeuHTdwrV+/HhUVFbCzs0P37t2xePFiSElJ4fDhw2KOWBQvLy8UFRXBy8sLeXl5WLVqFezt7TFgwADo6upCVVUVZ86caTAg0/SSv3fvHi5dugRCCFavXo13794hPDwcrVq1gq6uLtTV1RvcQ0v8km7cCbD2Fzc2NjZo164dvLy8cPjwYZw7dw5z587F/PnzuWvs7e1BCMG2bdvEGGnjPHv2DJs2bcIPP/wAa2tr1NTUYNasWdDS0oK3tzcAYM2aNVBQUICXlxcAoLCwEIqKiuIMG4DkV4dxdnbmjoXLycnB2NiYO4n45s0bLF26FGvXroWOjo5IugEtSLpxp6SLTI8fP0Z5eTnGjBkD4K/FdGlpKVxcXPDp0yfO70hwGosmJD3d8nOfkw8fPiAyMhJ//PEHfv75Z65Mu+DZFaT7WVlZUbFhlZubCzMzM3Ts2BG9e/fGs2fP4OTkhE6dOqFr167IysqCt7c3lJSUICcnxxnc0tL+km6cyvjvYUIHQ2IhhKC8vByzZ89GixYt0KxZM8yYMQPq6uqYPHkyCCHYsGEDPn78iIyMDHTv3p26Qc7GxgatW7fGhg0buO98fHwwaNAg6Ovrg8fjoVu3bli1ahU3iNPKgwcPEBcXxy22c3JyEBERgVatWkFHR6fRxba4aSo7SQBrf3EgWFS8ePECISEhcHZ2Rps2bRAUFISsrCxMnToVY8eOxfr169GiRQtOJKBlkQT81ZaZmZnw9fXFDz/8AFtbW1RUVGDWrFkYPXo0t7gWxE9L+0t6dZiXL18iOjqaE2kcHR0hIyMDExMT9O7dGzIyMggNDcWtW7ewefNmqio0NAXjTkkXmXg8HnJzc8Hn86GnpwcjIyPIyMhwz6dA7Hj79i3OnDnD3UfL81tRUYHc3Fzs3r2bS7fs06cPbt26JZJu2alTpwbpljT0nwMHDiAgIAA//vgjFBQUMHr0aPTv3x/5+fl48eIFHj16BBMTE+7UBp/Ph42NDaysrKg4CSfg6NGjiI6Ohp+fH06ePIns7Gx8/PgRy5cvx4QJE6CgoCByPS39pykYpzL+e5jQwZBYsrOz0bNnTxw8eBBt2rRB27Zt8fTpU5w6dQrjxo3D1KlTMWPGDLi4uKBZs2bUTdLr6uqgra2NESNGYOvWrQCAjx8/wsTEBFZWVpg8eTLOnz8PJycnJCQkoGPHjmKOuHGEJx2NLbYPHjwIbW1tzJw5U8yRiiLpO0kCWPuLn9LSUqxevRojRozAqlWrAADBwcF49eoVMjIyMHDgQK6sLC3x37t3j9ttFBY7NmzYgMGDB2P16tUoKyvDlClTMHz4cE5MoCV+Sa8O8+nTJ9TV1XG57wA4k1opKSmYmJigb9++SExMxPPnz7F06VLxBfsVJNW4U5JFJqC+/3/48IHzESkrKwMhBK1atRLxbSkpKUF0dDQWLVokznAbIOnplkB99ZTDhw+jV69e+PTpEzp16oSUlBTIy8ujvLwcb968wZAhQ7Bt2za0bNkSFhYWsLa2puIkx8aNG/HDDz9AR0cHHz58gL+/P5YvX46BAwdiyZIlyMvLQ3V1NeTk5DjvC4AekUnSjVMZ3w4mdDAkEm9vbygqKsLKygqJiYlwdHTEyZMn0bNnT5iamuLt27do0aIFqqqqsGnTJm4ngJZJliCOqqoq6OrqYtiwYfD09ISJiQkmTZqE5cuXA6ifpP/2228wMDAQc8T/nAcPHuDChQuora2FjY0NysrKqPBREEbSd5K+Bmv//54zZ86gsrISv/76K/ddWloatm/fjnXr1qFv374AgMDAQJSXl2PNmjUA6Bl/MjMzMW/ePDg7O8PIyAjAX7G9evUKpqamsLS0xLx580R2smlpf0mvDuPj44O3b9+iVatWsLS0RO/evVFXVwcZGRlUV1eDx+MBACd2CKCl/SW9Ooyki0z5+fnw8vKCh4cH2rdvj9u3b+P27ds4d+4cVFVVsXTpUkyYMKFBf6Gl/wCSnW4pTFxcHBITE6GpqQk9PT1UVlairKwMDx8+xNOnTyErKwtra2vcvHkTioqKVFRacXd3x8ePH7F7924A9e3q5uaGmpoadOrUCQ8ePMDx48fx8eNHZGVlYejQoeIN+G949OgRnJ2doa+vz4kdAGBqaopp06bhl19+EWN0DHHDhA6GxMHj8VBSUsLt5AHA1q1bUVdXh4KCAuTk5GD//v1o0aIFoqOjMW/ePAB0veQBcLnAVVVVmD17Nt68eQN/f3/o6+ujrq4OUlJSIvHS+JL/Eg8ePEB4eDhGjx4NfX19APS0f1PYSfo7WPv/NxBC8PHjRwQEBODFixeoqqrCypUr0bt3b/Tq1QteXl6YMWMGd1JCGNqe36SkJLi7u2PZsmWcYCM49nv8+HFUVVVh2bJl3PW0xF9bW4vq6mro6OhgypQpGDBgAMLDw7Fx40aRstu0VodxdXUFn88Hj8dDWloaxo0bJ3ICBahfdNvY2GDChAkiPi80IOnGnZIuMgH1O9YbNmxAt27dUFJSgps3b2Lo0KH48ccf8ezZMzx58gTBwcEN+hVtSGK6pQDh/hAdHY2bN29i9OjRmDhxIjp37izm6L4Mj8dDeXk5tmzZAqD+30BTUxP5+fmYNWsWunbtiujoaGo94ZqCcSrj28KEDoZEwePxEB4ejtDQUGhpaXG7jZcvX4aHhwfU1dW5kmrC0DRJEUYgdlRXV2Pu3LkYMmQIdUfchfm7OvECCCF49+4dVaXTBEjyThJrf/FSWVmJuro6tGzZEkB9FZX379/jzp07WL9+Pe7cuYPU1FQEBQVx1wD0jD83btzAx48f0bdvXwwaNAi///47bG1tYWJiInI6xdzcHGPHjsXixYvFGG1DJL06TFJSEo4fP86lAaWnp+PFixe4c+cO+vTpA2NjY67fCFJuaELSjTslXWQS5vjx48jIyMDTp09hY2MDdXV1dOvWDVVVVXBzc4O7uzvk5eXFHWajSGq65ecI/x1nz57FrVu3MGrUKEycOBGdOnVq9DpxcvDgQRw7dgwxMTFo3bo1QkNDERMTg9DQULRu3Rp+fn5o1aoV1qxZQ6UnnKQbpzLEAxM6GBKDh4cHysvLMXLkSM74b/z48dzvVlZWUFBQgJ+fHzUvln+C8MmOWbNmoWfPnggJCRF3WA0oKCjAkSNHkJOTg/nz5zdaJx74y128rKwMHz9+FNllpQVJ3Eli7S9e/Pz88OHDBzx58gT9+/fHrFmzMHnyZDRr1gxxcXG4cuUKunXrhsOHD+PEiRNUHFEWxtXVFXl5eWjZsiX++OMPaGpqwszMDCUlJXBwcMCsWbPQp08fXLhwAfLy8iIGybQgydVhgHqhad++fQgLC8Px48dx5coV1NbWokuXLqisrMTIkSOxYMECEZGDlneZpBt3SrrIJODv+oO1tTUUFRW5/i8JSEK65ZcQ/vf47bffEBMTAzMzs0ZP9YmbmJgY3L9/H5qamvjzzz+RmJgIX19fbo5w8+ZNmJubIz4+nspNEkByjVMZ4oP96zMkgtzcXKioqGDTpk0wNDTE4sWL4e3tjdu3b3PXGBsbIzc3Fzk5OVRMDIU5deoU3r9/j7q6uga/NWvWDDU1NZCTk0NsbCyVizwA6NChA+bMmYN+/frh5MmTSElJaXBNbW2tSAm1srIyMUT6ZQS6rqamJnR0dCAlJYWdO3dCSUkJhoaG+PPPP/Hq1SsxR9k4rP3Fh4uLC/7880/Y29sjICAA3bt3x7lz5zgjRh0dHaxbtw5GRkYwMzOjTuTw8vJCaWkpgoODsWvXLvB4PEhJSWHfvn1QUlLC1q1bUVZWhvv370NZWZkTORobr8RFXV0d1NXV4eLigsePH2PXrl2Qk5NDTEwMbt++DWdnZ/B4PBGRo66ujgqR4+TJk6ioqMC4cePQsmVLzJo1C9u3b8e0adPg7OyMjRs3YsCAAdyEXHiRTcO77OXLl+jQoQN27doFPz8/fPr0CYcOHUJmZiZqa2uhrKyMxYsX4/Tp0/j48SN1IgdQLwDn5+cDqD8NsWXLFpw5cwa1tbV48uQJV5Xkc5GJNqSkpETievDgAXx8fLBt2zYsW7YMzZs3F6nuJAloampixowZKCsr4wRjQDLiF/730NPTg7W1NZUiBwDMnj0bI0eOxPXr13HixAm4uLigZ8+eqKqqAiEEP/30EzZv3kydyLFx40bExcUBAGbMmAEVFRVUV1fD3d0d1dXVKC8vx/bt2/HLL78gNjaWu0/YlJfx/cJOdDCoRzA5EVQdEUxETp06hQMHDsDDwwPjxo1DdnY2zp8/L2JGRAMZGRlYu3YtwsPDRY6zf05FRQVatGjBKdC07ORJep34ryEJO0ms/cXLtWvXcPz4cQQHB3PfVVZWIiwsDElJSVi7di369OnTYOeIlp0kHo+HP/74A0ePHgXwV1yPHj3C4cOHMXr0aBgZGTXYhaclfkmvDkMIgYGBAXr37g0/Pz9ISUkhPT0dPXv2RIcOHbjraE0XknTjzpMnT8LAwACysrIwNTXF+/fvkZeXh7Vr12Lo0KFQV1dHUFAQFBQURNK3JInQ0FDIyclBSkoKxsbGAOjp/5Kebrlnzx7k5ORARUUF8+bNE3lmhRGkepSXlyMnJ0fE34UGhOeTly9fxrVr1zBo0CBMmzYN7du3bzDfpKX/NDXjVMa3R/y9mMH4Ci4uLnBxccGCBQuQlpaGmpoabjA2MjLCihUr4OnpicuXL6Nnz54ixme0oKGhASUlJaSnp3/xmtraWrRo0QLFxcXw9PREaWkpFSLHgQMHMGvWLNja2sLNzQ2xsbHg8/kYNmwYBg8ejIiICK7ygWCRbWNjQ00Jtb+D9p0k1v7ip7CwkCstWVdXh7q6OjRv3hxLlixBYWEhEhISAKDBpJCGSSIAdO7cGUVFRXjz5g2Av04IDBkyBJqamtypFOF4adkJy8zMxIoVK3Dq1CkA9W1aV1eHPn36YP369Th79ixOnz6NVq1a4caNG5zIQUv8AlPp06dPo7CwEC4uLpCSksLQoUNRUlKC48eP4927d1i1ahXatGlDncjh4+ODNWvWgMfj4dWrVyCEcKfGBKlCBw8exMuXLzF27FhO5KDl+SWE4NSpU3B2dkZ1dTUCAgLg6+uL+Ph4GBkZcacnU1NTUV1dLeZoGxIeHo6kpKQv/i44cbV06VIsWLCAOpGjoKAA7969w5kzZ3D//v0vXlddXQ0pKSkoKioiOzv7G0b4ddzd3ZGRkQFtbW106dKl0bQIoH7+Jicnxxkf03QSToDwyZMpU6ZgzJgxyMjIQHR0ND5+/NhgvklD/+HxeCgrK+NEjgcPHkBaWhpr1qzBtWvXcPPmTRw7dgwA0L59e07koLH9GeKjmbgDYDC+hIuLC0pLSxEUFITnz5+jX79+qK2tFRmADQ0NuSPXU6ZM4b6nQSQQUFtbi+bNm+Px48cYNmxYo7/LyMiAz+dj9erVWLlyJVq3bi2GSBuipKSEPn36QFZWFoWFhbh//z4CAwNF6sTn5uZydeKtrKyoKeH4T3eShg4dik6dOonsJNHSf1j7i58uXbqAz+cjPz+fO1VWXl4OeXl5jBkzBm3bthVzhI2TkpICFRUVWFlZQU5ODpaWlti8eTP69+/P7T5OnDgRaWlpDUznaGn/Pn36YP/+/XB3d0ddXR1+/fVXriy3qqoqTExMwOfzAYA7jULLIk/YzE9GRgYHDhyAubk5HBwc4O3tjdzcXNy4cQP37t1Dly5d4OHhAYCe+AXGnT4+PkhLS4OysjKkpKS4tA5ZWVl4eXnBxsYGKSkpIjvYNPQfQTuePn0aJiYmcHFxgb+/P4YOHYrXr1/jwoULmDhxInx8fKgUmQQiQXJyMuTk5Br1ZJKWluY8vsrKyvDhwweoqKhQ0X+Av9ItL1++jJMnT4IQ0uDdJJxuaWtrC0dHRzFFK0pkZCTy8/Oxd+9eAEB2djaSk5ORlZUFJSUlTJgwQaT9BZsMFhYWUFNTE3P0jSMQO6SkpKCrqwtpaWkkJCSgT58+mDBhgrjDE+HgwYO4efMmYmJiAEDEOFVRURGzZ89Gq1atICUl1cA4lZb+z6ADJnQwqCQ9PR18Pp/boSstLcWxY8dw5swZjB8/HuPHj+de/LQdlQWATZs2oVWrVhgwYACGDBkCXV1dzntA8GIEREUOa2trahapAnR0dAAAiYmJGDNmzBfrxHfq1Ak3b96Eo6MjFf4E/2SSCPyV7iHYSaLNuJO1v/jp0KEDioqKcPXqVcyYMQMKCgpcNYP09HSoqqqKOcKGODo64tmzZ2jevDmWLVuGFStWoLy8HI6OjvDz8+P6yPbt27nTKjQhXB1GS0sL27Ztg62tLQDg119/5Sa1N2/exNixY0XupWGS6+Pjg7Nnz2Lw4MEYPXo0FBQUMHHiRAQFBcHY2BgBAQGwtLREQEAAKioquJRGWkSOpKQkFBUVce/fTp064dy5cw2MO2VlZbFnzx7qjDslXWQC/rlIIFhk0yQSCKdbqqqqwsDAAJGRkQgPD0ddXd1X0y01NDTEGTpHRUUFN7ZHRkbi+vXryMzMRM+ePVFZWQk5OTmMGTOGa3+a5m9f22QQFjt0dHTQq1cvKuYMn9O5c2dMmDABCQkJnHHqzp07uY2FCRMmwNzcHIaGhtSlOzHognl0MKgkMzMTdnZ2cHJyQlpaGhISEtCxY0d069YN1dXV6Nq1K1asWEGlOz0A7NixA9XV1UhJSYG0tDSys7PRpUsXBAUFoUOHDiITquLiYqxevRpWVlZUvCQFSGqdeAGvXr3C5cuX8fz580bLSwqLTIJJIi2TLIC1P01EREQgJCQEc+bMQe/evTF48GD4+flBVlYWW7duFXd4Inh5eeHDhw/YvXs31q1bB3l5ebi7uwOoH5euX7+OjRs3Ijg4GC1bthQxLqRh/GwK1WGePHkCFxcX5Ofnw97eHpGRkaiqqkKHDh1QUVGB5ORkjBw5EocOHaLy/SXJ1WG+JDJ16dIFxsbG0NTUhKWlJVq1akWlyCTpnkwHDhxAQEAAfvzxRygoKGD06NHo378/8vPz8eLFCzx69AgmJibcWC84CUHL/Ofu3bsYPXo00tLSYGpqCmVlZWRkZMDS0hKjRo3C4MGD4e7ujhkzZkBLSwulpaWwtLSkRuT4p9XZBGJgWVkZCgsLqRQLzp07h4SEBKSkpCAoKAgaGhqoqqqCrKwspKSkEBcXx20GMRhfgp3oYFCFq6sr5s2bB01NTUydOhVbtmxBQUEBHBwcMGDAAKiqqiI+Ph4PHz4EQJ87/aFDh1BZWYm5c+eiV69eKCkpQfPmzXHv3j1cu3YNHh4ecHV1RZcuXbh8yb1792LlypVUvCSFEVb+9fX1ISUlhVu3bkFKSoraOvFNYSdJAGt/8SNoV0NDQygoKODBgweIjIzE6NGj0blzZ7i6ugKgZ5Hk4eGBwsJCLqd55MiRyMrK4n63tbWFrKws9PT0MG/ePJHqJDTEL1wdBgCSk5MRGxuLffv2Yd26ddi6dStXIlFZWRlr1qwBQE/8Li4u0NDQwOLFi7Ft2zY4OTmhtLQUYWFhAOoXUQKhoLS0lLr3l8C4c9y4cQgNDcWsWbMaNe6ktToMAMyZMwd3795FWloaZs6cicjISERERKBDhw6QlZVFaGgofv/9dxw6dIgTOWjxdPmSSDBs2DAoKCggIiICbdq0gYaGRgNPJlrmD5KcbpmZmYmVK1fC2dkZRkZGCAkJQVpaGkaMGIFevXpx1+Xk5KC4uBhAvahpb2+PwYMHiytsEf7pSSCBpwhNJ4EECN67s2bNQvPmzSEvL4+HDx+ic+fOnHEq8NeJV1rGfwadsBMdDKrYu3cvAgICEBkZCXV1dXz48AEtW7YU8aywtLSEhoYGrK2txRhpQ9zc3PD69WsoKirixo0buH79OhQUFLgBOCcnB8eOHUNqaiqCgoLQpk0bAH9VW6EVSakTL+k7SV+Ctf+34fOdVAGfi0gCfw4BtEyyIiMj4eHhgfv376N58+YICQnB/v37IScnh6lTp6K2thZGRkZQUVHBq1evuIk5LfFLenUYoP4khLm5Oby8vPDzzz8jIyMD9vb20NbWhoODQ6P30CJSSnp1GGGR6cWLF3BycsLs2bO51FaByPTw4UOUlpbCxcVFvAE3QlxcHA4fPoxevXrh06dP6NSpE1JSUkREgiFDhnAigYWFBZXG03FxcUhMTISmpuYX0y2tra1x8+ZNKCoqUpE6IXgOk5KSwOPxYGpqCiMjIwD174ZXr15BS0sLlpaWaNmyJbZs2SLmiEWR9JNAnyM8LsbGxiIlJQW9evWCvr4+lemWDHphQgeDCoR9K0JCQrB9+3YcP34cgwcPxpMnT3D37l0oKSnht99+g6KiIvz8/ADQM0kUPi5eVVUFNzc3uLi4oG3btiIxZmVl4cmTJ5g1a5aYI/53CP8Njx8/pmb3QpimMklsDNb+/y2Ojo7o0aMHbGxsvnqd8DgF0LPIfvXqFWfOqaSkBBUVFVy+fBn29vaoqanBixcvcOHCBXz48AGTJ0/GunXrANATP1BfxjE+Ph579uyBsrKySJ8/evQojh07hpiYGMjIyFCXLgH8lYp19+5dLFu2DJ6enjA0NERGRgbWrVuHiRMncidQaEPQD2pra2FiYoL27dvD398fcnJyeP36NW7fvs0ZdyooKGDTpk3iDrkBkiwyCSOJIoEASU+3FHD79m3weDyYmJhAT08Ply9fRmBgIHr16oW2bdty/Z+W8VPSNxm+hHB/iouLQ0JCAvT09KgzTmXQDRM6GGInNzcXrVu3Fjm1cezYMfj5+eHMmTNQVFTE4cOHwefz0alTJ24xQstLxs7ODp8+fcL+/fsBACdOnEB0dDRsbGwwfPhw7njs59AyyWoqdeIByZwksvYXLzweD0VFRdi1a9dXrxMsZEtLS3H37l1Mnjz5G0X4dXg8Htq1awc7OzsQQmBlZYU7d+4gPDwcffr0Ebn27du31OViC6rDdOzYEUFBQfjtt98aVIfJzs7Gvn37OIGbVgTvpDt37mD58uUiYsfy5cvB4/EwY8YMcYcpwucVC2pqamBubg55eXl4e3sjPT0doaGhaNGiBRQVFak07pRkkUlAUxEJhP+Os2fP4tatWxg1ahS16ZYCL50hQ4agV69eGDBgAPr06YPnz5/D0tISjo6O0NbWRn5+PqSkpLhTEzT1f0neZPi76mzC/eT333+nYs7AkCyYRwdDrBw/fhze3t7o3LkzZsyYgZKSEsyePRuzZs1CUVERFi9ejMjISKxdu1bkPlpeMtnZ2aiqquLSUMLCwhAQEICuXbti165dqKqqQosWLTBhwgQYGBiga9eu3L00vOTd3d1RUFCAOXPmoLy8vNE68YKdPuGcTicnJzFF3DjCLuJVVVW4efMmamtrORO66dOnY/r06dz1P/30kxij/QvW/uKFx+MhPj4eCxYs4L5rbGz5vDqSvb39tw61UTw9PfHx40fOa0NKSgp79+6FmZkZAgMD4erqijZt2nAnUQQiBy2LDEmvDvM50tLSqKurw5gxYxASEgJTU1MA9WXQT58+jW7duok5QlEkvTqMABkZGdTV1WH06NEICQnB8uXLAdS3+8aNG7F8+XL079+fOpFJGEn0ZGoM4b9jzpw5kJKSQkxMDJSVlUX+Blri79atG6KiopCdnY2JEydi9+7dkJKSwvTp0yEjIwM7OzvY2dlh2bJl3D20eLoIaMrV2YTLx/bu3ZtKsZ5BN+xEB0OslJWVYePGjUhOToaBgQFKS0uRlZWF33//HWPGjEFkZCQA4PLly+jevbvIS1TcbN++HWpqatDQ0MDhw4fx8OFDNG/eHGFhYZCWlkZ1dTXu3LmDW7duQUZGhjsuTguRkZG4evWqSJ34rKwskTrxABqtE0+bPwQgeTtJrP3FiyCt4+eff8b169fRqlUrWFpaQk5OTmQhJyxy2NjYwNLSkoqdME9PT/D5fK7qS2hoKKSlpbFkyRIQQmBmZobWrVvDx8dH5LQcLUh6dZjPvUKEET7ZsXTpUhw9epTrMzSJBJJeHeZzhNvd1NQU7u7uMDQ0xJ9//kmdyPQlJMWT6e+gPd3S19cXq1atQvv27REREYHg4GAEBgaid+/eSE9PR1paGrKyspCamgolJSVs3LhR3CE3iqSfBGpK1dkYdMJOdDDEwuHDh3Hx4kUcP34cdnZ2nBO6r68vFBQUkJOTA2lpaYwePRp8Pl9EwaVhkuXh4QE+n88dh126dCl2796NiooKbkIoJyeH8ePHY/z48dx9NE0SJblOfGNI2k4Sa3/xMnLkSMyfPx9AvZh05coV7Nu3DxYWFpzYAUBE5KAlp/nkyZM4efIkMjIyAABHjhxBbGwsl34jJSWF4OBg/Prrrzhz5gx1xpGSXh1m586dGDhwIKZMmdLo78InO86dOyeSQkRD/JJeHeZLIpNwux84cABLly6FiooKlSLTlxAeR/X09KCiokKdSPBP0i2Fd+L79u2Lly9fUpVuOWzYMOjp6SE2NhaGhobg8/lYsWIF/Pz8MGLECPTv37/BPTTN3wRI4kmgpladjUE5hMEQA4WFhWTlypXEysqKEEJIcXEx8fT0JObm5iQ3N7fRe2pra79liF/E1dWVqKurc5+rqqoIIYS8fPmSrF+/njg4OJCKigpCCCHV1dXcdXV1dd820C9w584dQgghT58+JVpaWmT+/Plk6NChJDg4mDx69IgQQgiPxyOJiYmEEEJKSkrI4sWLSXJysthi/jcIt7Pg76EJ1v7ixd3dnVhbW3Of6+rqSFVVFbl27RpxdXUlO3fu5J5pQggpKysjJiYm5N69e+IItwFnzpwhb9++JaampsTDw4OcPHmSLFiwgGRlZYlcR2t/iYiIIAMHDuTGyIMHD5IRI0aQsWPHEnd3d+Lq6kqePHlCSkpKRPoPLeO/u7s7WbNmzd+O54Kx/9OnT4QQeuInhJDr16+T/v37k4iICEIIIenp6URHR4ds3rz5i/fQ8v7asWMHuXTp0levEbT1y5cvv0VI/4rdu3cTZ2dnEhgYSPLz8794XWVlJSGkfvx58eLFtwrvb+HxeMTS0pJcvHiRREdHc3EKELR9TU0NIaR+brds2TLy7Nmzbx7r33HlyhUybtw4UlhYSAghJCQkhEydOpXcv3+fECLa52np/19COL6zZ88SU1NTcvfuXTFG1DjBwcFk+PDhZPXq1cTV1ZXExMSQly9fkjt37pCjR48SBwcHkp6ezl1fXFxMlixZQu37jEE/LHWFITb4fD4cHR0hIyODvXv3gs/nY/fu3Xj37h3c3d0bLfUobtzc3Ljcx6ysLERFRaFZs2acUv7q1SscPHgQ+fn52Lt3r0iFBhrIzMzEvHnzuDrxGRkZjdaJNzMzw7x58zBjxgwkJSWhVatWVOwqSbpxJ2t/8fLp0ydYWVnh9u3b0NHRwbZt27jfqqurkZSUhMuXL6N58+ZwcnKCjIwM3rx5g6qqKvTr10+MkdeTlZWFDRs2wMnJCW3btsWaNWtw9+5d3Lp1S+TfYs2aNejatSuXLkco2cmT9Oowrq6uiIyM5E7SfG7kKUD4uPXy5cuxY8cOavLKJdm4U3CScuvWrV/tz4J0P0Hpdlr6z+eeTDNnzhTpP8KeTMLH9Z2cnKCmpibGyOtpaumWAHDlyhXweDzExsaiXbt2OHToEHbu3Ino6GgoKyuLO7x/BaE8XQiQbONUhoQiVpmF8d1TXFxMVq5cSSwsLLjPjo6OJDQ0VLyBNUJiYiLx9/fnPpubm5MZM2ZwnwWK+rNnz0h4ePg3j+/vEMSXmJhIpkyZQsLCwrjfcnNzuRMEFhYWxN7eXiwxfg1J30li7S9eBO3/8uVL4ujoSIyNjcny5ctFrqmqqiIXL17kdrppo7KyktjY2JAtW7YQQggpKCggy5YtI+vWreOusbW1Ja6uruIK8Yu4ubmRrVu3EkLq/y0sLCzIsGHDGt11z87O/tbh/S3r168ndnZ2ZMGCBcTY2Jj7XvjUHiEN+z8tJ4GEETyrSUlJpH///tz7Kj09nWhpaZHz58+LM7xGWb9+vchJys/HHwHC7T9v3jxq+lJERAQ3zyGEkKysLHLr1i1y4sQJcu3aNe57QX8S7GTTtCt/9OhRbuyJiIggVlZWZObMmWTFihVkyZIlJDExkRtni4uLJeYkYkJCAvnxxx+5kx3Xr18Xb0CNIOkngYQ5d+4cWb9+PYmMjCRVVVWkpKSE5ObmkgsXLpCtW7eSXbt2EUIIuXHjBnny5ImYo2VIOuxEB0Ps8Pl8ODk5cRUDBLswNBEWFgYVFRWMGjVKZGfIwsICf/zxB86fPw+g4c7p559pQdLqxDe1nSTW/uIjPz8fhw8fxsSJE/Hbb7/hzZs3OHToEPe7cJvT9PwKdnnfvHkDZ2dnuLi4YNCgQSgoKICjoyPatWuHZs2aoUWLFvD09ARAT//x9PTEhw8fsGfPHpHvzczMoKio2KA6jABa2v/WrVu4du0aXF1dAQDGxsaora3FqVOnAPzV7wX/RiUlJbCysqLaU0eSjDtdXV3x6dMn5OXlgRCC48ePA0CD/vL5SYhVq1ZRsxN87Ngx5OXlcaavn3syrVy5EmPGjIGUlBR1nkx3797F6NGjkZaWBlNTUygrKyMjIwOWlpYYNWoUBg8eDHd3d8yYMQNaWlooLS2FpaUlNfH/E65cuQILCwukpqZCXl4eAD3jp6SfBBJAJNw4lSGhiE9jYXwv/JPcxuLiYjJ//nxy6NChf3Xft8LNzY1YWFiQBw8eiOTvE0KIpaUlmTlzppgi+3ucnZ2JmZkZ2bNnD/ntt9+4HdRnz56RKVOmkIsXL5Lq6mry559/ivij0JRTLsk7Saz9xcvatWvJpk2bRHbWw8PDiYmJCXn37h1Zu3Ztg5MdNHHz5k2Rz6WlpcTHx4fEx8dz3+Xn5xMDAwPO84gQevqPh4cHsbOz4z6HhIRw43xdXR0xMTEhq1evJiUlJeIK8avs37+fpKamNvje2NiY/PLLL9xnwXuBz+cTQ0NDavr/5+8rYYRPdqirq4s8I7T0n5s3bxJvb2/u86+//irS7oITEIKTHHw+n6rxR9I9mV6+fEmGDBnCnUBMT08np0+fJm/evBG5ztTUlDsJlJiYSKU/09/x9OlTcYfQgKZwEkgY4Xl9dHQ0cXBwIOHh4eT9+/dfvI7B+H+BCR2M/xzBROvvBi6BOR2tbNq0iaxatYo8ePCgwXHlX3/99atGbuJk165dRENDg8yYMYP4+/uTqVOnkmnTppGtW7eS6dOnk0GDBpGQkBCRe2h5yUj6JJEQ1v7i5OXLl2Tq1KlkwoQJZPny5WTt2rUkPz+f1NbWkrCwMHLr1i1SVlZGFi5cSIKDg8UdbgOePn1KBg0aRFxdXcmRI0e47+Pj44murq6IMCYsFNCySD1x4oRIusHhw4fJ3Llzydu3b7nv6urqiJGRETl8+LA4Qvwqr169Iurq6mTVqlXk6dOnDdIlFi5cSIyMjES+S0hI4MwMxY2kG3dKusgk6SKBpKdb/q/QMn4SItmbDF9CUoxTGU0DlrrC+M/YsWMHysrKUFpaCmdnZ7Rp06bR6wTH7UpLSyElJYVWrVp940gbJzs7G4qKimjdujX33Z49e/D7779j5cqVGDRoUKMl7mhB0uvES7pxJ2t/Orh58yYiIyOhqamJu3fvomfPnqioqEBdXR2UlZVhZmbW4Ag8TXz8+BEJCQm4evUqcnNzsWrVKgwcOBBXr16FoqIidHR0qEy3iYqKwqhRo+Dh4YEePXpAXV0dsbGx8PPzQ8+ePbnrUlJSqD3eXlpaCg8PD6SmpmLKlCmYM2cO+vfvL3KcXUdHBzo6OrCysgLQMJ1CXEi6cecff/yBmTNnYtKkSbCyskLfvn1FjusvWrQI1dXVXElcoD79oF27dhg2bJg4QhZB8BwmJSWBx+PB1NQURkZGAOrLa7569QpaWlqwtLREy5YtsWXLFjFH/HUkLd1S0mnq6UJEAoxTGU0DJnQw/hPc3NxQWFiIuXPnQkpKCmPHjkXz5s0BiA5wwjnNCxcuhK+vLwYNGiTO0AEAQUFB2LZtG5SVlTFq1CjIyspCS0sLI0aMgJ+fH2RkZGBkZIQhQ4ZQ68lx/vx5+Pr6cm7iBw8eRFhYGFcnvjFoib8pTBJZ+4uPyspKbrwBgNjYWNy9exdDhw5Ft27dUFpaii1btqCgoAC7du3CuHHjANDT/ocPH0Z+fj6qqqqgrq6OuXPnAgAOHTqEwsJC3Lp1C+3atYOCggJ27Ngh3mAbQdKrwwiTnp6O3bt3o7a2Fq1bt4aJiQk0NDSoXsw1heowkiwyfY6kiQQuLi7Iz8/HkCFD0KtXLwwYMAB9+vTB8+fPYWlpCUdHR2hrayM/Px9SUlJchTxa4pd0JH2TQdKrszGaFkzoYPyfc+zYMdy4cQNBQUEAgJycHGRmZuLhw4eYOnUqNDQ0UFdXB0IIN8miyXjr/fv36Ny5MywsLHD79m0cPHgQFy5cwB9//IGqqiqUlZUhLS0N7dq1Q0REhMjuJG1cvXoVbm5u3GI7NDQUJ0+ehL+/P4YNGyaysKBxkQFI3iRRGNb+3x5XV1fU1tZCT08Pw4cP5xZ4MTExuH79OoYPHw4jIyO8e/cOL168wKRJk8QcsSg8Hg8fP37ExIkTkZubizNnzkBNTQ0BAQEA6svg/v777wgPD0ezZs3g7e0t5ogbUlVVhbVr16JXr16wt7fHx48f4eDggM6dO8Pf3x9AvcjRunVr6uKPiIiAkpISJ34BwPHjx6GpqYm4uDhkZ2dj5cqVDcQOWp7fpmDcKUASRaamIBLs3r0b+/btg4qKCiZOnIiEhARISUlh+vTpuHjxIt69ewc7OzssW7aMu4eW/i/pSPomQ1MxTmU0HZjQwfg/Jzo6GuXl5TA2Nsbp06dx/fp1PH/+HN27d0dycjKio6OhqqoKAFx1BisrKypEjvXr16N79+6wsLAAAMybNw89evTAzp07AQBv375FdXU1Xr16hT/++AOmpqbiDPcfIUl14pvCJPFzWPt/W6ysrFBUVIQ///wT48aNQ7du3bBq1SoAQGJiIsLCwjB48GAsWLAALVu2BEBP/Hv37sWTJ084UQMAiouLoa+vj1GjRomkNwlXp6JpkSHJ1WEuXLgAW1tbtG3bFsbGxpg+fTpUVVVx+fJlJCQkYOvWrdi4cSMyMjLg4+OD7t27iztkESS9Ooyki0yAZIsEkp5u2dSQtE2GplSdjdGE+AY+IIzvBB6PR9LS0khsbCyZPHkyWb58ORk6dCg5ePAgSUlJIYQQ4u3tzZlulZaWEiMjIxGnd3Hi4eFBLC0tG3w/Z84csmjRoi/eR5Nx1ZeQhDrxhEi2cefXYO3/3yOIIyoqiuzevZtkZGSQ06dPEwMDA6Kjo0N27NhB3r9/TyIiIkRM9WhCeHysrq7mKkm8fPmS/Pzzz+T169cN7qGl/SW9OgwhhHz8+JEcOHCAGBkZEWNjYxIYGEisrKxIQUEB8fDw4KocnDlzRsyRNkTSjTvPnz9P1NXVyahRo8iOHTtIeno6qaysJOfOneOq9vj7+5OlS5eKmNnSgo+PDykoKCCE1Fd1mjp1Knn16hUhhJC0tDQSGRlJtm3bRhYuXEgcHR3FGeoXiYuLI+PGjePeUwcOHCBTpkz5ah+hZfyRdFh1Ngbjv4EJHYz/MwTO9ITUT3oTEhJIVlaWyDUrV64kFy9eJIQQkpKSQtLT0795nI3h7u4uUgLxwIEDIoshAwMDsmzZMnGE9n9GQkICUVdXJ2VlZdx3tLwkm8Ik8e9g7f9tKCgoINra2iQ6OpoQQsj69evJrFmziJWVFRkwYAC5ffs2dy0tk/Ta2lpSXV1NrK2tSVRUFPcdIfUx5ubmkmnTppFnz56JMcovI+nVYYTJzc0lx48fJwsXLiSJiYkkPDycLF26lCxcuJCYm5uLXEtL/5H06jCESLbIREjTEQmuXLki8neEhISQqVOncn1FOGYa45dUJHmTQdKrszGaNkzoYPw/Ixhs16xZQ7Zu3Sry26NHj8irV69IUVERMTc3J05OTg3uEzcHDhwggwYN4j4LSiBmZ2eLXDd58mQSGBj4rcP7P4XGOvGENJ1J4t/B2v+/RbBwPnfuHHFyciLW1tZk4cKF3MkImhZ2jRESEkImT57MndwQXrBaWFg0EI5poqCggISHh5NVq1YRAwMDcvHiRfLu3Tty7Ngxcu7cOUKIqLBBS/8JCAggaWlppKioiPsuLy+PBAQEkKVLl5Lc3FySn59PTp8+LfL+oomSkhJib29PtLW1ia+vL/n9998biEgzZ84ku3fv5j5/XiKdBiRRZBKmqYgECQkJIn9HaGgoGTp0aKMnyhj/b0j6JoOkl1BmNH2YRwfj/wnh6gZxcXFIS0uDg4MDgPq802PHjiE0NBQDBgyAgoIC/Pz8ANCTUwjUl7YKDQ1Fnz59oKioiPPnzzcogZiRkQENDQ0xRvl/C03tL6ApGHf+U1j7/7ekpaXB1tYWioqKDfwJADrbH6j3LNqyZQvevHkDT09PqKioAABsbW3RsmVLbvykBUmvDnPv3j0sXrwY48aNg5qaGtq2bQtzc3MQQlBdXY2goCCkpKRgzZo1GDJkCHcfjf1fEo07AwMDMX78eCgpKaFt27YA6s3Ao6KicOfOHfj7+6NZs2a4fv06kpOTqev/jSFJnkxf48qVK3B3d0dMTAzatWuHGzduYPz48eIOq8nBqrMxGP8tTOhg/M8EBATgypUrmDZtGrp16wZZWVkEBATg2LFjaNmyJTcQv379GoqKitxEhrZFRl1dHTIyMrB7924kJSUhPj6eM1kEgNWrV6NPnz6wsbEBQM9LpinSVCaJkkpTav/g4GBcunQJ4eHh4g6lUb40jmRkZCAyMhJxcXFQU1ODnJwc2rVrxxnP0TL+NIXqMADg5OSE0tJSzJo1C9u3b4eqqir69euHZcuWoaamBmfPngUhBCtWrBB3qCJIunFnUxKZPqepiARXrlyBhYUFUlNTIS8vD4C++VtToClsMkiacSrj+4EJHYx/jWCgTUhIQEFBAXJzc3Ht2jX06tULKSkpCAkJgZqaGjegScIgXVdXh2fPnmH//v3o0aMHHB0dAQB2dnZo1aoVtZP0pkhTmSRKKpLe/oIxJjMzE66urvDw8IC6urq4w/rXPHnyBDIyMpCWluZOk9EySWxK1WGuXbuGhIQEboyfNGkSPn36BBkZGYwcORKLFy+GpqammKMVRdKrwwiQVJHpn9BURIK0tDQMGDBA3GE0eSRpk6EpVGdjfD8woYPxr3n8+DEGDx4s8l11dTWkpaWxfft2JCUlwc/PT+JqYgtOdhw8eBCKioooLCxE69atqSuB+D3QVCaJkgrN7V9aWoqioiL06NHjq9dVV1fj4sWLmDVr1jeK7J+xdetWDB48GFOnTv1X99HS/gDg4+ODESNGYMaMGaipqYGUlBRkZGSQmZkJJycnbNmypcHknCaRQ5i3b9/C3Nwc1tbWePXqFRITE3H06FHExcWhqqoK+vr6AOiKv7CwEGfOnMGlS5cgIyODCRMm4MmTJ/D09MTu3bsxceJETJgwAVFRUTAwMBB3uA2QZJHp39CURAKaxp+miqRsMkhyCWXG9wcTOhj/ipCQEGzbto3bTZo7dy5kZGRErtm2bRvOnDmDyMhIdO3aVUyRNs7fDbYCscPLywtdu3bl8srZS/7b05QmiZIIje3P4/FQWFiIBw8eYM+ePRg8eHCjz2VVVRXk5ORQUlKCu3fvYsKECZCVlRVDxKK4u7ujuLj4b/0qBH4iVVVVqKqqQuvWrb9NgH9DXV0d6urqYGdnh0mTJkFfX1/k5N779++xePFi7N69WyKEbsH7IDo6Gk5OTtDU1MSRI0ca9BUaJ+l5eXlISEjA+fPnYWFhgbdv3yIuLg41NTVo3bo19u/fz11LY/yAZIpM/wts/sD4p9C8yeDr64tVq1ahffv2iIiIQHBwMAIDA9G7d2+kp6cjLS0NWVlZSE1NhZKSksjJPgZDXDQTdwAMyaJ79+5o3bo18vLyOBNPExMTaGpqQlVVFUB9uoeGhgZ1IgdQv4CQlZX94sRJWloa/fr1w44dO7j4aXnJfG8IFtms/cUDbe3v6uoKPp+PrVu3Ij8/Hx06dEBjOn1tbS0ncpiammLt2rVUiBzr16/H5cuXkZiYCOCvXe3Pqa2tRbNmzcDn82FpaYmNGzdSI3RIS0tDWloampqa2LNnDzQ1NaGsrMwJS126dEHfvn3RsmVLcYf6jxC8A4YPH46hQ4fCxsYGsrKyIibbwteJk8+NO7t06YIpU6agpKQEQUFB8Pf3x6RJkzjjTmFoiP9zCCHo0aMHTE1NsXr1ak5kAgAdHR2R62iM/99Aw/jJkAwmTZqEM2fOcCIHQE//GTZsGPT09BAbGwtDQ0Pw+XysWLGCM07t379/g3uawvPLkGzYiQ7GP0J4sNqyZQu6dOmCRYsWITg4GEFBQWjWrBmMjIygrKwsclSWlkXSjh07UFZWhtLSUjg7O6NNmzaNXifYSS0tLYW0tLTIy4bBYIiHlJQUBAYGIjg4GEC9f0VqairOnj2LSZMmYfbs2VBWVuaeXz6fD2tra1hbW3/Ruf5b4uzsDD6fj4KCAgwcOBAODg6NigEC8YPP58PW1hYrV67E6NGjxRDx15G06jD/BG9vbzx8+BCRkZHUTcybsnFndnY21q5di9WrV0NLS6uByMRgfK/QMn8WpikYpzK+L+h6ghjUIjxwtWzZEvfu3QNQb0A3ZMgQeHh4IC0tDXl5eSL30TBIu7m54eXLl9DS0sK0adNEJlHCOp9gJ7WkpAQLFizAq1evxBEug8H4jM6dO0NBQQFJSUk4ePAg/Pz8kJiYiJ9++gmJiYm4fPkyAHAih62tLTUix507d6CgoIC9e/ciLCwMT58+hY+PD6qqqkSuE4gcJSUlsLa2hoWFBRUiR2N7IW3atIGxsTH69esHY2NjLF26FCtWrICcnBwnckjKHkpdXR0AwNDQEPr6+lROzEeNGgV9fX20bNkSgwcPxpkzZ2Bubo5t27ahtLQUv/zyC8aNG4e7d++K3Efj3/I5PXv2xMCBA7FlyxYQQpjIwWD8/9Awf/4cbW1teHl5QVdXF0VFRVi2bBmMjY1hYmKCN2/eiIw5kjD+MJo+7EQH46tER0cjPz8fSkpK6NWrFwYNGoTKykrMnTsX5eXl6N69O0JCQiAnJwc+n8+dlKBFyT127Bhu3LiBoKAgAEBOTg4yMzPx8OFDTJ06FRoaGqirqwMhhNtJpWknmMH4ngkKCkLnzp2hq6sLLy8vvHnzBpmZmXBwcMCgQYPQt29f3Lp1C0lJSVi7di2qqqqwdOlS2NraYtSoUeIOHwcOHEC/fv0wYcIE7ruysjKYmJigT58+cHd3h5ycHPeb4Ciwg4ODxIw/NFeH2bNnD3JycqCiooJ58+ahQ4cOX72+qqoK7969Q+/evb9RhH9PUzbuFPSTjIwMJCcnY9GiReIOicFg/AMkxTiVwWBCB+OLuLm54cOHD+jRowfy8vKQk5ODJUuWQE9PD2FhYQgLC8Px48fRqlUrVFdXc3nwtIgcQL1QU15eDmNjY5w+fRrXr1/H8+fP0b17dyQnJyM6OprzFuHz+bCxsYGVlZXELDIYjKZKVVUVoqKiEBMTAwsLC4wdOxZ8Ph/S0tIinhUWFhYYMGAArKysUFNTgw8fPqBbt25ijLye169fY8aMGZg6dSoWLFiAkSNHcp4cZWVlMDMzg6qqKng8Hid2XL58GW3btsXIkSPFGToAya8O4+7ujoKCAsyZMwfl5eWYOXOmiKgkiFM4XWjNmjVYt24dlUaqkmbc2RREJgaD8WVoNk5lMAQwM1JGo/B4PFRUVCAgIAAA8P79e8THx+PAgQPo2LEjfvrpJ+zcuROPHz+GlpaWiNkfDZMsd3d3zJ8/H7KysggJCUFCQgJSU1NhbW2NJUuWYPjw4fDx8cHz58+hqqqKsrIyrFixAvb29kzkYDDEDCEEcnJy0NHRQYsWLbB3714UFRVBR0cHSUlJ+OOPP9C+fXtERUWhffv2sLKyAlCfukKDyAEASkpK+Pnnn/HmzRvcv38fdXV10NLSgpSUFFq1aoXg4GDMnTsXZ8+ehaGhIQBg8uTJ1IyfxcXFsLe3/+p1jVWHoWGSGxkZifz8fOzduxdAvQ9EcnIysrKyoKSkhAkTJkBaWlrE08XGxgYWFhZUihySZtz5ucikoKAg8vvXRCYGgyEZ0GycymAIYEIHowFBQUFITU3F0aNHAdQfne3cuTPmzJmDiooKXLp0Ce7u7tDV1cWjR4+gpaUl5ogb8uHDBxBCMGvWLLRt2xZVVVXw8PBAz549uWvevn3L5cBnZGTAw8ODO3rNYDDEQ2ZmJnfKSkFBAXPmzAEhBKdOnULz5s0xaNAgREVFQV5eHurq6rCzswNA306SnJwcpkyZgl27duH169fg8/mQkpLCmDFjOLEjJiZG5JQBDYvUplAdpqKigutDkZGRuH79OjIzM9GzZ09UVlZCTk4OY8aModK4tjEkqTpMUxOZGAzGl6GtOhuD8TlM6GA0oEuXLtDQ0MCtW7cwevRodO7cGUC9AZ2amhoiIyPB5/NhZmbG/UYLgh2tFi1a4MKFCxgwYAB+/PFHAMDjx49RU1OD9u3bw8nJCYqKityx7GHDhlExSWQwvmfCwsLg4eGBQYMGQUlJCWpqahg6dCi6du2KefPmITg4GG5ubti0aZPIfbRMsiIiIqCiosKlnkycOBF//vkn2rRpg7S0NFy4cAFSUlIYPXo0pKSkOJGDlp14QXWY3r17Y8OGDf+4OoyVlRWUlJTEELEod+/exejRozFs2DCYmpoiJSUFGRkZsLS0hJmZGQYPHgx3d3cA9aJAaWkp9SKHMMLGnZGRkVQadzY1kYnBYPw9NLx/GYzGYEIHowFz5swBAFy7dg0VFRWYPHky2rdvDwAYO3Ys+vXrB0IIJ3LQMkkX3t2aMmUK0tLSuN8IIXj06BFCQ0MxYMAAtGvXjqsOQMsiicH4nqmoqMCYMWMwa9YsEEIgKysLOTk5BAUFobi4GC1atMCjR49gbm6O/fv3Y9CgQQDqn20ant+YmBi4ublBXl4e69atg5KSEn766Sd07NgRT58+xdq1a7Ft2zZERESgU6dO6NOnD3cvDeOnoDqMYFycP38+fHx8Ghimfl4dhpZFamZmJlauXAlnZ2cYGRkhJCQEaWlpGDFiBHr16sVdl5OTg+LiYgD1Rqr29vYYPHiwuML+xwjeU4aGhlBRUaGizwjT1EUmBoPBYEgezIyUIYKwaHH27FncuHGDK23XvHlz2NnZQV5eHj4+PmKOVJSAgABcuXIF06ZNQ7du3SArK4uAgAAcO3YMLVu25P6m169fQ1FREW3btgXARA4GgwZiY2Nx48YNrF+/Hnl5eYiOjoasrCwWL16MDh06oLKyEi9evEBaWhqqqqqwcOFCcYfcgLq6OgQEBODw4cOYPn06OnbsiGfPnsHb2xurV6+GmZkZhg0bhuvXr2PWrFniDlcESa8OI3hvJSUlgcfjwdTUFEZGRgCAvLw8vHr1ClpaWrC0tETLli2xZcsWMUcsiqQbd2ZmZmLevHmcyJSRkdGoyGRmZoZ58+ZhxowZSEpKQqtWrSRCZGIwGAyGZMKEDkYDPhc7EhMTMXr0aMTHx6Nbt27w8PBocJ24EMSQkJCAgoIC5Obm4tq1a+jVqxdSUlIQEhICNTU1TtAQjpmG+BkMBnDv3j1cunQJhBCsXr0a7969Q3h4OFq1agVdXV2oq6s3uIcmkVIQS1VVFQICAhAfH4+QkBCcPn0aBQUFeP78OTp37oytW7dy99Ay/kh6dZjPuX37Nng8HkxMTKCnp4fLly8jMDAQvXr1Qtu2bbm0J1r6j6RXh5F0kYnBYDAYTRcmdDAaRXgS/ttvv+Ho0aNQU1ODr68vAHomiY8fP26wI1RdXQ1paWls374dSUlJ8PPzo2JCyGAwvsyDBw8QFxfHiR05OTmIiIhAq1atoKOj06jYIW727NmD2bNno2XLllwqX11dHXx9ffH06VMcOHAAVVVViIuLQ1paGjZs2CDmiBtSVVUFLy8vvHnzBmPGjIGmpiZXHQaoFzvmzp0LU1NTrjoMLSKNi4sL8vPzMWTIEPTq1QsDBgxAnz598Pz5c1haWsLR0RHa2trIz8+HlJQUunTpAoCe91dkZCSuXr0qYtyZlZUlYtwJoFHjzlGjRokz9EaRNJGJwWAwGE0bJnQwvojwZPb58+ecWEDLJCUkJATbtm2Dra0t2rZti7lz5zaoDrBt2zacOXMGkZGR6Nq1q5giZTAYX0J4nGlM7Dh48CC0tbUxc+ZMMUcqysOHDzF//nz88ssvKCkpwZgxY6Crq4tWrVoBALy9vfHgwQPs27dPZOyhRSQQ5tq1a9i1axf69OmD9u3bY+LEiVx1GKBeDBE+ZUALu3fvxr59+6CiooKJEyciISEBUlJSmD59Oi5evIh3797Bzs4Oy5Yt4+6hqf2PHTuGvLw82NvbN2rcuXLlSu7fgTbjTkkXmRgMBoPR9GFCx3fKP80JFkxwy8vLkZOTg759+37jSL9MfHw83N3dMXv2bOTl5eHly5cwMTGBpqYm5/oOAHFxcdDR0RFjpAwG45/y4MEDXLhwAbW1tbCxsUFZWRm6desm7rAaUFVVhWXLlqFnz56YMmUKfH19MXToUHTv3h2rVq0CAOzduxfNmjWDnZ0dVQvsz6vDAMDJkye56jClpaWYOXMmVx1GAC1/g6+vL1atWoX27dsjIiICwcHBCAwMRO/evZGeno60tDRkZWUhNTUVSkpK2Lhxo7hDFkFg3JmWlgZTU1MoKytzxp2jRo3ijDtnzJgBLS0tlJaWwtLSkhqRA5B8kYnBYDAYTR8mdHyH/C85wba2tnBycqIiBUR4srRlyxZ06dIFixYtQnBwMIKCgtCsWTMYGRlBWVkZBgYG3H1sJ4nBkAwePHiA8PBwjB49Gvr6+gDoWiQJxsbExESEhYVh165d4PP5mD9/PrKystC7d2907NgR3t7e6NGjh7jDFSEmJgZr165tUB3m0qVLePr0KWxtbbFt2za8e/cOFhYWItVhaOH8+fPw9fVFbGws2rVrh4MHDyIsLAx+fn5fFAJo6T+Sbtwp6SITg8FgML4fWHnZ74zIyEjk5+eL5AQnJyeL5ARLS0s3mhNMg8gBQMRMtGXLlrh37x4WLVqE4uJiDBkyBIaGhjh9+jRatGghch8TORgM8RIeHo6ePXtCS0vrq9cNHToUnTp1EhEJaFikChCkyHXt2hWFhYW4f/8+EhMT0blzZ8TFxeH8+fMoLy/n4qdlkQ0As2bNQnZ2Ng4fPoynT58iLy8PYWFh8Pb2xpEjR3Djxg2sWLEC169fp1LkAICZM2eiRYsW0NXVRWxsLExMTCAtLQ0XFxf4+/tj2LBhVBpPE0LQp08f7N+/HzweDwBgZGQEDQ0N5OXlISkpiTPubNu2LWbMmAEAf/u8fEuGDRsGPT09xMbGwtDQkKvAIxCZ+vfv3+AeWtqfwWAwGN8X7ETHd4Yk5wRHR0cjPz8fSkpK6NWrFwYNGoTKykrMnTsX5eXl6N69O0JCQiAnJwc+n482bdoAYJMsBoMGCgoKcOTIEeTk5GD+/PkYPnx4o9dVV1dDVlYWZWVl+PjxI3r27PmNI/13HDhwAFu2bMHQoUNx/PhxyMjIiJweo2n8keTqMI1x5coV8Hg87mTHoUOHsHPnTkRHR0NZWVnc4X0VSTbuvHr1Ktzc3Lh2Dw0NxcmTJ6kWmRgMBoPx/cFOdHwnCHKChw0bBlNTU6SkpHA5wWZmZlxOMFC/c1paWkqVyOHm5oYPHz6gR48eePToEXJycrBkyRLo6elh0aJFCAsLQ2BgIOTk5FBdXc1EDgaDMjp06IA5c+bg8uXLOHnyJAghDcaW2tpayMrKculyjo6OYor27xGMLXPmzEFSUhIWLlwIGRkZVFZWonnz5tx1NIw/n1eHkZOTg5WVFYqLi7F69eoG1WGEoSH+LzFp0iQAwOzZsxETE4OlS5dCVVWVOpGjMePOcePGYf/+/bC0tESnTp2gq6vLbTLQbtypra0NLy8v7kTNsmXLICUlBRMTkwYiE839h8FgMBhNGyZ0fAdkZmZi5cqVXE5wSEhIoznBOTk5KC4uBgA8efIE9vb2VOQE83g8VFRUICAgAADw/v17xMfH48CBA+jYsSN++ukn7Ny5E48fP4aWlhZkZWW5e9kki8EQL3l5edzCTVVVFQYGBoiMjER4eDjq6uq4MpmCkxyCk2RWVlbQ0NAQZ+hfRTC2dOrUCV26dMHhw4ehra0tInLQwMOHD7Fnzx68f/++QXUYNzc3eHt7Y/Hixdi3bx8WLlzI3ScpIrFA7BgzZgxSU1Mxfvx4AHSJBN26dUNUVBSys7MxceJE7N69mzPulJGRgZ2dXaPGnbTE3xiSIjIxGAwG4/uFpa40cQST1aSkJPB4PJiamsLIyAhA/QLk1atXXE5wy5YtsWXLFjFHLEpQUBB+++03HD16FIqKiiIGqadOnUJOTg7c3d3h6+uLDh06wNzcXNwhMxiM/58DBw4gICAAP/74IxQUFDB69Gj0798f+fn5ePHiBR49egQTExNO0BB4AllZWVFxkuzvPEUEi+nMzEycO3cONjY21AkEklwd5t+QlpaGAQMGiDsMEb4H484rV67AwsICqampkJeXB0CXyMRgMBiM7xcmdHxHSGJO8NmzZ3Hz5k1MmDABo0ePRufOnbnfrl+/jg0bNiAiIgIVFRUivzEYDPETFxeHw4cPo1evXvj06RM6deqElJQUyMvLo7y8HG/evMGQIUOwbds2tGzZEhYWFrC2thYpeyou/qmniEB8raioQHFxMXd6hQYkuTrM/wpN7y9Jrg7zb6BRZGIwGAwGg6WuNFGaSk7wnDlzAADXrl1DRUUFJk+ejPbt2wMAxo4di379+oEQwokckjhJZDCaKjo6OgCAxMREjBkzBnp6eqisrERZWRkePnyIp0+fQlZWFp06dcLNmzfh6OiIQYMGiTnqev6pp4hwCW5HR0eqhA5Jrg7zv0LT+0tSq8P8WwQiB23zBwaDwWB837ATHU2U3bt3Y9++fVBRUcHEiRORkJDA5QRfvHgR7969azQnmKZJlnA8Z8+exY0bNzBq1Cjo6+ujefPmsLOzg7y8PHx8fMQcKYPB+Bzh5zc6Oho3b97E6NGjMXHiRKpPXwl7igDAhw8fEBkZiT/++AM///zzVz1FaDiJ8iUkqTpMU0OSq8MwGAwGgyGpsBMdTQxBTrC1tTW6du2K4OBg/PLLL1i3bh2XEzx9+nSkpqYiIyND5F7aJrlSUlIilQ2A+p1hWVlZxMfHo1u3bvDw8ADAJukMBm0IP7/6+vqQkpLCrVu3ICUlhYkTJ6JTp07ctbQ8v1/yFBk2bBgUFBQQERGBNm3aQENDgxM5bGxsqKlO1RiSVB2mqcKMOxkMBoPB+PYwoaOJMWzYMOjp6SE2NhaGhobg8/lYsWIFlxPcv3//BvfQsshojM/FDikpKRw9ehRqamqcyMGOyzIYdNLY8xsTEwNlZWURoYOW8UdJSQl9+vSBrKwsl+oRGBgo4imSm5vLeYpYWVlRLXIAklMdpqkjCdVhGAwGg8FoSrDUlSbI1atX4ebmxh2TDQ0NxcmTJyU6J1g4zufPn0NNTQ0AmyQyGJKA8PP7+PFjKspWf4m4uDgkJiZCU1Pzi54i1tbWuHnzJhQVFanwFGkK1WG+F5hxJ4PBYDAY3wYmdDRRJCkneM+ePcjJyYGKigrmzZuHDh06NHpdVVUV5OTkUF5ejpycHPTt2/cbR8pgMD6nqTy/kuop0hSqw3yPMJGewWAwGIz/FvaWbaJMmjQJXl5emD17NoqKirB06VLs3LmTOpHD3d0dGRkZ0NbWRpcuXaCgoCDye11dHYD6SbqcnBz4fD6srKy47xkMhvhoSs+vIM0GAPT19TF+/HgkJyfj+vXr+PDhg8i1NO0PCKrD9OvXDydPnkRKSkqDa4Srw1hYWKCwsFAMkTKEYSIHg8FgMBj/LcyjowlDe05wZGQk8vPzsXfvXgBAdnY2kpOTkZWVBSUlJUyYMAHS0tKoqalBs2bNOOM/CwsLLnWFwWCIh6b4/EqSp4hwdRhVVVUYGBggMjIS4eHhqKur+2p1GA0NDXGGzmAwGAwGg/Gfw4SOJs6kSZNw5swZyMvLc9/RIHIAQEVFBVRVVQHUL5quX7+OzMxM9OzZE5WVlZCTk8OYMWO4RZK1tTX1xn8MxvdCU31+hcUOPT09qKioUOcp0hSrwzAYDAaDwWD8X8KEju8AgfEZLSc57t69i9GjR2PYsGEwNTVFSkoKMjIyYGlpCTMzMwwePBju7u4A6hcdpaWlErNIYjCaOpL+/P4TTxEpKSnOU6Rv3754+fIlVZ4iTbE6DIPBYDAYDMb/JUzo+I6gQeTIzMzEypUr4ezsDCMjI4SEhCAtLQ0jRoxAr169uOtycnJQXFwMAHjy5Ans7e2p21VlML43JP35dXd3R0FBAebMmYPy8vJGPUWkpaVFPEVsbW3h5OQkpogbR0dHBwCQmJiIMWPGfLE6TKdOnXDz5k04OjpSUR2GwWAwGAwG41vBqq4wvhmC4+BJSUng8XgwNTWFkZERgPp881evXkFLSwuWlpZo2bIltmzZIuaIGQyGAEl/fiMjI3H16lURT5GsrCwRTxEAjXqKCPwuaEBSq8MwGAwGg8FgfEvYiQ7GN0MwOdfS0oKHhwd4PB5qa2uhp6eHpKQkBAYGolevXmjbti02bdoEgJ50Gwbje0fSn9+m4iki7CGir68PKSkp3Lp1C1JSUpg4caKIaaqwKMJgMBgMBoPxPcGEDsZ/jouLC/Lz8zFkyBD06tULAwYMwLhx47B//35YWlqiU6dO0NXVxZgxYyAlJcVVEqBpkcRgfK9I+vMr6Z4ijSFJ1WEYDAaDwWAwxAETOhj/Od26dUNUVBSys7MxceJE7N69G1JSUpg+fTpkZGRgZ2cHOzs7LFu2jLuHEELFIonB+N6R5OdX0j1FvoYkVIdhMBgMBoPBEBfMo4Pxn+Hr64tVq1ahffv2iIiIQHBwMAIDA9G7d2+kp6cjLS0NWVlZSE1NhZKSEjZu3CjukBkMxv+PpD+/ku4p8k+qwwDgqsOUl5cjJyeHquowDAaDwWAwGOKCnehg/GcMGzYMenp6iI2NhaGhIfh8PlasWAE/Pz+MGDEC/fv3b3APyylnMOhA0p9fSfYUaSrVYRgMBoPBYDDEBRM6GP8ZM2fORIsWLaCrq4vY2FiYmJhAWloaLi4u8Pf3x7Bhw0QWRjQtkhiM7x1JfX4l3VMkMjIS+fn5ItVhkpOTRarDSEtLN1odRk1NTczRMxgMBoPBYNABEzoY/yna2trw8vLiFkvLli2DlJQUTExMEB0dDWVlZe5aGhZJDAbjLyTx+ZVkTxGg6VSHYTAYDAaDwRAnTOhg/OdMmjQJADB79mzExMRg6dKlUFVVFVkkMRgMOpGU51fgKWJtbY2uXbsiODgYv/zyC9atW8d5ikyfPh2pqanIyMgQuZcGkaYpVodhMBgMBoPBEBdM6GB8EwSLpTFjxiA1NRXjx48HQM9xcQaD8WUk4fmVZE+RplwdhsFgMBgMBkMcsKorjG9KWloaBgwYIO4wGAzG/wDtz+/Vq1fh5uaG2NhYtGvXDqGhoTh58iTVniKSXh2GwWAwGAwGg0aY0MEQCzTtBDMYjH8Hzc/vlStXwOPxOLHj0KFD2LlzZwNPERq5ffs2eDweTExMoKenh8uXL1NdHYbBYDAYDAaDVpjQwWAwGIwmxZUrV+Du7o6YmBi0a9cON27c4NJtaKGx6jB9+vTB8+fPYWlpCUdHR2hrayM/P5/K6jAMBoPBYDAYNMM8OhgMBoPRpJAETxFJrw7DYDAYDAaDQTPsRAeDwWAwmiQ0eooIqsO0b98eERERCA4ORmBgIHr37s1Vh8nKykJqaiqUlJSwceNGcYfMYDAYDAaDIXEwoYPBYDAYTRqaTnKcP38evr6+nIfIwYMHERYWxlWHaQxajFMZDAaDwWAwJAWWusJgMBiMJg0tIgcAzJw5Ey1atICuri5iY2NhYmICaWlpuLi4UF0dhsFgMBgMBkOSoGf2x2AwGAzGd4C2tja8vLygq6uLoqIiLFu2DMbGxjAxMcGbN29EhA0mcjAYDAaDwWD8e9iJDgaDwWAwvjECw9TZs2cjJiYGS5cuhaqqKvUlcBkMBoPBYDAkAebRwWAwGAyGmLhy5QosLCyQmpoKeXl5AHR5ijAYDAaDwWBIIkzoYDAYDAZDjNBYHYbBYDAYDAZDkmFCB4PBYDAYFMBOcjAYDAaDwWD838CEDgaDwWAwGAwGg8FgMBhNBrZ1xGAwGAwGg8FgMBgMBqPJwIQOBoPBYDAYDAaDwWAwGE0GJnQwGAwGg8FgMBgMBoPBaDIwoYPBYDAYDAaDwWAwGAxGk4EJHQwGg8FgMBgMBoPBYDCaDP8fNpsVQu3PZIgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculer les corrélations entre features\n",
    "corr = sk_id_installments.corr(method='pearson')\n",
    "corr = pd.melt(corr.reset_index(), id_vars='index')\n",
    "corr.columns = ['x', 'y', 'value']\n",
    "# visualiser les corrélations entre features\n",
    "heatmap(\n",
    "    x=corr['x'],\n",
    "    y=corr['y'],\n",
    "    size=corr['value'].abs(),\n",
    "    color=corr['value']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>INS_N_FAULT_ADJ_RATE</td>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>INS_N_FAULT_ADJ_RATE</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>INS_N_STATUS_0_SUM</td>\n",
       "      <td>INS_N_INSTAL_COUNT</td>\n",
       "      <td>0.992855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>INS_N_INSTAL_COUNT</td>\n",
       "      <td>INS_N_STATUS_0_SUM</td>\n",
       "      <td>0.992855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>INS_N_FAULT_GRAVITY</td>\n",
       "      <td>INS_N_FAULT_ADJ_RATE</td>\n",
       "      <td>0.950424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>INS_N_FAULT_ADJ_RATE</td>\n",
       "      <td>INS_N_FAULT_GRAVITY</td>\n",
       "      <td>0.950424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>INS_N_FAULT_GRAVITY</td>\n",
       "      <td>0.950422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>INS_N_FAULT_GRAVITY</td>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>0.950422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>INS_N_AMT_OVERDUE_RATIO_MEAN</td>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>0.838338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>INS_N_FAULT_RATE</td>\n",
       "      <td>INS_N_AMT_OVERDUE_RATIO_MEAN</td>\n",
       "      <td>0.838338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                x                             y     value\n",
       "254          INS_N_FAULT_ADJ_RATE              INS_N_FAULT_RATE  1.000000\n",
       "286              INS_N_FAULT_RATE          INS_N_FAULT_ADJ_RATE  1.000000\n",
       "233            INS_N_STATUS_0_SUM            INS_N_INSTAL_COUNT  0.992855\n",
       "217            INS_N_INSTAL_COUNT            INS_N_STATUS_0_SUM  0.992855\n",
       "287           INS_N_FAULT_GRAVITY          INS_N_FAULT_ADJ_RATE  0.950424\n",
       "271          INS_N_FAULT_ADJ_RATE           INS_N_FAULT_GRAVITY  0.950424\n",
       "269              INS_N_FAULT_RATE           INS_N_FAULT_GRAVITY  0.950422\n",
       "253           INS_N_FAULT_GRAVITY              INS_N_FAULT_RATE  0.950422\n",
       "246  INS_N_AMT_OVERDUE_RATIO_MEAN              INS_N_FAULT_RATE  0.838338\n",
       "150              INS_N_FAULT_RATE  INS_N_AMT_OVERDUE_RATIO_MEAN  0.838338"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = corr[corr['value'] != 1]\n",
    "corr['value'] = abs(corr['value'])\n",
    "corr = corr.sort_values(by=['value'], ascending=False)\n",
    "corr.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate over Credit balance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Credit card brings much more complex features to handle than credit, mainly because the client could use it (or not) in many ways. To get straight to the point, we'll try to catch and keep only the big picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# third level\n",
    "credit_balance = pd.read_csv('Data/POS_CASH_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>SK_ID_PREV</th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>CNT_INSTALMENT</th>\n",
       "      <th>CNT_INSTALMENT_FUTURE</th>\n",
       "      <th>NAME_CONTRACT_STATUS</th>\n",
       "      <th>SK_DPD</th>\n",
       "      <th>SK_DPD_DEF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1803195</td>\n",
       "      <td>182943</td>\n",
       "      <td>-31</td>\n",
       "      <td>48.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1715348</td>\n",
       "      <td>367990</td>\n",
       "      <td>-33</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1784872</td>\n",
       "      <td>397406</td>\n",
       "      <td>-32</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1903291</td>\n",
       "      <td>269225</td>\n",
       "      <td>-35</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2341044</td>\n",
       "      <td>334279</td>\n",
       "      <td>-35</td>\n",
       "      <td>36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001353</th>\n",
       "      <td>2448283</td>\n",
       "      <td>226558</td>\n",
       "      <td>-20</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>843</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001354</th>\n",
       "      <td>1717234</td>\n",
       "      <td>141565</td>\n",
       "      <td>-19</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001355</th>\n",
       "      <td>1283126</td>\n",
       "      <td>315695</td>\n",
       "      <td>-21</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>609</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001356</th>\n",
       "      <td>1082516</td>\n",
       "      <td>450255</td>\n",
       "      <td>-22</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Active</td>\n",
       "      <td>614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001357</th>\n",
       "      <td>1259607</td>\n",
       "      <td>174278</td>\n",
       "      <td>-52</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10001358 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_PREV  SK_ID_CURR  MONTHS_BALANCE  CNT_INSTALMENT  \\\n",
       "0            1803195      182943             -31            48.0   \n",
       "1            1715348      367990             -33            36.0   \n",
       "2            1784872      397406             -32            12.0   \n",
       "3            1903291      269225             -35            48.0   \n",
       "4            2341044      334279             -35            36.0   \n",
       "...              ...         ...             ...             ...   \n",
       "10001353     2448283      226558             -20             6.0   \n",
       "10001354     1717234      141565             -19            12.0   \n",
       "10001355     1283126      315695             -21            10.0   \n",
       "10001356     1082516      450255             -22            12.0   \n",
       "10001357     1259607      174278             -52            16.0   \n",
       "\n",
       "          CNT_INSTALMENT_FUTURE NAME_CONTRACT_STATUS  SK_DPD  SK_DPD_DEF  \n",
       "0                          45.0               Active       0           0  \n",
       "1                          35.0               Active       0           0  \n",
       "2                           9.0               Active       0           0  \n",
       "3                          42.0               Active       0           0  \n",
       "4                          35.0               Active       0           0  \n",
       "...                         ...                  ...     ...         ...  \n",
       "10001353                    0.0               Active     843           0  \n",
       "10001354                    0.0               Active     602           0  \n",
       "10001355                    0.0               Active     609           0  \n",
       "10001356                    0.0               Active     614           0  \n",
       "10001357                    0.0            Completed       0           0  \n",
       "\n",
       "[10001358 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3:80: E501 line too long (83 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# clean df and focus on active months\n",
    "credit_balance.dropna(inplace=True)\n",
    "credit_balance = credit_balance[credit_balance['NAME_CONTRACT_STATUS'] == 'Active']\n",
    "credit_balance.drop(columns=['NAME_CONTRACT_STATUS'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4:48: E225 missing whitespace around operator\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dpd_def tolerance filtering ratio: 0.393702\n"
     ]
    }
   ],
   "source": [
    "# get an idea of filtering DPD effect\n",
    "dpd_count = credit_balance[credit_balance['SK_DPD'] != 0].shape[0]\n",
    "dpd_def_count = credit_balance[credit_balance['SK_DPD_DEF'] != 0].shape[0]\n",
    "print('dpd_def tolerance filtering ratio: %f' %(dpd_def_count/dpd_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost 40% of actual overdue are filtered, due to low laon amounts ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get loan age - in months and term for each previous loan\n",
    "credit_balance_agg = credit_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min'],\n",
    "     'CNT_INSTALMENT': ['size']})\n",
    "credit_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'POS_N_PREV_LOAN_AGE_MONTH',\n",
    "    'POS_N_PREV_LOAN_TERM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (88 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of DPD overdue we need to catch min of CNT_INSTALMENT with SK_DPD > 0\n",
    "dpd_credit_balance = credit_balance[credit_balance['SK_DPD'] != 0]\n",
    "dpd_credit_balance_agg = dpd_credit_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'CNT_INSTALMENT': ['min']})\n",
    "dpd_credit_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'POS_N_DPD_PREV_EARLIER_OVERDUE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (96 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of DPD_DEF overdue we need to catch min of CNT_INSTALMENT with SK_DPD_DEF > 0\n",
    "dpd_def_credit_balance = credit_balance[credit_balance['SK_DPD_DEF'] != 0]\n",
    "dpd_def_credit_balance_agg = dpd_def_credit_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'CNT_INSTALMENT': ['min']})\n",
    "dpd_def_credit_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'POS_N_DPD_DEF_PREV_EARLIER_OVERDUE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (98 > 79 characters)\n",
      "3:80: E501 line too long (102 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# merge with raw earlier overdue\n",
    "credit_balance_agg = credit_balance_agg.merge(dpd_credit_balance_agg, how='left', on='SK_ID_PREV')\n",
    "credit_balance_agg = credit_balance_agg.merge(dpd_def_credit_balance_agg, how='left', on='SK_ID_PREV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dpd_credit_balance_agg\n",
    "del dpd_def_credit_balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (164 > 79 characters)\n",
      "3:80: E501 line too long (81 > 79 characters)\n",
      "4:80: E501 line too long (84 > 79 characters)\n",
      "5:80: E501 line too long (172 > 79 characters)\n",
      "6:80: E501 line too long (85 > 79 characters)\n",
      "7:80: E501 line too long (88 > 79 characters)\n",
      "8:80: E501 line too long (98 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# manua FE\n",
    "credit_balance_agg['POS_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'] = 1 - credit_balance_agg['POS_N_DPD_PREV_EARLIER_OVERDUE'] / credit_balance_agg['POS_N_PREV_LOAN_TERM']\n",
    "credit_balance_agg.drop(columns=['POS_N_DPD_PREV_EARLIER_OVERDUE'], inplace=True)\n",
    "credit_balance_agg['POS_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "credit_balance_agg['POS_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'] = 1 - credit_balance_agg['POS_N_DPD_DEF_PREV_EARLIER_OVERDUE'] / credit_balance_agg['POS_N_PREV_LOAN_TERM']\n",
    "credit_balance_agg.drop(columns=['POS_N_DPD_DEF_PREV_EARLIER_OVERDUE'], inplace=True)\n",
    "credit_balance_agg['POS_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "credit_balance_agg['POS_N_PREV_LOAN_AGE'] = - credit_balance_agg['POS_N_PREV_LOAN_AGE_MONTH'] / 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "credit_balance_agg will be merged later on SK_ID_PREV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:27: E251 unexpected spaces around keyword / parameter equals\n",
      "2:29: E251 unexpected spaces around keyword / parameter equals\n",
      "10:80: E501 line too long (102 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess POS_CASH_balance.csv\n",
    "def pos_cash(pos, num_rows = None, nan_as_category=False):\n",
    "    # Features\n",
    "    aggregations = {\n",
    "        'MONTHS_BALANCE': ['max', 'mean'],\n",
    "        'SK_DPD': ['min', 'max', 'mean'],\n",
    "        'SK_DPD_DEF': ['min', 'max', 'mean']\n",
    "    }\n",
    "    pos_agg = pos.groupby('SK_ID_CURR').agg(aggregations)\n",
    "    pos_agg.columns = pd.Index(['POS_' + e[0] + \"_\" + e[1].upper() for e in pos_agg.columns.tolist()])\n",
    "    # Count pos cash accounts\n",
    "    pos_agg['POS_N_POS_COUNT'] = pos.groupby('SK_ID_CURR').size()\n",
    "    return pos_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess POS_CASH_balance.csv\n",
    "sk_id_credit_balance = pos_cash(credit_balance, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "del credit_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>feature</th>\n",
       "      <th>abs_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>POS_N_POS_COUNT</td>\n",
       "      <td>0.035816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POS_MONTHS_BALANCE_MEAN</td>\n",
       "      <td>0.033448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POS_SK_DPD_DEF_MAX</td>\n",
       "      <td>0.018046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>POS_SK_DPD_DEF_MEAN</td>\n",
       "      <td>0.009336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POS_MONTHS_BALANCE_MAX</td>\n",
       "      <td>0.005359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POS_SK_DPD_MIN</td>\n",
       "      <td>0.004939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POS_SK_DPD_MEAN</td>\n",
       "      <td>0.004899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POS_SK_DPD_MAX</td>\n",
       "      <td>0.004256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>POS_SK_DPD_DEF_MIN</td>\n",
       "      <td>0.003984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>0.002108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature  abs_corr\n",
       "9          POS_N_POS_COUNT  0.035816\n",
       "2  POS_MONTHS_BALANCE_MEAN  0.033448\n",
       "7       POS_SK_DPD_DEF_MAX  0.018046\n",
       "8      POS_SK_DPD_DEF_MEAN  0.009336\n",
       "1   POS_MONTHS_BALANCE_MAX  0.005359\n",
       "3           POS_SK_DPD_MIN  0.004939\n",
       "5          POS_SK_DPD_MEAN  0.004899\n",
       "4           POS_SK_DPD_MAX  0.004256\n",
       "6       POS_SK_DPD_DEF_MIN  0.003984\n",
       "0               SK_ID_CURR  0.002108"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge to target for corr study\n",
    "pos_target = target.join(sk_id_credit_balance, how='left', on='SK_ID_CURR')\n",
    "target_corrs(pos_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most interesting feature study through plot\n",
    "kde_plot(pos_target, 'POS_N_POS_COUNT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                    0\n",
       "TARGET                        0\n",
       "POS_MONTHS_BALANCE_MAX    18270\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_target.isna().sum().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those pos aggregative data produce missing values once merged with SK_ID, meaning we have no previous internal knowledge about those clients before application.\n",
    "POS_N_POS_COUNT is therefore zero for those 18270 SK_ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculer les corrélations entre features\n",
    "corr = sk_id_credit_balance.corr(method='pearson')\n",
    "corr = pd.melt(corr.reset_index(), id_vars='index')\n",
    "corr.columns = ['x', 'y', 'value']\n",
    "# visualiser les corrélations entre features\n",
    "heatmap(\n",
    "    x=corr['x'],\n",
    "    y=corr['y'],\n",
    "    size=corr['value'].abs(),\n",
    "    color=corr['value']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate over Card balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# third level\n",
    "card_balance = pd.read_csv('Data/credit_card_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# same with card_balance\n",
    "card_balance = card_balance[card_balance['NAME_CONTRACT_STATUS'] == 'Active']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "card_balance.drop(columns=['NAME_CONTRACT_STATUS'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "card_balance.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dpd_def tolerance filtering ratio:  57.89\n"
     ]
    }
   ],
   "source": [
    "# filtering effect\n",
    "card_dpd_count = card_balance[card_balance['SK_DPD'] != 0].shape[0]\n",
    "card_dpd_def_count = card_balance[card_balance['SK_DPD_DEF'] != 0].shape[0]\n",
    "print('dpd_def tolerance filtering ratio: ',\n",
    "      round(100 * (card_dpd_def_count/card_dpd_count), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tolerance level seems higher for credit card overdue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get loan age - in months and term for each previous loan\n",
    "card_balance_agg = card_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min', 'size']})\n",
    "card_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'CC_N_PREV_LOAN_AGE_MONTH',\n",
    "    'CC_N_PREV_LOAN_TERM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (88 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of DPD overdue we need to catch min of CNT_INSTALMENT with SK_DPD > 0\n",
    "dpd_card_balance = card_balance[card_balance['SK_DPD'] != 0]\n",
    "dpd_card_balance_agg = dpd_card_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min']})\n",
    "dpd_card_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'CC_N_DPD_PREV_EARLIER_OVERDUE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (96 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of DPD_DEF overdue we need to catch min of CNT_INSTALMENT with SK_DPD_DEF > 0\n",
    "dpd_def_card_balance = card_balance[card_balance['SK_DPD_DEF'] != 0]\n",
    "dpd_def_card_balance_agg = dpd_def_card_balance.groupby(\n",
    "    'SK_ID_PREV',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min']})\n",
    "dpd_def_card_balance_agg.columns = [\n",
    "    'SK_ID_PREV',\n",
    "    'CC_N_DPD_DEF_PREV_EARLIER_OVERDUE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (92 > 79 characters)\n",
      "3:80: E501 line too long (96 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# merge with raw earlier overdue\n",
    "card_balance_agg = card_balance_agg.merge(dpd_card_balance_agg, how='left', on='SK_ID_PREV')\n",
    "card_balance_agg = card_balance_agg.merge(dpd_def_card_balance_agg, how='left', on='SK_ID_PREV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "del dpd_card_balance_agg\n",
    "del dpd_def_card_balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (155 > 79 characters)\n",
      "4:80: E501 line too long (81 > 79 characters)\n",
      "5:80: E501 line too long (152 > 79 characters)\n",
      "6:80: E501 line too long (163 > 79 characters)\n",
      "7:80: E501 line too long (82 > 79 characters)\n",
      "8:80: E501 line too long (85 > 79 characters)\n",
      "9:80: E501 line too long (160 > 79 characters)\n",
      "10:80: E501 line too long (92 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# manual FE\n",
    "card_balance_agg['CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'] = 1 - card_balance_agg['CC_N_DPD_PREV_EARLIER_OVERDUE'] / card_balance_agg['CC_N_PREV_LOAN_TERM']\n",
    "card_balance_agg.drop(columns=['CC_N_DPD_PREV_EARLIER_OVERDUE'], inplace=True)\n",
    "card_balance_agg['CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "card_balance_agg['CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'] = card_balance_agg['CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO'].apply(lambda x: x if x > 0 else 0)\n",
    "card_balance_agg['CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'] = 1 - card_balance_agg['CC_N_DPD_DEF_PREV_EARLIER_OVERDUE'] / card_balance_agg['CC_N_PREV_LOAN_TERM']\n",
    "card_balance_agg.drop(columns=['CC_N_DPD_DEF_PREV_EARLIER_OVERDUE'], inplace=True)\n",
    "card_balance_agg['CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "card_balance_agg['CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'] = card_balance_agg['CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO'].apply(lambda x: x if x > 0 else 0)\n",
    "card_balance_agg['CC_N_PREV_LOAN_AGE'] = - card_balance_agg['CC_N_PREV_LOAN_AGE_MONTH'] / 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "card_balance_agg will be merged later on SK_ID_PREV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:37: E251 unexpected spaces around keyword / parameter equals\n",
      "2:39: E251 unexpected spaces around keyword / parameter equals\n",
      "2:61: E251 unexpected spaces around keyword / parameter equals\n",
      "2:63: E251 unexpected spaces around keyword / parameter equals\n",
      "4:34: E251 unexpected spaces around keyword / parameter equals\n",
      "4:45: E251 unexpected spaces around keyword / parameter equals\n",
      "4:47: E251 unexpected spaces around keyword / parameter equals\n",
      "6:80: E501 line too long (99 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess credit_card_balance.csv\n",
    "def credit_card_balance(cc, num_rows = None, nan_as_category = True):\n",
    "    # General aggregations\n",
    "    cc.drop(['SK_ID_PREV'], axis= 1, inplace = True)\n",
    "    cc_agg = cc.groupby('SK_ID_CURR').agg(['min', 'max', 'mean', 'sum', 'var'])\n",
    "    cc_agg.columns = pd.Index(['CC_' + e[0] + \"_\" + e[1].upper() for e in cc_agg.columns.tolist()])\n",
    "    # Count credit card lines\n",
    "    cc_agg['N_CC_COUNT'] = cc.groupby('SK_ID_CURR').size()\n",
    "    return cc_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess credit_card_balance.csv\n",
    "sk_id_card_balance = credit_card_balance(card_balance, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (85 > 79 characters)\n",
      "5:80: E501 line too long (99 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# get late use by applicant of any credit card (max of drawing amount / credit_limit)\n",
    "last_month = -6\n",
    "late_card_use = card_balance[card_balance['MONTHS_BALANCE'] >= last_month]\n",
    "late_card_use = late_card_use[late_card_use['AMT_CREDIT_LIMIT_ACTUAL'] > 0]\n",
    "late_card_use['N_CC_USE'] = late_card_use['AMT_BALANCE'] / late_card_use['AMT_CREDIT_LIMIT_ACTUAL']\n",
    "late_card_use = late_card_use.groupby(\n",
    "    'SK_ID_CURR'\n",
    ")['N_CC_USE'].max().reset_index().rename(\n",
    "    columns={'N_CC_USE': 'N_CC_USE_LATE_MONTH_'+str(last_month*-1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (89 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# merge with late_card_use\n",
    "sk_id_card_balance = sk_id_card_balance.merge(late_card_use, how='left', on='SK_ID_CURR')\n",
    "sk_id_card_balance['N_CC_USE_LATE_MONTH_6'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (85 > 79 characters)\n",
      "5:80: E501 line too long (99 > 79 characters)\n",
      "10:80: E501 line too long (89 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# get late use by applicant of any credit card (max of drawing amount / credit_limit)\n",
    "last_month = -2\n",
    "late_card_use = card_balance[card_balance['MONTHS_BALANCE'] >= last_month]\n",
    "late_card_use = late_card_use[late_card_use['AMT_CREDIT_LIMIT_ACTUAL'] > 0]\n",
    "late_card_use['N_CC_USE'] = late_card_use['AMT_BALANCE'] / late_card_use['AMT_CREDIT_LIMIT_ACTUAL']\n",
    "late_card_use = late_card_use.groupby(\n",
    "    'SK_ID_CURR'\n",
    ")['N_CC_USE'].max().reset_index().rename(\n",
    "    columns={'N_CC_USE': 'N_CC_USE_LATE_MONTH_'+str(last_month*-1)})\n",
    "sk_id_card_balance = sk_id_card_balance.merge(late_card_use, how='left', on='SK_ID_CURR')\n",
    "sk_id_card_balance['N_CC_USE_LATE_MONTH_2'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "del late_card_use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "del card_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>abs_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>N_CC_USE_LATE_MONTH_2</td>\n",
       "      <td>0.146919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>N_CC_USE_LATE_MONTH_6</td>\n",
       "      <td>0.144717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>CC_CNT_DRAWINGS_ATM_CURRENT_MEAN</td>\n",
       "      <td>0.115231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>CC_CNT_DRAWINGS_CURRENT_MAX</td>\n",
       "      <td>0.101448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>CC_CNT_DRAWINGS_ATM_CURRENT_MAX</td>\n",
       "      <td>0.088022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CC_AMT_BALANCE_MEAN</td>\n",
       "      <td>0.086335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>CC_AMT_TOTAL_RECEIVABLE_MEAN</td>\n",
       "      <td>0.085628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CC_AMT_RECIVABLE_MEAN</td>\n",
       "      <td>0.085624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>CC_AMT_RECEIVABLE_PRINCIPAL_MEAN</td>\n",
       "      <td>0.085216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>CC_CNT_DRAWINGS_CURRENT_MEAN</td>\n",
       "      <td>0.081544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CC_CNT_DRAWINGS_POS_CURRENT_MAX</td>\n",
       "      <td>0.080711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CC_AMT_INST_MIN_REGULARITY_MEAN</td>\n",
       "      <td>0.074022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CC_AMT_DRAWINGS_ATM_CURRENT_MEAN</td>\n",
       "      <td>0.073374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CC_AMT_BALANCE_MAX</td>\n",
       "      <td>0.068804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>CC_AMT_TOTAL_RECEIVABLE_MAX</td>\n",
       "      <td>0.068085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CC_AMT_RECIVABLE_MAX</td>\n",
       "      <td>0.068069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>CC_AMT_RECEIVABLE_PRINCIPAL_MAX</td>\n",
       "      <td>0.066923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CC_AMT_INST_MIN_REGULARITY_MAX</td>\n",
       "      <td>0.064154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CC_AMT_BALANCE_MIN</td>\n",
       "      <td>0.063511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>CC_AMT_RECIVABLE_MIN</td>\n",
       "      <td>0.062943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>CC_AMT_TOTAL_RECEIVABLE_MIN</td>\n",
       "      <td>0.062943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>CC_AMT_RECEIVABLE_PRINCIPAL_MIN</td>\n",
       "      <td>0.062592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CC_MONTHS_BALANCE_MEAN</td>\n",
       "      <td>0.062117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>CC_CNT_DRAWINGS_CURRENT_VAR</td>\n",
       "      <td>0.061833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CC_MONTHS_BALANCE_MIN</td>\n",
       "      <td>0.061305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>CC_CNT_DRAWINGS_POS_CURRENT_MEAN</td>\n",
       "      <td>0.061210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>CC_CNT_DRAWINGS_ATM_CURRENT_VAR</td>\n",
       "      <td>0.059931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>N_CC_COUNT</td>\n",
       "      <td>0.059460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CC_MONTHS_BALANCE_SUM</td>\n",
       "      <td>0.059017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CC_MONTHS_BALANCE_VAR</td>\n",
       "      <td>0.057368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CC_AMT_DRAWINGS_CURRENT_MEAN</td>\n",
       "      <td>0.056342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CC_AMT_DRAWINGS_ATM_CURRENT_MAX</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CC_AMT_DRAWINGS_CURRENT_MAX</td>\n",
       "      <td>0.052695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>CC_CNT_DRAWINGS_POS_CURRENT_VAR</td>\n",
       "      <td>0.051082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>CC_CNT_DRAWINGS_CURRENT_SUM</td>\n",
       "      <td>0.050756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>CC_CNT_DRAWINGS_ATM_CURRENT_SUM</td>\n",
       "      <td>0.050472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CC_AMT_INST_MIN_REGULARITY_VAR</td>\n",
       "      <td>0.044198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CC_AMT_CREDIT_LIMIT_ACTUAL_SUM</td>\n",
       "      <td>0.044095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>CC_CNT_INSTALMENT_MATURE_CUM_SUM</td>\n",
       "      <td>0.041491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CC_AMT_DRAWINGS_ATM_CURRENT_SUM</td>\n",
       "      <td>0.038513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CC_AMT_BALANCE_VAR</td>\n",
       "      <td>0.038417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>CC_CNT_DRAWINGS_POS_CURRENT_SUM</td>\n",
       "      <td>0.038416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>CC_AMT_TOTAL_RECEIVABLE_VAR</td>\n",
       "      <td>0.038241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>CC_AMT_RECIVABLE_VAR</td>\n",
       "      <td>0.038237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>CC_AMT_RECEIVABLE_PRINCIPAL_VAR</td>\n",
       "      <td>0.037364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CC_AMT_PAYMENT_CURRENT_MAX</td>\n",
       "      <td>0.029485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CC_AMT_PAYMENT_TOTAL_CURRENT_MAX</td>\n",
       "      <td>0.028477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>CC_CNT_INSTALMENT_MATURE_CUM_MEAN</td>\n",
       "      <td>0.028389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>CC_CNT_INSTALMENT_MATURE_CUM_VAR</td>\n",
       "      <td>0.026153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>CC_CNT_INSTALMENT_MATURE_CUM_MIN</td>\n",
       "      <td>0.025549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               feature  abs_corr\n",
       "103              N_CC_USE_LATE_MONTH_2  0.146919\n",
       "102              N_CC_USE_LATE_MONTH_6  0.144717\n",
       "68    CC_CNT_DRAWINGS_ATM_CURRENT_MEAN  0.115231\n",
       "72         CC_CNT_DRAWINGS_CURRENT_MAX  0.101448\n",
       "67     CC_CNT_DRAWINGS_ATM_CURRENT_MAX  0.088022\n",
       "8                  CC_AMT_BALANCE_MEAN  0.086335\n",
       "63        CC_AMT_TOTAL_RECEIVABLE_MEAN  0.085628\n",
       "58               CC_AMT_RECIVABLE_MEAN  0.085624\n",
       "53    CC_AMT_RECEIVABLE_PRINCIPAL_MEAN  0.085216\n",
       "73        CC_CNT_DRAWINGS_CURRENT_MEAN  0.081544\n",
       "82     CC_CNT_DRAWINGS_POS_CURRENT_MAX  0.080711\n",
       "38     CC_AMT_INST_MIN_REGULARITY_MEAN  0.074022\n",
       "18    CC_AMT_DRAWINGS_ATM_CURRENT_MEAN  0.073374\n",
       "7                   CC_AMT_BALANCE_MAX  0.068804\n",
       "62         CC_AMT_TOTAL_RECEIVABLE_MAX  0.068085\n",
       "57                CC_AMT_RECIVABLE_MAX  0.068069\n",
       "52     CC_AMT_RECEIVABLE_PRINCIPAL_MAX  0.066923\n",
       "37      CC_AMT_INST_MIN_REGULARITY_MAX  0.064154\n",
       "6                   CC_AMT_BALANCE_MIN  0.063511\n",
       "56                CC_AMT_RECIVABLE_MIN  0.062943\n",
       "61         CC_AMT_TOTAL_RECEIVABLE_MIN  0.062943\n",
       "51     CC_AMT_RECEIVABLE_PRINCIPAL_MIN  0.062592\n",
       "3               CC_MONTHS_BALANCE_MEAN  0.062117\n",
       "75         CC_CNT_DRAWINGS_CURRENT_VAR  0.061833\n",
       "1                CC_MONTHS_BALANCE_MIN  0.061305\n",
       "83    CC_CNT_DRAWINGS_POS_CURRENT_MEAN  0.061210\n",
       "70     CC_CNT_DRAWINGS_ATM_CURRENT_VAR  0.059931\n",
       "101                         N_CC_COUNT  0.059460\n",
       "4                CC_MONTHS_BALANCE_SUM  0.059017\n",
       "5                CC_MONTHS_BALANCE_VAR  0.057368\n",
       "23        CC_AMT_DRAWINGS_CURRENT_MEAN  0.056342\n",
       "17     CC_AMT_DRAWINGS_ATM_CURRENT_MAX  0.055040\n",
       "22         CC_AMT_DRAWINGS_CURRENT_MAX  0.052695\n",
       "85     CC_CNT_DRAWINGS_POS_CURRENT_VAR  0.051082\n",
       "74         CC_CNT_DRAWINGS_CURRENT_SUM  0.050756\n",
       "69     CC_CNT_DRAWINGS_ATM_CURRENT_SUM  0.050472\n",
       "40      CC_AMT_INST_MIN_REGULARITY_VAR  0.044198\n",
       "14      CC_AMT_CREDIT_LIMIT_ACTUAL_SUM  0.044095\n",
       "89    CC_CNT_INSTALMENT_MATURE_CUM_SUM  0.041491\n",
       "19     CC_AMT_DRAWINGS_ATM_CURRENT_SUM  0.038513\n",
       "10                  CC_AMT_BALANCE_VAR  0.038417\n",
       "84     CC_CNT_DRAWINGS_POS_CURRENT_SUM  0.038416\n",
       "65         CC_AMT_TOTAL_RECEIVABLE_VAR  0.038241\n",
       "60                CC_AMT_RECIVABLE_VAR  0.038237\n",
       "55     CC_AMT_RECEIVABLE_PRINCIPAL_VAR  0.037364\n",
       "42          CC_AMT_PAYMENT_CURRENT_MAX  0.029485\n",
       "47    CC_AMT_PAYMENT_TOTAL_CURRENT_MAX  0.028477\n",
       "88   CC_CNT_INSTALMENT_MATURE_CUM_MEAN  0.028389\n",
       "90    CC_CNT_INSTALMENT_MATURE_CUM_VAR  0.026153\n",
       "86    CC_CNT_INSTALMENT_MATURE_CUM_MIN  0.025549"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge to target for corr study\n",
    "cc_target = target.merge(sk_id_card_balance, how='left', on='SK_ID_CURR')\n",
    "cc_corrs = target_corrs(cc_target)\n",
    "cc_corrs.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most interesting feature study through plot\n",
    "kde_plot(cc_target, 'N_CC_USE_LATE_MONTH_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                    0\n",
       "TARGET                        0\n",
       "CC_MONTHS_BALANCE_MIN    220673\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_target.isna().sum().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those cc aggregative data produce missing values once merged with SK_ID, meaning we have no previous internal knowledge about those clients before application according to credit card details.\n",
    "N_CC_COUNT is therefore zero for those 220606 SK_ID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate \"over\" Previous credits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many inconsistencies between the previous loans and the 3 sources of balance / installments details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'POS_N_PREV_LOAN_AGE_MONTH', 'POS_N_PREV_LOAN_TERM',\n",
       "       'POS_N_DPD_PREV_EARLINESS_OVERDUE_RATIO',\n",
       "       'POS_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO', 'POS_N_PREV_LOAN_AGE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_balance_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'CC_N_PREV_LOAN_AGE_MONTH', 'CC_N_PREV_LOAN_TERM',\n",
       "       'CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO',\n",
       "       'CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO', 'CC_N_PREV_LOAN_AGE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_balance_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'NC_LOAN_TERM', 'NC_EARLINESS_OVERDUE_RATIO_DEF',\n",
       "       'NC_EARLINESS_OVERDUE_RATIO', 'NC_LOAN_AGE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5:80: E501 line too long (83 > 79 characters)\n",
      "6:80: E501 line too long (83 > 79 characters)\n",
      "10:10: E251 unexpected spaces around keyword / parameter equals\n",
      "11:12: E251 unexpected spaces around keyword / parameter equals\n",
      "11:14: E251 unexpected spaces around keyword / parameter equals\n"
     ]
    }
   ],
   "source": [
    "# prepare concatenation\n",
    "credit_balance_agg = credit_balance_agg.rename(\n",
    "    columns={\n",
    "        'POS_N_PREV_LOAN_TERM': 'NC_LOAN_TERM',\n",
    "        'POS_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO': 'NC_EARLINESS_OVERDUE_RATIO',\n",
    "        'POS_N_DPD_PREV_EARLINESS_OVERDUE_RATIO': 'NC_EARLINESS_OVERDUE_RATIO_DEF',\n",
    "        'POS_N_PREV_LOAN_AGE': 'NC_LOAN_AGE'})\n",
    "credit_balance_agg.drop(\n",
    "    ['POS_N_PREV_LOAN_AGE_MONTH'],\n",
    "    axis= 1,\n",
    "    inplace = True)\n",
    "credit_balance_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SK_ID_PREV', 'NC_LOAN_TERM', 'NC_EARLINESS_OVERDUE_RATIO',\n",
       "       'NC_EARLINESS_OVERDUE_RATIO_DEF', 'NC_LOAN_AGE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6:80: E501 line too long (86 > 79 characters)\n",
      "10:10: E251 unexpected spaces around keyword / parameter equals\n",
      "11:12: E251 unexpected spaces around keyword / parameter equals\n",
      "11:14: E251 unexpected spaces around keyword / parameter equals\n"
     ]
    }
   ],
   "source": [
    "# prepare concatenation\n",
    "card_balance_agg = card_balance_agg.rename(\n",
    "    columns={\n",
    "        'CC_N_PREV_LOAN_TERM': 'NC_LOAN_TERM',\n",
    "        'CC_N_DPD_PREV_EARLINESS_OVERDUE_RATIO': 'NC_EARLINESS_OVERDUE_RATIO',\n",
    "        'CC_N_DPD_DEF_PREV_EARLINESS_OVERDUE_RATIO': 'NC_EARLINESS_OVERDUE_RATIO_DEF',\n",
    "        'CC_N_PREV_LOAN_AGE': 'NC_LOAN_AGE'})\n",
    "card_balance_agg.drop(\n",
    "    ['CC_N_PREV_LOAN_AGE_MONTH'],\n",
    "    axis= 1,\n",
    "    inplace = True)\n",
    "card_balance_agg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform concat\n",
    "balance_agg = pd.concat([card_balance_agg, credit_balance_agg])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "del card_balance_agg\n",
    "del credit_balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (120 > 79 characters)\n",
      "3:80: E501 line too long (128 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# clean features\n",
    "balance_agg['NC_EARLINESS_OVERDUE_RATIO'] = balance_agg['NC_EARLINESS_OVERDUE_RATIO'].apply(lambda x: x if x > 0 else 0)\n",
    "balance_agg['NC_EARLINESS_OVERDUE_RATIO_DEF'] = balance_agg['NC_EARLINESS_OVERDUE_RATIO_DEF'].apply(lambda x: x if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load previous\n",
    "with open(\"Data/previous.pkl\", mode=\"rb\") as df:\n",
    "    previous = pickle.load(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge balance_agg of loans data as a first aggregation step\n",
    "previous = previous.merge(balance_agg, how='left', on='SK_ID_PREV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1669868, 41)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "previous.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "del balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (93 > 79 characters)\n",
      "7:64: E251 unexpected spaces around keyword / parameter equals\n",
      "8:60: E251 unexpected spaces around keyword / parameter equals\n",
      "9:71: E251 unexpected spaces around keyword / parameter equals\n",
      "10:59: E251 unexpected spaces around keyword / parameter equals\n",
      "11:62: E251 unexpected spaces around keyword / parameter equals\n",
      "13:80: E501 line too long (85 > 79 characters)\n",
      "14:80: E501 line too long (117 > 79 characters)\n",
      "15:60: E222 multiple spaces after operator\n",
      "15:80: E501 line too long (119 > 79 characters)\n",
      "16:80: E501 line too long (137 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# manual FE\n",
    "previous['DAYS_DUE_GAP'] = previous['DAYS_FIRST_DUE'] - previous['DAYS_LAST_DUE_1ST_VERSION']\n",
    "previous['AMT_CREDIT'].replace(0, np.nan, inplace=True)\n",
    "previous['AMT_APPLICATION'].replace(0, np.nan, inplace=True)\n",
    "previous['AMT_GOODS_PRICE'].replace(0, np.nan, inplace=True)\n",
    "# Days 365.243 values -> nan\n",
    "previous['DAYS_FIRST_DRAWING'].replace(365243, np.nan, inplace= True)\n",
    "previous['DAYS_FIRST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "previous['DAYS_LAST_DUE_1ST_VERSION'].replace(365243, np.nan, inplace= True)\n",
    "previous['DAYS_LAST_DUE'].replace(365243, np.nan, inplace= True)\n",
    "previous['DAYS_TERMINATION'].replace(365243, np.nan, inplace= True)\n",
    "# Add feature: value ask / value received percentage\n",
    "previous['N_APP_CREDIT_RATIO'] = previous['AMT_APPLICATION'] / previous['AMT_CREDIT']\n",
    "previous['N_ADJ_CREDIT_RATIO'] = (previous['AMT_APPLICATION'] - previous['AMT_CREDIT']) / previous['AMT_APPLICATION']\n",
    "previous['N_ADJ_INTEREST_EVAL'] = (previous['AMT_CREDIT'] -  previous['AMT_GOODS_PRICE']) / previous['AMT_GOODS_PRICE']\n",
    "previous['N_CREDIT_PROLONG_RATIO'] = (previous['DAYS_LAST_DUE'] - previous['DAYS_LAST_DUE_1ST_VERSION']) / (previous['DAYS_DUE_GAP'] - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:41: E251 unexpected spaces around keyword / parameter equals\n",
      "2:43: E251 unexpected spaces around keyword / parameter equals\n",
      "2:65: E251 unexpected spaces around keyword / parameter equals\n",
      "2:67: E251 unexpected spaces around keyword / parameter equals\n",
      "3:60: E251 unexpected spaces around keyword / parameter equals\n",
      "28:1: W293 blank line contains whitespace\n",
      "29:80: E501 line too long (87 > 79 characters)\n",
      "30:80: E501 line too long (105 > 79 characters)\n",
      "34:80: E501 line too long (117 > 79 characters)\n",
      "39:80: E501 line too long (114 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess previous_applications.csv\n",
    "def previous_applications(prev, num_rows = None, nan_as_category = True):\n",
    "    prev, cat_cols = one_hot_encoder(prev, nan_as_category= True)\n",
    "    # Previous applications numeric features\n",
    "    num_aggregations = {\n",
    "        'AMT_ANNUITY': ['min', 'max', 'mean'],\n",
    "        'AMT_APPLICATION': ['min', 'max', 'mean'],\n",
    "        'AMT_CREDIT': ['min', 'max', 'mean'],\n",
    "        'N_APP_CREDIT_RATIO': ['min', 'max', 'mean', 'var'],\n",
    "        'N_ADJ_CREDIT_RATIO': ['min', 'max', 'mean', 'var'],\n",
    "        'N_ADJ_INTEREST_EVAL': ['min', 'max', 'mean', 'var'],\n",
    "        'N_CREDIT_PROLONG_RATIO': ['min', 'max', 'mean', 'var'],\n",
    "        'AMT_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "        'AMT_GOODS_PRICE': ['min', 'max', 'mean'],\n",
    "        'RATE_DOWN_PAYMENT': ['min', 'max', 'mean'],\n",
    "        'DAYS_DECISION': ['min', 'max', 'mean'],\n",
    "        'CNT_PAYMENT': ['mean', 'sum', 'var'],\n",
    "        'NC_LOAN_TERM': ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'NC_EARLINESS_OVERDUE_RATIO': ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'NC_EARLINESS_OVERDUE_RATIO_DEF': ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'NC_LOAN_AGE': ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'NFLAG_INSURED_ON_APPROVAL': ['sum', 'mean']\n",
    "    }\n",
    "    # Previous applications categorical features\n",
    "    cat_aggregations = {}\n",
    "    for cat in cat_cols:\n",
    "        cat_aggregations[cat] = ['mean']\n",
    "    \n",
    "    prev_agg = prev.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})\n",
    "    prev_agg.columns = pd.Index(['PREV_' + e[0] + \"_\" + e[1].upper() for e in prev_agg.columns.tolist()])\n",
    "    # Previous Applications: Approved Applications - only numerical features\n",
    "    approved = prev[prev['NAME_CONTRACT_STATUS_Approved'] == 1]\n",
    "    approved_agg = approved.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "    approved_agg.columns = pd.Index(['APPROVED_' + e[0] + \"_\" + e[1].upper() for e in approved_agg.columns.tolist()])\n",
    "    prev_agg = prev_agg.join(approved_agg, how='left', on='SK_ID_CURR')\n",
    "    # Previous Applications: Refused Applications - only numerical features\n",
    "    refused = prev[prev['NAME_CONTRACT_STATUS_Refused'] == 1]\n",
    "    refused_agg = refused.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "    refused_agg.columns = pd.Index(['REFUSED_' + e[0] + \"_\" + e[1].upper() for e in refused_agg.columns.tolist()])\n",
    "    prev_agg = prev_agg.join(refused_agg, how='left', on='SK_ID_CURR')\n",
    "    return prev_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess previous_applications.csv\n",
    "sk_id_previous = previous_applications(previous, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "del previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 365 columns.\n",
      "There are 177 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 96,
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6:80: E501 line too long (112 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(sk_id_previous)\n",
    "# get the missing curve\n",
    "missing_list = []\n",
    "for i in np.arange(0, 101, 1):\n",
    "    missing = sk_id_previous.shape[1] - len(list(missing_values.index[missing_values['% of Total Values'] > i]))\n",
    "    missing_list.append(missing)\n",
    "pd.DataFrame(missing_list).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_missing_col(df, threshold=90):\n",
    "    miss_data = pd.DataFrame((df.isnull().sum()) * 100 / df.shape[0])\n",
    "    miss_data_col = miss_data[miss_data[0] > threshold].index\n",
    "    data_new = df[[i for i in df.columns if i not in miss_data_col]]\n",
    "    return data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_id_previous_clean = remove_missing_col(sk_id_previous)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate over Bureau & Bureau_balance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute info from bureau_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load previous\n",
    "with open(\"Data/bureau.pkl\", mode=\"rb\") as df:\n",
    "    bureau = pickle.load(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# third level\n",
    "bureau_balance = pd.read_csv('Data/bureau_balance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (95 > 79 characters)\n",
      "3:80: E501 line too long (82 > 79 characters)\n",
      "4:80: E501 line too long (82 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# to get earliness of overdue we need to catch min of MONTHS_BALANCE out of !=C, X, or 0 STATUS\n",
    "focus_bureau_balance = bureau_balance[bureau_balance['STATUS'] != 'C']\n",
    "focus_bureau_balance = focus_bureau_balance[focus_bureau_balance['STATUS'] != 'X']\n",
    "focus_bureau_balance = focus_bureau_balance[focus_bureau_balance['STATUS'] != '0']\n",
    "focus_bureau_balance_agg = focus_bureau_balance.groupby(\n",
    "    'SK_ID_BUREAU',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min']})\n",
    "focus_bureau_balance_agg.columns = [\n",
    "    'SK_ID_BUREAU',\n",
    "    'N_BUR_EARLIER_OVERDUE']\n",
    "bureau = bureau.merge(focus_bureau_balance_agg, how='left', on='SK_ID_BUREAU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:80: E501 line too long (80 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# get credit AGE in months since closure\n",
    "closing_bureau_balance = bureau_balance[bureau_balance['STATUS'] == 'C']\n",
    "closing_bureau_balance_agg = closing_bureau_balance.groupby(\n",
    "    'SK_ID_BUREAU',\n",
    "    as_index=False).agg(\n",
    "    {'STATUS': ['count']})\n",
    "closing_bureau_balance_agg.columns = [\n",
    "    'SK_ID_BUREAU',\n",
    "    'N_CREDIT_CLOSING_MONTHS']\n",
    "bureau = bureau.merge(closing_bureau_balance_agg, how='left', on='SK_ID_BUREAU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "del closing_bureau_balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:35: W291 trailing whitespace\n"
     ]
    }
   ],
   "source": [
    "# to get duration of bureau credit \n",
    "term_bureau_balance = bureau_balance[bureau_balance['STATUS'] != 'C']\n",
    "term_bureau_balance = term_bureau_balance[term_bureau_balance['STATUS'] != 'X']\n",
    "term_bureau_balance_agg = term_bureau_balance.groupby(\n",
    "    'SK_ID_BUREAU',\n",
    "    as_index=False).agg(\n",
    "    {'MONTHS_BALANCE': ['min']})\n",
    "term_bureau_balance_agg.columns = [\n",
    "    'SK_ID_BUREAU',\n",
    "    'N_BUR_START_OBSERVED']\n",
    "bureau = bureau.merge(term_bureau_balance_agg, how='left', on='SK_ID_BUREAU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "del term_bureau_balance_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (106 > 79 characters)\n",
      "5:80: E501 line too long (101 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# about earlier overdue\n",
    "bureau['N_BUR_EARLINESS_OVERDUE_RATIO'] = bureau['N_BUR_EARLIER_OVERDUE'] / bureau['N_BUR_START_OBSERVED']\n",
    "bureau.drop(columns=['N_BUR_EARLIER_OVERDUE'], inplace=True)\n",
    "bureau['N_BUR_EARLINESS_OVERDUE_RATIO'].fillna(0, inplace=True)\n",
    "bureau['N_BUR_AMT_CREDIT_DEBT_RATE'] = bureau['AMT_CREDIT_SUM_DEBT'] / (1 + bureau['AMT_CREDIT_SUM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:44: E251 unexpected spaces around keyword / parameter equals\n",
      "2:46: E251 unexpected spaces around keyword / parameter equals\n",
      "2:68: E251 unexpected spaces around keyword / parameter equals\n",
      "2:70: E251 unexpected spaces around keyword / parameter equals\n",
      "4:66: W291 trailing whitespace\n",
      "11:80: E501 line too long (91 > 79 characters)\n",
      "13:51: E251 unexpected spaces around keyword / parameter equals\n",
      "31: E203 whitespace before ':'\n",
      "32:75: W291 trailing whitespace\n",
      "36:26: E701 multiple statements on one line (colon)\n",
      "37:22: E701 multiple statements on one line (colon)\n",
      "38:80: E501 line too long (91 > 79 characters)\n",
      "39:80: E501 line too long (108 > 79 characters)\n",
      "43:80: E501 line too long (111 > 79 characters)\n",
      "48:80: E501 line too long (111 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# Preprocess bureau.csv and bureau_balance.csv\n",
    "def bureau_and_balance(bureau, bb, num_rows = None, nan_as_category = True):\n",
    "    bb, bb_cat = one_hot_encoder(bb, nan_as_category)\n",
    "    bureau, bureau_cat = one_hot_encoder(bureau, nan_as_category) \n",
    "    # Bureau balance: Perform aggregations and merge with bureau.csv\n",
    "    bb_aggregations = {'MONTHS_BALANCE': ['min', 'max', 'size']}\n",
    "    for col in bb_cat:\n",
    "        bb_aggregations[col] = ['mean']\n",
    "    print(bb_aggregations)\n",
    "    bb_agg = bb.groupby('SK_ID_BUREAU').agg(bb_aggregations)\n",
    "    bb_agg.columns = pd.Index([e[0] + \"_\" + e[1].upper() for e in bb_agg.columns.tolist()])\n",
    "    bureau = bureau.join(bb_agg, how='left', on='SK_ID_BUREAU')\n",
    "    bureau.drop(['SK_ID_BUREAU'], axis=1, inplace= True)\n",
    "    # Bureau and bureau_balance numeric features\n",
    "    num_aggregations = {\n",
    "        'DAYS_CREDIT': ['min', 'max', 'mean', 'var'],\n",
    "        'DAYS_CREDIT_ENDDATE': ['min', 'max', 'mean'],\n",
    "        'CREDIT_DAY_OVERDUE': ['max', 'mean'],\n",
    "        'AMT_CREDIT_MAX_OVERDUE': ['max', 'mean', 'sum'],\n",
    "        'AMT_CREDIT_SUM': ['max', 'mean', 'sum'],\n",
    "        'AMT_CREDIT_SUM_DEBT': ['max', 'mean', 'sum'],\n",
    "        'AMT_CREDIT_SUM_OVERDUE': ['max', 'mean', 'sum'],\n",
    "        'AMT_CREDIT_SUM_LIMIT': ['max', 'mean', 'sum'],\n",
    "        'AMT_ANNUITY':  ['max', 'mean', 'sum'],\n",
    "        'CNT_CREDIT_PROLONG': ['sum'],\n",
    "        'MONTHS_BALANCE_MIN': ['min'],\n",
    "        'MONTHS_BALANCE_MAX': ['max'],\n",
    "        'MONTHS_BALANCE_SIZE': ['mean', 'sum'],\n",
    "        'N_CREDIT_CLOSING_MONTHS': ['max', 'mean'],\n",
    "        'N_BUR_START_OBSERVED' : ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'N_BUR_EARLINESS_OVERDUE_RATIO': ['min', 'max', 'mean', 'sum', 'var'],\n",
    "        'N_BUR_AMT_CREDIT_DEBT_RATE': ['min', 'max', 'mean', 'sum', 'var']       \n",
    "    }\n",
    "    # Bureau and bureau_balance categorical features\n",
    "    cat_aggregations = {}\n",
    "    for cat in bureau_cat: cat_aggregations[cat] = ['mean']\n",
    "    for cat in bb_cat: cat_aggregations[cat + \"_MEAN\"] = ['mean']\n",
    "    bureau_agg = bureau.groupby('SK_ID_CURR').agg({**num_aggregations, **cat_aggregations})\n",
    "    bureau_agg.columns = pd.Index(['BUR_' + e[0] + \"_\" + e[1].upper() for e in bureau_agg.columns.tolist()])\n",
    "    # Bureau: Active credits - using only numerical aggregations\n",
    "    active = bureau[bureau['CREDIT_ACTIVE_Active'] == 1]\n",
    "    active_agg = active.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "    active_agg.columns = pd.Index(['ACTIVE_' + e[0] + \"_\" + e[1].upper() for e in active_agg.columns.tolist()])\n",
    "    bureau_agg = bureau_agg.join(active_agg, how='left', on='SK_ID_CURR')\n",
    "    # Bureau: Closed credits - using only numerical aggregations\n",
    "    closed = bureau[bureau['CREDIT_ACTIVE_Closed'] == 1]\n",
    "    closed_agg = closed.groupby('SK_ID_CURR').agg(num_aggregations)\n",
    "    closed_agg.columns = pd.Index(['CLOSED_' + e[0] + \"_\" + e[1].upper() for e in closed_agg.columns.tolist()])\n",
    "    bureau_agg = bureau_agg.join(closed_agg, how='left', on='SK_ID_CURR')\n",
    "    return bureau_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'MONTHS_BALANCE': ['min', 'max', 'size'], 'STATUS_0': ['mean'], 'STATUS_1': ['mean'], 'STATUS_2': ['mean'], 'STATUS_3': ['mean'], 'STATUS_4': ['mean'], 'STATUS_5': ['mean'], 'STATUS_C': ['mean'], 'STATUS_X': ['mean'], 'STATUS_nan': ['mean']}\n"
     ]
    }
   ],
   "source": [
    "# Preprocess bureau.csv and bureau_balance.csv\n",
    "sk_id_bureau = bureau_and_balance(bureau, bureau_balance, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "del bureau\n",
    "del bureau_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 169 columns.\n",
      "There are 135 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oFPb9FAoFNBqNi8slf9SuM6Lw2FUUHb+KLoMF+KR5wMcGBgAvLJ+G+TPGubFCIu/l9AevGo0GK1euxOOPP46UlBSo1eoea2ULguDU2tmVlZXOlgS1Wu30sb7KG3s2WWz43y870K4f/GcyNgGoaTbCbBHwUGwoEuIcW5N9ZHgQhgdooVZrB12LmLzx5+xu7Nk1nAr5K1euYOXKlXjqqafw7LPPAgAiIyOh1X77D6mlpQVKpdLhcycmJkIud3yND7VajeTkZIeP82Xe2HOXwYxXdp1CVbUOcVHDEIDBPyRjTtIYLJs/EeMih3llz+7Gnv2Dsz0bjcY+B8cOh7xOp8PPfvYzvPDCC3jsscfs26OjoyGXy+2FFhQUIC0tzeGCyXfpu814+d0v8PW1m/jVimSkT48RuyQiv+dwyO/fvx8tLS3YvXs3du/eDQCYP38+Vq9ejR07dmDTpk3Q6XRISEhATk6Oywsm79GuM+Ldwkq0dxoBAE2tXWhu7cK6n8zA7KR7RK6OiAAHQr60tBQA8Mwzz+CZZ5654z7x8fHYv3+/Swoj73az04BNb5ehqUWP+6KHAwBGRsixcmkivvdQpMjVEdFtvOOVHHajvRub3i6Dtq0bm1c+jKSJiv4PIiJRMOSpX0039Hi3oBJ6gxkAcF2rR7fRjJd/noqE8aNFro6I+sKQpz5d1+qw8Q8n0G2y4r57bi3odW/UMGQveADx944SuToi6g9Dnu6qTtOJTW+fgMUq4PX/mI377hkudklE5CCGvB/66koL3j90AWaLrc/9Gpp1CA4OxGv/MRtxkc4ty0tE4mLI+5kzXzdj659OYUSEHNGK8D73nTxhDJ7KeBCxY/l8UiJfxZD3I+VVGrz259OIVoTj1VWzMDzc8TuLici3MOQl5uzXzfhL8Xnc6fGl15o6EBc1DK88NwvDhoZ4vjgi8jiGvMQcOlWL6y16JI4f0+t7E2JG4KdZCXwmKZEfYchLiM0m4KvLLXg4MQprsqeLXQ4ReYEBPf6PfMM1TSc69CZMvr/3KJ6I/BNDXkIqLt1a6nnKBIY8Ed3CkJeQisstiBwdBuWoMLFLISIvwZCXCKtNQOWVFkyZwMXCiOhbDHmJqG5oh95gwWRO1RDRv2HIS0TFZc7HE1FvDHmJqLjcghhlOEYNGyJ2KUTkRRjyEmCx2vCvqzc4iieiXhjyEnC5rg0Gk5UfuhJRL7zj1UdcvHYT+f97ERdqW+3bzGYLZIXNMJlvLRmceD+f0kREPTHkvVyDVoc/HqjA2YtaRITJ8HBiFIKDb/0BptVqoVDcGr3HjY3gqpJE1MuAQl6n02H58uV4++23ERMTg7KyMrz++uswGo3IyMjAmjVrAABVVVXYuHEj9Ho9ZsyYgZdffhnBwfzvyGD8pfg8LtS24unMh7B41r0IG/Lt4mJqtRrJyUkiVkdE3q7fOflz584hOzsbNTU1AACDwYANGzZg586dKC4uRmVlJY4ePQoAWLt2LTZv3oxDhw5BEATk5+e7tXh/cKPNgAfiRmHZ/Ik9Ap6IaCD6Dfn8/Hxs2bIFSqUSAFBRUYG4uDjExsYiODgYWVlZKCkpQUNDAwwGA6ZOnQoAUKlUKCkpcWvx/uCmzogREZyGISLn9DuXsnXr1h6vm5ub7fPAAKBUKqHRaHptVygU0Gg0DhdUWVnp8DG3qdVqp4/1RoIgoLW9G0Z92117k1rPA8Ge/QN7dg2HJ8xtNhsCAgLsrwVBQEBAwF23OyoxMRFyueMj11vz08kOH+fNugxmWP7agAfuH4fk5Im9vi/FnvvDnv0Dex44o9HY5+DY4evkIyMjodVq7a+1Wi2USmWv7S0tLfYpHnJOu84EAJyuISKnORzySUlJqK6uRm1tLaxWK4qKipCWlobo6GjI5XL7nxsFBQVIS0tzecH+pK3TCIAhT0TOc3i6Ri6XY9u2bcjNzYXRaER6ejoWLVoEANixYwc2bdoEnU6HhIQE5OTkuLxgf9KmMwAARvD6dyJy0oBDvrS01P51amoqCgsLe+0THx+P/fv3u6Yy4kieiAaNa9d4sdshzztZichZDHkv1qYzIiJMhuAg/piIyDlMDy/WxhuhiGiQGPJerK3TiBHhfAgIETmPIe/F2jqNGB4eInYZROTDGPJejNM1RDRYDHkvZTJb0WWwMOSJaFAY8l6qTffNNfKckyeiQWDIe6nb18iP5EieiAaBIe+lbo/k+cErEQ0GQ95LfbukAadriMh5DHkvxXVriMgVGPJeql1nRKg8GHJZkNilEJEPY8h7qbZOXiNPRIPHkPdSbToj15EnokFjyHupmxzJE5ELMOS91K3FyRjyRDQ4DHkvZLXa0Nll4kieiAaNIe+F2vUmALx8kogGjyHvhezXyHO6hogGaVAhX1BQgMzMTGRmZmL79u0AgLKyMmRlZWHBggXIy8tzSZH+hs92JSJXcTrku7u7sXXrVuzZswcFBQUoLy9HaWkpNmzYgJ07d6K4uBiVlZU4evSoK+v1C206AwAuTkZEg+d0yFutVthsNnR3d8NiscBisSA8PBxxcXGIjY1FcHAwsrKyUFJS4sp6/UJbJ+fkicg1gp09MDw8HKtXr0ZGRgZCQ0Mxc+ZMNDc3Q6FQ2PdRKpXQaDQuKdSftOmMCAkORKjc6R8PERGAQYT8hQsX8NFHH+HIkSOIiIjAr3/9a9TU1CAgIMC+jyAIPV4PRGVlpbMlQa1WO32sN7lS04pQeQDOnDnT775S6dkR7Nk/sGfXcDrkjx8/jtTUVIwePRoAoFKpsGvXLgQFfbugllarhVKpdOi8iYmJkMsdn6ZQq9VITk52+Dhv9N6xo4gaI++3Hyn1PFDs2T+w54EzGo19Do6dnpOPj49HWVkZurq6IAgCSktLkZSUhOrqatTW1sJqtaKoqAhpaWnOvoVfutrQjst1bZidFC12KUQkAU6P5OfMmYPz589DpVJBJpNh8uTJyM3NxezZs5Gbmwuj0Yj09HQsWrTIlfVKXnFZNUKCA/H9mbFil0JEEjCoT/aee+45PPfccz22paamorCwcFBF+asugxlHz9TjkWnRiAjjY/+IaPB4x6sXOaKuh8FkxeJZ94ldChFJBEPeSwiCgE/KqnF/zHBMjB0hdjlEJBEMeS9xvroVtU2dyEi9z+HLTomI7oZ323iQ2WLF2we+wqW6m72+19ZpxNAhwUifxqtqiMh1GPIeYjRb8dqfT+PMhWYkxysRHNTzjyjlyDA8nBiFIbzLlYhciIniAQaTBa/+6RQqLrcg94mpWJASJ3ZJROQnGPIe8H/fP4OvLrfgheXTMX8Gr38nIs/hB68ecO6SFj9IiWPAE5HHMeTdzGyxoctggWJEqNilEJEfYsi7WWfXrbXhhw3lHaxE5HkMeTfr0N8OeT4AhIg8jyHvZh36W89r5UieiMTAkHezb0fyDHki8jyGvJu16xjyRCQehryb3R7JRzDkiUgEDHk369AbMTRU1msZAyIiT2DyuFmH3sSpGiISDUPezRjyRCQmhrybMeSJSEwMeTfr0BkZ8kQkGoa8GwmC8M1Inne7EpE4BhXypaWlUKlUyMjIwKuvvgoAKCsrQ1ZWFhYsWIC8vDyXFOmrjCYrTBYbhnMkT0QicTrk6+rqsGXLFuzcuROFhYU4f/48jh49ig0bNmDnzp0oLi5GZWUljh496sp6fQrvdiUisTkd8ocPH8bixYsRGRkJmUyGvLw8hIaGIi4uDrGxsQgODkZWVhZKSkpcWa9PYcgTkdicfjJUbW0tZDIZVq1ahcbGRsydOxcTJ06EQqGw76NUKqHRaFxSqC/iCpREJDanQ95qtaK8vBx79uxBWFgYfvGLX2DIkCEICAiw7yMIQo/XA1FZWelsSVCr1U4f6w4V1V0AgGs1F9HVKnPLe3hbz57Anv0De3YNp0N+zJgxSE1NxahRowAAjz76KEpKShAUFGTfR6vVQqlUOnTexMREyOWOj3zVajWSk5MdPs6d6vVXALRiVsp0RIS5fsrGG3t2N/bsH9jzwBmNxj4Hx07Pyc+bNw/Hjx9HR0cHrFYrjh07hkWLFqG6uhq1tbWwWq0oKipCWlqas2/h8zr0JgQGBmDoEPeM4omI+uP0SD4pKQkrV67EihUrYDabMXv2bGRnZ2P8+PHIzc2F0WhEeno6Fi1a5Mp6fUqH3oRhYSEIDHRsyoqIyFWcDnkAWLZsGZYtW9ZjW2pqKgoLCwdVlFR06I1cYpiIRMU7Xt2I69YQkdgY8m7EkCcisTHk3ahDx5AnInEx5N3EZhPQ0cWQJyJxMeTdpMtghs0m8G5XIhIVQ95Nbi9pMDycI3kiEg9D3k24OBkReQOGvJsw5InIGzDk3aRDbwTAFSiJSFwMeTdp13EkT0TiY8i7SYfeBFlwIIaEBPW/MxGRmzDk3aRDb8LwoSEOr6dPRORKDHk3ubWkAefjiUhcDHk36dAbOR9PRKIb1FLD/qqlrRttOmOf+7R2GBAfN8pDFRER3RlD3kGfn6lH3vtq2IT+9501ZYj7CyIi6gND3gGf/fMa/nvfWSSOH4OlaeP73DcgIAAJ40d7qDIiojvzm5AXBAFX6tthsdqcOv5C7U386WAlkiYosPHZ72FIiN/8X0dEPsxvkuqIug55fz07qHMkxyux4ZnvIUTGa9+JyDf4TcgXHa9GjDIcP1862anjg4MD8NB9oxEcxAuSiMh3+EXIX65rw6W6Njz32GRMj1eKXQ4Rkce4ZFi6fft2rF+/HgBQVlaGrKwsLFiwAHl5ea44/aB9crIG8pAgzJ8RK3YpREQeNeiQP3nyJD7++GMAgMFgwIYNG7Bz504UFxejsrISR48eHXSRg6HvNuPo2XqkT4vB0FCZqLUQEXnaoEK+ra0NeXl5WLVqFQCgoqICcXFxiI2NRXBwMLKyslBSUuKSQp1VWl4Ho8mKjNR7Ra2DiEgMg5qT37x5M9asWYPGxkYAQHNzMxQKhf37SqUSGo3GoXNWVlY6XY9are7xWhAEHCjV4J5RMrQ3X4G62elTe63v9uwP2LN/YM+u4XTIf/jhh4iKikJqaioOHDgAALDZbD1WXRQEweFVGBMTEyGXO76wl1qtRnJyMs5X38Cfi87DbLXBarWhpcOC1U9ORXJynMPn9Ha3e/Yn7Nk/sOeBMxqNfQ6OnQ754uJiaLVaLF26FO3t7ejq6kJDQwOCgr69hlyr1UKp9NzVLLpuM/5rTzlsAjA+ejgA4N6oYXhkWozHaiAi8iZOh/zu3bvtXx84cACnT5/Gyy+/jAULFqC2thYxMTEoKirC448/7pJCB+J//vYVWjuN+K/cRzBp3EiPvS8Rkbdy6XXycrkc27ZtQ25uLoxGI9LT07Fo0SJXvsVdVdV1o7T8Bp58dBIDnojoGy4JeZVKBZVKBQBITU1FYWGhK047YG2dRhw8fRPjo4fjyR884NH3JiLyZpK4R7+q5gbMFgFrsqdDFiyJloiIXEISiZg6+R78WhWFe6OGiV0KEZFXkUTIA4BcJplWiIhchslIRCRhDHkiIgljyBMRSRhDnohIwhjyREQSxpAnIpIwr3n8nyAIAACTyeT0OYxGo6vK8Rns2T+wZ//gTM+3M/N2hn5XgHC373hYZ2cnLl68KHYZREQ+adKkSYiIiOi13WtC3mazQa/XQyaTObwGPRGRvxIEAWazGUOHDkVgYO8ZeK8JeSIicj1+8EpEJGEMeSIiCWPIExFJGEOeiEjCGPJERBLGkCcikjCGPBGRhEki5A8ePIjFixdjwYIF2Lt3r9jluMVbb72FzMxMZGZm4re//S0AoKysDFlZWViwYAHy8vJErtB9tm/fjvXr1wOQfs+lpaVQqVTIyMjAq6++CkD6PRcUFNh/t7dv3w5Auj3rdDosWbIE9fX1AO7eZ1VVFVQqFRYuXIiNGzfCYrE4/6aCj2tqahLmzZsn3Lx5U9Dr9UJWVpZw6dIlsctyqRMnTghPPvmkYDQaBZPJJOTk5AgHDx4U0tPThWvXrglms1l49tlnhc8//1zsUl2urKxMSElJEV588UWhu7tb0j1fu3ZNmDNnjtDY2CiYTCYhOztb+PzzzyXdc1dXlzBz5kzhxo0bgtlsFpYtWyZ89tlnkuz5yy+/FJYsWSIkJCQIdXV1ff4+Z2ZmCmfPnhUEQRBeeuklYe/evU6/r8+P5MvKyvDwww9jxIgRCAsLw8KFC1FSUiJ2WS6lUCiwfv16hISEQCaT4f7770dNTQ3i4uIQGxuL4OBgZGVlSa7vtrY25OXlYdWqVQCAiooKSfd8+PBhLF68GJGRkZDJZMjLy0NoaKike7ZarbDZbOju7obFYoHFYkF4eLgke87Pz8eWLVugVCoB3P33uaGhAQaDAVOnTgUAqFSqQfXvNatQOqu5uRkKhcL+WqlUoqKiQsSKXG/ixIn2r2tqavDJJ5/gJz/5Sa++NRqNGOW5zebNm7FmzRo0NjYCuPPPWko919bWQiaTYdWqVWhsbMTcuXMxceJESfccHh6O1atXIyMjA6GhoZg5c6Zkf85bt27t8fpufX53u0KhGFT/Pj+St9lsPRY0EwRBsgucXbp0Cc8++yzWrVuH2NhYSff94YcfIioqCqmpqfZtUv9ZW61WnDx5Eq+99hr27duHiooK1NXVSbrnCxcu4KOPPsKRI0dw7NgxBAYGoqamRtI933a332dX/577/Eg+MjIS5eXl9tdardb+55CUqNVqPP/889iwYQMyMzNx+vRpaLVa+/el1ndxcTG0Wi2WLl2K9vZ2dHV1oaGhAUFBQfZ9pNbzmDFjkJqailGjRgEAHn30UZSUlEi65+PHjyM1NRWjR48GcGtqYteuXZLu+bbIyMg7/hv+7vaWlpZB9e/zI/lZs2bh5MmTaG1tRXd3Nz799FOkpaWJXZZLNTY24pe//CV27NiBzMxMAEBSUhKqq6tRW1sLq9WKoqIiSfW9e/duFBUVoaCgAM8//zzmz5+Pd999V9I9z5s3D8ePH0dHRwesViuOHTuGRYsWSbrn+Ph4lJWVoaurC4IgoLS0VPK/27fdrc/o6GjI5XKo1WoAt64+Gkz/Pj+SHzt2LNasWYOcnByYzWYsW7YMU6ZMEbssl9q1axeMRiO2bdtm37Z8+XJs27YNubm5MBqNSE9Px6JFi0Ss0v3kcrmke05KSsLKlSuxYsUKmM1mzJ49G9nZ2Rg/frxke54zZw7Onz8PlUoFmUyGyZMnIzc3F7Nnz5Zsz7f19fu8Y8cObNq0CTqdDgkJCcjJyXH6fbiePBGRhPn8dA0REd0dQ56ISMIY8kREEsaQJyKSMIY8EZGEMeSJiCSMIU9EJGEMeSIiCfv/x+tQfhuqXyIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6:80: E501 line too long (110 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(sk_id_bureau)\n",
    "# get the missing curve\n",
    "missing_list = []\n",
    "for i in np.arange(0, 101, 1):\n",
    "    missing = sk_id_bureau.shape[1] - len(list(missing_values.index[missing_values['% of Total Values'] > i]))\n",
    "    missing_list.append(missing)\n",
    "pd.DataFrame(missing_list).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treat Applications\n",
    "We proceed with manual FE on any applicant's profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Data/any_app.pkl\", mode=\"rb\") as df:\n",
    "    any_applications = pickle.load(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:70: W291 trailing whitespace\n"
     ]
    }
   ],
   "source": [
    "# Preprocess application_train.csv and application_test.csv\n",
    "def application_train_test(df, num_rows=None, nan_as_category=False): \n",
    "    # Categorical features with Binary encode (0 or 1; two categories)\n",
    "    df = label_encoder(df)\n",
    "    # Categorical features with One-Hot encode\n",
    "    df, cat_cols = one_hot_encoder(df, nan_as_category)\n",
    "    return df, cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 columns were label encoded. ['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']\n"
     ]
    }
   ],
   "source": [
    "# Preprocess application_train.csv and application_test.csv\n",
    "application, catApp = application_train_test(any_applications, num_rows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "del any_applications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge to target for corr study\n",
    "app_target = application[application['TARGET'].notna()]\n",
    "app_target_corrs = target_corrs(app_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>abs_corr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>YEARS_BIRTH</td>\n",
       "      <td>0.078242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>0.074957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>N_REJECT_RATE</td>\n",
       "      <td>0.074425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>N_ADJ_REJECT_RECENCY_INDEX</td>\n",
       "      <td>0.068731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>N_DAYS_EMPLOYED_RATIO</td>\n",
       "      <td>0.067952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>REGION_RATING_CLIENT_W_CITY</td>\n",
       "      <td>0.060895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>REGION_RATING_CLIENT</td>\n",
       "      <td>0.058901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>NAME_INCOME_TYPE_Working</td>\n",
       "      <td>0.057483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>N_FLAG_REJECTED</td>\n",
       "      <td>0.057421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>NAME_EDUCATION_TYPE_Higher education</td>\n",
       "      <td>0.056593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  feature  abs_corr\n",
       "123                           YEARS_BIRTH  0.078242\n",
       "11                          DAYS_EMPLOYED  0.074957\n",
       "119                         N_REJECT_RATE  0.074425\n",
       "116            N_ADJ_REJECT_RECENCY_INDEX  0.068731\n",
       "124                 N_DAYS_EMPLOYED_RATIO  0.067952\n",
       "23            REGION_RATING_CLIENT_W_CITY  0.060895\n",
       "22                   REGION_RATING_CLIENT  0.058901\n",
       "143              NAME_INCOME_TYPE_Working  0.057483\n",
       "115                       N_FLAG_REJECTED  0.057421\n",
       "145  NAME_EDUCATION_TYPE_Higher education  0.056593"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_target_corrs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# most interesting feature study through plot\n",
    "kde_plot(app_target, 'N_REJECT_RATE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 280 columns.\n",
      "There are 58 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Qt8y/6U0ib3gJnygEdPSO36g7N5OtDCiweF8xoiA78/UALEOjU+7zeUcfFqfMY2thCjiGPFEQDQyOoPbNEz7tu/XxzCBXQ5GIIU8URB268SXFe8vWIjUpYdL9ogAkLeBZPAUeQ54oiM7eXBa5IjOJq2ZIFLzwShQkbreAs+dMyM1UMOBJNAx5oiDRG4YxZHWwoySJiiFPFCQdvePz8ewNT2JiyBMFyVmdicsiSXQMeaIgcDhd6Dpv4VQNiY4hTxQEX10YhMPpQh6nakhkDHmiIOjoNSE6Ogo5GYvELoUiHEOeKAjO6kx46IF7MTdeJnYpFOEY8kQBZh1x4Nyla1xVQyGBIU8UYJ3nzHAL4EVXCgkMeaIA69CZMEceg4eW3Ct2KUQMeaJA6+g1IScjCbEx/OdF4uOnkCiAjIMjMJhtXDpJIYMhTxRAZ2+2FuZ8PIUKhjxRAHXcbC28OGWe2KUQAWDIEwWM2y3grI6thSm0MOSJAkRvGMawzYG8Zclil0LkwZAnCpCO3gEAQG5mksiVEH2LIU8UIB29bC1MoYf3eCXyg8Ppwv899jVGHa47nus+b8ETa5bOflFEU2DIE/mh85wZ7x/XYY48FtHRt19cnRsvw7rcNJEqI7o7hjyRH4wWGwDgn6v+Dgvnx4tcDZF3nJMn8kP/4AjiYqNx7zy52KUQ+YQhT+SHfosNKYsSuA6ewoZP0zX19fVoaWkBABQWFqKyshJ79uyBVqvFnDnjKwl+8YtfYOPGjejp6UFNTQ1sNhtWrlyJV155BbGxnBUiaei3jCB10VyxyyDymdf0bW9vR1tbG5qamhAVFYXnnnsOx44dQ1dXF9555x0kJ9/+xY+Kigrs3bsXeXl5qK6uRmNjI7Zv3x60ARDNFkEQ0G+xYfn3uQ6ewofX6RqFQoGqqirExcVBJpMhIyMDV65cwZUrV1BdXY3i4mK88cYbcLvd6Ovrw+joKPLy8gAAKpUKra2twR4D0awYsjow6nDxTJ7Citcz+czMTM+f9Xo9Wlpa8O677+LUqVN4+eWXMW/ePJSVleHQoUPIzMyEQvFt9z2FQgGj0RicyolmWf/g+Mqa1EUJIldC5DufJ8t1Oh3KyspQWVmJBx98EH/4wx88z+3YsQOHDx9GRkbGbRekBEHw+wJVV1eXX/vfSqvVTvvYcMUxz55O/QgAwNKvh/ZG36y+Nt/nyBCMMfsU8lqtFuXl5aiuroZSqcTXX38NvV6PoqIiAONhHhsbi9TUVJhMJs9xZrP5jjl7b3JyciCX+788TavVIj8/3+/jwhnHPLvODX4NYBDr161EfNzsLSbg+xwZpjtmu90+5cmx1zl5g8GAnTt3QqPRQKlUAhgP9ddeew1DQ0NwOp04ePAgNm7ciLS0NMjlcs9vo+bmZhQUFPhdNFEo6reMYOF8+awGPNFMef20NjQ0wG63o66uzrOtpKQEzz//PLZt24axsTFs2rQJW7ZsAQBoNBqo1WpYrVZkZ2ejtLQ0eNUTzSKDxYaUhZyPp/DiNeTVajXUavVdn/vpT396x7asrCwcOnRo5pURhRijxYYVvHcrhRl+45XIBw6nC5bhUaQu5PJJCi8MeSIfDFwdgSAAKVw+SWGGIU/kg37L+PJJfhGKwg1DnsgHEy2G+UUoCjcMeSIf9A+OIE4WwxbDFHYY8kQ+MJhtSF00ly2GKeww5Il8YBwcQSrXyFMYYsgTeTHRYpgXXSkcMeSJvJhoMZzCkKcwxJAn8oIthimcMeSJvPCskee3XSkMMeSJvOi/uUae33alcMSQJ/Ki32LDwvnxkMtixC6FyG8MeSIv+i0jXFlDYYshT+SF0WLjRVcKWwx5oimwxTCFO4Y80RTYYpjCHUOeaAoTyyfvY8hTmGLIE03h2xbDnK6h8MSQJ5qCwTLeYngBWwxTmGLIE01hojEZWwxTuGLIE02BLYYp3DHkiSbBFsMkBQx5okmwxTBJAUOeaBITLYa5fJLCGUOeaBKeFsMMeQpjDHmiSUy0GE5mSwMKYwx5okmwxTBJAUOeaBJsMUxS4FPI19fXQ6lUQqlUYv/+/bc9984772DHjh2exz09PVCpVCgqKkJNTQ3GxsYCWzHRLGGLYZICryHf3t6OtrY2NDU14fDhw+ju7saxY8cAAOfOncObb7552/4VFRWora3F0aNHIQgCGhsbg1M5URB5Wgwz5CnMeQ15hUKBqqoqxMXFQSaTISMjA1euXIHD4UBtbS3Ky8s9+/b19WF0dBR5eXkAAJVKhdbW1qAVTxQsEy2GOV1D4S7W2w6ZmZmeP+v1erS0tOC9997Db37zG/zkJz9Benq65/mBgQEoFArPY4VCAaPRGOCSiYLPYL55826urKEw5zXkJ+h0OpSVlaGyshJ9fX0wGAzYs2cPTp486dnH7Xbf1shJEAS/Gzt1dXX5tf+ttFrttI8NVxxzcBw/cw3R0cA143loB/VBfz1v+D5HhmCM2aeQ12q1KC8vR3V1NZRKJfbs2QOdToennnoKIyMjMJvN+OUvf4mKigqYTCbPcWazGcnJyX4VlJOTA7nc/7auWq0W+fn5fh8Xzjjm4PmXf/sMP1i6CGsfWRX01/KG73NkmO6Y7Xb7lCfHXkPeYDBg586dOHDgANasWQMAeP311z3Pnzx5EvX19fjd734HAJDL5Z5im5ubUVBQ4HfRRGIastpxvm8IPy3KErsUohnzGvINDQ2w2+2oq6vzbCspKcG2bdvuur9Go4FarYbVakV2djZKS0sDVy3RLOg8Z4YgALnLFN53JgpxXkNerVZDrVZP+vzq1auxevVqz+OsrCwcOnQoMNURieCszoSE+Fhkpi8QuxSiGeM3Xom+o6PXhOXfT0JMDP95UPjjp5joFgazDcbBEeRlcqqGpIEhT3SLDt346jDOx5NUMOSJbnG214Ske+KRpkgUuxSigGDIE93kcgs4qzMhb1my31/iIwpVDHmim/7z8jVYbzg5VUOSwpAnwnjXyT82dWKOPAY/ZMiThDDkKeIJgoB//n+d6L14Dbu35eOeRP/bahCFKoY8RbyP2vU4duointm4DGuW3yd2OUQB5XMXShLf52f60HJC73l8/fp1/OXkF+IVJIJAj1mAgJ5vBrHq4RRs38ReNSQ9DPkw4XIL+NOH3XC53LgvaXx5nwABbkEQubLZFYwxP7rifvzD1lxER3NFDUkPQz5MnPl6AKarN1BVugqP5t4PgO1Yicg7zsmHidYTeixIlONH2alil0JEYYQhHwYsQzfw1x4jNvzoAchi+ZYRke+YGGHg2KmLcLsFbFq9ROxSiCjMMORDnMst4OOTF5C3TIH7khLELoeIwgwvvM4yu9MFy9ANn/f/Sn8Vpqs38PfFOUGsioikiiE/i65eH8X/+F//hoGrvoc8ACyYJ8fqHF5wJSL/MeRnyZjLjX3/5zSuWR34h5+sQLzc97/6791/D2J5lyIimgaG/CxpaO5C93kL/vtP8/HY36SLXQ4RRQjJhfyoYwz//tUAXK7Q+SbopYHrOPLFN/ivhRkMeCKaVZIL+X89dRF/bPoPscu4Q94yBf6b8mGxyyCiCCO5kNcbhjFvrgz7fvG3Ypdym/sViYhhbxQimmWSC/lLxutYnDIPi1PmiV0KEZHoJLdk45LRyoAnIrpJUiE/ZLXj+ogD6ckMeSIiQGIhf8l4HQCwOCVR5EqIiEKDtEJ+wAoAWMwzeSIiABIL+cvG65DHxSBpwRyxSyEiCgk+hXx9fT2USiWUSiX2798PAPjzn/8MpVKJzZs3Y9++fRBu3pKtp6cHKpUKRUVFqKmpwdjYWPCq/45LxutIT07kbdyIiG7yGvLt7e1oa2tDU1MTDh8+jO7ubrz99tt4++238f777+ODDz7AmTNn8MUX4zdXrqioQG1tLY4ePQpBENDY2Bj0QUy4NGDlVA0R0S28hrxCoUBVVRXi4uIgk8mQkZGBqKgofPjhh5g7dy6Gh4dhtVoxf/589PX1YXR0FHl5eQAAlUqF1tbWYI8BAGB3umG+dgPpvOhKROThNeQzMzM9oa3X69HS0oLCwkLIZDI0NjZiw4YNUCgUyMrKwsDAABQKhedYhUIBo9EYtOJvZR4enxbimTwR0bd8/sarTqdDWVkZKisrsXTpUgDA008/DZVKhT179qC+vh6FhYWIivp2PlwQhNse+6Krq8uv/SeYh50AgGHzRWi1hmn9jHCk1WrFLmHWccyRgWMODJ9CXqvVory8HNXV1VAqlTAYDLhy5Qry8/MRGxsLpVKJ9957D8888wxMJpPnOLPZjOTkZL8KysnJgVwu928UAP614xPEREdhQ+GPIqb3ularRX5+vthlzCqOOTJwzL6z2+1Tnhx7TUODwYCdO3dCo9FAqVQCAK5fv46KigoMDw9DEAQcPXoU+fn5SEtLg1wu9/w2am5uRkFBgd9FT4d52In7khIiJuCJiHzh9Uy+oaEBdrsddXV1nm0lJSV4/vnnUVJSgpiYGKxcuRI///nPAQAajQZqtRpWqxXZ2dkoLS0NXvW3MA2PYdmSe2fltYiIwoXXkFer1VCr1Xd9rqSk5I5tWVlZOHTo0Mwr84NzzI3B62NIT+bKGiKiW0libsNgtkIQwO6TRETfIYmQZ88aIqK7k0TIj425ER8XxekaIqLvkMSdoQp+mAaZw4B4uSSGQ0QUMJI4k4+KioJcJomhEBEFFJORiEjCGPJERBLGkCcikjCGPBGRhDHkiYgkjCFPRCRhIbOwfOIesQ6HY9o/w263B6qcsMExRwaOOTJMZ8wTmTmRod8VJUz2zCy7fv06ent7xS6DiCgsLVu2DPPm3dnaJWRC3u12w2azQSaT+X03KSKiSCUIApxOJxISEhAdfecMfMiEPBERBR4vvBIRSRhDnohIwhjyREQSxpAnIpIwhjwRkYQx5ImIJIwhT0QkYZII+Q8++ACbN2/Gpk2b8O6774pdTlDU19dDqVRCqVRi//79AID29nYUFxdj06ZNOHDggMgVBs++fftQVVUFQPpj/uSTT6BSqfDkk09i7969AKQ/5ubmZs9ne9++fQCkO2ar1YotW7bg8uXLACYfZ09PD1QqFYqKilBTU4OxsbHpv6gQ5vr7+4X169cLV69eFWw2m1BcXCzodDqxywqoL774QnjmmWcEu90uOBwOobS0VPjggw+EwsJC4eLFi4LT6RSeffZZ4bPPPhO71IBrb28XVq9eLbz00kvCjRs3JD3mixcvCuvWrRMMBoPgcDiEbdu2CZ999pmkxzwyMiKsWrVKsFgsgtPpFLZu3SocP35ckmPu6OgQtmzZImRnZwuXLl2a8vOsVCqFM2fOCIIgCHv27BHefffdab9u2J/Jt7e345FHHsGCBQswd+5cFBUVobW1VeyyAkqhUKCqqgpxcXGQyWTIyMiAXq/HkiVLsHjxYsTGxqK4uFhy47527RoOHDiAF154AQDQ2dkp6TEfO3YMmzdvRmpqKmQyGQ4cOIA5c+ZIeswulwtutxs3btzA2NgYxsbGkJiYKMkxNzY24uWXX0ZycjKAyT/PfX19GB0dRV5eHgBApVLNaPwh04VyugYGBqBQKDyPk5OT0dnZKWJFgZeZmen5s16vR0tLC372s5/dMW6j0ShGeUFTW1uL3bt3w2AwALj7ey2lMV+4cAEymQwvvPACDAYDHnvsMWRmZkp6zImJiXjxxRfx5JNPYs6cOVi1apVk3+df//rXtz2ebJzf3a5QKGY0/rA/k3e73bc1NBMEQbINznQ6HZ599llUVlZi8eLFkh73+++/j/vuuw9r1qzxbJP6e+1yuXDixAm89tprOHjwIDo7O3Hp0iVJj/mrr77CX/7yF3z66af4/PPPER0dDb1eL+kxT5js8xzoz3nYn8mnpqbi9OnTnscmk8nz3yEp0Wq1KC8vR3V1NZRKJU6dOgWTyeR5Xmrj/uijj2AymfDUU09haGgIIyMj6OvrQ0xMjGcfqY05KSkJa9aswcKFCwEAGzZsQGtrq6TH3NbWhjVr1mDRokUAxqcmGhoaJD3mCampqXf9N/zd7WazeUbjD/sz+bVr1+LEiRMYHBzEjRs38PHHH6OgoEDssgLKYDBg586d0Gg0UCqVAIDc3Fx88803uHDhAlwuF44cOSKpcf/pT3/CkSNH0NzcjPLycjz++ON46623JD3m9evXo62tDcPDw3C5XPj888/xxBNPSHrMWVlZaG9vx8jICARBwCeffCL5z/aEycaZlpYGuVwOrVYLYHz10UzGH/Zn8ikpKdi9ezdKS0vhdDqxdetWrFixQuyyAqqhoQF2ux11dXWebSUlJairq8OuXbtgt9tRWFiIJ554QsQqg08ul0t6zLm5uXjuueewfft2OJ1OPProo9i2bRsefPBByY553bp1+PLLL6FSqSCTybB8+XLs2rULjz76qGTHPGGqz7NGo4FarYbVakV2djZKS0un/TrsJ09EJGFhP11DRESTY8gTEUkYQ56ISMIY8kREEsaQJyKSMIY8EZGEMeSJiCSMIU9EJGH/H64D98NI+oyGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6:80: E501 line too long (109 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)\n",
    "# get the missing curve\n",
    "missing_list = []\n",
    "for i in np.arange(0, 101, 1):\n",
    "    missing = application.shape[1] - len(list(missing_values.index[missing_values['% of Total Values'] > i]))\n",
    "    missing_list.append(missing)\n",
    "pd.DataFrame(missing_list).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge any applicant's knowledge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize df size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nprint('application :')\\napplication = convert_types(application, print_info=True)\\nprint('sk_id_bureau :')\\nsk_id_bureau = convert_types(sk_id_bureau, print_info=True)\\nprint('sk_id_previous_clean :')\\nsk_id_previous_clean = convert_types(sk_id_previous_clean, print_info=True)\\nprint('sk_id_focus_credit_balance :')\\nsk_id_focus_credit_balance = convert_types(sk_id_focus_credit_balance, print_info=True)\\nprint('sk_id_card_balance :')\\nsk_id_card_balance = convert_types(sk_id_card_balance, print_info=True)\\nprint('sk_id_installments :')\\nsk_id_installments = convert_types(sk_id_installments, print_info=True)\\nprint('sk_id_installments_agg :')\\nsk_id_installments_agg = convert_types(sk_id_installments_agg, print_info=True)\\n\""
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9:80: E501 line too long (87 > 79 characters)\n",
      "16:80: E501 line too long (724 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "print('application :')\n",
    "application = convert_types(application, print_info=True)\n",
    "print('sk_id_bureau :')\n",
    "sk_id_bureau = convert_types(sk_id_bureau, print_info=True)\n",
    "print('sk_id_previous_clean :')\n",
    "sk_id_previous_clean = convert_types(sk_id_previous_clean, print_info=True)\n",
    "print('sk_id_focus_credit_balance :')\n",
    "sk_id_focus_credit_balance = convert_types(sk_id_focus_credit_balance, print_info=True)\n",
    "print('sk_id_card_balance :')\n",
    "sk_id_card_balance = convert_types(sk_id_card_balance, print_info=True)\n",
    "print('sk_id_installments :')\n",
    "sk_id_installments = convert_types(sk_id_installments, print_info=True)\n",
    "print('sk_id_installments_agg :')\n",
    "sk_id_installments_agg = convert_types(sk_id_installments_agg, print_info=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge data & save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 280 columns.\n",
      "There are 58 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial shape: (356251, 280)\n",
      "with sk_id_bureau: (356251, 449)\n"
     ]
    }
   ],
   "source": [
    "print(\"initial shape:\", application.shape)\n",
    "application = application.merge(sk_id_bureau, how='left', on='SK_ID_CURR')\n",
    "print(\"with sk_id_bureau:\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 449 columns.\n",
      "There are 227 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_bureau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with sk_id_previous_clean: (356251, 793)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (82 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "application = application.merge(sk_id_previous_clean, how='left', on='SK_ID_CURR')\n",
    "print(\"with sk_id_previous_clean:\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 793 columns.\n",
      "There are 571 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_previous_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with sk_id_installments_agg agg (356251, 823)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (84 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "application = application.merge(sk_id_installments_agg, how='left', on='SK_ID_CURR')\n",
    "print(\"with sk_id_installments_agg agg\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 823 columns.\n",
      "There are 601 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_installments_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with installments agg (356251, 840)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (80 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "application = application.merge(sk_id_installments, how='left', on='SK_ID_CURR')\n",
    "print(\"with installments agg\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 840 columns.\n",
      "There are 618 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_installments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with credit balance agg (356251, 849)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (82 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "application = application.merge(sk_id_credit_balance, how='left', on='SK_ID_CURR')\n",
    "print(\"with credit balance agg\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 849 columns.\n",
      "There are 627 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_credit_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "with card balance agg (356251, 952)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1:80: E501 line too long (80 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "application = application.merge(sk_id_card_balance, how='left', on='SK_ID_CURR')\n",
    "print(\"with card balance agg\", application.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 952 columns.\n",
      "There are 730 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sk_id_card_balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SK_ID_CURR']\n"
     ]
    }
   ],
   "source": [
    "columns = application.columns.tolist()\n",
    "key = 'SK_ID'\n",
    "cols_with_sk_id = [item for item in columns if key in item]\n",
    "print(cols_with_sk_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Data/application_miss.pkl\", mode=\"wb\") as df:\n",
    "    pickle.dump(application, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "507ec6b1-99d0-4324-a3ed-bdea2f916227",
    "_uuid": "58851dfef481f32b3026e89b086534ea3683440d"
   },
   "source": [
    "## Examine Missing Values\n",
    "\n",
    "Next we can look at the number and percentage of missing values in each column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 952 columns.\n",
      "There are 730 columns that have missing values.\n"
     ]
    }
   ],
   "source": [
    "# check for missing values\n",
    "missing_values = missing_values_table(application)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_CREDIT_CLOSING_MONTHS_MEAN</th>\n",
       "      <td>320144</td>\n",
       "      <td>89.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_CREDIT_CLOSING_MONTHS_MAX</th>\n",
       "      <td>320144</td>\n",
       "      <td>89.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MAX</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MIN</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_DOWN_PAYMENT_MAX</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_RATE_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_DOWN_PAYMENT_MIN</th>\n",
       "      <td>303648</td>\n",
       "      <td>85.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_CNT_PAYMENT_VAR</th>\n",
       "      <td>300607</td>\n",
       "      <td>84.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_APP_CREDIT_RATIO_VAR</th>\n",
       "      <td>300467</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_CREDIT_RATIO_VAR</th>\n",
       "      <td>300467</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_INTEREST_EVAL_VAR</th>\n",
       "      <td>300462</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_START_OBSERVED_VAR</th>\n",
       "      <td>289600</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_START_OBSERVED_VAR</th>\n",
       "      <td>272577</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_ANNUITY_MAX</th>\n",
       "      <td>263525</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_ANNUITY_MEAN</th>\n",
       "      <td>263525</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_ANNUITY_MAX</th>\n",
       "      <td>261054</td>\n",
       "      <td>73.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_ANNUITY_MEAN</th>\n",
       "      <td>261054</td>\n",
       "      <td>73.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_OTHER_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_ATM_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_POS_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_DEF_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECIVABLE_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_TOTAL_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_TOTAL_RECEIVABLE_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_ATM_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_INSTALMENT_MATURE_CUM_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_BALANCE_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_CREDIT_LIMIT_ACTUAL_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_POS_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_MONTHS_BALANCE_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_OTHER_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECEIVABLE_PRINCIPAL_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_CURRENT_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_INST_MIN_REGULARITY_VAR</th>\n",
       "      <td>253749</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_CREDIT_CLOSING_MONTHS_MAX</th>\n",
       "      <td>253588</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_CREDIT_CLOSING_MONTHS_MEAN</th>\n",
       "      <td>253588</td>\n",
       "      <td>71.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_CC_USE_LATE_MONTH_2</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_INST_MIN_REGULARITY_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_OTHER_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_ATM_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_ATM_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_ATM_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_OTHER_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_INST_MIN_REGULARITY_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_OTHER_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_OTHER_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_POS_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_POS_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_POS_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_POS_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_INST_MIN_REGULARITY_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_INST_MIN_REGULARITY_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECEIVABLE_PRINCIPAL_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_TOTAL_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_ATM_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_OTHER_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_OTHER_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_OTHER_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_OTHER_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_POS_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_POS_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_POS_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_POS_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_INSTALMENT_MATURE_CUM_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_INSTALMENT_MATURE_CUM_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_INSTALMENT_MATURE_CUM_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_INSTALMENT_MATURE_CUM_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_ATM_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_TOTAL_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_ATM_CURRENT_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_PAYMENT_TOTAL_CURRENT_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECEIVABLE_PRINCIPAL_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECEIVABLE_PRINCIPAL_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECEIVABLE_PRINCIPAL_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECIVABLE_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECIVABLE_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECIVABLE_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_RECIVABLE_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_TOTAL_RECEIVABLE_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_TOTAL_RECEIVABLE_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_TOTAL_RECEIVABLE_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_TOTAL_RECEIVABLE_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_CNT_DRAWINGS_ATM_CURRENT_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_CREDIT_LIMIT_ACTUAL_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_DRAWINGS_ATM_CURRENT_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_CREDIT_LIMIT_ACTUAL_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_MONTHS_BALANCE_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_CC_USE_LATE_MONTH_6</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_CC_COUNT</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_CREDIT_LIMIT_ACTUAL_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_DEF_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_DEF_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_DEF_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_MONTHS_BALANCE_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_MONTHS_BALANCE_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_DEF_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_SK_DPD_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_MONTHS_BALANCE_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_BALANCE_SUM</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_BALANCE_MAX</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_BALANCE_MIN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_AMT_BALANCE_MEAN</th>\n",
       "      <td>252783</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_START_OBSERVED_MIN</th>\n",
       "      <td>250571</td>\n",
       "      <td>70.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_START_OBSERVED_MEAN</th>\n",
       "      <td>250571</td>\n",
       "      <td>70.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_START_OBSERVED_MAX</th>\n",
       "      <td>250571</td>\n",
       "      <td>70.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>248357</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>248357</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>248357</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_ANNUITY_MEAN</th>\n",
       "      <td>248159</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_ANNUITY_MIN</th>\n",
       "      <td>248159</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_ANNUITY_MAX</th>\n",
       "      <td>248159</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_CNT_PAYMENT_MEAN</th>\n",
       "      <td>248159</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_CREDIT_RATIO_MEAN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_CREDIT_RATIO_MAX</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_CREDIT_RATIO_MIN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_APP_CREDIT_RATIO_MAX</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_APP_CREDIT_RATIO_MIN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_APP_CREDIT_RATIO_MEAN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_APPLICATION_MAX</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_APPLICATION_MIN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_APPLICATION_MEAN</th>\n",
       "      <td>247467</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_INTEREST_EVAL_MAX</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_INTEREST_EVAL_MEAN</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_N_ADJ_INTEREST_EVAL_MIN</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_GOODS_PRICE_MEAN</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_GOODS_PRICE_MAX</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_GOODS_PRICE_MIN</th>\n",
       "      <td>247462</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_CREDIT_CLOSING_MONTHS_MAX</th>\n",
       "      <td>246930</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_CREDIT_CLOSING_MONTHS_MEAN</th>\n",
       "      <td>246930</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>246859</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>246859</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>246859</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_START_OBSERVED_VAR</th>\n",
       "      <td>245990</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_MONTHS_BALANCE_MIN_MIN</th>\n",
       "      <td>245102</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_MONTHS_BALANCE_MAX_MAX</th>\n",
       "      <td>245102</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_MONTHS_BALANCE_SIZE_MEAN</th>\n",
       "      <td>245102</td>\n",
       "      <td>68.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_CREDIT_MAX</th>\n",
       "      <td>243798</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_CREDIT_MIN</th>\n",
       "      <td>243798</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_AMT_CREDIT_MEAN</th>\n",
       "      <td>243798</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_START_OBSERVED_MIN</th>\n",
       "      <td>243601</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_START_OBSERVED_MAX</th>\n",
       "      <td>243601</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_START_OBSERVED_MEAN</th>\n",
       "      <td>243601</td>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>242977</td>\n",
       "      <td>68.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>242977</td>\n",
       "      <td>68.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>242977</td>\n",
       "      <td>68.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MODE</th>\n",
       "      <td>241106</td>\n",
       "      <td>67.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_MEDI</th>\n",
       "      <td>241106</td>\n",
       "      <td>67.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMIN_AVG</th>\n",
       "      <td>241106</td>\n",
       "      <td>67.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_ANNUITY_MAX</th>\n",
       "      <td>238027</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_ANNUITY_MEAN</th>\n",
       "      <td>238027</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_DAYS_DECISION_MAX</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_NC_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_NC_LOAN_AGE_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_NC_LOAN_TERM_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_CNT_PAYMENT_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_DAYS_DECISION_MEAN</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_NFLAG_INSURED_ON_APPROVAL_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REFUSED_DAYS_DECISION_MIN</th>\n",
       "      <td>237983</td>\n",
       "      <td>66.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_MONTHS_BALANCE_MIN_MIN</th>\n",
       "      <td>237756</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_MONTHS_BALANCE_SIZE_MEAN</th>\n",
       "      <td>237756</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_MONTHS_BALANCE_MAX_MAX</th>\n",
       "      <td>237756</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MODE</th>\n",
       "      <td>236304</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_MEDI</th>\n",
       "      <td>236304</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BUILD_AVG</th>\n",
       "      <td>236304</td>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <td>235239</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_START_OBSERVED_MIN</th>\n",
       "      <td>225478</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_START_OBSERVED_MAX</th>\n",
       "      <td>225478</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_START_OBSERVED_MEAN</th>\n",
       "      <td>225478</td>\n",
       "      <td>63.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN</th>\n",
       "      <td>223254</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_MAX_OVERDUE_MAX</th>\n",
       "      <td>223254</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_3_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_MONTHS_BALANCE_MAX_MAX</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_C_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_X_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_nan_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_MONTHS_BALANCE_MIN_MIN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_0_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_5_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_1_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_2_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_STATUS_4_MEAN_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_MONTHS_BALANCE_SIZE_MEAN</th>\n",
       "      <td>221709</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>210842</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MEDI</th>\n",
       "      <td>210842</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_MODE</th>\n",
       "      <td>210842</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_AVG</th>\n",
       "      <td>207583</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MODE</th>\n",
       "      <td>207583</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASEMENTAREA_MEDI</th>\n",
       "      <td>207583</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_VAR</th>\n",
       "      <td>207021</td>\n",
       "      <td>58.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_AVG</th>\n",
       "      <td>195764</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MODE</th>\n",
       "      <td>195764</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAREA_MEDI</th>\n",
       "      <td>195764</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_VAR</th>\n",
       "      <td>189928</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>189928</td>\n",
       "      <td>53.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_AVG</th>\n",
       "      <td>189079</td>\n",
       "      <td>53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MODE</th>\n",
       "      <td>189079</td>\n",
       "      <td>53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ELEVATORS_MEDI</th>\n",
       "      <td>189079</td>\n",
       "      <td>53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN</th>\n",
       "      <td>186355</td>\n",
       "      <td>52.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_MAX_OVERDUE_MAX</th>\n",
       "      <td>186355</td>\n",
       "      <td>52.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_MEDI</th>\n",
       "      <td>179947</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_AVG</th>\n",
       "      <td>179947</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APARTMENTS_MODE</th>\n",
       "      <td>179947</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_MODE</th>\n",
       "      <td>178406</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_MEDI</th>\n",
       "      <td>178406</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_AVG</th>\n",
       "      <td>178406</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MEDI</th>\n",
       "      <td>177901</td>\n",
       "      <td>49.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_MODE</th>\n",
       "      <td>177901</td>\n",
       "      <td>49.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAREA_AVG</th>\n",
       "      <td>177901</td>\n",
       "      <td>49.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MEDI</th>\n",
       "      <td>176340</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_MODE</th>\n",
       "      <td>176340</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLOORSMAX_AVG</th>\n",
       "      <td>176340</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MEDI</th>\n",
       "      <td>172862</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_AVG</th>\n",
       "      <td>172862</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BEGINEXPLUATATION_MODE</th>\n",
       "      <td>172862</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOTALAREA_MODE</th>\n",
       "      <td>171054</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_VAR</th>\n",
       "      <td>170424</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_CREDIT_PROLONG_RATIO_VAR</th>\n",
       "      <td>163385</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_PROLONG_RATIO_VAR</th>\n",
       "      <td>163385</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_VAR</th>\n",
       "      <td>150025</td>\n",
       "      <td>42.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>150025</td>\n",
       "      <td>42.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_MAX_OVERDUE_MEAN</th>\n",
       "      <td>143284</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_MAX_OVERDUE_MAX</th>\n",
       "      <td>143284</td>\n",
       "      <td>40.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_LIMIT_MAX</th>\n",
       "      <td>138151</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN</th>\n",
       "      <td>138151</td>\n",
       "      <td>38.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_LIMIT_MAX</th>\n",
       "      <td>126314</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN</th>\n",
       "      <td>126314</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MAX</th>\n",
       "      <td>116394</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MIN</th>\n",
       "      <td>116394</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN</th>\n",
       "      <td>116394</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <td>116391</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <td>116391</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <td>114909</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_ENDDATE_MAX</th>\n",
       "      <td>114909</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_ENDDATE_MEAN</th>\n",
       "      <td>114909</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_TERM_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_TERM_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_AGE_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_AGE_VAR</th>\n",
       "      <td>112728</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_CREDIT_RATIO_VAR</th>\n",
       "      <td>112132</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_INTEREST_EVAL_VAR</th>\n",
       "      <td>112132</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_APP_CREDIT_RATIO_VAR</th>\n",
       "      <td>112132</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_AMT_CREDIT_DEBT_RATE_VAR</th>\n",
       "      <td>107106</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_CNT_PAYMENT_VAR</th>\n",
       "      <td>106928</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_MAX</th>\n",
       "      <td>104443</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_MEAN</th>\n",
       "      <td>104443</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_MAX_OVERDUE_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_CREDIT_DAY_OVERDUE_MAX</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_MEAN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_MIN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_DEBT_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_OVERDUE_MAX</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_DAYS_CREDIT_MAX</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_OVERDUE_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_N_BUR_START_OBSERVED_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_MONTHS_BALANCE_SIZE_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_CNT_CREDIT_PROLONG_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_ANNUITY_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM</th>\n",
       "      <td>104440</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MIN</th>\n",
       "      <td>101920</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MAX</th>\n",
       "      <td>101920</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN</th>\n",
       "      <td>101920</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <td>101919</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <td>101919</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_INTEREST_EVAL_VAR</th>\n",
       "      <td>92300</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_CREDIT_RATIO_VAR</th>\n",
       "      <td>92300</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_APP_CREDIT_RATIO_VAR</th>\n",
       "      <td>92300</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_VAR</th>\n",
       "      <td>91964</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>91964</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CNT_PAYMENT_VAR</th>\n",
       "      <td>91315</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_ENDDATE_MEAN</th>\n",
       "      <td>90083</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_ENDDATE_MAX</th>\n",
       "      <td>90083</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <td>90083</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_MAX</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_MEAN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_OVERDUE_MAX</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_DEBT_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_OVERDUE_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_SUM_LIMIT_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_ANNUITY_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_CNT_CREDIT_PROLONG_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_MONTHS_BALANCE_SIZE_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_START_OBSERVED_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_AMT_CREDIT_MAX_OVERDUE_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_MIN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_MAX</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_DAYS_CREDIT_MEAN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_CREDIT_DAY_OVERDUE_MAX</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSED_CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <td>88330</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_LIMIT_MAX</th>\n",
       "      <td>75752</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_LIMIT_MEAN</th>\n",
       "      <td>75752</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <td>64648</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_DAYS_EMPLOYED_RATIO</th>\n",
       "      <td>64648</td>\n",
       "      <td>18.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MAX</th>\n",
       "      <td>58818</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MIN</th>\n",
       "      <td>58818</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN</th>\n",
       "      <td>58818</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_DEBT_MAX</th>\n",
       "      <td>58816</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_DEBT_MEAN</th>\n",
       "      <td>58816</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_ENDDATE_MAX</th>\n",
       "      <td>53029</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_ENDDATE_MEAN</th>\n",
       "      <td>53029</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_ENDDATE_MIN</th>\n",
       "      <td>53029</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_MAX</th>\n",
       "      <td>50446</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_MEAN</th>\n",
       "      <td>50446</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_CURRENCY_currency 3_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_CURRENCY_currency 2_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_CURRENCY_currency 1_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_CURRENCY_nan_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_ACTIVE_nan_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_ACTIVE_Sold_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_ACTIVE_Closed_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_ACTIVE_Bad debt_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_TYPE_Cash loans_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_ACTIVE_Active_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_TYPE_Credit card_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_TYPE_nan_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_CURRENCY_currency 4_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_MONTHS_BALANCE_SIZE_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_AMT_CREDIT_DEBT_RATE_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_ANNUITY_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_LIMIT_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_START_OBSERVED_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_OVERDUE_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_OVERDUE_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_OVERDUE_MAX</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_DEBT_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_SUM_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_AMT_CREDIT_MAX_OVERDUE_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_DAY_OVERDUE_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CREDIT_DAY_OVERDUE_MAX</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_MEAN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_MAX</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_CNT_CREDIT_PROLONG_SUM</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUR_DAYS_CREDIT_MIN</th>\n",
       "      <td>50444</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TARGET</th>\n",
       "      <td>48744</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_CREDIT_PROLONG_RATIO_MIN</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_PROLONG_RATIO_MEAN</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_PROLONG_RATIO_MAX</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_PROLONG_RATIO_MIN</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_CREDIT_PROLONG_RATIO_MEAN</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_CREDIT_PROLONG_RATIO_MAX</th>\n",
       "      <td>43992</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MAX</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MIN</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_DOWN_PAYMENT_MAX</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_RATE_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_DOWN_PAYMENT_MIN</th>\n",
       "      <td>40203</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_RATE_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_RATE_DOWN_PAYMENT_MAX</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_RATE_DOWN_PAYMENT_MIN</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_DOWN_PAYMENT_MEAN</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_DOWN_PAYMENT_MAX</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_DOWN_PAYMENT_MIN</th>\n",
       "      <td>37502</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_INTEREST_EVAL_MAX</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_CREDIT_RATIO_MAX</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_CREDIT_RATIO_MIN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_GOODS_PRICE_MEAN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_INTEREST_EVAL_MEAN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_APP_CREDIT_RATIO_MAX</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_APP_CREDIT_RATIO_MIN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_GOODS_PRICE_MAX</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_GOODS_PRICE_MIN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_APPLICATION_MEAN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_APPLICATION_MAX</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_APPLICATION_MIN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_INTEREST_EVAL_MIN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_APP_CREDIT_RATIO_MEAN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_N_ADJ_CREDIT_RATIO_MEAN</th>\n",
       "      <td>19677</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_MAX</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_MIN</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_DEF_MEAN</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_N_POS_COUNT</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_DEF_MAX</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_MONTHS_BALANCE_MEAN</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_MONTHS_BALANCE_MAX</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_MEAN</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POS_SK_DPD_DEF_MIN</th>\n",
       "      <td>19221</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_TERM_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_AGE_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_AGE_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_TERM_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_AGE_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_TERM_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_TERM_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_TERM_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_AGE_MIN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_TERM_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_AGE_MAX</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_AGE_MEAN</th>\n",
       "      <td>19073</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NFLAG_INSURED_ON_APPROVAL_MEAN</th>\n",
       "      <td>18915</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NFLAG_INSURED_ON_APPROVAL_MEAN</th>\n",
       "      <td>18915</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_CREDIT_MEAN</th>\n",
       "      <td>18566</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_CREDIT_MIN</th>\n",
       "      <td>18566</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_CREDIT_MAX</th>\n",
       "      <td>18566</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_ANNUITY_MAX</th>\n",
       "      <td>18559</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_ANNUITY_MEAN</th>\n",
       "      <td>18559</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_AMT_ANNUITY_MIN</th>\n",
       "      <td>18559</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_TERM_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_DAYS_DECISION_MIN</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NC_LOAN_AGE_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_CNT_PAYMENT_MEAN</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_NFLAG_INSURED_ON_APPROVAL_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_CNT_PAYMENT_SUM</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_DAYS_DECISION_MEAN</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPROVED_DAYS_DECISION_MAX</th>\n",
       "      <td>18557</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_APP_CREDIT_RATIO_MIN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_APPLICATION_MIN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_APPLICATION_MAX</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_GOODS_PRICE_MEAN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_GOODS_PRICE_MAX</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_GOODS_PRICE_MIN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_APPLICATION_MEAN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_APP_CREDIT_RATIO_MAX</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_INTEREST_EVAL_MEAN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_INTEREST_EVAL_MAX</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_INTEREST_EVAL_MIN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_CREDIT_RATIO_MEAN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_CREDIT_RATIO_MAX</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_ADJ_CREDIT_RATIO_MIN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_APP_CREDIT_RATIO_MEAN</th>\n",
       "      <td>18503</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_ANNUITY_MEAN</th>\n",
       "      <td>17878</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_ANNUITY_MAX</th>\n",
       "      <td>17878</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_ANNUITY_MIN</th>\n",
       "      <td>17878</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CNT_PAYMENT_MEAN</th>\n",
       "      <td>17876</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_CREDIT_MAX</th>\n",
       "      <td>17650</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_CREDIT_MEAN</th>\n",
       "      <td>17650</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_AMT_CREDIT_MIN</th>\n",
       "      <td>17650</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LOAN_AGE_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_PROLONG_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_VAR</th>\n",
       "      <td>17645</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PAYMENT_TYPE_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_SCOFR_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_SCO_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_LIMIT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_HC_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_CLIENT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PAYMENT_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PAYMENT_TYPE_Cashless from the account of the employer_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PAYMENT_TYPE_Non-cash from your account_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_STATUS_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_STATUS_Unused offer_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_STATUS_Refused_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_STATUS_Canceled_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Medicine_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_STATUS_Approved_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Business development_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NFLAG_INSURED_ON_APPROVAL_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_AGE_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NC_LOAN_TERM_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CNT_PAYMENT_SUM</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_DAYS_DECISION_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_DAYS_DECISION_MAX</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_DAYS_DECISION_MIN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Building a house or an annex_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Buying a garage_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Buying a holiday home / land_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_XAP_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Wedding / gift / holiday_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_VERIF_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Refusal to name the goal_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Purchase of electronic equipment_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Payments on other loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Other_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Money for a third person_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Journey_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Hobby_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Gasification / water supply_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Furniture_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Everyday expenses_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Education_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Buying a used car_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Buying a new car_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CASH_LOAN_PURPOSE_Buying a home_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_SYSTEM_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_XAP_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Mobile_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS mobile with interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS industry with interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS household without interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS household with interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Cash_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_Card Street_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_middle_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_low_normal_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_low_action_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_high_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS industry without interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS mobile without interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_HC_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS other with interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_NOTUSED_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_APPROVED_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_XAP_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_VERIF_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_SYSTEM_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_SCOFR_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_SCO_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_LIMIT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_HC_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_CLIENT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_TYPE_Revolving loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CREDIT_TYPE_Cash loans_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_PRODUCT_COMBINATION_POS others without interest_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_YIELD_GROUP_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Tourism_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PRODUCT_TYPE_walk-in_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PRODUCT_TYPE_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_POS_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_Cash_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_Cars_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PORTFOLIO_Cards_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Weapon_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Vehicles_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Tourism_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipment_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Other_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Office Appliances_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PRODUCT_TYPE_x-sell_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_PRODUCT_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_MLM partners_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Jewelry_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Industry_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Furniture_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Consumer electronics_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Construction_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Car dealer_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Stone_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Regional / Local_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Credit and cash offices_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Country-wide_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Contact center_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_CLIENT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CODE_REJECT_REASON_REJECTED_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_LIMIT_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CLIENT_TYPE_New_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_SCO_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Animals_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Additional Service_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CLIENT_TYPE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CLIENT_TYPE_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CLIENT_TYPE_Repeater_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_CLIENT_TYPE_Refreshed_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Clothing and Accessories_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Other_B_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Other_A_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Group of people_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Family_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_TYPE_SUITE_Children_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_CODE_REJECT_REASON_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Auto Accessories_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Computers_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Homewares_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Insurance_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Construction Materials_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Education_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Fitness_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Furniture_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Gardening_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_House Construction_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Direct Sales_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Jewelry_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_NAME_GOODS_CATEGORY_Medical Supplies_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_nan_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_XNA_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_VERIF_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_SYSTEM_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PREV_N_CONTRACT_STATUS_REJECTED_SCOFR_MEAN</th>\n",
       "      <td>17398</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INSTAL_COUNT</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_FAULT_RATE</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_PROLONG_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_PROLONG_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_PROLONG_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_PROLONG_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_DAYS_INSTALMENT_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_STATUS_0_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_AMT_INSTALMENT_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_AMT_INSTALMENT_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_AMT_INSTALMENT_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_AMT_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_AMT_OVERDUE_RATIO_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INS_AMT_OVERDUE_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INS_DPD_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INS_DPD_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INS_DPD_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_DAYS_ENTRY_PAYMENT_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_INS_DBD_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_FAULT_GRAVITY</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LOAN_AGE_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_SUM</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INS_N_FAULT_ADJ_RATE</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LOAN_AGE_MIN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LOAN_AGE_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_LOAN_AGE_MEAN</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MAX</th>\n",
       "      <td>16677</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1050</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1050</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1050</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBS_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <td>1050</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <td>278</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_CREDIT_TERM</th>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_ANNUITY_INCOME_RATIO</th>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N_INCOME_PER_PERSON</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Missing Values  \\\n",
       "ACTIVE_N_CREDIT_CLOSING_MONTHS_MEAN                         320144   \n",
       "ACTIVE_N_CREDIT_CLOSING_MONTHS_MAX                          320144   \n",
       "REFUSED_RATE_DOWN_PAYMENT_MAX                               303648   \n",
       "REFUSED_RATE_DOWN_PAYMENT_MIN                               303648   \n",
       "REFUSED_AMT_DOWN_PAYMENT_MEAN                               303648   \n",
       "REFUSED_AMT_DOWN_PAYMENT_MAX                                303648   \n",
       "REFUSED_RATE_DOWN_PAYMENT_MEAN                              303648   \n",
       "REFUSED_AMT_DOWN_PAYMENT_MIN                                303648   \n",
       "REFUSED_CNT_PAYMENT_VAR                                     300607   \n",
       "REFUSED_N_APP_CREDIT_RATIO_VAR                              300467   \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_VAR                              300467   \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_VAR                             300462   \n",
       "ACTIVE_N_BUR_START_OBSERVED_VAR                             289600   \n",
       "CLOSED_N_BUR_START_OBSERVED_VAR                             272577   \n",
       "ACTIVE_AMT_ANNUITY_MAX                                      263525   \n",
       "ACTIVE_AMT_ANNUITY_MEAN                                     263525   \n",
       "CLOSED_AMT_ANNUITY_MAX                                      261054   \n",
       "CLOSED_AMT_ANNUITY_MEAN                                     261054   \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_VAR                           253749   \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_VAR                             253749   \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_VAR                             253749   \n",
       "CC_AMT_PAYMENT_CURRENT_VAR                                  253749   \n",
       "CC_SK_DPD_DEF_VAR                                           253749   \n",
       "CC_AMT_RECIVABLE_VAR                                        253749   \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_VAR                            253749   \n",
       "CC_AMT_TOTAL_RECEIVABLE_VAR                                 253749   \n",
       "CC_SK_DPD_VAR                                               253749   \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_VAR                             253749   \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_VAR                            253749   \n",
       "CC_AMT_BALANCE_VAR                                          253749   \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_VAR                              253749   \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_VAR                             253749   \n",
       "CC_AMT_DRAWINGS_CURRENT_VAR                                 253749   \n",
       "CC_MONTHS_BALANCE_VAR                                       253749   \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_VAR                           253749   \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_VAR                             253749   \n",
       "CC_CNT_DRAWINGS_CURRENT_VAR                                 253749   \n",
       "CC_AMT_INST_MIN_REGULARITY_VAR                              253749   \n",
       "CLOSED_N_CREDIT_CLOSING_MONTHS_MAX                          253588   \n",
       "CLOSED_N_CREDIT_CLOSING_MONTHS_MEAN                         253588   \n",
       "CC_AMT_PAYMENT_CURRENT_MAX                                  252783   \n",
       "CC_AMT_PAYMENT_CURRENT_MIN                                  252783   \n",
       "CC_AMT_PAYMENT_CURRENT_MEAN                                 252783   \n",
       "N_CC_USE_LATE_MONTH_2                                       252783   \n",
       "CC_AMT_INST_MIN_REGULARITY_SUM                              252783   \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MIN                           252783   \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MIN                             252783   \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MAX                             252783   \n",
       "CC_SK_DPD_MAX                                               252783   \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_SUM                             252783   \n",
       "CC_AMT_DRAWINGS_CURRENT_MIN                                 252783   \n",
       "CC_AMT_DRAWINGS_CURRENT_MAX                                 252783   \n",
       "CC_AMT_DRAWINGS_CURRENT_MEAN                                252783   \n",
       "CC_AMT_DRAWINGS_CURRENT_SUM                                 252783   \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MAX                           252783   \n",
       "CC_AMT_INST_MIN_REGULARITY_MEAN                             252783   \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MEAN                          252783   \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_SUM                           252783   \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MIN                             252783   \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MAX                             252783   \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MEAN                            252783   \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_SUM                             252783   \n",
       "CC_AMT_INST_MIN_REGULARITY_MIN                              252783   \n",
       "CC_AMT_INST_MIN_REGULARITY_MAX                              252783   \n",
       "CC_AMT_PAYMENT_CURRENT_SUM                                  252783   \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MAX                             252783   \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MIN                            252783   \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_SUM                             252783   \n",
       "CC_CNT_DRAWINGS_CURRENT_MAX                                 252783   \n",
       "CC_CNT_DRAWINGS_CURRENT_MEAN                                252783   \n",
       "CC_CNT_DRAWINGS_CURRENT_SUM                                 252783   \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MIN                           252783   \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MAX                           252783   \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MEAN                          252783   \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_SUM                           252783   \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MIN                             252783   \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MAX                             252783   \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MEAN                            252783   \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_SUM                             252783   \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MIN                            252783   \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MAX                            252783   \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MEAN                           252783   \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_SUM                            252783   \n",
       "CC_CNT_DRAWINGS_CURRENT_MIN                                 252783   \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MEAN                            252783   \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MAX                            252783   \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MAX                             252783   \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN                           252783   \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_SUM                            252783   \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MIN                             252783   \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN                             252783   \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MEAN                            252783   \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_SUM                             252783   \n",
       "CC_AMT_RECIVABLE_MIN                                        252783   \n",
       "CC_AMT_RECIVABLE_MAX                                        252783   \n",
       "CC_AMT_RECIVABLE_MEAN                                       252783   \n",
       "CC_AMT_RECIVABLE_SUM                                        252783   \n",
       "CC_AMT_TOTAL_RECEIVABLE_MIN                                 252783   \n",
       "CC_AMT_TOTAL_RECEIVABLE_MAX                                 252783   \n",
       "CC_AMT_TOTAL_RECEIVABLE_MEAN                                252783   \n",
       "CC_AMT_TOTAL_RECEIVABLE_SUM                                 252783   \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MIN                             252783   \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_SUM                              252783   \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MEAN                            252783   \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MAX                              252783   \n",
       "CC_MONTHS_BALANCE_MEAN                                      252783   \n",
       "N_CC_USE_LATE_MONTH_6                                       252783   \n",
       "N_CC_COUNT                                                  252783   \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MIN                              252783   \n",
       "CC_SK_DPD_DEF_MEAN                                          252783   \n",
       "CC_SK_DPD_DEF_MAX                                           252783   \n",
       "CC_SK_DPD_DEF_MIN                                           252783   \n",
       "CC_SK_DPD_SUM                                               252783   \n",
       "CC_SK_DPD_MEAN                                              252783   \n",
       "CC_MONTHS_BALANCE_MIN                                       252783   \n",
       "CC_MONTHS_BALANCE_MAX                                       252783   \n",
       "CC_SK_DPD_DEF_SUM                                           252783   \n",
       "CC_SK_DPD_MIN                                               252783   \n",
       "CC_MONTHS_BALANCE_SUM                                       252783   \n",
       "CC_AMT_BALANCE_SUM                                          252783   \n",
       "CC_AMT_BALANCE_MAX                                          252783   \n",
       "CC_AMT_BALANCE_MIN                                          252783   \n",
       "CC_AMT_BALANCE_MEAN                                         252783   \n",
       "ACTIVE_N_BUR_START_OBSERVED_MIN                             250571   \n",
       "ACTIVE_N_BUR_START_OBSERVED_MEAN                            250571   \n",
       "ACTIVE_N_BUR_START_OBSERVED_MAX                             250571   \n",
       "COMMONAREA_MODE                                             248357   \n",
       "COMMONAREA_MEDI                                             248357   \n",
       "COMMONAREA_AVG                                              248357   \n",
       "REFUSED_AMT_ANNUITY_MEAN                                    248159   \n",
       "REFUSED_AMT_ANNUITY_MIN                                     248159   \n",
       "REFUSED_AMT_ANNUITY_MAX                                     248159   \n",
       "REFUSED_CNT_PAYMENT_MEAN                                    248159   \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MEAN                             247467   \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MAX                              247467   \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MIN                              247467   \n",
       "REFUSED_N_APP_CREDIT_RATIO_MAX                              247467   \n",
       "REFUSED_N_APP_CREDIT_RATIO_MIN                              247467   \n",
       "REFUSED_N_APP_CREDIT_RATIO_MEAN                             247467   \n",
       "REFUSED_AMT_APPLICATION_MAX                                 247467   \n",
       "REFUSED_AMT_APPLICATION_MIN                                 247467   \n",
       "REFUSED_AMT_APPLICATION_MEAN                                247467   \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MAX                             247462   \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MEAN                            247462   \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MIN                             247462   \n",
       "REFUSED_AMT_GOODS_PRICE_MEAN                                247462   \n",
       "REFUSED_AMT_GOODS_PRICE_MAX                                 247462   \n",
       "REFUSED_AMT_GOODS_PRICE_MIN                                 247462   \n",
       "BUR_N_CREDIT_CLOSING_MONTHS_MAX                             246930   \n",
       "BUR_N_CREDIT_CLOSING_MONTHS_MEAN                            246930   \n",
       "NONLIVINGAPARTMENTS_MEDI                                    246859   \n",
       "NONLIVINGAPARTMENTS_MODE                                    246859   \n",
       "NONLIVINGAPARTMENTS_AVG                                     246859   \n",
       "BUR_N_BUR_START_OBSERVED_VAR                                245990   \n",
       "ACTIVE_MONTHS_BALANCE_MIN_MIN                               245102   \n",
       "ACTIVE_MONTHS_BALANCE_MAX_MAX                               245102   \n",
       "ACTIVE_MONTHS_BALANCE_SIZE_MEAN                             245102   \n",
       "REFUSED_AMT_CREDIT_MAX                                      243798   \n",
       "REFUSED_AMT_CREDIT_MIN                                      243798   \n",
       "REFUSED_AMT_CREDIT_MEAN                                     243798   \n",
       "CLOSED_N_BUR_START_OBSERVED_MIN                             243601   \n",
       "CLOSED_N_BUR_START_OBSERVED_MAX                             243601   \n",
       "CLOSED_N_BUR_START_OBSERVED_MEAN                            243601   \n",
       "LIVINGAPARTMENTS_AVG                                        242977   \n",
       "LIVINGAPARTMENTS_MODE                                       242977   \n",
       "LIVINGAPARTMENTS_MEDI                                       242977   \n",
       "FLOORSMIN_MODE                                              241106   \n",
       "FLOORSMIN_MEDI                                              241106   \n",
       "FLOORSMIN_AVG                                               241106   \n",
       "BUR_AMT_ANNUITY_MAX                                         238027   \n",
       "BUR_AMT_ANNUITY_MEAN                                        238027   \n",
       "REFUSED_DAYS_DECISION_MAX                                   237983   \n",
       "REFUSED_NC_EARLINESS_OVERDUE_RATIO_SUM                      237983   \n",
       "REFUSED_NC_LOAN_AGE_SUM                                     237983   \n",
       "REFUSED_NC_LOAN_TERM_SUM                                    237983   \n",
       "REFUSED_CNT_PAYMENT_SUM                                     237983   \n",
       "REFUSED_DAYS_DECISION_MEAN                                  237983   \n",
       "REFUSED_NFLAG_INSURED_ON_APPROVAL_SUM                       237983   \n",
       "REFUSED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                  237983   \n",
       "REFUSED_DAYS_DECISION_MIN                                   237983   \n",
       "CLOSED_MONTHS_BALANCE_MIN_MIN                               237756   \n",
       "CLOSED_MONTHS_BALANCE_SIZE_MEAN                             237756   \n",
       "CLOSED_MONTHS_BALANCE_MAX_MAX                               237756   \n",
       "YEARS_BUILD_MODE                                            236304   \n",
       "YEARS_BUILD_MEDI                                            236304   \n",
       "YEARS_BUILD_AVG                                             236304   \n",
       "OWN_CAR_AGE                                                 235239   \n",
       "BUR_N_BUR_START_OBSERVED_MIN                                225478   \n",
       "BUR_N_BUR_START_OBSERVED_MAX                                225478   \n",
       "BUR_N_BUR_START_OBSERVED_MEAN                               225478   \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN                          223254   \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MAX                           223254   \n",
       "BUR_STATUS_3_MEAN_MEAN                                      221709   \n",
       "BUR_MONTHS_BALANCE_MAX_MAX                                  221709   \n",
       "BUR_STATUS_C_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_X_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_nan_MEAN_MEAN                                    221709   \n",
       "BUR_MONTHS_BALANCE_MIN_MIN                                  221709   \n",
       "BUR_STATUS_0_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_5_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_1_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_2_MEAN_MEAN                                      221709   \n",
       "BUR_STATUS_4_MEAN_MEAN                                      221709   \n",
       "BUR_MONTHS_BALANCE_SIZE_MEAN                                221709   \n",
       "LANDAREA_AVG                                                210842   \n",
       "LANDAREA_MEDI                                               210842   \n",
       "LANDAREA_MODE                                               210842   \n",
       "BASEMENTAREA_AVG                                            207583   \n",
       "BASEMENTAREA_MODE                                           207583   \n",
       "BASEMENTAREA_MEDI                                           207583   \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                       207021   \n",
       "NONLIVINGAREA_AVG                                           195764   \n",
       "NONLIVINGAREA_MODE                                          195764   \n",
       "NONLIVINGAREA_MEDI                                          195764   \n",
       "ACTIVE_DAYS_CREDIT_VAR                                      189928   \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                    189928   \n",
       "ELEVATORS_AVG                                               189079   \n",
       "ELEVATORS_MODE                                              189079   \n",
       "ELEVATORS_MEDI                                              189079   \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN                          186355   \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_MAX                           186355   \n",
       "APARTMENTS_MEDI                                             179947   \n",
       "APARTMENTS_AVG                                              179947   \n",
       "APARTMENTS_MODE                                             179947   \n",
       "ENTRANCES_MODE                                              178406   \n",
       "ENTRANCES_MEDI                                              178406   \n",
       "ENTRANCES_AVG                                               178406   \n",
       "LIVINGAREA_MEDI                                             177901   \n",
       "LIVINGAREA_MODE                                             177901   \n",
       "LIVINGAREA_AVG                                              177901   \n",
       "FLOORSMAX_MEDI                                              176340   \n",
       "FLOORSMAX_MODE                                              176340   \n",
       "FLOORSMAX_AVG                                               176340   \n",
       "YEARS_BEGINEXPLUATATION_MEDI                                172862   \n",
       "YEARS_BEGINEXPLUATATION_AVG                                 172862   \n",
       "YEARS_BEGINEXPLUATATION_MODE                                172862   \n",
       "TOTALAREA_MODE                                              171054   \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                       170424   \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_VAR                         163385   \n",
       "PREV_N_CREDIT_PROLONG_RATIO_VAR                             163385   \n",
       "CLOSED_DAYS_CREDIT_VAR                                      150025   \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                    150025   \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_MEAN                             143284   \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_MAX                              143284   \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_MAX                             138151   \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN                            138151   \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_MAX                             126314   \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN                            126314   \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                       116394   \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                       116394   \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                      116394   \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN                             116391   \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_MAX                              116391   \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MIN                              114909   \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MAX                              114909   \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MEAN                             114909   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_VAR                     112728   \n",
       "APPROVED_NC_LOAN_TERM_VAR                                   112728   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR                     112728   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_VAR                         112728   \n",
       "PREV_NC_LOAN_TERM_VAR                                       112728   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR                 112728   \n",
       "APPROVED_NC_LOAN_AGE_VAR                                    112728   \n",
       "PREV_NC_LOAN_AGE_VAR                                        112728   \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_VAR                             112132   \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_VAR                            112132   \n",
       "APPROVED_N_APP_CREDIT_RATIO_VAR                             112132   \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                          107106   \n",
       "APPROVED_CNT_PAYMENT_VAR                                    106928   \n",
       "ACTIVE_AMT_CREDIT_SUM_MAX                                   104443   \n",
       "ACTIVE_AMT_CREDIT_SUM_MEAN                                  104443   \n",
       "ACTIVE_CREDIT_DAY_OVERDUE_MEAN                              104440   \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_SUM                           104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_SUM                                   104440   \n",
       "ACTIVE_CREDIT_DAY_OVERDUE_MAX                               104440   \n",
       "ACTIVE_DAYS_CREDIT_MEAN                                     104440   \n",
       "ACTIVE_DAYS_CREDIT_MIN                                      104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_SUM                              104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MAX                           104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN                          104440   \n",
       "ACTIVE_DAYS_CREDIT_MAX                                      104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_SUM                           104440   \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                    104440   \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                       104440   \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                   104440   \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                    104440   \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                    104440   \n",
       "ACTIVE_N_BUR_START_OBSERVED_SUM                             104440   \n",
       "ACTIVE_MONTHS_BALANCE_SIZE_SUM                              104440   \n",
       "ACTIVE_CNT_CREDIT_PROLONG_SUM                               104440   \n",
       "ACTIVE_AMT_ANNUITY_SUM                                      104440   \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM                             104440   \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                       101920   \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                       101920   \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                      101920   \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_MEAN                             101919   \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_MAX                              101919   \n",
       "PREV_N_ADJ_INTEREST_EVAL_VAR                                 92300   \n",
       "PREV_N_ADJ_CREDIT_RATIO_VAR                                  92300   \n",
       "PREV_N_APP_CREDIT_RATIO_VAR                                  92300   \n",
       "BUR_DAYS_CREDIT_VAR                                          91964   \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                        91964   \n",
       "PREV_CNT_PAYMENT_VAR                                         91315   \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MEAN                              90083   \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MAX                               90083   \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MIN                               90083   \n",
       "CLOSED_AMT_CREDIT_SUM_MAX                                    88330   \n",
       "CLOSED_AMT_CREDIT_SUM_MEAN                                   88330   \n",
       "CLOSED_AMT_CREDIT_SUM_SUM                                    88330   \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_MAX                            88330   \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_SUM                               88330   \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                     88330   \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN                           88330   \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_SUM                            88330   \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_SUM                              88330   \n",
       "CLOSED_AMT_ANNUITY_SUM                                       88330   \n",
       "CLOSED_CNT_CREDIT_PROLONG_SUM                                88330   \n",
       "CLOSED_MONTHS_BALANCE_SIZE_SUM                               88330   \n",
       "CLOSED_N_BUR_START_OBSERVED_SUM                              88330   \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                     88330   \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                    88330   \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                        88330   \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_SUM                            88330   \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                     88330   \n",
       "CLOSED_DAYS_CREDIT_MIN                                       88330   \n",
       "CLOSED_DAYS_CREDIT_MAX                                       88330   \n",
       "CLOSED_DAYS_CREDIT_MEAN                                      88330   \n",
       "CLOSED_CREDIT_DAY_OVERDUE_MAX                                88330   \n",
       "CLOSED_CREDIT_DAY_OVERDUE_MEAN                               88330   \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_MAX                                 75752   \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_MEAN                                75752   \n",
       "DAYS_EMPLOYED                                                64648   \n",
       "N_DAYS_EMPLOYED_RATIO                                        64648   \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                           58818   \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                           58818   \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                          58818   \n",
       "BUR_AMT_CREDIT_SUM_DEBT_MAX                                  58816   \n",
       "BUR_AMT_CREDIT_SUM_DEBT_MEAN                                 58816   \n",
       "BUR_DAYS_CREDIT_ENDDATE_MAX                                  53029   \n",
       "BUR_DAYS_CREDIT_ENDDATE_MEAN                                 53029   \n",
       "BUR_DAYS_CREDIT_ENDDATE_MIN                                  53029   \n",
       "BUR_AMT_CREDIT_SUM_MAX                                       50446   \n",
       "BUR_AMT_CREDIT_SUM_MEAN                                      50446   \n",
       "BUR_CREDIT_CURRENCY_currency 3_MEAN                          50444   \n",
       "BUR_CREDIT_CURRENCY_currency 2_MEAN                          50444   \n",
       "BUR_CREDIT_CURRENCY_currency 1_MEAN                          50444   \n",
       "BUR_CREDIT_CURRENCY_nan_MEAN                                 50444   \n",
       "BUR_CREDIT_ACTIVE_nan_MEAN                                   50444   \n",
       "BUR_CREDIT_ACTIVE_Sold_MEAN                                  50444   \n",
       "BUR_CREDIT_ACTIVE_Closed_MEAN                                50444   \n",
       "BUR_CREDIT_ACTIVE_Bad debt_MEAN                              50444   \n",
       "BUR_CREDIT_TYPE_Cash loans_MEAN                              50444   \n",
       "BUR_CREDIT_ACTIVE_Active_MEAN                                50444   \n",
       "BUR_CREDIT_TYPE_Credit card_MEAN                             50444   \n",
       "BUR_CREDIT_TYPE_nan_MEAN                                     50444   \n",
       "BUR_CREDIT_CURRENCY_currency 4_MEAN                          50444   \n",
       "BUR_MONTHS_BALANCE_SIZE_SUM                                  50444   \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                           50444   \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                        50444   \n",
       "BUR_AMT_ANNUITY_SUM                                          50444   \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_SUM                                 50444   \n",
       "BUR_N_BUR_START_OBSERVED_SUM                                 50444   \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_SUM                               50444   \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_MEAN                              50444   \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_MAX                               50444   \n",
       "BUR_AMT_CREDIT_SUM_DEBT_SUM                                  50444   \n",
       "BUR_AMT_CREDIT_SUM_SUM                                       50444   \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_SUM                               50444   \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                        50444   \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                        50444   \n",
       "BUR_CREDIT_DAY_OVERDUE_MEAN                                  50444   \n",
       "BUR_CREDIT_DAY_OVERDUE_MAX                                   50444   \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                       50444   \n",
       "BUR_DAYS_CREDIT_MEAN                                         50444   \n",
       "BUR_DAYS_CREDIT_MAX                                          50444   \n",
       "BUR_CNT_CREDIT_PROLONG_SUM                                   50444   \n",
       "BUR_DAYS_CREDIT_MIN                                          50444   \n",
       "TARGET                                                       48744   \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MIN                          43992   \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MEAN                             43992   \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MAX                              43992   \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MIN                              43992   \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MEAN                         43992   \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MAX                          43992   \n",
       "APPROVED_AMT_DOWN_PAYMENT_MEAN                               40203   \n",
       "APPROVED_RATE_DOWN_PAYMENT_MAX                               40203   \n",
       "APPROVED_RATE_DOWN_PAYMENT_MIN                               40203   \n",
       "APPROVED_AMT_DOWN_PAYMENT_MAX                                40203   \n",
       "APPROVED_RATE_DOWN_PAYMENT_MEAN                              40203   \n",
       "APPROVED_AMT_DOWN_PAYMENT_MIN                                40203   \n",
       "PREV_RATE_DOWN_PAYMENT_MEAN                                  37502   \n",
       "PREV_RATE_DOWN_PAYMENT_MAX                                   37502   \n",
       "PREV_RATE_DOWN_PAYMENT_MIN                                   37502   \n",
       "PREV_AMT_DOWN_PAYMENT_MEAN                                   37502   \n",
       "PREV_AMT_DOWN_PAYMENT_MAX                                    37502   \n",
       "PREV_AMT_DOWN_PAYMENT_MIN                                    37502   \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MAX                             19677   \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MAX                              19677   \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MIN                              19677   \n",
       "APPROVED_AMT_GOODS_PRICE_MEAN                                19677   \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MEAN                            19677   \n",
       "APPROVED_N_APP_CREDIT_RATIO_MAX                              19677   \n",
       "APPROVED_N_APP_CREDIT_RATIO_MIN                              19677   \n",
       "APPROVED_AMT_GOODS_PRICE_MAX                                 19677   \n",
       "APPROVED_AMT_GOODS_PRICE_MIN                                 19677   \n",
       "APPROVED_AMT_APPLICATION_MEAN                                19677   \n",
       "APPROVED_AMT_APPLICATION_MAX                                 19677   \n",
       "APPROVED_AMT_APPLICATION_MIN                                 19677   \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MIN                             19677   \n",
       "APPROVED_N_APP_CREDIT_RATIO_MEAN                             19677   \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MEAN                             19677   \n",
       "POS_SK_DPD_MAX                                               19221   \n",
       "POS_SK_DPD_MIN                                               19221   \n",
       "POS_SK_DPD_DEF_MEAN                                          19221   \n",
       "POS_N_POS_COUNT                                              19221   \n",
       "POS_SK_DPD_DEF_MAX                                           19221   \n",
       "POS_MONTHS_BALANCE_MEAN                                      19221   \n",
       "POS_MONTHS_BALANCE_MAX                                       19221   \n",
       "POS_SK_DPD_MEAN                                              19221   \n",
       "POS_SK_DPD_DEF_MIN                                           19221   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX                  19073   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN                 19073   \n",
       "APPROVED_NC_LOAN_TERM_MIN                                    19073   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN                  19073   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MEAN                     19073   \n",
       "APPROVED_NC_LOAN_AGE_MEAN                                    19073   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MAX                      19073   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MIN                      19073   \n",
       "APPROVED_NC_LOAN_AGE_MAX                                     19073   \n",
       "APPROVED_NC_LOAN_TERM_MEAN                                   19073   \n",
       "APPROVED_NC_LOAN_AGE_MIN                                     19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MEAN                         19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX                      19073   \n",
       "PREV_NC_LOAN_TERM_MEAN                                       19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MIN                          19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MAX                          19073   \n",
       "PREV_NC_LOAN_TERM_MIN                                        19073   \n",
       "PREV_NC_LOAN_TERM_MAX                                        19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN                      19073   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN                     19073   \n",
       "PREV_NC_LOAN_AGE_MIN                                         19073   \n",
       "APPROVED_NC_LOAN_TERM_MAX                                    19073   \n",
       "PREV_NC_LOAN_AGE_MAX                                         19073   \n",
       "PREV_NC_LOAN_AGE_MEAN                                        19073   \n",
       "PREV_NFLAG_INSURED_ON_APPROVAL_MEAN                          18915   \n",
       "APPROVED_NFLAG_INSURED_ON_APPROVAL_MEAN                      18915   \n",
       "APPROVED_AMT_CREDIT_MEAN                                     18566   \n",
       "APPROVED_AMT_CREDIT_MIN                                      18566   \n",
       "APPROVED_AMT_CREDIT_MAX                                      18566   \n",
       "APPROVED_AMT_ANNUITY_MAX                                     18559   \n",
       "APPROVED_AMT_ANNUITY_MEAN                                    18559   \n",
       "APPROVED_AMT_ANNUITY_MIN                                     18559   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_SUM                      18557   \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                  18557   \n",
       "APPROVED_NC_LOAN_TERM_SUM                                    18557   \n",
       "APPROVED_DAYS_DECISION_MIN                                   18557   \n",
       "APPROVED_NC_LOAN_AGE_SUM                                     18557   \n",
       "APPROVED_CNT_PAYMENT_MEAN                                    18557   \n",
       "APPROVED_NFLAG_INSURED_ON_APPROVAL_SUM                       18557   \n",
       "APPROVED_CNT_PAYMENT_SUM                                     18557   \n",
       "APPROVED_DAYS_DECISION_MEAN                                  18557   \n",
       "APPROVED_DAYS_DECISION_MAX                                   18557   \n",
       "PREV_N_APP_CREDIT_RATIO_MIN                                  18503   \n",
       "PREV_AMT_APPLICATION_MIN                                     18503   \n",
       "PREV_AMT_APPLICATION_MAX                                     18503   \n",
       "PREV_AMT_GOODS_PRICE_MEAN                                    18503   \n",
       "PREV_AMT_GOODS_PRICE_MAX                                     18503   \n",
       "PREV_AMT_GOODS_PRICE_MIN                                     18503   \n",
       "PREV_AMT_APPLICATION_MEAN                                    18503   \n",
       "PREV_N_APP_CREDIT_RATIO_MAX                                  18503   \n",
       "PREV_N_ADJ_INTEREST_EVAL_MEAN                                18503   \n",
       "PREV_N_ADJ_INTEREST_EVAL_MAX                                 18503   \n",
       "PREV_N_ADJ_INTEREST_EVAL_MIN                                 18503   \n",
       "PREV_N_ADJ_CREDIT_RATIO_MEAN                                 18503   \n",
       "PREV_N_ADJ_CREDIT_RATIO_MAX                                  18503   \n",
       "PREV_N_ADJ_CREDIT_RATIO_MIN                                  18503   \n",
       "PREV_N_APP_CREDIT_RATIO_MEAN                                 18503   \n",
       "PREV_AMT_ANNUITY_MEAN                                        17878   \n",
       "PREV_AMT_ANNUITY_MAX                                         17878   \n",
       "PREV_AMT_ANNUITY_MIN                                         17878   \n",
       "PREV_CNT_PAYMENT_MEAN                                        17876   \n",
       "PREV_AMT_CREDIT_MAX                                          17650   \n",
       "PREV_AMT_CREDIT_MEAN                                         17650   \n",
       "PREV_AMT_CREDIT_MIN                                          17650   \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_VAR                   17645   \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_VAR                               17645   \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_VAR                     17645   \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_VAR                    17645   \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_VAR                            17645   \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_VAR                17645   \n",
       "PREV_NAME_PAYMENT_TYPE_XNA_MEAN                              17398   \n",
       "PREV_CODE_REJECT_REASON_SCOFR_MEAN                           17398   \n",
       "PREV_CODE_REJECT_REASON_SCO_MEAN                             17398   \n",
       "PREV_CODE_REJECT_REASON_LIMIT_MEAN                           17398   \n",
       "PREV_CODE_REJECT_REASON_HC_MEAN                              17398   \n",
       "PREV_CODE_REJECT_REASON_CLIENT_MEAN                          17398   \n",
       "PREV_NAME_PAYMENT_TYPE_nan_MEAN                              17398   \n",
       "PREV_NAME_PAYMENT_TYPE_Cashless from the accoun...           17398   \n",
       "PREV_NAME_PAYMENT_TYPE_Non-cash from your accou...           17398   \n",
       "PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN            17398   \n",
       "PREV_NAME_CONTRACT_STATUS_nan_MEAN                           17398   \n",
       "PREV_NAME_CONTRACT_STATUS_Unused offer_MEAN                  17398   \n",
       "PREV_NAME_CONTRACT_STATUS_Refused_MEAN                       17398   \n",
       "PREV_NAME_CONTRACT_STATUS_Canceled_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Medicine_MEAN                       17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN                17398   \n",
       "PREV_NAME_CONTRACT_STATUS_Approved_MEAN                      17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Business developmen...           17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_nan_MEAN                     17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN               17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN                 17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN                17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN                  17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN                17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN                  17398   \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN                  17398   \n",
       "PREV_NAME_CONTRACT_TYPE_nan_MEAN                             17398   \n",
       "PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN                 17398   \n",
       "PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN                  17398   \n",
       "PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN                      17398   \n",
       "PREV_NFLAG_INSURED_ON_APPROVAL_SUM                           17398   \n",
       "PREV_NC_LOAN_AGE_SUM                                         17398   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                      17398   \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_SUM                          17398   \n",
       "PREV_NC_LOAN_TERM_SUM                                        17398   \n",
       "PREV_CNT_PAYMENT_SUM                                         17398   \n",
       "PREV_DAYS_DECISION_MEAN                                      17398   \n",
       "PREV_DAYS_DECISION_MAX                                       17398   \n",
       "PREV_DAYS_DECISION_MIN                                       17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Building a house or...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a garage_MEAN             17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_nan_MEAN                         17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a holiday ho...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN                         17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_XAP_MEAN                         17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Wedding / gift / ho...           17398   \n",
       "PREV_CODE_REJECT_REASON_VERIF_MEAN                           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN                     17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Refusal to name the...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Purchase of electro...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Payments on other l...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Other_MEAN                       17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Money for a third p...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN                    17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Journey_MEAN                     17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Hobby_MEAN                       17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Gasification / wate...           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Furniture_MEAN                   17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Everyday expenses_MEAN           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Education_MEAN                   17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN                 17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a used car_MEAN           17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a new car_MEAN            17398   \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a home_MEAN               17398   \n",
       "PREV_CODE_REJECT_REASON_SYSTEM_MEAN                          17398   \n",
       "PREV_CODE_REJECT_REASON_XAP_MEAN                             17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Mobile_MEAN                         17398   \n",
       "PREV_PRODUCT_COMBINATION_POS mobile with intere...           17398   \n",
       "PREV_PRODUCT_COMBINATION_POS industry with inte...           17398   \n",
       "PREV_PRODUCT_COMBINATION_POS household without ...           17398   \n",
       "PREV_PRODUCT_COMBINATION_POS household with int...           17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN            17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN               17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN              17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN            17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN               17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN              17398   \n",
       "PREV_PRODUCT_COMBINATION_Cash_MEAN                           17398   \n",
       "PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN                    17398   \n",
       "PREV_PRODUCT_COMBINATION_Card Street_MEAN                    17398   \n",
       "PREV_NAME_YIELD_GROUP_nan_MEAN                               17398   \n",
       "PREV_NAME_YIELD_GROUP_middle_MEAN                            17398   \n",
       "PREV_NAME_YIELD_GROUP_low_normal_MEAN                        17398   \n",
       "PREV_NAME_YIELD_GROUP_low_action_MEAN                        17398   \n",
       "PREV_NAME_YIELD_GROUP_high_MEAN                              17398   \n",
       "PREV_PRODUCT_COMBINATION_POS industry without i...           17398   \n",
       "PREV_PRODUCT_COMBINATION_POS mobile without int...           17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_HC_MEAN                      17398   \n",
       "PREV_PRODUCT_COMBINATION_POS other with interes...           17398   \n",
       "PREV_N_CONTRACT_STATUS_NOTUSED_MEAN                          17398   \n",
       "PREV_N_CONTRACT_STATUS_APPROVED_MEAN                         17398   \n",
       "PREV_N_CODE_REJECT_REASON_nan_MEAN                           17398   \n",
       "PREV_CODE_REJECT_REASON_XNA_MEAN                             17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_XAP_MEAN                  17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_VERIF_MEAN                17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SYSTEM_MEAN               17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SCOFR_MEAN                17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SCO_MEAN                  17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_LIMIT_MEAN                17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_HC_MEAN                   17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_CLIENT_MEAN               17398   \n",
       "PREV_N_CREDIT_TYPE_nan_MEAN                                  17398   \n",
       "PREV_N_CREDIT_TYPE_Revolving loans_MEAN                      17398   \n",
       "PREV_N_CREDIT_TYPE_Cash loans_MEAN                           17398   \n",
       "PREV_PRODUCT_COMBINATION_nan_MEAN                            17398   \n",
       "PREV_PRODUCT_COMBINATION_POS others without int...           17398   \n",
       "PREV_NAME_YIELD_GROUP_XNA_MEAN                               17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_nan_MEAN                           17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_XNA_MEAN                           17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Tourism_MEAN                       17398   \n",
       "PREV_NAME_PRODUCT_TYPE_walk-in_MEAN                          17398   \n",
       "PREV_NAME_PRODUCT_TYPE_XNA_MEAN                              17398   \n",
       "PREV_NAME_PORTFOLIO_nan_MEAN                                 17398   \n",
       "PREV_NAME_PORTFOLIO_XNA_MEAN                                 17398   \n",
       "PREV_NAME_PORTFOLIO_POS_MEAN                                 17398   \n",
       "PREV_NAME_PORTFOLIO_Cash_MEAN                                17398   \n",
       "PREV_NAME_PORTFOLIO_Cars_MEAN                                17398   \n",
       "PREV_NAME_PORTFOLIO_Cards_MEAN                               17398   \n",
       "PREV_NAME_GOODS_CATEGORY_nan_MEAN                            17398   \n",
       "PREV_NAME_GOODS_CATEGORY_XNA_MEAN                            17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Weapon_MEAN                         17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Vehicles_MEAN                       17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Tourism_MEAN                        17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN              17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipme...           17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Other_MEAN                          17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Office Appliances_MEAN              17398   \n",
       "PREV_NAME_PRODUCT_TYPE_x-sell_MEAN                           17398   \n",
       "PREV_NAME_PRODUCT_TYPE_nan_MEAN                              17398   \n",
       "PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN                       17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN                      17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_MLM partners_MEAN                  17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Jewelry_MEAN                       17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Industry_MEAN                      17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Furniture_MEAN                     17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Consumer electronics_...           17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Construction_MEAN                  17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN                  17398   \n",
       "PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN               17398   \n",
       "PREV_CHANNEL_TYPE_Car dealer_MEAN                            17398   \n",
       "PREV_CHANNEL_TYPE_nan_MEAN                                   17398   \n",
       "PREV_CHANNEL_TYPE_Stone_MEAN                                 17398   \n",
       "PREV_CHANNEL_TYPE_Regional / Local_MEAN                      17398   \n",
       "PREV_CHANNEL_TYPE_Credit and cash offices_MEAN               17398   \n",
       "PREV_CHANNEL_TYPE_Country-wide_MEAN                          17398   \n",
       "PREV_CHANNEL_TYPE_Contact center_MEAN                        17398   \n",
       "PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN            17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_CLIENT_MEAN                  17398   \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_XNA_MEAN                  17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_LIMIT_MEAN                   17398   \n",
       "PREV_NAME_CLIENT_TYPE_New_MEAN                               17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SCO_MEAN                     17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN                    17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Animals_MEAN                        17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Additional Service_MEAN             17398   \n",
       "PREV_NAME_CLIENT_TYPE_nan_MEAN                               17398   \n",
       "PREV_NAME_CLIENT_TYPE_XNA_MEAN                               17398   \n",
       "PREV_NAME_CLIENT_TYPE_Repeater_MEAN                          17398   \n",
       "PREV_NAME_CLIENT_TYPE_Refreshed_MEAN                         17398   \n",
       "PREV_NAME_TYPE_SUITE_nan_MEAN                                17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Clothing and Accessori...           17398   \n",
       "PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN                      17398   \n",
       "PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN                    17398   \n",
       "PREV_NAME_TYPE_SUITE_Other_B_MEAN                            17398   \n",
       "PREV_NAME_TYPE_SUITE_Other_A_MEAN                            17398   \n",
       "PREV_NAME_TYPE_SUITE_Group of people_MEAN                    17398   \n",
       "PREV_NAME_TYPE_SUITE_Family_MEAN                             17398   \n",
       "PREV_NAME_TYPE_SUITE_Children_MEAN                           17398   \n",
       "PREV_CODE_REJECT_REASON_nan_MEAN                             17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Auto Accessories_MEAN               17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Computers_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Homewares_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Insurance_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Construction Materials...           17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Education_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN           17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Fitness_MEAN                        17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Furniture_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Gardening_MEAN                      17398   \n",
       "PREV_NAME_GOODS_CATEGORY_House Construction_MEAN             17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Direct Sales_MEAN                   17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Jewelry_MEAN                        17398   \n",
       "PREV_NAME_GOODS_CATEGORY_Medical Supplies_MEAN               17398   \n",
       "PREV_N_CONTRACT_STATUS_nan_MEAN                              17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_XNA_MEAN                     17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_VERIF_MEAN                   17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SYSTEM_MEAN                  17398   \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SCOFR_MEAN                   17398   \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_SUM                    16677   \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MEAN                    16677   \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MAX                     16677   \n",
       "INS_N_INSTAL_COUNT                                           16677   \n",
       "INS_N_FAULT_RATE                                             16677   \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MIN                            16677   \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MEAN                   16677   \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MAX                    16677   \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MIN                    16677   \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_SUM                            16677   \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MEAN                           16677   \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MAX                            16677   \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_SUM                     16677   \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MIN                     16677   \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MIN                   16677   \n",
       "INS_DAYS_INSTALMENT_MEAN                                     16677   \n",
       "INS_N_STATUS_0_SUM                                           16677   \n",
       "INS_AMT_INSTALMENT_SUM                                       16677   \n",
       "INS_AMT_INSTALMENT_MEAN                                      16677   \n",
       "INS_AMT_INSTALMENT_MAX                                       16677   \n",
       "INS_N_AMT_OVERDUE_RATIO_MEAN                                 16677   \n",
       "INS_N_AMT_OVERDUE_RATIO_MAX                                  16677   \n",
       "INS_N_INS_AMT_OVERDUE_MEAN                                   16677   \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MAX                   16677   \n",
       "INS_N_INS_DPD_SUM                                            16677   \n",
       "INS_N_INS_DPD_MEAN                                           16677   \n",
       "INS_N_INS_DPD_MAX                                            16677   \n",
       "INS_DAYS_ENTRY_PAYMENT_MAX                                   16677   \n",
       "INS_N_INS_DBD_MEAN                                           16677   \n",
       "INS_N_FAULT_GRAVITY                                          16677   \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_SUM                16677   \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_SUM                               16677   \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_SUM                   16677   \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MIN                16677   \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MEAN               16677   \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MEAN                  16677   \n",
       "INS_N_FAULT_ADJ_RATE                                         16677   \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MIN                               16677   \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MAX                               16677   \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MEAN                              16677   \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MAX                16677   \n",
       "DEF_60_CNT_SOCIAL_CIRCLE                                      1050   \n",
       "OBS_60_CNT_SOCIAL_CIRCLE                                      1050   \n",
       "DEF_30_CNT_SOCIAL_CIRCLE                                      1050   \n",
       "OBS_30_CNT_SOCIAL_CIRCLE                                      1050   \n",
       "AMT_GOODS_PRICE                                                278   \n",
       "AMT_ANNUITY                                                     36   \n",
       "N_CREDIT_TERM                                                   36   \n",
       "N_ANNUITY_INCOME_RATIO                                          36   \n",
       "N_INCOME_PER_PERSON                                              2   \n",
       "CNT_FAM_MEMBERS                                                  2   \n",
       "DAYS_LAST_PHONE_CHANGE                                           1   \n",
       "\n",
       "                                                    % of Total Values  \n",
       "ACTIVE_N_CREDIT_CLOSING_MONTHS_MEAN                              89.9  \n",
       "ACTIVE_N_CREDIT_CLOSING_MONTHS_MAX                               89.9  \n",
       "REFUSED_RATE_DOWN_PAYMENT_MAX                                    85.2  \n",
       "REFUSED_RATE_DOWN_PAYMENT_MIN                                    85.2  \n",
       "REFUSED_AMT_DOWN_PAYMENT_MEAN                                    85.2  \n",
       "REFUSED_AMT_DOWN_PAYMENT_MAX                                     85.2  \n",
       "REFUSED_RATE_DOWN_PAYMENT_MEAN                                   85.2  \n",
       "REFUSED_AMT_DOWN_PAYMENT_MIN                                     85.2  \n",
       "REFUSED_CNT_PAYMENT_VAR                                          84.4  \n",
       "REFUSED_N_APP_CREDIT_RATIO_VAR                                   84.3  \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_VAR                                   84.3  \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_VAR                                  84.3  \n",
       "ACTIVE_N_BUR_START_OBSERVED_VAR                                  81.3  \n",
       "CLOSED_N_BUR_START_OBSERVED_VAR                                  76.5  \n",
       "ACTIVE_AMT_ANNUITY_MAX                                           74.0  \n",
       "ACTIVE_AMT_ANNUITY_MEAN                                          74.0  \n",
       "CLOSED_AMT_ANNUITY_MAX                                           73.3  \n",
       "CLOSED_AMT_ANNUITY_MEAN                                          73.3  \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_VAR                                71.2  \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_VAR                                  71.2  \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_VAR                                  71.2  \n",
       "CC_AMT_PAYMENT_CURRENT_VAR                                       71.2  \n",
       "CC_SK_DPD_DEF_VAR                                                71.2  \n",
       "CC_AMT_RECIVABLE_VAR                                             71.2  \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_VAR                                 71.2  \n",
       "CC_AMT_TOTAL_RECEIVABLE_VAR                                      71.2  \n",
       "CC_SK_DPD_VAR                                                    71.2  \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_VAR                                  71.2  \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_VAR                                 71.2  \n",
       "CC_AMT_BALANCE_VAR                                               71.2  \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_VAR                                   71.2  \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_VAR                                  71.2  \n",
       "CC_AMT_DRAWINGS_CURRENT_VAR                                      71.2  \n",
       "CC_MONTHS_BALANCE_VAR                                            71.2  \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_VAR                                71.2  \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_VAR                                  71.2  \n",
       "CC_CNT_DRAWINGS_CURRENT_VAR                                      71.2  \n",
       "CC_AMT_INST_MIN_REGULARITY_VAR                                   71.2  \n",
       "CLOSED_N_CREDIT_CLOSING_MONTHS_MAX                               71.2  \n",
       "CLOSED_N_CREDIT_CLOSING_MONTHS_MEAN                              71.2  \n",
       "CC_AMT_PAYMENT_CURRENT_MAX                                       71.0  \n",
       "CC_AMT_PAYMENT_CURRENT_MIN                                       71.0  \n",
       "CC_AMT_PAYMENT_CURRENT_MEAN                                      71.0  \n",
       "N_CC_USE_LATE_MONTH_2                                            71.0  \n",
       "CC_AMT_INST_MIN_REGULARITY_SUM                                   71.0  \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MIN                                71.0  \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MIN                                  71.0  \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MAX                                  71.0  \n",
       "CC_SK_DPD_MAX                                                    71.0  \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_SUM                                  71.0  \n",
       "CC_AMT_DRAWINGS_CURRENT_MIN                                      71.0  \n",
       "CC_AMT_DRAWINGS_CURRENT_MAX                                      71.0  \n",
       "CC_AMT_DRAWINGS_CURRENT_MEAN                                     71.0  \n",
       "CC_AMT_DRAWINGS_CURRENT_SUM                                      71.0  \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MAX                                71.0  \n",
       "CC_AMT_INST_MIN_REGULARITY_MEAN                                  71.0  \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_MEAN                               71.0  \n",
       "CC_AMT_DRAWINGS_OTHER_CURRENT_SUM                                71.0  \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MIN                                  71.0  \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MAX                                  71.0  \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_MEAN                                 71.0  \n",
       "CC_AMT_DRAWINGS_POS_CURRENT_SUM                                  71.0  \n",
       "CC_AMT_INST_MIN_REGULARITY_MIN                                   71.0  \n",
       "CC_AMT_INST_MIN_REGULARITY_MAX                                   71.0  \n",
       "CC_AMT_PAYMENT_CURRENT_SUM                                       71.0  \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MAX                                  71.0  \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MIN                                 71.0  \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_SUM                                  71.0  \n",
       "CC_CNT_DRAWINGS_CURRENT_MAX                                      71.0  \n",
       "CC_CNT_DRAWINGS_CURRENT_MEAN                                     71.0  \n",
       "CC_CNT_DRAWINGS_CURRENT_SUM                                      71.0  \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MIN                                71.0  \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MAX                                71.0  \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_MEAN                               71.0  \n",
       "CC_CNT_DRAWINGS_OTHER_CURRENT_SUM                                71.0  \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MIN                                  71.0  \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MAX                                  71.0  \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_MEAN                                 71.0  \n",
       "CC_CNT_DRAWINGS_POS_CURRENT_SUM                                  71.0  \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MIN                                 71.0  \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MAX                                 71.0  \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_MEAN                                71.0  \n",
       "CC_CNT_INSTALMENT_MATURE_CUM_SUM                                 71.0  \n",
       "CC_CNT_DRAWINGS_CURRENT_MIN                                      71.0  \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MEAN                                 71.0  \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MAX                                 71.0  \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MAX                                  71.0  \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_MEAN                                71.0  \n",
       "CC_AMT_PAYMENT_TOTAL_CURRENT_SUM                                 71.0  \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MIN                                  71.0  \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MEAN                                  71.0  \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_MEAN                                 71.0  \n",
       "CC_AMT_RECEIVABLE_PRINCIPAL_SUM                                  71.0  \n",
       "CC_AMT_RECIVABLE_MIN                                             71.0  \n",
       "CC_AMT_RECIVABLE_MAX                                             71.0  \n",
       "CC_AMT_RECIVABLE_MEAN                                            71.0  \n",
       "CC_AMT_RECIVABLE_SUM                                             71.0  \n",
       "CC_AMT_TOTAL_RECEIVABLE_MIN                                      71.0  \n",
       "CC_AMT_TOTAL_RECEIVABLE_MAX                                      71.0  \n",
       "CC_AMT_TOTAL_RECEIVABLE_MEAN                                     71.0  \n",
       "CC_AMT_TOTAL_RECEIVABLE_SUM                                      71.0  \n",
       "CC_CNT_DRAWINGS_ATM_CURRENT_MIN                                  71.0  \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_SUM                                   71.0  \n",
       "CC_AMT_DRAWINGS_ATM_CURRENT_MEAN                                 71.0  \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MAX                                   71.0  \n",
       "CC_MONTHS_BALANCE_MEAN                                           71.0  \n",
       "N_CC_USE_LATE_MONTH_6                                            71.0  \n",
       "N_CC_COUNT                                                       71.0  \n",
       "CC_AMT_CREDIT_LIMIT_ACTUAL_MIN                                   71.0  \n",
       "CC_SK_DPD_DEF_MEAN                                               71.0  \n",
       "CC_SK_DPD_DEF_MAX                                                71.0  \n",
       "CC_SK_DPD_DEF_MIN                                                71.0  \n",
       "CC_SK_DPD_SUM                                                    71.0  \n",
       "CC_SK_DPD_MEAN                                                   71.0  \n",
       "CC_MONTHS_BALANCE_MIN                                            71.0  \n",
       "CC_MONTHS_BALANCE_MAX                                            71.0  \n",
       "CC_SK_DPD_DEF_SUM                                                71.0  \n",
       "CC_SK_DPD_MIN                                                    71.0  \n",
       "CC_MONTHS_BALANCE_SUM                                            71.0  \n",
       "CC_AMT_BALANCE_SUM                                               71.0  \n",
       "CC_AMT_BALANCE_MAX                                               71.0  \n",
       "CC_AMT_BALANCE_MIN                                               71.0  \n",
       "CC_AMT_BALANCE_MEAN                                              71.0  \n",
       "ACTIVE_N_BUR_START_OBSERVED_MIN                                  70.3  \n",
       "ACTIVE_N_BUR_START_OBSERVED_MEAN                                 70.3  \n",
       "ACTIVE_N_BUR_START_OBSERVED_MAX                                  70.3  \n",
       "COMMONAREA_MODE                                                  69.7  \n",
       "COMMONAREA_MEDI                                                  69.7  \n",
       "COMMONAREA_AVG                                                   69.7  \n",
       "REFUSED_AMT_ANNUITY_MEAN                                         69.7  \n",
       "REFUSED_AMT_ANNUITY_MIN                                          69.7  \n",
       "REFUSED_AMT_ANNUITY_MAX                                          69.7  \n",
       "REFUSED_CNT_PAYMENT_MEAN                                         69.7  \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MEAN                                  69.5  \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MAX                                   69.5  \n",
       "REFUSED_N_ADJ_CREDIT_RATIO_MIN                                   69.5  \n",
       "REFUSED_N_APP_CREDIT_RATIO_MAX                                   69.5  \n",
       "REFUSED_N_APP_CREDIT_RATIO_MIN                                   69.5  \n",
       "REFUSED_N_APP_CREDIT_RATIO_MEAN                                  69.5  \n",
       "REFUSED_AMT_APPLICATION_MAX                                      69.5  \n",
       "REFUSED_AMT_APPLICATION_MIN                                      69.5  \n",
       "REFUSED_AMT_APPLICATION_MEAN                                     69.5  \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MAX                                  69.5  \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MEAN                                 69.5  \n",
       "REFUSED_N_ADJ_INTEREST_EVAL_MIN                                  69.5  \n",
       "REFUSED_AMT_GOODS_PRICE_MEAN                                     69.5  \n",
       "REFUSED_AMT_GOODS_PRICE_MAX                                      69.5  \n",
       "REFUSED_AMT_GOODS_PRICE_MIN                                      69.5  \n",
       "BUR_N_CREDIT_CLOSING_MONTHS_MAX                                  69.3  \n",
       "BUR_N_CREDIT_CLOSING_MONTHS_MEAN                                 69.3  \n",
       "NONLIVINGAPARTMENTS_MEDI                                         69.3  \n",
       "NONLIVINGAPARTMENTS_MODE                                         69.3  \n",
       "NONLIVINGAPARTMENTS_AVG                                          69.3  \n",
       "BUR_N_BUR_START_OBSERVED_VAR                                     69.0  \n",
       "ACTIVE_MONTHS_BALANCE_MIN_MIN                                    68.8  \n",
       "ACTIVE_MONTHS_BALANCE_MAX_MAX                                    68.8  \n",
       "ACTIVE_MONTHS_BALANCE_SIZE_MEAN                                  68.8  \n",
       "REFUSED_AMT_CREDIT_MAX                                           68.4  \n",
       "REFUSED_AMT_CREDIT_MIN                                           68.4  \n",
       "REFUSED_AMT_CREDIT_MEAN                                          68.4  \n",
       "CLOSED_N_BUR_START_OBSERVED_MIN                                  68.4  \n",
       "CLOSED_N_BUR_START_OBSERVED_MAX                                  68.4  \n",
       "CLOSED_N_BUR_START_OBSERVED_MEAN                                 68.4  \n",
       "LIVINGAPARTMENTS_AVG                                             68.2  \n",
       "LIVINGAPARTMENTS_MODE                                            68.2  \n",
       "LIVINGAPARTMENTS_MEDI                                            68.2  \n",
       "FLOORSMIN_MODE                                                   67.7  \n",
       "FLOORSMIN_MEDI                                                   67.7  \n",
       "FLOORSMIN_AVG                                                    67.7  \n",
       "BUR_AMT_ANNUITY_MAX                                              66.8  \n",
       "BUR_AMT_ANNUITY_MEAN                                             66.8  \n",
       "REFUSED_DAYS_DECISION_MAX                                        66.8  \n",
       "REFUSED_NC_EARLINESS_OVERDUE_RATIO_SUM                           66.8  \n",
       "REFUSED_NC_LOAN_AGE_SUM                                          66.8  \n",
       "REFUSED_NC_LOAN_TERM_SUM                                         66.8  \n",
       "REFUSED_CNT_PAYMENT_SUM                                          66.8  \n",
       "REFUSED_DAYS_DECISION_MEAN                                       66.8  \n",
       "REFUSED_NFLAG_INSURED_ON_APPROVAL_SUM                            66.8  \n",
       "REFUSED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                       66.8  \n",
       "REFUSED_DAYS_DECISION_MIN                                        66.8  \n",
       "CLOSED_MONTHS_BALANCE_MIN_MIN                                    66.7  \n",
       "CLOSED_MONTHS_BALANCE_SIZE_MEAN                                  66.7  \n",
       "CLOSED_MONTHS_BALANCE_MAX_MAX                                    66.7  \n",
       "YEARS_BUILD_MODE                                                 66.3  \n",
       "YEARS_BUILD_MEDI                                                 66.3  \n",
       "YEARS_BUILD_AVG                                                  66.3  \n",
       "OWN_CAR_AGE                                                      66.0  \n",
       "BUR_N_BUR_START_OBSERVED_MIN                                     63.3  \n",
       "BUR_N_BUR_START_OBSERVED_MAX                                     63.3  \n",
       "BUR_N_BUR_START_OBSERVED_MEAN                                    63.3  \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MEAN                               62.7  \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_MAX                                62.7  \n",
       "BUR_STATUS_3_MEAN_MEAN                                           62.2  \n",
       "BUR_MONTHS_BALANCE_MAX_MAX                                       62.2  \n",
       "BUR_STATUS_C_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_X_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_nan_MEAN_MEAN                                         62.2  \n",
       "BUR_MONTHS_BALANCE_MIN_MIN                                       62.2  \n",
       "BUR_STATUS_0_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_5_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_1_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_2_MEAN_MEAN                                           62.2  \n",
       "BUR_STATUS_4_MEAN_MEAN                                           62.2  \n",
       "BUR_MONTHS_BALANCE_SIZE_MEAN                                     62.2  \n",
       "LANDAREA_AVG                                                     59.2  \n",
       "LANDAREA_MEDI                                                    59.2  \n",
       "LANDAREA_MODE                                                    59.2  \n",
       "BASEMENTAREA_AVG                                                 58.3  \n",
       "BASEMENTAREA_MODE                                                58.3  \n",
       "BASEMENTAREA_MEDI                                                58.3  \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                            58.1  \n",
       "NONLIVINGAREA_AVG                                                55.0  \n",
       "NONLIVINGAREA_MODE                                               55.0  \n",
       "NONLIVINGAREA_MEDI                                               55.0  \n",
       "ACTIVE_DAYS_CREDIT_VAR                                           53.3  \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                         53.3  \n",
       "ELEVATORS_AVG                                                    53.1  \n",
       "ELEVATORS_MODE                                                   53.1  \n",
       "ELEVATORS_MEDI                                                   53.1  \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_MEAN                               52.3  \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_MAX                                52.3  \n",
       "APARTMENTS_MEDI                                                  50.5  \n",
       "APARTMENTS_AVG                                                   50.5  \n",
       "APARTMENTS_MODE                                                  50.5  \n",
       "ENTRANCES_MODE                                                   50.1  \n",
       "ENTRANCES_MEDI                                                   50.1  \n",
       "ENTRANCES_AVG                                                    50.1  \n",
       "LIVINGAREA_MEDI                                                  49.9  \n",
       "LIVINGAREA_MODE                                                  49.9  \n",
       "LIVINGAREA_AVG                                                   49.9  \n",
       "FLOORSMAX_MEDI                                                   49.5  \n",
       "FLOORSMAX_MODE                                                   49.5  \n",
       "FLOORSMAX_AVG                                                    49.5  \n",
       "YEARS_BEGINEXPLUATATION_MEDI                                     48.5  \n",
       "YEARS_BEGINEXPLUATATION_AVG                                      48.5  \n",
       "YEARS_BEGINEXPLUATATION_MODE                                     48.5  \n",
       "TOTALAREA_MODE                                                   48.0  \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                            47.8  \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_VAR                              45.9  \n",
       "PREV_N_CREDIT_PROLONG_RATIO_VAR                                  45.9  \n",
       "CLOSED_DAYS_CREDIT_VAR                                           42.1  \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                         42.1  \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_MEAN                                  40.2  \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_MAX                                   40.2  \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_MAX                                  38.8  \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_MEAN                                 38.8  \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_MAX                                  35.5  \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_MEAN                                 35.5  \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                            32.7  \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                            32.7  \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                           32.7  \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_MEAN                                  32.7  \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_MAX                                   32.7  \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MIN                                   32.3  \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MAX                                   32.3  \n",
       "ACTIVE_DAYS_CREDIT_ENDDATE_MEAN                                  32.3  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_VAR                          31.6  \n",
       "APPROVED_NC_LOAN_TERM_VAR                                        31.6  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR                          31.6  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_VAR                              31.6  \n",
       "PREV_NC_LOAN_TERM_VAR                                            31.6  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_VAR                      31.6  \n",
       "APPROVED_NC_LOAN_AGE_VAR                                         31.6  \n",
       "PREV_NC_LOAN_AGE_VAR                                             31.6  \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_VAR                                  31.5  \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_VAR                                 31.5  \n",
       "APPROVED_N_APP_CREDIT_RATIO_VAR                                  31.5  \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_VAR                               30.1  \n",
       "APPROVED_CNT_PAYMENT_VAR                                         30.0  \n",
       "ACTIVE_AMT_CREDIT_SUM_MAX                                        29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_MEAN                                       29.3  \n",
       "ACTIVE_CREDIT_DAY_OVERDUE_MEAN                                   29.3  \n",
       "ACTIVE_AMT_CREDIT_MAX_OVERDUE_SUM                                29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_SUM                                        29.3  \n",
       "ACTIVE_CREDIT_DAY_OVERDUE_MAX                                    29.3  \n",
       "ACTIVE_DAYS_CREDIT_MEAN                                          29.3  \n",
       "ACTIVE_DAYS_CREDIT_MIN                                           29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_DEBT_SUM                                   29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MAX                                29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_MEAN                               29.3  \n",
       "ACTIVE_DAYS_CREDIT_MAX                                           29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_OVERDUE_SUM                                29.3  \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                         29.3  \n",
       "ACTIVE_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                            29.3  \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                        29.3  \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                         29.3  \n",
       "ACTIVE_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                         29.3  \n",
       "ACTIVE_N_BUR_START_OBSERVED_SUM                                  29.3  \n",
       "ACTIVE_MONTHS_BALANCE_SIZE_SUM                                   29.3  \n",
       "ACTIVE_CNT_CREDIT_PROLONG_SUM                                    29.3  \n",
       "ACTIVE_AMT_ANNUITY_SUM                                           29.3  \n",
       "ACTIVE_AMT_CREDIT_SUM_LIMIT_SUM                                  29.3  \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                            28.6  \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                            28.6  \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                           28.6  \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_MEAN                                  28.6  \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_MAX                                   28.6  \n",
       "PREV_N_ADJ_INTEREST_EVAL_VAR                                     25.9  \n",
       "PREV_N_ADJ_CREDIT_RATIO_VAR                                      25.9  \n",
       "PREV_N_APP_CREDIT_RATIO_VAR                                      25.9  \n",
       "BUR_DAYS_CREDIT_VAR                                              25.8  \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_VAR                            25.8  \n",
       "PREV_CNT_PAYMENT_VAR                                             25.6  \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MEAN                                  25.3  \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MAX                                   25.3  \n",
       "CLOSED_DAYS_CREDIT_ENDDATE_MIN                                   25.3  \n",
       "CLOSED_AMT_CREDIT_SUM_MAX                                        24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_MEAN                                       24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_SUM                                        24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_MAX                                24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_DEBT_SUM                                   24.8  \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                         24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_MEAN                               24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_OVERDUE_SUM                                24.8  \n",
       "CLOSED_AMT_CREDIT_SUM_LIMIT_SUM                                  24.8  \n",
       "CLOSED_AMT_ANNUITY_SUM                                           24.8  \n",
       "CLOSED_CNT_CREDIT_PROLONG_SUM                                    24.8  \n",
       "CLOSED_MONTHS_BALANCE_SIZE_SUM                                   24.8  \n",
       "CLOSED_N_BUR_START_OBSERVED_SUM                                  24.8  \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                         24.8  \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                        24.8  \n",
       "CLOSED_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                            24.8  \n",
       "CLOSED_AMT_CREDIT_MAX_OVERDUE_SUM                                24.8  \n",
       "CLOSED_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                         24.8  \n",
       "CLOSED_DAYS_CREDIT_MIN                                           24.8  \n",
       "CLOSED_DAYS_CREDIT_MAX                                           24.8  \n",
       "CLOSED_DAYS_CREDIT_MEAN                                          24.8  \n",
       "CLOSED_CREDIT_DAY_OVERDUE_MAX                                    24.8  \n",
       "CLOSED_CREDIT_DAY_OVERDUE_MEAN                                   24.8  \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_MAX                                     21.3  \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_MEAN                                    21.3  \n",
       "DAYS_EMPLOYED                                                    18.1  \n",
       "N_DAYS_EMPLOYED_RATIO                                            18.1  \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MAX                               16.5  \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MIN                               16.5  \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_MEAN                              16.5  \n",
       "BUR_AMT_CREDIT_SUM_DEBT_MAX                                      16.5  \n",
       "BUR_AMT_CREDIT_SUM_DEBT_MEAN                                     16.5  \n",
       "BUR_DAYS_CREDIT_ENDDATE_MAX                                      14.9  \n",
       "BUR_DAYS_CREDIT_ENDDATE_MEAN                                     14.9  \n",
       "BUR_DAYS_CREDIT_ENDDATE_MIN                                      14.9  \n",
       "BUR_AMT_CREDIT_SUM_MAX                                           14.2  \n",
       "BUR_AMT_CREDIT_SUM_MEAN                                          14.2  \n",
       "BUR_CREDIT_CURRENCY_currency 3_MEAN                              14.2  \n",
       "BUR_CREDIT_CURRENCY_currency 2_MEAN                              14.2  \n",
       "BUR_CREDIT_CURRENCY_currency 1_MEAN                              14.2  \n",
       "BUR_CREDIT_CURRENCY_nan_MEAN                                     14.2  \n",
       "BUR_CREDIT_ACTIVE_nan_MEAN                                       14.2  \n",
       "BUR_CREDIT_ACTIVE_Sold_MEAN                                      14.2  \n",
       "BUR_CREDIT_ACTIVE_Closed_MEAN                                    14.2  \n",
       "BUR_CREDIT_ACTIVE_Bad debt_MEAN                                  14.2  \n",
       "BUR_CREDIT_TYPE_Cash loans_MEAN                                  14.2  \n",
       "BUR_CREDIT_ACTIVE_Active_MEAN                                    14.2  \n",
       "BUR_CREDIT_TYPE_Credit card_MEAN                                 14.2  \n",
       "BUR_CREDIT_TYPE_nan_MEAN                                         14.2  \n",
       "BUR_CREDIT_CURRENCY_currency 4_MEAN                              14.2  \n",
       "BUR_MONTHS_BALANCE_SIZE_SUM                                      14.2  \n",
       "BUR_N_BUR_AMT_CREDIT_DEBT_RATE_SUM                               14.2  \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_SUM                            14.2  \n",
       "BUR_AMT_ANNUITY_SUM                                              14.2  \n",
       "BUR_AMT_CREDIT_SUM_LIMIT_SUM                                     14.2  \n",
       "BUR_N_BUR_START_OBSERVED_SUM                                     14.2  \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_SUM                                   14.2  \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_MEAN                                  14.2  \n",
       "BUR_AMT_CREDIT_SUM_OVERDUE_MAX                                   14.2  \n",
       "BUR_AMT_CREDIT_SUM_DEBT_SUM                                      14.2  \n",
       "BUR_AMT_CREDIT_SUM_SUM                                           14.2  \n",
       "BUR_AMT_CREDIT_MAX_OVERDUE_SUM                                   14.2  \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MIN                            14.2  \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MAX                            14.2  \n",
       "BUR_CREDIT_DAY_OVERDUE_MEAN                                      14.2  \n",
       "BUR_CREDIT_DAY_OVERDUE_MAX                                       14.2  \n",
       "BUR_N_BUR_EARLINESS_OVERDUE_RATIO_MEAN                           14.2  \n",
       "BUR_DAYS_CREDIT_MEAN                                             14.2  \n",
       "BUR_DAYS_CREDIT_MAX                                              14.2  \n",
       "BUR_CNT_CREDIT_PROLONG_SUM                                       14.2  \n",
       "BUR_DAYS_CREDIT_MIN                                              14.2  \n",
       "TARGET                                                           13.7  \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MIN                              12.3  \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MEAN                                 12.3  \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MAX                                  12.3  \n",
       "PREV_N_CREDIT_PROLONG_RATIO_MIN                                  12.3  \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MEAN                             12.3  \n",
       "APPROVED_N_CREDIT_PROLONG_RATIO_MAX                              12.3  \n",
       "APPROVED_AMT_DOWN_PAYMENT_MEAN                                   11.3  \n",
       "APPROVED_RATE_DOWN_PAYMENT_MAX                                   11.3  \n",
       "APPROVED_RATE_DOWN_PAYMENT_MIN                                   11.3  \n",
       "APPROVED_AMT_DOWN_PAYMENT_MAX                                    11.3  \n",
       "APPROVED_RATE_DOWN_PAYMENT_MEAN                                  11.3  \n",
       "APPROVED_AMT_DOWN_PAYMENT_MIN                                    11.3  \n",
       "PREV_RATE_DOWN_PAYMENT_MEAN                                      10.5  \n",
       "PREV_RATE_DOWN_PAYMENT_MAX                                       10.5  \n",
       "PREV_RATE_DOWN_PAYMENT_MIN                                       10.5  \n",
       "PREV_AMT_DOWN_PAYMENT_MEAN                                       10.5  \n",
       "PREV_AMT_DOWN_PAYMENT_MAX                                        10.5  \n",
       "PREV_AMT_DOWN_PAYMENT_MIN                                        10.5  \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MAX                                  5.5  \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MAX                                   5.5  \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MIN                                   5.5  \n",
       "APPROVED_AMT_GOODS_PRICE_MEAN                                     5.5  \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MEAN                                 5.5  \n",
       "APPROVED_N_APP_CREDIT_RATIO_MAX                                   5.5  \n",
       "APPROVED_N_APP_CREDIT_RATIO_MIN                                   5.5  \n",
       "APPROVED_AMT_GOODS_PRICE_MAX                                      5.5  \n",
       "APPROVED_AMT_GOODS_PRICE_MIN                                      5.5  \n",
       "APPROVED_AMT_APPLICATION_MEAN                                     5.5  \n",
       "APPROVED_AMT_APPLICATION_MAX                                      5.5  \n",
       "APPROVED_AMT_APPLICATION_MIN                                      5.5  \n",
       "APPROVED_N_ADJ_INTEREST_EVAL_MIN                                  5.5  \n",
       "APPROVED_N_APP_CREDIT_RATIO_MEAN                                  5.5  \n",
       "APPROVED_N_ADJ_CREDIT_RATIO_MEAN                                  5.5  \n",
       "POS_SK_DPD_MAX                                                    5.4  \n",
       "POS_SK_DPD_MIN                                                    5.4  \n",
       "POS_SK_DPD_DEF_MEAN                                               5.4  \n",
       "POS_N_POS_COUNT                                                   5.4  \n",
       "POS_SK_DPD_DEF_MAX                                                5.4  \n",
       "POS_MONTHS_BALANCE_MEAN                                           5.4  \n",
       "POS_MONTHS_BALANCE_MAX                                            5.4  \n",
       "POS_SK_DPD_MEAN                                                   5.4  \n",
       "POS_SK_DPD_DEF_MIN                                                5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX                       5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN                      5.4  \n",
       "APPROVED_NC_LOAN_TERM_MIN                                         5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN                       5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MEAN                          5.4  \n",
       "APPROVED_NC_LOAN_AGE_MEAN                                         5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MAX                           5.4  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_MIN                           5.4  \n",
       "APPROVED_NC_LOAN_AGE_MAX                                          5.4  \n",
       "APPROVED_NC_LOAN_TERM_MEAN                                        5.4  \n",
       "APPROVED_NC_LOAN_AGE_MIN                                          5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MEAN                              5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MAX                           5.4  \n",
       "PREV_NC_LOAN_TERM_MEAN                                            5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MIN                               5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_MAX                               5.4  \n",
       "PREV_NC_LOAN_TERM_MIN                                             5.4  \n",
       "PREV_NC_LOAN_TERM_MAX                                             5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MIN                           5.4  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_MEAN                          5.4  \n",
       "PREV_NC_LOAN_AGE_MIN                                              5.4  \n",
       "APPROVED_NC_LOAN_TERM_MAX                                         5.4  \n",
       "PREV_NC_LOAN_AGE_MAX                                              5.4  \n",
       "PREV_NC_LOAN_AGE_MEAN                                             5.4  \n",
       "PREV_NFLAG_INSURED_ON_APPROVAL_MEAN                               5.3  \n",
       "APPROVED_NFLAG_INSURED_ON_APPROVAL_MEAN                           5.3  \n",
       "APPROVED_AMT_CREDIT_MEAN                                          5.2  \n",
       "APPROVED_AMT_CREDIT_MIN                                           5.2  \n",
       "APPROVED_AMT_CREDIT_MAX                                           5.2  \n",
       "APPROVED_AMT_ANNUITY_MAX                                          5.2  \n",
       "APPROVED_AMT_ANNUITY_MEAN                                         5.2  \n",
       "APPROVED_AMT_ANNUITY_MIN                                          5.2  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_SUM                           5.2  \n",
       "APPROVED_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                       5.2  \n",
       "APPROVED_NC_LOAN_TERM_SUM                                         5.2  \n",
       "APPROVED_DAYS_DECISION_MIN                                        5.2  \n",
       "APPROVED_NC_LOAN_AGE_SUM                                          5.2  \n",
       "APPROVED_CNT_PAYMENT_MEAN                                         5.2  \n",
       "APPROVED_NFLAG_INSURED_ON_APPROVAL_SUM                            5.2  \n",
       "APPROVED_CNT_PAYMENT_SUM                                          5.2  \n",
       "APPROVED_DAYS_DECISION_MEAN                                       5.2  \n",
       "APPROVED_DAYS_DECISION_MAX                                        5.2  \n",
       "PREV_N_APP_CREDIT_RATIO_MIN                                       5.2  \n",
       "PREV_AMT_APPLICATION_MIN                                          5.2  \n",
       "PREV_AMT_APPLICATION_MAX                                          5.2  \n",
       "PREV_AMT_GOODS_PRICE_MEAN                                         5.2  \n",
       "PREV_AMT_GOODS_PRICE_MAX                                          5.2  \n",
       "PREV_AMT_GOODS_PRICE_MIN                                          5.2  \n",
       "PREV_AMT_APPLICATION_MEAN                                         5.2  \n",
       "PREV_N_APP_CREDIT_RATIO_MAX                                       5.2  \n",
       "PREV_N_ADJ_INTEREST_EVAL_MEAN                                     5.2  \n",
       "PREV_N_ADJ_INTEREST_EVAL_MAX                                      5.2  \n",
       "PREV_N_ADJ_INTEREST_EVAL_MIN                                      5.2  \n",
       "PREV_N_ADJ_CREDIT_RATIO_MEAN                                      5.2  \n",
       "PREV_N_ADJ_CREDIT_RATIO_MAX                                       5.2  \n",
       "PREV_N_ADJ_CREDIT_RATIO_MIN                                       5.2  \n",
       "PREV_N_APP_CREDIT_RATIO_MEAN                                      5.2  \n",
       "PREV_AMT_ANNUITY_MEAN                                             5.0  \n",
       "PREV_AMT_ANNUITY_MAX                                              5.0  \n",
       "PREV_AMT_ANNUITY_MIN                                              5.0  \n",
       "PREV_CNT_PAYMENT_MEAN                                             5.0  \n",
       "PREV_AMT_CREDIT_MAX                                               5.0  \n",
       "PREV_AMT_CREDIT_MEAN                                              5.0  \n",
       "PREV_AMT_CREDIT_MIN                                               5.0  \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_VAR                        5.0  \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_VAR                                    5.0  \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_VAR                          5.0  \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_VAR                         5.0  \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_VAR                                 5.0  \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_VAR                     5.0  \n",
       "PREV_NAME_PAYMENT_TYPE_XNA_MEAN                                   4.9  \n",
       "PREV_CODE_REJECT_REASON_SCOFR_MEAN                                4.9  \n",
       "PREV_CODE_REJECT_REASON_SCO_MEAN                                  4.9  \n",
       "PREV_CODE_REJECT_REASON_LIMIT_MEAN                                4.9  \n",
       "PREV_CODE_REJECT_REASON_HC_MEAN                                   4.9  \n",
       "PREV_CODE_REJECT_REASON_CLIENT_MEAN                               4.9  \n",
       "PREV_NAME_PAYMENT_TYPE_nan_MEAN                                   4.9  \n",
       "PREV_NAME_PAYMENT_TYPE_Cashless from the accoun...                4.9  \n",
       "PREV_NAME_PAYMENT_TYPE_Non-cash from your accou...                4.9  \n",
       "PREV_NAME_PAYMENT_TYPE_Cash through the bank_MEAN                 4.9  \n",
       "PREV_NAME_CONTRACT_STATUS_nan_MEAN                                4.9  \n",
       "PREV_NAME_CONTRACT_STATUS_Unused offer_MEAN                       4.9  \n",
       "PREV_NAME_CONTRACT_STATUS_Refused_MEAN                            4.9  \n",
       "PREV_NAME_CONTRACT_STATUS_Canceled_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Medicine_MEAN                            4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Urgent needs_MEAN                     4.9  \n",
       "PREV_NAME_CONTRACT_STATUS_Approved_MEAN                           4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Business developmen...                4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_nan_MEAN                          4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_WEDNESDAY_MEAN                    4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_TUESDAY_MEAN                      4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_THURSDAY_MEAN                     4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_SUNDAY_MEAN                       4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_SATURDAY_MEAN                     4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_MONDAY_MEAN                       4.9  \n",
       "PREV_WEEKDAY_APPR_PROCESS_START_FRIDAY_MEAN                       4.9  \n",
       "PREV_NAME_CONTRACT_TYPE_nan_MEAN                                  4.9  \n",
       "PREV_NAME_CONTRACT_TYPE_Revolving loans_MEAN                      4.9  \n",
       "PREV_NAME_CONTRACT_TYPE_Consumer loans_MEAN                       4.9  \n",
       "PREV_NAME_CONTRACT_TYPE_Cash loans_MEAN                           4.9  \n",
       "PREV_NFLAG_INSURED_ON_APPROVAL_SUM                                4.9  \n",
       "PREV_NC_LOAN_AGE_SUM                                              4.9  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_DEF_SUM                           4.9  \n",
       "PREV_NC_EARLINESS_OVERDUE_RATIO_SUM                               4.9  \n",
       "PREV_NC_LOAN_TERM_SUM                                             4.9  \n",
       "PREV_CNT_PAYMENT_SUM                                              4.9  \n",
       "PREV_DAYS_DECISION_MEAN                                           4.9  \n",
       "PREV_DAYS_DECISION_MAX                                            4.9  \n",
       "PREV_DAYS_DECISION_MIN                                            4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Building a house or...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a garage_MEAN                  4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_nan_MEAN                              4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a holiday ho...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_XNA_MEAN                              4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_XAP_MEAN                              4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Wedding / gift / ho...                4.9  \n",
       "PREV_CODE_REJECT_REASON_VERIF_MEAN                                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Repairs_MEAN                          4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Refusal to name the...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Purchase of electro...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Payments on other l...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Other_MEAN                            4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Money for a third p...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Medicine_MEAN                         4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Journey_MEAN                          4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Hobby_MEAN                            4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Gasification / wate...                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Furniture_MEAN                        4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Everyday expenses_MEAN                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Education_MEAN                        4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Car repairs_MEAN                      4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a used car_MEAN                4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a new car_MEAN                 4.9  \n",
       "PREV_NAME_CASH_LOAN_PURPOSE_Buying a home_MEAN                    4.9  \n",
       "PREV_CODE_REJECT_REASON_SYSTEM_MEAN                               4.9  \n",
       "PREV_CODE_REJECT_REASON_XAP_MEAN                                  4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Mobile_MEAN                              4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS mobile with intere...                4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS industry with inte...                4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS household without ...                4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS household with int...                4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: middle_MEAN                 4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: low_MEAN                    4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash X-Sell: high_MEAN                   4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: middle_MEAN                 4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: low_MEAN                    4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash Street: high_MEAN                   4.9  \n",
       "PREV_PRODUCT_COMBINATION_Cash_MEAN                                4.9  \n",
       "PREV_PRODUCT_COMBINATION_Card X-Sell_MEAN                         4.9  \n",
       "PREV_PRODUCT_COMBINATION_Card Street_MEAN                         4.9  \n",
       "PREV_NAME_YIELD_GROUP_nan_MEAN                                    4.9  \n",
       "PREV_NAME_YIELD_GROUP_middle_MEAN                                 4.9  \n",
       "PREV_NAME_YIELD_GROUP_low_normal_MEAN                             4.9  \n",
       "PREV_NAME_YIELD_GROUP_low_action_MEAN                             4.9  \n",
       "PREV_NAME_YIELD_GROUP_high_MEAN                                   4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS industry without i...                4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS mobile without int...                4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_HC_MEAN                           4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS other with interes...                4.9  \n",
       "PREV_N_CONTRACT_STATUS_NOTUSED_MEAN                               4.9  \n",
       "PREV_N_CONTRACT_STATUS_APPROVED_MEAN                              4.9  \n",
       "PREV_N_CODE_REJECT_REASON_nan_MEAN                                4.9  \n",
       "PREV_CODE_REJECT_REASON_XNA_MEAN                                  4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_XAP_MEAN                       4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_VERIF_MEAN                     4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SYSTEM_MEAN                    4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SCOFR_MEAN                     4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_SCO_MEAN                       4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_LIMIT_MEAN                     4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_HC_MEAN                        4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_CLIENT_MEAN                    4.9  \n",
       "PREV_N_CREDIT_TYPE_nan_MEAN                                       4.9  \n",
       "PREV_N_CREDIT_TYPE_Revolving loans_MEAN                           4.9  \n",
       "PREV_N_CREDIT_TYPE_Cash loans_MEAN                                4.9  \n",
       "PREV_PRODUCT_COMBINATION_nan_MEAN                                 4.9  \n",
       "PREV_PRODUCT_COMBINATION_POS others without int...                4.9  \n",
       "PREV_NAME_YIELD_GROUP_XNA_MEAN                                    4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_nan_MEAN                                4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_XNA_MEAN                                4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Tourism_MEAN                            4.9  \n",
       "PREV_NAME_PRODUCT_TYPE_walk-in_MEAN                               4.9  \n",
       "PREV_NAME_PRODUCT_TYPE_XNA_MEAN                                   4.9  \n",
       "PREV_NAME_PORTFOLIO_nan_MEAN                                      4.9  \n",
       "PREV_NAME_PORTFOLIO_XNA_MEAN                                      4.9  \n",
       "PREV_NAME_PORTFOLIO_POS_MEAN                                      4.9  \n",
       "PREV_NAME_PORTFOLIO_Cash_MEAN                                     4.9  \n",
       "PREV_NAME_PORTFOLIO_Cars_MEAN                                     4.9  \n",
       "PREV_NAME_PORTFOLIO_Cards_MEAN                                    4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_nan_MEAN                                 4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_XNA_MEAN                                 4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Weapon_MEAN                              4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Vehicles_MEAN                            4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Tourism_MEAN                             4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Sport and Leisure_MEAN                   4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Photo / Cinema Equipme...                4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Other_MEAN                               4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Office Appliances_MEAN                   4.9  \n",
       "PREV_NAME_PRODUCT_TYPE_x-sell_MEAN                                4.9  \n",
       "PREV_NAME_PRODUCT_TYPE_nan_MEAN                                   4.9  \n",
       "PREV_CHANNEL_TYPE_AP+ (Cash loan)_MEAN                            4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Clothing_MEAN                           4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_MLM partners_MEAN                       4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Jewelry_MEAN                            4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Industry_MEAN                           4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Furniture_MEAN                          4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Consumer electronics_...                4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Construction_MEAN                       4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Connectivity_MEAN                       4.9  \n",
       "PREV_NAME_SELLER_INDUSTRY_Auto technology_MEAN                    4.9  \n",
       "PREV_CHANNEL_TYPE_Car dealer_MEAN                                 4.9  \n",
       "PREV_CHANNEL_TYPE_nan_MEAN                                        4.9  \n",
       "PREV_CHANNEL_TYPE_Stone_MEAN                                      4.9  \n",
       "PREV_CHANNEL_TYPE_Regional / Local_MEAN                           4.9  \n",
       "PREV_CHANNEL_TYPE_Credit and cash offices_MEAN                    4.9  \n",
       "PREV_CHANNEL_TYPE_Country-wide_MEAN                               4.9  \n",
       "PREV_CHANNEL_TYPE_Contact center_MEAN                             4.9  \n",
       "PREV_CHANNEL_TYPE_Channel of corporate sales_MEAN                 4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_CLIENT_MEAN                       4.9  \n",
       "PREV_N_CODE_REJECT_REASON_REJECTED_XNA_MEAN                       4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_LIMIT_MEAN                        4.9  \n",
       "PREV_NAME_CLIENT_TYPE_New_MEAN                                    4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SCO_MEAN                          4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Audio/Video_MEAN                         4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Animals_MEAN                             4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Additional Service_MEAN                  4.9  \n",
       "PREV_NAME_CLIENT_TYPE_nan_MEAN                                    4.9  \n",
       "PREV_NAME_CLIENT_TYPE_XNA_MEAN                                    4.9  \n",
       "PREV_NAME_CLIENT_TYPE_Repeater_MEAN                               4.9  \n",
       "PREV_NAME_CLIENT_TYPE_Refreshed_MEAN                              4.9  \n",
       "PREV_NAME_TYPE_SUITE_nan_MEAN                                     4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Clothing and Accessori...                4.9  \n",
       "PREV_NAME_TYPE_SUITE_Unaccompanied_MEAN                           4.9  \n",
       "PREV_NAME_TYPE_SUITE_Spouse, partner_MEAN                         4.9  \n",
       "PREV_NAME_TYPE_SUITE_Other_B_MEAN                                 4.9  \n",
       "PREV_NAME_TYPE_SUITE_Other_A_MEAN                                 4.9  \n",
       "PREV_NAME_TYPE_SUITE_Group of people_MEAN                         4.9  \n",
       "PREV_NAME_TYPE_SUITE_Family_MEAN                                  4.9  \n",
       "PREV_NAME_TYPE_SUITE_Children_MEAN                                4.9  \n",
       "PREV_CODE_REJECT_REASON_nan_MEAN                                  4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Auto Accessories_MEAN                    4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Computers_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Homewares_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Insurance_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Construction Materials...                4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Education_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Consumer Electronics_MEAN                4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Fitness_MEAN                             4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Furniture_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Gardening_MEAN                           4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_House Construction_MEAN                  4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Direct Sales_MEAN                        4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Jewelry_MEAN                             4.9  \n",
       "PREV_NAME_GOODS_CATEGORY_Medical Supplies_MEAN                    4.9  \n",
       "PREV_N_CONTRACT_STATUS_nan_MEAN                                   4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_XNA_MEAN                          4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_VERIF_MEAN                        4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SYSTEM_MEAN                       4.9  \n",
       "PREV_N_CONTRACT_STATUS_REJECTED_SCOFR_MEAN                        4.9  \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_SUM                         4.7  \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MEAN                         4.7  \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MAX                          4.7  \n",
       "INS_N_INSTAL_COUNT                                                4.7  \n",
       "INS_N_FAULT_RATE                                                  4.7  \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MIN                                 4.7  \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MEAN                        4.7  \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MAX                         4.7  \n",
       "INSAGG_INS_N_PREV_INS_AMT_MAX_OVERDUE_MIN                         4.7  \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_SUM                                 4.7  \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MEAN                                4.7  \n",
       "INSAGG_INS_N_PREV_CNT_PROLONG_MAX                                 4.7  \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_SUM                          4.7  \n",
       "INSAGG_INS_N_PREV_CNT_TOT_INSTALMENT_MIN                          4.7  \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MIN                        4.7  \n",
       "INS_DAYS_INSTALMENT_MEAN                                          4.7  \n",
       "INS_N_STATUS_0_SUM                                                4.7  \n",
       "INS_AMT_INSTALMENT_SUM                                            4.7  \n",
       "INS_AMT_INSTALMENT_MEAN                                           4.7  \n",
       "INS_AMT_INSTALMENT_MAX                                            4.7  \n",
       "INS_N_AMT_OVERDUE_RATIO_MEAN                                      4.7  \n",
       "INS_N_AMT_OVERDUE_RATIO_MAX                                       4.7  \n",
       "INS_N_INS_AMT_OVERDUE_MEAN                                        4.7  \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MAX                        4.7  \n",
       "INS_N_INS_DPD_SUM                                                 4.7  \n",
       "INS_N_INS_DPD_MEAN                                                4.7  \n",
       "INS_N_INS_DPD_MAX                                                 4.7  \n",
       "INS_DAYS_ENTRY_PAYMENT_MAX                                        4.7  \n",
       "INS_N_INS_DBD_MEAN                                                4.7  \n",
       "INS_N_FAULT_GRAVITY                                               4.7  \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_SUM                     4.7  \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_SUM                                    4.7  \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_SUM                        4.7  \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MIN                     4.7  \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MEAN                    4.7  \n",
       "INSAGG_INS_N_PREV_LAST_DAYS_INSTALMENT_MEAN                       4.7  \n",
       "INS_N_FAULT_ADJ_RATE                                              4.7  \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MIN                                    4.7  \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MAX                                    4.7  \n",
       "INSAGG_INS_N_PREV_LOAN_AGE_MEAN                                   4.7  \n",
       "INSAGG_INS_N_PREV_EARLINESS_OVERDUE_RATIO_MAX                     4.7  \n",
       "DEF_60_CNT_SOCIAL_CIRCLE                                          0.3  \n",
       "OBS_60_CNT_SOCIAL_CIRCLE                                          0.3  \n",
       "DEF_30_CNT_SOCIAL_CIRCLE                                          0.3  \n",
       "OBS_30_CNT_SOCIAL_CIRCLE                                          0.3  \n",
       "AMT_GOODS_PRICE                                                   0.1  \n",
       "AMT_ANNUITY                                                       0.0  \n",
       "N_CREDIT_TERM                                                     0.0  \n",
       "N_ANNUITY_INCOME_RATIO                                            0.0  \n",
       "N_INCOME_PER_PERSON                                               0.0  \n",
       "CNT_FAM_MEMBERS                                                   0.0  \n",
       "DAYS_LAST_PHONE_CHANGE                                            0.0  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 145,
     "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"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4:80: E501 line too long (109 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# get the missing curve\n",
    "missing_list = []\n",
    "for i in np.arange(0, 101, 1):\n",
    "    missing = application.shape[1] - len(list(missing_values.index[missing_values['% of Total Values'] > i]))\n",
    "    missing_list.append(missing)\n",
    "pd.DataFrame(missing_list).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A first set of features can be safely removed and is the result of wrong aggregation method during \"automated\" FE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2:80: E501 line too long (85 > 79 characters)\n"
     ]
    }
   ],
   "source": [
    "# get the list of features to remove\n",
    "remove_missing = list(missing_values.index[missing_values['% of Total Values'] > 85])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ACTIVE_N_CREDIT_CLOSING_MONTHS_MEAN',\n",
       " 'ACTIVE_N_CREDIT_CLOSING_MONTHS_MAX',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MAX',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MIN',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MEAN',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MAX',\n",
       " 'REFUSED_RATE_DOWN_PAYMENT_MEAN',\n",
       " 'REFUSED_AMT_DOWN_PAYMENT_MIN']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "application.drop(columns=remove_missing, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Model agnostic\" Boruta Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from boruta import BorutaPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# keep training applications only\n",
    "train = application[application['TARGET'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set index with SK_ID_CURR\n",
    "train = train.set_index('SK_ID_CURR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = train.drop(columns=['TARGET'])\n",
    "y_ = train['TARGET']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample, _, y_sample, _ = train_test_split(\n",
    "        X_,\n",
    "        y_,\n",
    "        stratify=y_,\n",
    "        test_size=0.8,\n",
    "        random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(\n",
    "    missing_values=np.nan,\n",
    "    strategy='mean')\n",
    "imputer.fit(X_sample)\n",
    "X_sample = imputer.transform(X_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define random forest classifier, with utilising all cores and\n",
    "# sampling in proportion to y labels\n",
    "rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define Boruta feature selection method\n",
    "feat_selector = BorutaPy(rf, n_estimators='auto', max_iter=15, verbose=2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find all relevant features - features should be selected\n",
    "feat_selector.fit(X_sample, y_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train.drop(columns=['TARGET']).columns\n",
    "y = feat_selector.ranking_\n",
    "df = pd.DataFrame({'feature': x, 'rank': y})\n",
    "df = df.sort_values(by=['rank'], ascending=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get support features\n",
    "support = train.drop(columns=['TARGET']).columns[feat_selector.support_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected = []\n",
    "selected = ['SK_ID_CURR'] + ['TARGET'] + support.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_app = application.filter(selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"Data/sel_app.pkl\", mode=\"wb\") as df:\n",
    "    pickle.dump(sel_app, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del sel_app\n",
    "del train\n",
    "del application"
   ]
  }
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